Augmented Reality (AR) technology has revolutionised how brands approach marketing. AR allows brands to create immersive experiences seamlessly blending virtual and real worlds. This technology has opened up new opportunities for marketers to connect with their customers and create a more engaging and personalised experience. 

AR is the future of marketing —it allows brands to create immersive experiences that are personalised and engaging. AR is also becoming more accessible as technology continues to evolve. As more people adopt AR-enabled devices like smartphones and smart glasses, the opportunities for brands to use AR in marketing will only increase.

This blog post will discuss how brands can use Augmented Reality in marketing, why AR is the future, which brands are leading in AR, and some use cases in industries like banking, travel, retail, medical, and fitness.

How Brands Can Use Augmented Reality (AR) in Marketing

  • Product Visualisation: Brands can use AR to create virtual product demos that allow customers to visualise how a product looks and functions in the real world. For example, Ikea’s AR app allows customers to visualise furniture in their homes before purchasing.
  • Interactive Ads: Brands can use AR to create interactive ads that allow customers to engage with their products more effectively. For example, Pepsi used AR to create a bus shelter ad that allowed customers to play a game of soccer with virtual players.
  • Virtual Try-On: Brands can use AR to create virtual try-on experiences that allow customers to see how products will look on them before making a purchase. Sephora’s AR app allows customers to try on makeup virtually.
  • Gamification: Brands can use AR to create gamified experiences that allow customers to interact with their products in a more engaging way. McDonald’s used AR to create a Monopoly-themed game that customers could play in-store.

How can CMOs prove to the board these new technologies are working and that they should implement them? 

To prove the effectiveness of AR in marketing, marketers must measure the impact of AR on their campaign metrics. This can be done by tracking Key Performance Indicators (KPIs) like engagement, brand awareness, sales, and customer satisfaction. Brands can measure AR’s impact using various methods, including surveys, A/B testing, and analytics tools. By demonstrating a clear ROI and a positive influence on these KPIs, CMOs can make a compelling case to the board for implementing AR and other new technologies.

Let’s look at the differences between VR and AR in marketing.

Virtual Reality (VR) and Augmented Reality (AR) are two technologies that are often confused but have some essential distinctions.

Virtual Reality is a technology that immerses users in a completely virtual environment, often through a headset. VR experiences can be used in marketing to create fully immersive experiences that allow customers to explore a product or service in a virtual environment. For example, car companies like Audi have used VR to create virtual test drives enabling customers to experience a car before purchasing.

On the other hand, Augmented Reality is a technology that overlays virtual content onto the real world, often through a smartphone or tablet. AR experiences can be used in marketing to create interactive and personalised experiences that allow customers to engage with a product or service in the real world. For instance, beauty companies like L’Oreal have used AR to create virtual try-on experiences that allow customers to see how makeup will look on their faces before making a purchase.

One of the main differences between VR and AR in marketing is the level of immersion. VR provides a fully immersive experience that can transport users to a virtual environment, while AR provides a more interactive and personalised experience that overlays virtual content in the real world.

VR and AR in marketing also require different levels of technology needed. VR experiences typically require more advanced technology, like a VR headset, while AR experiences can be created using a smartphone or tablet.

VR and AR are two different technologies that can be used in marketing to create different experiences. CMOs should consider the goals of their marketing campaign and the preferences of their target audience when deciding which technology to use.

The most notable and significant difference between AR and VR is that AR adds things to a person’s existing world, and VR immerses them in a new world, so while the VR world is a new reality, AR supplements an existing reality.

Which of these technologies is better for marketers?

The excitement and enthusiasm for AR and VR technologies are evident, yet how these technologies influence consumers remains uncertain. Tim Hilken of Maastricht University in The Netherlands undertook a research project to understand better AR and VR’s impacts on the consumer experience. The results indicated that both technologies could effectively achieve marketing objectives with different outcomes. Specifically, AR proved more efficient at generating higher purchase intentions, while VR elicited more favourable attitudes towards the brand.

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Why Prioritise Augmented Reality Over Virtual Reality?

In most cases, consumers would naturally prefer AR over VR, and here’s why. 

Augmented Reality grants a new dimension of depth to our world, enhancing our senses and immersing us in a new reality beyond the limitations of what we perceive. Unlike Virtual Reality, AR empowers us to live in our Reality while unlocking infinite possibilities that can improve our lives.

Humans have an innate desire to stay grounded in the real world, and AR provides the perfect solution to make that happen. Simply overlaying computer-generated content onto our everyday experiences, we can step into an enhanced metaverse while firmly planted in the real world.

When it comes to VR, it can be isolating and somewhat disorienting for many individuals, and most people do not want to spend hours looking at screens right in front of their eyes.

The benefits of AR, however, are numerous. Its interactive capabilities unprecedentedly open up the potential for education, work, travel, and entertainment. Whether it’s a museum tour or a surgical procedure demonstration, AR takes learning to a new level. Imagine experiencing art and museums, getting around a new city, or even redecorating your home without leaving your front door.

AR brings to life a whole new universe of possibilities. With an immersive first-person perspective and the freedom to customise your view to your preferences, AR provides an extraordinary sensory experience that both VR and the real world alone could never replicate. AR allows you to see what others see in real-time.

Augmented Reality has limitless potential to improve our lives, and it’s time we embrace the real-world solutions it offers.

The use of AR in the Metaverse

The metaverse is a term used to describe a virtual world where people can interact with each other and digital objects in a fully immersive way. It is a fully realised virtual universe that is not constrained by the physical world’s limitations. In the metaverse, users can create and customise their digital avatars and interact with other users in real-time.

On the other hand, Augmented Reality (AR) is a technology that overlays digital content onto the physical world. AR enhances the real world by adding virtual objects, images, and information visible to users through mobile devices, smart glasses, or other AR-enabled devices.

While the metaverse and AR are related to creating virtual experiences, the critical difference is the level of immersion. The metaverse is a fully immersive digital world where users can interact with each other and digital objects in a virtual environment. In contrast, AR enhances the real world by adding virtual elements on top of it.

As augmented reality technology grows more accessible and cost-effective, more people use it. Marketers can pair AR with the metaverse to develop vivid experiences.

In augmented Reality (AR), data tags and overlays pop up over virtual and tangible objects, providing details and action points so you can see real and virtual things simultaneously.

Augmented Reality in the metaverse offers unprecedented opportunities for marketers. By utilising AR-based product visualisations, brands can create emotionally engaging, interactive experiences that can be accessed from anywhere. This could revolutionise the marketing industry by breaking the bounds of traditional advertising and bringing the advantages of AR to the masses.

Persistent Augmented Reality

A report shows that over 50% of smartphone owners already use AR apps when shopping. 

Augmented Reality (AR) offers a way to blend virtual elements into the real world. With Persistent AR, digital imagery and sounds become part of the world beyond when you’re using them, allowing for a longer-term experience and presence. Persistent AR is seen as an essential tool to facilitate the evolution of the metaverse.

Persistent AR is an innovative technology that overlays digital objects in real-world environments seamlessly and persistently. In other words, it allows users to interact with virtual elements in the real world for an extended period without interruptions. Persistent AR has gained immense popularity in various fields, including entertainment, education, gaming, and retail, to name a few.

A noteworthy example of Persistent AR is the game “Pokémon GO.” This game overlays digital creatures in the user’s real-world environment and allows them to interact with them in real time. The game uses the smartphone’s camera and GPS to track the user’s movements, enabling the creatures to follow them. This type of persistent AR is also known as location-based AR.

Another example of persistent AR is the app “IKEA Place.” This app allows users to place digital furniture in their real-world environment and see how it would look. The app uses the smartphone camera to measure the room’s dimensions, ensuring the digital furniture is proportionate and scaled correctly.

The retail industry uses Persistent AR to enhance the shopping experience. Many cosmetic companies have launched AR try-on apps allowing customers to try different makeup products virtually. These apps use facial recognition technology to map the customer’s face, allowing them to see how different products look on their skin.

Persistent AR is revolutionising how we interact with digital content in the real world. Its endless possibilities offer numerous benefits in various industries, making it an essential tool for the future of technology.

Persistent Augmented Reality (AR) technology enables the placement of virtual objects in the same location each time they are viewed. Apple ARKit and Google ARCore, amongst other mobile AR APIs, allow this kind of experience to be created. Marketers can use this by deploying virtual billboards that stay visible within a given area of the metaverse.

Platforms and brands leading in AR

  • Apple: Apple’s AR kit has made it easy for developers to create AR experiences for iOS devices. Apple has also integrated AR into its products, such as the AR-enabled Measure app.
  • Snapchat: Snapchat’s AR filters and lenses have been a hit with users, allowing brands to create their own AR filters and lenses for advertising.
  • Google: Google’s ARCore has made it easy for developers to create AR experiences for Android devices. Google has also integrated AR into its products, such as the AR-enabled Google Maps.
  • Facebook: Facebook’s Spark AR Studio allows developers to create AR experiences for Facebook, Instagram, and Messenger. Facebook has also launched AR ads allowing brands to create interactive AR ads.

Use cases of AR across Industries

Augmented Reality is a game-changer in the marketing field. It provides brands with a new platform to create immersive experiences that are personalised and engaging. The application of AR in industries like banking, travel, retail, medicine, and fitness highlights the potential for AR to transform the way we interact with products and services. As more brands embrace AR, we expect to see more innovative and exciting use cases emerge.

Here are some noteworthy examples of how various industries are utilising AR technology. 

  • Banking: AR can be used in banking to create virtual banking experiences that allow customers to manage their finances in a more engaging way. Bank of America’s AR app lets customers visualise their account balances and transactions.
  • Travel: AR can be used to create virtual tours that allow customers to explore destinations before they book their trips. Marriott’s AR app allows customers to explore hotel rooms and amenities before booking.
  • Retail: AR can create virtual shopping experiences that allow customers to try on products and visualise how they will look in their homes. Ikea’s AR app allows customers to see how appliances and furniture will look in their homes.
  • Medical: AR can be used in medicine to create virtual training experiences that allow medical professionals to practice procedures in a safe and controlled environment. For example, AccuVein’s AR device enables medical professionals to see veins under the skin to facilitate IV insertions.
  • Fitness: AR can be used in fitness to create virtual workout experiences that allow customers to participate in personalised and engaging workouts. Peloton’s AR app allows customers to participate in virtual cycling classes and see their progress in real-time.
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How Can AR Be Used for Marketing Today?

While it’s still early days for AR, and we have not quite reached widespread adoption, brands must start thinking about using AR capabilities now to prepare for the future once it is accessible enough for rapid mass adoption. It is essential to understand what makes the technology better and what practical use cases exist for this technology. How can AR help brands position their products in the best way possible? And how can it enhance experience and engagement? 

The technology enables marketers to position their products and services favourably while allowing customers to build an experience around the products that pique their interest. In this way, AR opens a world of possibilities not only on how but also where and the scenarios in which the product may be used.

In a world where customers want to be in control of how they want to engage with brands, AR can become an essential component of a marketer’s toolkit. 

Pairing AR in the metaverse to elevate brand experiences.

How does AR marketing fit into the metaverse for brands? 

While the mass adoption of the metaverse is still in progress, there is a significant number of regular users that brands can leverage. 

Marketers must understand that the metaverse extends beyond virtual worlds, gaming, and PR events. The metaverse’s larger opportunity for brands lies in the real world rather than within metaverse worlds like Horizon Worlds, Decentraland, Roblox, or the Sandbox.

The most significant advantage of using AR is the endless possibilities it creates that do not exist in real life. 

In the metaverse, AR will allow consumers to interact with and experience a product before buying it, creating a solid connection between the buyer and the product. Online shoppers can use virtual try-on filters to see how they would look wearing hats, shoes, watches, and clothing, among other items, without visiting a store. This increases customer satisfaction and significantly reduces returns and exchanges. Similarly, furniture shoppers can test whether a product will fit in their living rooms and many other products.

What does this mean to brands when it comes to AR marketing in the metaverse? Although we haven’t yet reached the mass adoption stage, brands have enough consumers to begin testing AR as a marketing and engagement tool. 

Imagine virtual artwork on our living room wall that is animated and keeps changing; people can also interact with it. Or consider assembling furniture or other complex products, where AR can provide step-by-step visual instructions overlayed directly onto the assembled objects. AR can be integrated into gaming experiences, allowing virtual objects and characters to interact with the real world. For example, players can use their smartphones to see virtual creatures overlaid in their physical environment and engage in augmented reality battles or quests.

These scenarios are meaningful opportunities for brands to engage with prospects and customers. Currently, many brands use AR to enhance their marketing efforts, and most often, this is accomplished through AR-enhanced mobile apps.

We know that today’s consumers prefer shopping online, and AR helps provide what is missing from the online shopping experience. With AR, they can try things out and interact with a product before purchasing it, mimicking the in-store experience more closely. 

The rapid pace of technological advancements and the continuous implementation of innovative ideas in immersive Reality suggests that the emergence of the metaverse is inevitable. The metaverse is set to be constructed collectively, with many imaginative individuals contributing creative concepts and practical applications daily. And AR, more than VR, will be at the forefront of this metaverse. 

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In today’s hypercompetitive market, pricing and promotion optimisation have become crucial for brands’ success. With the rise of price-sensitive consumers, companies must find ways to offer value without sacrificing profit margins. And that’s where data analytics comes in!

According to a recent study, companies that use data analytics to optimise pricing and promotions see an average revenue increase of 2-7%. That’s a significant boost to your bottom line!

But what exactly is a price-sensitive consumer? Well, studies have shown that nearly 60% of shoppers are price-sensitive when making purchase decisions. These consumers are highly aware of prices and will compare prices between products and brands to get the best value for their money.

As a marketer or market researcher, understanding the behaviour of price-sensitive consumers is essential for developing effective pricing and promotion strategies. Data analytics lets you gain insights into their purchasing patterns, preferences, and attitudes toward pricing and promotions.

This blog will explore how data analytics can help you optimise pricing and promotions for price-sensitive consumers. We’ll cover different pricing strategies, promotions and discounts, data collection and analysis, and provide real-world case studies and best practices. So, let’s dive in and learn how to use data analytics to boost your revenue and attract more price-sensitive consumers!

Understanding Price-Sensitive Consumers: Unlocking the Secrets of Their Behavior

Have you ever wondered what drives price-sensitive consumers to make purchasing decisions? Understanding their behaviour is the key to unlocking the secrets of their buying patterns and preferences.

Research shows that price-sensitive consumers are not necessarily bargain hunters but value seekers. They are looking for products and services that offer the best value for their money, not necessarily the cheapest option. Therefore, they tend to be loyal to brands that provide consistent quality, even if they are slightly more expensive.

One way to understand the behaviour of price-sensitive consumers is by analysing their demographics. Studies show that age, income, and education level are key factors that influence their purchasing decisions. For instance, younger, lower-income consumers tend to be more price-sensitive than older, more affluent consumers.

Another way to gain insight into the behaviour of price-sensitive consumers is by looking at their shopping habits. They tend to be more likely to buy on sale or during promotions, and they tend to be more willing to switch brands to save money. In fact, nearly 60% of price-sensitive consumers will switch brands if they find a better deal.

Understanding the psychology behind price-sensitive consumers is also important. They tend to experience more guilt and regret when making purchasing decisions, which can influence their behaviour. Therefore, offering clear and transparent pricing and promotions can help ease their guilt and increase their satisfaction with their purchase.

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Choosing the Right Pricing Strategy: How to Optimise Value for Price-Sensitive Consumers

Choosing the right pricing strategy is crucial for attracting and retaining price-sensitive consumers. With so many options available, it can be challenging to determine which strategy is right for your brand.

One common pricing strategy is cost-plus pricing, where a business adds a markup to its production costs to set a price. However, this strategy does not take into account the value perceived by consumers, and it may not be effective for price-sensitive consumers.

Another popular pricing strategy is value-based pricing, which sets a price based on the perceived value of the product or service to the customer. This strategy is particularly effective for price-sensitive consumers because it focuses on delivering value rather than simply offering the lowest price.

In fact, research shows that nearly 70% of consumers are willing to pay more for products and services that provide a superior experience. By focusing on value-based pricing, businesses can attract price-sensitive consumers looking for quality and value over the cheapest option.

Dynamic pricing is another pricing strategy that is effective for price-sensitive consumers. This strategy adjusts prices based on demand, allowing businesses to charge more during peak times and offer discounts during slower periods. This strategy can be particularly effective for businesses in industries with high demand fluctuations, such as the travel industry.

Ultimately, the right pricing strategy for your business will depend on your industry, product, or service, and target audience. By understanding the behaviour of price-sensitive consumers and the different pricing strategies available, you can develop a pricing strategy that maximises value and attracts price-sensitive consumers.

Promotions and Discounts: The Key to Attracting Price-Sensitive Consumers

Promotions and discounts are powerful tools for attracting price-sensitive consumers. In fact, nearly 90% of consumers say that promotions and discounts influence their purchasing decisions.

One popular promotion strategy is flash sales, which offer a limited-time discount on products or services. These sales can create a sense of urgency and scarcity, encouraging consumers to purchase before the promotion ends. Flash sales can be particularly effective for attracting price-sensitive consumers looking for a good deal.

Coupons are another effective promotion strategy. Research shows that nearly 80% of consumers use coupons when shopping. Coupons can be distributed through various channels, such as social media, email, or direct mail. They can also be personalised to target specific consumer segments, such as price-sensitive consumers who have previously purchased a product or service from your business.

Loyalty programs are another effective way to attract price-sensitive consumers. These programs offer rewards, discounts, or other incentives to customers who make repeat purchases or engage with your business in other ways. Loyalty programs can be particularly effective for retaining price-sensitive consumers and encouraging them to make repeat purchases.

It’s important to note that while promotions and discounts can effectively attract price-sensitive consumers, they can also reduce your profit margins. Therefore, it’s essential to carefully consider the cost of each promotion or discount and its potential return on investment.

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Data Collection and Analysis: Using Insights to Develop Effective Promotions and Discounts

Data collection and analysis are essential for developing effective promotions and discounts that appeal to price-sensitive consumers while maximising profitability.

One way to collect data is through sales data analysis. By analysing sales data, you can identify which products or services are popular among price-sensitive consumers and develop promotions or discounts to increase their value perception.

Another way to collect data is through surveys. Surveys can provide valuable insights into the behaviour and preferences of price-sensitive consumers. For instance, you can use surveys to determine which promotions or discounts appeal to price-sensitive consumers or what factors influence their purchasing decisions.

Social media analytics is another valuable source of data. Social media platforms provide a wealth of information about consumer behaviour, such as what types of products or services they are interested in and what kinds of promotions or discounts they respond to.

Once you have collected data, it’s important to analyse it to gain insights into the behaviour of price-sensitive consumers. This can involve using statistical methods to identify patterns or trends in the data, such as which promotions or discounts are most effective or which consumer segments are most price-sensitive.

Using data analysis, you can develop promotions and discounts tailored to the behaviour and preferences of price-sensitive consumers. This can increase the effectiveness of your promotions and discounts while also maximising profitability.

Case Studies: Real-World Examples of Using Data Analytics to Optimise Pricing and Promotions for Price-Sensitive Consumers

Using data analytics to optimise pricing and promotions is not just a theoretical concept; many companies have successfully implemented these strategies to increase revenue and attract price-sensitive consumers. Let’s look at some real-world case studies.

Case Study 1: Amazon

Amazon is a leader in using data analytics to optimise pricing and promotions. The company uses sophisticated algorithms to adjust prices based on demand and competitor pricing dynamically. For instance, during the holiday season, Amazon adjusts prices every 10 minutes to ensure they offer the best deal to price-sensitive consumers.

Additionally, Amazon uses data analytics to personalise promotions and discounts for individual consumers. By analysing customer data, Amazon can offer targeted promotions that appeal to price-sensitive consumers and increase their value perception.

Case Study 2: Walmart

Walmart is another company that has successfully used data analytics to optimise pricing and promotions for price-sensitive consumers. The company uses algorithms to analyse sales data and identify trends and patterns in consumer behaviour. This allows Walmart to develop targeted promotions that appeal to specific consumer segments, such as price-sensitive consumers.

Walmart also uses data analytics to optimise its pricing strategies. For instance, the company has found that offering lower prices on certain items can increase foot traffic and increase sales of other, higher-margin items.

Case Study 3: Starbucks

Starbucks has also used data analytics to optimise its pricing and promotions strategies. The company analyses sales data to identify popular products among price-sensitive consumers and develop targeted promotions and discounts.

Additionally, Starbucks uses loyalty programs to retain price-sensitive consumers. The company’s rewards program offers personalised promotions and discounts to members based on their purchasing history, encouraging them to make repeat purchases and increasing their value perception.

These case studies demonstrate the power of data analytics in optimising pricing and promotions for price-sensitive consumers. By using data to gain insights into consumer behaviour and preferences, businesses can develop strategies that appeal to price-sensitive consumers while maximising profitability.

Best Practices: Actionable Recommendations for Optimising Pricing and Promotions for Price-Sensitive Consumers

Now that we’ve explored the importance of data analytics in optimising pricing and promotions for price-sensitive consumers, let’s summarise the key takeaways and provide actionable recommendations for marketers and market researchers.

  1. Understand the behaviour of price-sensitive consumers: By analysing demographics, shopping habits, and psychology, you can develop strategies that appeal to price-sensitive consumers.
  2. Choose the right pricing strategy: Consider value-based pricing, dynamic pricing, and other strategies focusing on delivering value rather than simply offering the lowest price.
  3. Use promotions and discounts strategically: Use flash sales, coupons, and loyalty programs to attract price-sensitive consumers while maximising profitability.
  4. Collect and analyse data: Use sales data analysis, surveys, and social media analytics to gain insights into consumer behaviour and preferences.
  5. Personalise promotions and discounts: Use data analysis to develop personalised promotions and discounts that appeal to specific consumer segments.
  6. Optimise pricing and promotion strategies continuously: Use data analysis to adjust your pricing and promotion strategies based on consumer behaviour and market trends.

By following these best practices, you can develop effective pricing and promotion strategies that appeal to price-sensitive consumers while maximising profitability. Remember, using data analytics is key to achieving this goal.

The Future of Pricing and Promotions: Emerging Trends and Technologies

As technology advances, the future of pricing and promotions is constantly evolving. Let’s explore some emerging trends and technologies shaping the future of pricing and promotions for price-sensitive consumers.

  1. Artificial Intelligence (AI): AI is becoming increasingly important in pricing and promotions. AI algorithms can analyse vast amounts of data and identify patterns and trends in consumer behaviour, allowing businesses to develop personalised promotions and discounts that appeal to price-sensitive consumers.
  2. Augmented Reality (AR): AR technology can be used to enhance the shopping experience for price-sensitive consumers. For instance, AR can be used to provide virtual try-on experiences for clothing and makeup products, allowing consumers to see how the products look before making a purchase.
  3. Subscription Services: Subscription services are becoming more popular among price-sensitive consumers. By offering a subscription service, businesses can provide consistent value to consumers while increasing revenue and encouraging repeat purchases.
  4. Dynamic Pricing: Dynamic pricing is becoming more sophisticated, with businesses using AI algorithms to adjust prices in real time based on demand and consumer behaviour. This allows brands to offer personalised pricing that appeals to price-sensitive consumers while maximising profitability.
  5. Mobile Payments: Mobile payments are becoming more popular among price-sensitive consumers, with nearly 80% of consumers using mobile payments at least once a week. By offering mobile payment options, businesses can make purchasing more convenient and appealing to price-sensitive consumers.

As these emerging trends and technologies evolve, brands must adapt and use data analytics to stay ahead of the competition. By embracing these trends and using data to gain insights into consumer behaviour, businesses can develop effective pricing and promotion strategies that appeal to price-sensitive consumers and maximise profitability.

Using Data Analytics to Optimise Pricing and Promotions for Price-Sensitive Consumers

In today’s hypercompetitive market, brands must find ways to appeal to price-sensitive consumers while maximising profitability. Using data analytics, brands can gain insights into consumer behaviour and develop effective pricing and promotion strategies that appeal to price-sensitive consumers.

Research shows that nearly 60% of shoppers are price-sensitive when making purchase decisions. This is a significant percentage of consumers that brands cannot afford to ignore.

Using data analytics to understand the behaviour of price-sensitive consumers, businesses can develop pricing and promotion strategies that maximise value and appeal to their preferences. This can increase revenue, attract new customers, and retain existing ones.

From understanding the behaviour of price-sensitive consumers to choosing the right pricing strategy, strategically using promotions and discounts, collecting and analysing data, personalising promotions and discounts, and optimising pricing and promotion strategies continuously, businesses can use data analytics to stay ahead of the competition and appeal to price-sensitive consumers.

As technology evolves, businesses must adapt and embrace emerging trends and technologies, such as AI, AR, subscription services, dynamic pricing, and mobile payments, to continue attracting price-sensitive consumers and increasing revenue.

Data analytics is a powerful tool for businesses to optimise pricing and promotions for price-sensitive consumers. Using data analytics to understand consumer behaviour and preferences, brands can develop effective pricing and promotion strategies that appeal to price-sensitive consumers while maximising profitability. 

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Imagine this: it’s the year 2033, and you’re a market researcher tasked with analysing a massive dataset of consumer responses to a new product launch. 

In the past, this would have taken you weeks, if not months, of manually sorting through surveys, analysing focus group transcripts, and summarising the findings. But now, with the help of Large Language Models (LLMs), the task is completed in a matter of days. 

You simply upload the dataset to your computer, and within minutes, the LLM has sorted through and prioritised the responses, highlighting key themes and sentiment analyses that give you a comprehensive understanding of what consumers think about the product.

This hypothetical scenario may seem far-fetched, but with the rapid advancement of LLM technology in recent years, it’s closer than you might think. Large Language Models have the potential to revolutionise the market research industry, transforming the way we analyse and interpret data and making our jobs easier and more efficient.

But what exactly are Large Language Models, and how do they work? This article will explore the world of LLMs and their impact on market research. We’ll delve into their potential uses in market research, including summarising responses, automating reporting, and identifying themes and sentiments. We’ll also discuss the potential risks of using LLMs in market research. 

What are Large Language Models?

Before we dive into how Large Language Models (LLMs) are changing market research, let’s take a step back and explore what LLMs are and how they work.

At their core, LLMs are algorithms designed to predict the next word or phrase in a sequence based on the relationships between words in a large dataset. To accomplish this, LLMs use a technique called unsupervised learning, where the algorithm is given a large amount of data and left to find patterns and relationships on its own.

One of the most well-known examples of LLMs is ChatGPT (Generative Pre-trained Transformer), developed by OpenAI. ChatGPT is one of the largest LLMs, with 175 billion parameters, allowing it to perform various tasks with impressive accuracy.

So how does an LLM work in practice? Let’s take a simple example: predicting the next word in the sentence “The cat sat on the ____”. An LLM trained on a large dataset would be able to predict that the most likely word to complete the sentence is “mat”, followed by “chair”, “table”, and so on.

The power of LLMs comes from their ability to learn statistical relationships between words through their co-occurrences in large datasets. An LLM can identify patterns and correlations between words and phrases that a human researcher might miss by analysing massive amounts of text data.

But it’s important to note that LLMs are not sentient beings and do not wholly understand language. Instead, they rely on statistical associations and correlations to make predictions, sometimes leading to errors or misunderstandings.

Despite these limitations, the potential applications of LLMs in market research are vast and varied. In the next section, we’ll explore some of the ways LLMs are changing the field of market research.

The Potential of Large Language Models in Market Research

Large Language Models have the potential to revolutionise the way market research is conducted. They can speed up processes, enhance accuracy, and identify trends that human researchers might miss. 

Here are some of the potential applications of LLMs in market research:

  1. Summarisation: Market research generates vast amounts of data through surveys, qualitative interviews, and focus groups. LLMs can quickly summarise, order, and prioritise responses, allowing researchers to create a narrative for clients more efficiently.
  2. Automated reporting: Market research also produces large volumes of quantitative data that need sorting, summarising, and presenting. LLMs can quickly organise and create draft headlines based on charts, tables, models, and executive summaries.
  3. Topic/theme identification: LLMs can analyse different attitudinal datasets or open APIs to digital platforms, identify themes, and assess sentiment, affinity, and brand perceptions, providing researchers with insights to refine their research.
  4. Prediction: LLMs can extract embeddings (mathematical representations) that other machine learning models can use to predict outcomes of interest. For instance, they can predict the performance of a TV ad based on the dialogue or relate people’s qualitative experience interacting with a service representative to their brand loyalty or churn.
  5. Intelligent interviewing: Conversational AI can be used to automate and standardise the process of designing quant questionnaires. Additionally, conversational AI will come on in leaps and bounds, responding to previous answers and routing questions accordingly.
  6. Text data cleaning: Cleaning text data is crucial to the operational process. LLMs can check for gibberish and spelling errors much better than autocorrect ever did.
  7. Creative Writing: LLMs can be used to create discussion guides, initial drafts of presentations, marketing copy, and concept statements.
  8. Conversational search queries: With LLMs, an intelligent agent can sit on top of data platforms, analysing potentially massive databases and fetching results back in natural language.
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These are just a few examples of how LLMs are changing market research. LLMs offer a level of efficiency, accuracy, and scalability unparalleled by traditional market research methods. However, there are risks associated with using LLMs, which we will explore in the next section.

Risks Associated with Large Language Models

While Large Language Models offer immense potential to the market research industry, there are risks associated with their use. Here are some of the risks that researchers and organisations should be aware of:

  1. Hallucinations and false predictions: LLMs may make incorrect predictions, particularly when they encounter novel or ambiguous data. Sometimes, they may even make things up or ‘hallucinate,’ leading to false predictions.
  2. Bias reinforcement: LLMs learn from the data they are trained on. If the training data contains biases, the LLM may reinforce them in its predictions.
  3. Ethical issues: LLMs can raise ethical issues concerning privacy, consent, and intellectual property. For instance, using data scraped from social media platforms without users’ consent may raise ethical concerns.
  4. Limited understanding: LLMs are limited in understanding language and interpreting data. They rely on statistical associations and correlations to make predictions, and there are limitations to how much they can understand and learn.
  5. Legal issues: There may be legal issues related to the use of LLMs, particularly regarding intellectual property and privacy laws.
  6. Lack of transparency: LLMs are often black boxes, meaning it is difficult to understand how they arrive at their predictions. This lack of transparency can be problematic, particularly when the predictions have significant implications.
  7. Dependence on data quality: LLMs require high-quality data to perform effectively. If the data used to train an LLM is of low quality, the predictions made by the model may be inaccurate.

Large Language Models offer immense potential to the market research industry, allowing researchers to process vast amounts of data more efficiently and accurately than ever. However, researchers and organisations must be aware of the risks associated with their use and take steps to mitigate them. 

LLMs are not a magic solution that can replace human researchers entirely, but they can significantly enhance the work that researchers do. The key is to approach LLMs with caution, ensuring that they are used ethically and responsibly to realise their full potential.

Best Practices for Using Large Language Models in Market Research

To ensure that Large Language Models are used ethically and responsibly in market research, following some best practices is essential. Here are some guidelines for using LLMs in market research:

  1. Understand the limitations: It’s crucial to understand the limitations of LLMs and to avoid overestimating their capabilities. LLMs are not sentient beings and cannot replace human researchers entirely.
  2. Use high-quality data: LLMs require high-quality data to perform effectively. Researchers should ensure that the data used to train an LLM is representative, unbiased, and of high quality.
  3. Address potential biases: LLMs may learn from biased data and reinforce those biases in their predictions. Researchers should be aware of this risk and take steps to address potential biases in the data.
  4. Ensure transparency: LLMs are often black boxes, making understanding how they arrive at their predictions difficult. Researchers should ensure that the LLMs used in their research are transparent and that the methods used to arrive at predictions are clearly documented.
  5. Ethical considerations: Researchers should be aware of ethical considerations related to privacy, consent, and intellectual property when using LLMs in market research. It’s essential to obtain participants’ consent and ensure that data is used ethically.
  6. Verify predictions: It’s crucial to verify the predictions made by LLMs to ensure their accuracy. Researchers should take a critical approach to LLM predictions and verify them through human review.
  7. Partner with experts: LLMs are complex and require expertise to use effectively. Researchers should partner with experts in the field to ensure that LLMs are used correctly and ethically.

By following these best practices, researchers can use LLMs effectively in market research and ensure they are used ethically and responsibly. LLMs offer immense potential to the market research industry, and by using them responsibly, we can unlock their full potential while avoiding potential risks.

The Future of Large Language Models in Market Research

As we have seen, Large Language Models offer immense potential to the market research industry. With their ability to process vast amounts of data more efficiently and accurately than ever, LLMs can revolutionise market research. However, their use must be approached with caution, and researchers must take steps to mitigate potential risks.

The future of Large Language Models in market research is exciting. With advances in technology and data quality, LLMs will become more sophisticated and effective, enabling researchers to gain insights into consumer behaviour and preferences that were previously impossible to obtain. As LLMs evolve, we can expect them to play an increasingly critical role in the market research industry.

However, it’s important to remember that LLMs are not a replacement for human researchers. While they can significantly enhance researchers’ work, they cannot replace human insight and intuition. LLMs should be used with human researchers, and their predictions should always be verified through human review.

Large Language Models are changing the face of market research, offering new and exciting possibilities for the industry. While risks are associated with their use, they can be mitigated through responsible and ethical use. By following best practices and partnering with experts in the field, market researchers can harness the full potential of Large Language Models to gain insights into consumer behaviour and preferences that were previously impossible to obtain. The future of market research is bright, and Large Language Models will undoubtedly play a critical role in shaping it.

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The Ethical Considerations of Large Language Models

While the potential of Large Language Models is vast, ethical considerations must be taken into account. One of the most significant concerns is the potential for bias in the data used to train Large Language Models.

Large Language Models are trained on massive datasets that include vast amounts of text from a wide range of sources. However, these datasets can consist of biases and stereotypes in the data. For example, suppose a dataset includes a disproportionate amount of text from male authors. In that case, the Large Language Model may learn to associate certain words or concepts with men more than women.

This can have significant implications for the accuracy and fairness of the predictions made by Large Language Models. For example, if a Large Language Model is used to make hiring recommendations, it may unintentionally perpetuate gender or racial biases in the data used to train it.

Another concern is the potential for Large Language Models to generate misleading or harmful content. Large Language Models can generate fake news, propaganda, or hate speech, which can have significant real-world consequences.

To address these concerns, businesses and researchers must take steps to mitigate the risks associated with Large Language Models. This includes using diverse and representative datasets to train models, ensuring transparency in the use of Large Language Models, and actively monitoring and addressing potential biases in the predictions made by the models.

While Large Language Models offer immense potential to businesses and researchers, their use must be approached with caution and responsibility. By addressing the ethical considerations associated with Large Language Models, we can ensure that they are used to benefit society as a whole.

Final thoughts

Large Language Models are changing how we interact with technology, opening up new possibilities for businesses and researchers alike. From market research and customer service to content creation and data analysis, Large Language Models have the potential to revolutionise the way we operate in almost every industry.

However, as with any new technology, there are ethical considerations that must be taken into account. Ensuring the accuracy and fairness of Large Language Models is critical, particularly regarding decision-making processes that can have significant real-world consequences.

Moving forward, brands and researchers must approach the use of Large Language Models with caution and responsibility, taking steps to address the ethical considerations associated with this technology. By doing so, we can ensure that Large Language Models are used to benefit society as a whole rather than perpetuating biases and perpetuating harm.

Overall, the potential of Large Language Models is enormous, and we’re just beginning to scratch the surface of what this technology can do. The future of business and research is bright, and with Large Language Models leading the way, we’re sure to see some exciting developments in the years to come.

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The workplace is changing rapidly, and one of the most significant drivers of this change is automation. From factory floors to office cubicles, machines are taking over many tasks humans once did. While this shift has undoubtedly brought benefits in terms of efficiency and productivity, it has also raised concerns about the future of work. Will there be enough jobs for humans in an automated world? And what skills will be most valuable in this new landscape?

One thing that’s clear is that human creativity will remain essential, even as machines become increasingly sophisticated. While automation can handle routine tasks and process large amounts of data, it cannot replicate the unique perspective and problem-solving abilities of the human mind. Creativity will remain a critical asset in the workplace of the future.

In this article, we’ll explore the rise of automation and its impact on the workforce. We’ll also discuss the value of human creativity and its role in the future of work. Finally, we’ll offer some tips and strategies for companies that want to foster creativity in their workforce and stay ahead of the curve in this rapidly changing landscape.

The Rise of Automation

Automation is not a new phenomenon, but recent technological advances have made it more widespread than ever before. From self-driving cars to chatbots, machines are taking over an increasing number of tasks that were once done by humans. According to a recent report, up to 375 million workers (about 14% of the global workforce) may need to switch occupations or acquire new skills by 2030 due to automation.

Some industries are more likely to be impacted than others. For example, manufacturing has already seen significant job losses due to automation, and service industries like retail and hospitality are also at risk. Even traditionally white-collar jobs like accounting and legal services are not immune to automation, as machines become better at analyzing data and processing information.

While automation can bring benefits in increased efficiency and lower costs, it also has drawbacks. One of the main concerns is that it will lead to job losses, particularly in industries where routine tasks are being automated. There are also concerns about the impact on the quality of jobs that remain, as many of the tasks that cannot be automated are low-paying and low-skilled.

Despite these concerns, there are also reasons to be optimistic about the future of work. As automation takes over routine tasks, there will be a growing need for workers who can think creatively and develop innovative solutions to complex problems. This is where human creativity comes in.

The Value of Human Creativity

One of the main advantages of human creativity is that it allows us to do things that machines cannot. While machines are great at processing large amounts of data and following set rules, they cannot think outside the box or come up with truly novel ideas.

Creativity is also essential for innovation. To stay competitive, companies must constantly come up with new products, services, and ways of doing things. This requires the ability to think creatively and the willingness to take risks and try new things.

  • 90% of business leaders believe that the skills needed in the future will differ from those required today (source: Deloitte).

Another benefit of human creativity is that it allows us to connect with other people on an emotional level. Machines may be able to process information and provide answers, but they cannot replicate the empathy and understanding that comes from human interaction. This is particularly important in industries like healthcare and education, where human connection is essential to the work being done.

“The future of work is not about replacing humans with machines; it’s about augmenting human capabilities with technology.” – Satya Nadella, CEO of Microsoft.

In an increasingly automated world, the value of human creativity will only continue to grow. As machines take over routine tasks, workers who can think creatively and come up with innovative solutions will be more valuable than ever. This means that companies will need to invest in fostering creativity in their workforce and finding ways to tap into their employees’ unique perspectives and problem-solving abilities.

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The Future of Work

As automation continues to transform industries, the workforce is likely to change in significant ways. Some jobs will become obsolete, while new roles will emerge due to automation.

For example, there will be an increasing demand for workers who can design and program machines, as well as those who can manage and maintain them. There will also be a growing need for workers who can analyse and use data to make informed decisions. However, even in these roles, creativity will remain essential.

One area where creativity plays a critical role is problem-solving. As machines take over routine tasks, workers will be free to focus on more complex problems that require a human touch. This could include customer service, product design, and strategic planning.

  • Creativity will be one of the top three most important skills for workers in 2025 (source: World Economic Forum).

To succeed in this new landscape, workers must be adaptable and willing to learn new skills. They will also need to be comfortable with ambiguity and able to think creatively about complex problems.

For companies, this means investing in their workforce and providing opportunities for training and development. It also means creating a culture that values creativity and encourages collaboration and innovation.

The Role of Market Research

Market research can play a valuable role in helping companies stay ahead of the curve within the changing work landscape. By conducting research and gathering insights about the skills and attributes that will be most valuable in the future, companies can better prepare their workforce and position themselves for success.

Market research can be beneficial in identifying the skills and attributes that will be most in demand in the future. For example, a company might conduct research to identify the skills required for jobs that are likely to emerge due to automation. They might also gather insights about the skills that will be most valuable in industries that are likely to be less impacted by automation.

Market research can also help companies better understand the needs and preferences of their workforce. For example, a company might conduct research to gather insights about what motivates employees and what types of work environments are most conducive to creativity and innovation.

Finally, market research can help companies identify opportunities for innovation and growth. By gathering insights about changing customer needs and preferences, companies can develop new products and services that meet those needs and stay ahead of the competition.

  • Investment in retraining and reskilling could generate up to $11.5 trillion in global economic activity by 2028 (source: Oxford Economics).

To succeed in the future of work, companies must be proactive and adaptive. By leveraging the insights provided by market research, they can position themselves for success and ensure that their workforce is equipped with the skills and attributes needed to thrive in an increasingly automated world.

Fostering Creativity in the Workplace

Companies must foster creativity in their workforce to stay competitive in an increasingly automated world. Here are some tips and strategies for doing so:

  1. Encourage Collaboration: Collaboration is essential for creativity. Encourage your employees to work together and share ideas. Create opportunities for cross-functional teams to work on projects together.
  2. Provide Training and Development: Invest in your workforce by providing opportunities for training and development. This could include things like workshops, courses, and coaching.
  3. Create a Culture of Innovation: Foster a culture that values innovation and encourages employees to take risks and try new things. Celebrate successes and learn from failures.
  4. Embrace Diversity: A diverse workforce brings diverse perspectives and ideas. Embrace diversity and create a culture that values inclusivity.
  5. Provide Time and Space for Creativity: Creativity requires time and space to flourish. Provide your employees with the time and resources they need to be creative.

Industries Where Human Creativity is Critical

While healthcare and education are two industries where human connection is essential, there are many other industries where creativity plays a critical role. Here are some examples:

  1. Advertising: Advertising is all about creativity. Companies need to be able to create compelling messages and visuals that capture the attention of their target audience. This requires creative thinking and the ability to anticipate trends and stay ahead of the competition.
  2. Design: Designers create everything from product packaging to digital interfaces. To be successful in this field, designers need to be able to think creatively and come up with innovative solutions to design problems.
  3. The Arts: From visual art to music to theatre, the arts are all about human creativity. Whether it’s composing a new piece of music or creating a new painting, artists rely on their creativity to express themselves and connect with their audience.
  4. Fashion: The fashion industry is all about creativity and innovation. From designing new clothing lines to creating eye-catching window displays, fashion professionals need to be able to think outside the box and come up with new and exciting ideas.
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Preparing Students for the Jobs of the Future

With automation rapidly transforming the workforce, it’s essential to consider how education can be adapted to prepare students for future jobs. Here are some ways that education can help prepare students for the changing landscape of work:

  1. Teaching Problem-Solving Skills: Problem-solving is a critical skill in an automated world. Workers will need to be able to analyse complex problems and come up with creative solutions. Schools can teach problem-solving skills by allowing students to work on real-world problems and encouraging them to collaborate and think creatively.
  2. Fostering Critical Thinking: Critical thinking is another vital skill for the future of work. Workers must be able to analyse data and information and make informed decisions. Schools can foster critical thinking by teaching students how to evaluate information and arguments and encouraging them to think critically about the world around them.
  3. Encouraging Creativity: As discussed, human creativity will be a critical asset in an automated world. Schools can encourage creativity by providing students with opportunities to express themselves through art, music, and writing and by encouraging them to think outside the box and develop innovative solutions to problems.
  4. Teaching Digital Skills: As automation becomes more widespread, digital skills will become increasingly important. Schools can prepare students for the future of work by teaching them how to use technology effectively and adapt to new digital tools and platforms.

The Impact of Automation on Workers

While automation has many benefits in terms of increased efficiency and productivity, it also has the potential to impact workers negatively. Here are some of the potential negative impacts of automation on workers:

  1. Job Loss: The most apparent impact of automation is the potential for job loss. As machines take over routine tasks, workers in these fields may find themselves out of work. This can be particularly difficult for workers lacking the skills or resources to transition to new roles.
  2. Reduced Job Security: Even workers not directly impacted by automation may find themselves at risk of reduced job security. As companies increasingly rely on automation to cut costs and increase efficiency, workers may face layoffs or reduced hours.
  3. Lower Wages: In some cases, automation can lead to lower wages for workers. This may happen if machines can perform tasks more quickly and efficiently than humans, decreasing the value of human labour.
  4. Need for Retraining: For workers displaced by automation, retraining will be essential. However, it may be difficult for some workers to access the resources and support needed to learn new skills and transition to new roles.

As automation continues to transform the workforce, it will be necessary for companies and policymakers to consider how to mitigate the potential negative impacts on workers. This could include investing in programs to retrain displaced workers, providing job security and fair wages, and supporting workers as they adapt to the changing work landscape.

  • 30% of workers are at high risk of being displaced by automation by the mid-2030s (source: PwC).

Overall, while automation has many benefits, it’s important to remember that it also has the potential to impact workers significantly. By taking steps to mitigate these impacts, we can ensure that the benefits of automation are shared more equitably and that workers can thrive in the changing work landscape.

In the face of automation, getting caught up in concerns about job loss and economic disruption is easy. However, it’s important to remember that automation also brings benefits in terms of increased efficiency and productivity. The key is to find the right balance between automation and human creativity.

“In an increasingly automated world, creativity is the new literacy.” – Gerard Adams, entrepreneur and investor.

As we’ve seen in this article, human creativity will remain essential in the future of work. While machines are great at routine tasks and processing large amounts of data, they cannot replicate the unique perspective and problem-solving abilities of the human mind. Companies that foster creativity in their workforce will be better positioned to thrive in the changing work landscape.

In addition to fostering creativity, companies must consider the impact of automation on workers. While automation can bring many benefits, it also has the potential to negatively impact workers through job loss, reduced job security, and lower wages. As such, companies and policymakers should consider how to mitigate these impacts and ensure that workers can thrive in an increasingly automated world.

“Automation can liberate human beings from the burden of repetitive work and free us to pursue more creative and fulfilling activities.” – Klaus Schwab, Founder and Executive Chairman of the World Economic Forum.

Market research can play an important role in helping companies stay ahead of the curve and identify the skills and attributes that will be most valuable in the future. By leveraging the insights provided by market research, companies can position themselves for success and ensure that their workforce is equipped with the skills and attributes needed to thrive in an increasingly automated world.

Preparing students for the jobs of the future will require a combination of traditional academic skills and newer digital and creative skills. By adapting their curriculum and teaching methods, schools can help ensure that students have the skills and attributes needed to succeed in an increasingly automated world.

The future of work is likely to be characterised by a blend of automation and human creativity. By balancing these two forces, companies can position themselves for success and ensure they can thrive in the changing work landscape. However, companies and policymakers need to consider the impact of automation on workers and for schools to prepare students for the jobs of the future. By taking a holistic approach, we can ensure that the benefits of automation are shared more equitably and that workers and students can thrive in the changing landscape of work.

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With the proliferation of smartphones and tablets, it’s no surprise that more and more people are completing surveys on their mobile devices. But what does this mean for marketers, product managers, and market researchers? 

In this article, we’ll explore how mobile devices have changed the survey landscape and why it’s crucial to design mobile-friendly surveys. We’ll dive into the various question types, discuss their effectiveness on mobile devices, and provide best practices for designing surveys that work well on screens of all sizes.

But first, let’s take a step back and consider how mobile devices have changed our interaction with technology. These devices have revolutionised how we communicate, consume content, and engage with brands in just a few short years. People spend more time on their phones than ever before, and this trend will continue.

As marketers and researchers, we must keep up with these changes and adapt our strategies accordingly. By understanding the impact of mobile devices on survey responses, we can design surveys that are more engaging, more effective, and ultimately more valuable for our businesses. So let’s dive in and explore the exciting world of mobile surveys!

The Mobile Survey Landscape

The mobile survey landscape is constantly evolving, and staying up-to-date with the latest trends and statistics is essential. According to Statista, in 2023, the current number of smartphone users in the world today is 6.92 billion, meaning 86.29% of the world’s population owns a smartphone. This means that a large percentage of survey respondents are completing surveys on their mobile devices.

While mobile surveys offer many benefits, such as increased convenience and accessibility, they also present some unique challenges. One of the biggest challenges is the limited screen size of mobile devices. It’s crucial to design surveys that are optimised for smaller screens, with clear and concise questions and answer options.

In a survey by Google, 94% of respondents reported using their smartphones to take surveys.

Another challenge is user attention span. Mobile users often multitask and are easily distracted, so surveys must be engaging and easy to complete. If a survey takes too long or requires too much effort, respondents will likely abandon it before completing it.

Despite these challenges, mobile surveys can be highly effective when designed correctly. In fact, a study found that mobile surveys have a completion rate that is 10% higher than desktop surveys. Additionally, mobile surveys tend to have higher response rates and lower costs, making them an attractive option for brands.

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Understanding Question Types

Understanding the different types of survey questions is crucial to designing effective mobile surveys. Let’s closely examine some of the most common question types and how they work on mobile devices.

Open-ended questions allow respondents to provide their own answers and can be useful for collecting qualitative data. However, they can be more challenging to answer on a mobile device, as they often require more typing and can be harder to read on a smaller screen. In contrast, closed-ended questions provide a set of predefined answer options, such as yes or no, and are often easier to answer on a mobile device.

Multiple-choice questions are a popular closed-ended question type, where respondents are given a set of answer options to choose from. These can be effective on mobile devices if the options are clear and easy to read. However, if the options are too lengthy or complex, they may be difficult to read on a small screen.

Rating scales are another common question type, where respondents are asked to rate their level of agreement or satisfaction on a scale of 1 to 5 or 1 to 10. Rating scales can be effective on mobile devices if they are designed to fit the smaller screen size, and the rating options are clearly labelled and easy to select.

Research by Quirk’s Media found that surveys optimised for mobile devices are completed 30-40% faster than those optimised for desktops.

It’s worth noting that some question types, such as matrix questions or grid questions, can be challenging to answer on a mobile device. These types of questions require respondents to evaluate multiple items, which can be difficult to do on a smaller screen.

Best Practices for Mobile-Friendly Surveys

Designing surveys that are mobile-friendly is crucial to maximising completion rates and gathering accurate data. Here are some best practices for designing mobile-friendly surveys:

  1. Keep it concise: Mobile users have limited attention spans, so it’s essential to keep survey questions and answer options short and to the point. Avoid using long or complicated sentences, and consider breaking up longer questions into smaller, more manageable chunks.
  2. Use clear formatting: Use a clear and easy-to-read font, with a font size of at least 14 points, to ensure the text is readable on smaller screens. Use plenty of white space between questions and answer options to help respondents navigate the survey more easily.
  3. Optimise for different devices: Make sure your survey is optimised for different screen sizes and device types. Test your survey on different devices to ensure it looks and functions correctly on each one.
  4. Keep answer options consistent: Make sure that answer options are consistent throughout the survey. This will make it easier for respondents to understand the question and select the appropriate answer.
  5. Provide clear instructions: Provide clear and concise instructions at the beginning of the survey to help respondents understand how to complete the survey. Include instructions on navigating the survey and how long it is expected to take.
  6. Use skip logic: Skip logic allows respondents to skip questions that are not relevant to them, which can help to reduce survey fatigue and improve completion rates. However, ensure that skip logic is used sparingly, as it can add complexity to the survey.
  7. Test and iterate: Testing and iterating are essential parts of survey design. Test your survey on a small sample of respondents before launching it to a larger audience, and use their feedback to make improvements.
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Key Takeaways

Mobile devices have revolutionised how people interact with technology, including completing surveys. To maximise response rates and gather accurate data, it’s essential to design mobile-friendly surveys.

This means selecting the right question types and optimising surveys for different screen sizes and devices.

Key takeaways from this blog post include:

  • Mobile devices are an important platform for survey completion and should be taken into consideration when designing surveys.
  • Closed-ended questions, such as multiple-choice questions and rating scales, tend to work better on mobile devices than open-ended questions.
  • Mobile surveys should be concise, well-formatted, and optimised for different devices.
  • Best practices for mobile surveys include keeping answer options consistent, providing clear instructions, and testing and iterating.

Brands and researchers can create engaging, effective surveys that provide valuable insights into consumer behaviour and preferences by using a mobile-first approach and following these best practices.

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Did you know that, on average, shoppers spend just two seconds deciding whether to pick up a product or not? In that short amount of time, packaging has to grab their attention, communicate key information, and entice them to purchase. As a marketer, understanding the psychology behind the packaging is essential for capturing those fleeting moments and making the most of your opportunity on the shelf. In this blog, we’ll explore the fascinating research into shopping behaviour and eye-tracking studies and show you how to design packaging that influences consumers’ decisions. So, if you want to know the secrets to gain consumers’ attention from a shelf, keep reading!

The Science of Shopping Behavior

To create effective packaging, it’s essential to understand how shoppers behave in a store. Numerous studies into shopping behaviour offer key insights into how to design packaging that resonates with your target audience.

One important insight is that shoppers tend to make decisions based on emotion rather than logic. Packaging that conveys a sense of excitement, pleasure, or indulgence is more likely to attract their attention than packaging that simply lists the product’s features.

Another crucial insight is that shoppers look at a product’s upper left corner first. This area should contain the most vital information, such as the product’s name or a key benefit. 

It is important to note that this insight is based on eye-tracking studies conducted primarily in Western societies, such as the United States and Europe. Shoppers in other countries may have different eye-tracking patterns or prioritise different areas of a product’s packaging. 

Finally, shoppers prefer products that are easy to understand and use. Clear and concise communication on packaging regarding the product and its usage will help the product stand out on the shelf.

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The Power of Eye-Tracking Studies

While studies into shopping behaviour can provide valuable insights, they can also be limited by the self-reported nature of the data. Researchers have turned to eye-tracking studies to get a more accurate picture of how shoppers behave in-store.

Eye-tracking technology allows researchers to see where shoppers are looking and for how long. This provides a more objective way of measuring shopper behaviour and can reveal insights that might not be captured through self-reported data.

One key finding from eye-tracking studies is that shoppers focus on the front of the package first, then move on to the sides and back. That means that the front of your package needs to be eye-catching and convey essential information clearly and concisely.

Another important insight from eye-tracking studies is that shoppers tend to look at products at eye level more than those that are higher or lower. If your product is on a lower or higher shelf, you may need to use packaging design elements that stand out even more to attract attention.

Eye-tracking studies can also reveal how shoppers scan a package for information. For example, they tend to look at the product name, the image or graphic, and then any claims or benefits listed on the front of the package.

By using the insights from eye-tracking studies, you can design packaging that is even more effective at attracting attention and communicating key information to your target audience. 

Designing Packaging to Stand Out

Now that we better understand how shoppers behave in-store and the insights gained from eye-tracking studies, let’s explore some specific design elements that can help your packaging stand out on the shelf.

Colour

Colour is one of the most powerful design elements for attracting attention. Using bold and bright colours can help your product stand out. Consider using colours not commonly seen in your product category to make your product even more distinctive. 

However, colour can be perceived differently in different countries, and marketers need to be aware of these differences when designing packaging for a global audience. For example, in Western cultures, black is often associated with luxury and sophistication, while in some Eastern cultures, it is associated with mourning and sadness. Similarly, the colour red is often associated with love and passion in Western cultures, while in some Asian cultures, it is associated with luck and prosperity.

It’s also important to consider the context in which the product will be sold, as colours can have different meanings in different contexts. For example, green may be associated with nature and health in some contexts, but it may relate to money and finance in others. By carefully considering the cultural and contextual implications of colour, marketers can create packaging that effectively communicates the product’s value and resonates with the target audience.

Typography

Typography refers to the style, size, and arrangement of text on your packaging. Using clear and easy-to-read fonts can help shoppers quickly understand what your product is and what it offers. It is also important to note there can be differences in buyer behaviour and preferences regarding typography in different cultures. 

For example, in many Asian cultures, calligraphy and other forms of intricate handwriting are highly valued, and this may influence the types of typography that are preferred by consumers. Similarly, different scripts and writing systems may have different connotations and associations in different cultures, impacting buyer behaviour. 

It’s worth noting that typography can also significantly impact accessibility and readability for people with visual impairments or other disabilities. Designing clear and easy-to-read typography can help ensure your product is accessible to the broadest possible audience, regardless of cultural or linguistic background.

Imagery

Compelling imagery can help your product stand out and connect with shoppers emotionally. Consider using high-quality photos or illustrations that convey the benefit or feeling your product provides.

Packaging Shape

The shape of your packaging can also help it stand out on the shelf. Consider using unique shapes or structures different from the typical packaging in your product category.

Branding 

Finally, branding can also play a crucial role in attracting attention and building loyalty. Ensure your packaging design is consistent with your brand identity and conveys your brand values.

Real-World Examples of Successful Packaging Design

One of the best ways to learn about effective packaging design is to look at real-world examples. Here are a few successful packaging designs that have used the principles we’ve discussed:

  1. Burt’s Bees: Burt’s Bees packaging stands out on the shelf with its natural, earthy colours and simple, elegant typography. Using natural images and illustrations of bees and flowers helps to communicate the brand’s commitment to using natural ingredients.
  2. Oatly: Oatly’s packaging for their dairy-free milk products stands out on the shelf with its bold use of typography and graphics. The packaging features a simple black and white design with clever and irreverent messaging, such as “It’s like milk, but made for humans.”
  3. Chobani: Chobani’s yoghurt packaging features a distinctive, curved shape that differentiates it from other yoghurt brands. The packaging also features bold, colourful graphics and typography that help it stand out on the shelf.
  4. Method: Method’s cleaning product packaging features bright, cheerful colours and playful illustrations that help it stand out from the typically bland and boring cleaning products. The packaging also features witty product names that add to the brand’s playful personality.
  5. Nivea: Nivea’s skincare packaging features a simple, classic design that has become synonymous with the brand. The packaging features a clean, white background with the brand’s iconic blue logo, which helps it stand out on the shelf and communicate its commitment to quality skincare products.

A Case Study on Packaging that Missed its Mark

While the above are great examples of packaging that resonate well with buyers, marketers can also learn from many packaging failures.

Case Study: Bic For Her

In 2012, Bic introduced a line of pens called “Bic For Her,” marketed as pens designed specifically for women. The pens featured pastel colours and a thinner barrel size and were priced higher than regular pens. The packaging also included a tagline “Designed to fit comfortably in a woman’s hand.”

The product was met with widespread criticism and mockery on social media, with many people questioning why women would need pens explicitly designed for them. Some critics also pointed out that the pens were more expensive than regular pens, despite offering no significant additional features or benefits.

There are several steps that Bic could have taken to avoid the Bic For Her disaster. Here are a few possible strategies:

  1. Conduct Research: Before launching a new product, it’s essential to conduct thorough market research to understand the needs and preferences of your target audience. In the case of Bic For Her, Bic could have conducted surveys or focus groups to better understand whether there was a demand for pens designed specifically for women.
  2. Avoid Stereotypes: The marketing of Bic For Her relied heavily on gender stereotypes, such as the idea that women have delicate hands that require special pens. To avoid this, Bic could have focused on creating marketing messages that were more inclusive and resonated with a diverse range of consumers.
  3. Price the Product Appropriately: One of the criticisms of Bic For Her was that the pens were priced higher than regular pens, despite offering no significant additional features or benefits. To avoid this, Bic could have priced the product more competitively or provided clear and compelling reasons why the pens were worth the higher price.
  4. Test the Product: Before launching a new product, testing it with a smaller audience is vital to see how it is received. In the case of Bic For Her, Bic could have tested the pens with a smaller group of consumers to see whether the product resonated with them before launching it on a larger scale.
  5. Learn from Feedback: When the negative feedback about Bic For Her started to emerge, Bic could have responded more quickly and effectively to address the concerns. 

By taking these steps, Bic could have avoided the Bic For Her disaster and created a product that resonated with consumers and drove sales. The key is to understand your target audience, create marketing messages that are inclusive and relevant, and be responsive to feedback and criticism when it arises.

While Bic For Her was widely criticised, the brand was able to learn from its mistakes and move forward. In subsequent marketing campaigns, Bic focused on creating messages that resonated with all consumers, regardless of gender. By acknowledging their missteps and making changes based on feedback, Bic was able to salvage its brand reputation and avoid making similar mistakes in the future.

Putting It All Together

Now that we’ve explored the psychology of shopping behaviour, the power of eye-tracking studies, and specific design elements that make packaging stand out, let’s bring it all together.

A deep understanding of your target audience is essential to create effective packaging. What are their values, preferences, and pain points? How can your packaging address those needs and stand out from the competition?

Once you clearly understand your audience, you can incorporate the design elements we’ve discussed. Consider using bold, bright colours, clear and easy-to-read typography, compelling imagery, unique packaging shapes, and consistent branding.

It’s also important to communicate key information clearly and concisely. What is your product? What are the key benefits or features? Why should consumers choose your product over the competition?

Finally, don’t be afraid to be creative and have fun with your packaging design. Consumers are drawn to brands that have personalities and stand out from the crowd. By incorporating unique design elements and messaging that reflect your brand’s personality and values, you can create packaging that resonates with your target audience and leads to increased sales.

Testing Your Packaging Design

After you’ve invested time and resources into creating effective packaging, testing your design to ensure it resonates with your target audience is important. Here are a few methods for testing your packaging design:

  1. Surveys: One of the simplest ways to test your packaging design is to survey your target audience. You can show them different packaging designs and ask for feedback on their preferred design and why. This can provide valuable insights into what design elements are most appealing to your audience.
  2. Focus groups: Conducting a focus group is another effective method for testing your packaging design. This involves bringing together a group of individuals from your target audience and showing them your packaging design. You can then ask for their feedback on what they like and don’t like about the design and what changes they would suggest.
  3. A/B testing: A/B testing involves creating two different versions of your packaging design and testing them against each other to see which performs better. This can be done through online surveys or by conducting in-store tests.
  4. Eye-tracking studies: Eye-tracking studies can provide valuable insights into how shoppers interact with your packaging design. Eye-tracking technology lets you see which design elements attract the most attention and how shoppers scan the package for information.

Testing your packaging design ensures that it resonates with your target audience and leads to increased sales. This investment in testing can ultimately save you time and money in the long run by ensuring that your packaging design is effective before it goes to market.

Adapting Your Packaging Design Over Time

Even the most effective packaging designs may need to be adapted over time to stay relevant and resonant with your target audience. Here are a few reasons why you may need to adapt your packaging design:

  1. Changes in consumer preferences: Consumer preferences and values can change over time, which may require you to adapt your packaging design to stay relevant. For example, if consumers become more concerned about sustainability, you may need to incorporate eco-friendly packaging materials into your design.
  2. Changes in the competitive landscape: Your competitors may change their packaging designs, requiring you to adapt your design to stand out from the crowd. Keeping an eye on your competitors and their packaging designs can help you stay ahead of the curve.
  3. New product features or benefits: If your product evolves and offers new features or benefits, you may need to update your packaging design to communicate those changes effectively.
  4. New marketing strategies: If you change your marketing strategy, you may need to adapt your packaging design to align with those changes. For example, if you shift your focus to a new target audience, you may need to adapt your packaging design to appeal to that audience.

Packaging design captures consumers’ attention, communicates key information, and drives sales. By understanding the psychology of shopping behaviour, utilising eye-tracking studies, and incorporating key design elements, you can create packaging that stands out on the shelf and resonates with your target audience.

Starting with a deep understanding of your target audience, you can incorporate design elements such as bold colours, easy-to-read typography, compelling imagery, unique packaging shapes, and consistent branding to create effective packaging. Communication of key information clearly and concisely is important, as is creativity and personality in your design to stand out from the competition.

Testing your packaging design using surveys, focus groups, A/B testing, and eye-tracking studies is essential to ensure it resonates with your target audience. Regular evaluation and adaptation of your packaging design can help you stay relevant and effective over time.

By investing in effective packaging design, you can set your product apart from the competition and increase your chances of success in the competitive world of retail. So, take the time to invest in your packaging research and design, and watch as your sales soar.

Kadence International has expertise with the world’s leading brands in package testing. Get in touch or submit a research brief.

Microsoft recently made a substantial commitment to OpenAI’s ChatGPT —a chatbot released late last year, announcing its intent to invest $10 billion, while tech giant Google is scrambling to produce a rival for ChatGPT called Bard.

Artificial Intelligence (AI) is advancing at a remarkable rate, raising several questions about the dangers and risks of an AI takeover in every walk of life. 

Artificial Intelligence (AI), the ability of computers to perform tasks that typically require human intelligence, such as speech and image recognition, iterative learning, and creative thinking, has been a touchstone of hope and anxiety for decades. 

AI is within reach of many industries, including healthcare, education, retail, and, believe it or not, mining. Of course, the field of market research is no exception. 

In market research, will AI put jobs on the chopping block, or will it set off a renaissance of new market research innovation and jobs? Will machine learning in market research annihilate the human element or propel the industry forward with accelerated momentum?

Stephen Hawkings cautioned the world about the rise of artificial intelligence.

Stephen-Hawking-quote-on-AI

These questions have been a major source of anxiety for many. But before we dive into these questions, let’s look at a brief history of AI, the types of AI, and how to use AI. 

A Brief History of Artificial Intelligence

Let’s look at how AI has evolved over the past few decades. 

history-of-ai

Artificial Intelligence can be divided based on capabilities and functionalities.

There are three types of Artificial Intelligence-based on capabilities. 

Narrow AI

Also known as Weak AI, Narrow AI specialises in one task and cannot exceed its boundaries. This subset of AI is advancing in that single domain, becoming more ubiquitous in everyday life as machine learning and deep learning progress.
Let’s look at the capabilities of Narrow AI using real-world examples. From the iPhone’s Siri virtual assistant to self-driving cars, utilising vision recognition and product recommendation engines, this type of AI utilises pre-programmed abilities to serve users but often fails to assist with tasks outside its scope. On the other hand, IBM Watson is an advanced data analytics processor that employs natural language processing, an advanced technology that deciphers human language for syntax and significance. Watson has the power to rapidly perform analytics on enormous volumes of data to respond to human inquiries accurately. Interestingly, Watson competed and outsmarted a contestant on the popular TV game show Jeopardy!

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Other popular examples of Narrow AI include Google Translate, image recognition software, recommendation systems, spam filters, and Google’s page-ranking algorithm.

General AI

General AI, otherwise known as strong AI, is a form of Artificial Intelligence (AI) that has the potential to understand and learn any task a human being can. AI researchers have yet to develop this technology to its fullest capacity, needing a method to give machines the ability to think cognitively. 

Fujitsu has built the K computer – one of the world’s fastest supercomputers- to create strong AI. Meanwhile, China’s Tianhe-2 has been deemed the most powerful supercomputer in the world, as it can calculate 33.86 petaflops (quadrillions of cps). This still needs to catch up to what the human brain can accomplish.

Super AI

Artificial Superintelligence (AI) has surpassed human intelligence and can do any task more efficiently than a person. Super AI is imagined to be so close to the human sentiment that it not only comprehends them but can create its own feelings, requirements, opinions, and wishes. As of now, its concept remains just hypothetical. However, it is thought to possess significant abilities like contemplating, resolving issues, and generating its own judgments and decisions.

In terms of functionality, there are four main types of Artificial Intelligence.

  1. Purely Reactive

    As the name suggests, these AI machines do not use any data or memory. They specialise in one field. An example would be in a chess game where the machine observes the player’s moves and makes the best possible decision to win.
  2. Limited Memory

These machines use previous data, but memory is limited. They have enough previous data to make decisions, but their memory is minimal. An example is suggesting users a convenience store based on the location data.

  1. Theory of Mind

This type of AI goes beyond hard data and can interpret emotions and thoughts. 

  1. Self-Aware


Self-aware machines are smart as well as conscious. These are the future of AI. 

So how does AI work?

Artificial Intelligence utilises an abundance of data and intelligent algorithms, in tandem with high-speed processing, to understand patterns in the data and self-teach accordingly.

Artificial Intelligence is expected to revolutionise the market research industry in several ways:

  1. Data Collection and Analysis: AI-powered tools can collect, process and analyse large amounts of data faster and more accurately than humans, leading to more comprehensive and actionable insights.
  2. Customer Insight: AI can help uncover hidden patterns and connections in customer data, providing deeper and more personalised insights into consumer behaviour.
  3. Predictive Analytics: AI-powered predictive analytics can help companies anticipate consumer behaviour, market trends, and buying patterns, allowing businesses to make more informed decisions.
  4. Sentiment Analysis: AI can analyse large volumes of customer feedback, social media data, and other unstructured data sources to provide insight into consumer opinions and emotions.
  5. Chatbots and Virtual Assistants: AI-powered chatbots and virtual assistants can interact with consumers and collect data, freeing up researchers’ time to focus on analysis and interpretation.
  6. Improved Efficiency and Accuracy: AI can automate repetitive tasks, reducing errors and allowing market researchers to focus on more creative and strategic initiatives.

Artificial Intelligence is a powerful tool that can significantly enhance the work of market researchers, but it is unlikely to replace them completely. 

Here are some ways in which AI will not replace the role of market researchers:

  1. Interpretation and Analysis: AI can provide vast amounts of data and insights, but it is up to market researchers to interpret the results and provide meaningful insights and recommendations.
  2. Creative Problem-Solving: Market researchers are responsible for devising and executing research strategies, which requires creative problem-solving and human intuition. AI is not capable of replacing this critical aspect of the research process.
  3. Ethical Considerations: AI operates based on algorithms and data inputs, sometimes resulting in biased or unethical outcomes. Market researchers must consider ethical considerations and ensure that research methods and results align with the values and goals of the organisation.
  4. Communication and Presentation: Market researchers are responsible for communicating the research results to stakeholders, which requires strong communication skills, the ability to tell a story, and the ability to present data in an engaging and actionable manner.
  5. Contextual Understanding: AI operates based on patterns and algorithms, but it cannot replace the human understanding of context, culture, and individual circumstances that is critical to providing meaningful insights.

AI disruptions are everywhere.

AI is quickly disrupting how brands approach customer service, product creation, marketing, and data analysis. Companies are beginning to integrate AI into many aspects of their operations. Here are a few of the major brand disruptions that AI is bringing to the market:

One brand that has caused a disruption in the marketplace through AI is Amazon. By incorporating machine learning, Amazon has made real-time decisions to respond to customer needs. By taking into account past customer behaviours and preferences, Amazon offers personalised product recommendations. Amazon has also used AI-enabled facial recognition technology in their warehouses to automate and improve the inventory process.

jeff-bezos-quote-on-personalization

Another brand that has disrupted the market using AI is Microsoft. Microsoft provides businesses with advanced predictive analytics and natural language processing capabilities through its Azure AI platform. With these features, businesses can use machine learning to develop more accurate forecasting models. Additionally, brands can more effectively identify customer trends and behaviour, enabling them to respond quickly to changing customer demands.

Finally, Tesla is another company that has used AI to disrupt the market. The company has enabled its autonomous vehicles to read and recognise their environment using computer vision. This allows their cars to recognise traffic signals and lane markings, resulting in a safer driving experience. Additionally, their AI-powered Autopilot system allows their cars to make real-time adjustments to improve their driving performance.

Tesla-and-ai

Will robots dominate the world?

Artificial Intelligence has progressed at a phenomenal rate, and its expansive possibilities have prompted fears about the probability of an AI takeover. 

In Nick Bostrom’s book Superintelligence, the opening story “The Unfinished Fable of the Sparrows” offers a parable of how AI’s growing strength and abilities can stir feelings of unease and worry. The story follows some sparrows that sought a pet owl, disregarding the worries of one sparrow that cautioned about the difficulty of controlling such a creature. Instead of addressing this doubt, the group simply deferred the issue for future resolution.

Elon Musk, the founder of Tesla, SpaceX, and Neurolink, has also openly expressed the potential dangers of AI. However, since the benefits of AI are enormous, he suggests a regulatory body to minimise the dangers and risks associated with it. 

Elon-Musk-quote-on-AI

Overall, while AI can significantly enhance the work of market researchers, it will not replace their expertise and creativity. Instead, market researchers will likely embrace AI as a tool that enables them to do their jobs more efficiently and effectively.

In today’s world, data has become an essential asset for businesses. However, collecting data alone is insufficient; it must be analyzed and turned into meaningful insights. This is where predictive analytics comes in. 

Predictive analytics is the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and make predictions about future events or trends.

Predictive analytics has been around for a long time, with roots dating back to the early 1800s. One of the earliest known examples of predictive analytics is the work of the English statistician Francis Galton, who used statistical techniques to predict the height of children based on the height of their parents. Since then, predictive analytics has evolved significantly and is now a critical component of modern business intelligence.

Predictive analytics has many names, such as advanced analytics, data mining, and machine learning. However, they all refer to the same basic concept of using data to make predictions.

The importance of predictive analytics in market research cannot be overstated. With the abundance of data available today, businesses need to be able to make informed decisions quickly to stay ahead of the competition. Predictive analytics can help companies to identify trends, predict customer behaviour, and optimise pricing strategies. According to a survey by McKinsey & Company, companies that use predictive analytics are twice as likely to be in the top quartile of financial performance within their industry.

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The Importance of Predictive Analytics in Market Research

The importance of predictive analytics in market research lies in its ability to provide businesses with the insights they need to make informed decisions and stay ahead of the competition. Brands can predict future behaviour and adjust their strategies by analyzing historical data and identifying patterns and trends.

One example of the power of predictive analytics is the case of Target, a large retail chain. Target analyzed its customers’ purchasing patterns and used that data to predict when customers were most likely to become pregnant. With this information, Target could send targeted advertisements and coupons to these customers, increasing sales and customer loyalty.

Another real-world example is how predictive analytics helped the Seattle Seahawks win the Super Bowl in 2014. The team used predictive analytics to analyze their opponents’ behaviour and tendencies, allowing them to make strategic decisions during the game. 

According to a study by Forbes Insights, businesses that use predictive analytics are more likely to experience improved customer engagement, increased profitability, and better overall business performance. 

Benefits of Predictive Analytics in Market Research

The benefits of predictive analytics in market research are numerous, and businesses that use this technique can gain valuable insights that can inform their decision-making process. According to a study by Harvard Business Review, businesses that use predictive analytics are more likely to experience increased revenue and profitability.

Here are some of the key benefits of using predictive analytics in market research:

  1. Improved accuracy in forecasting: Predictive analytics can help businesses forecast future trends and outcomes with a high degree of accuracy. This can help brands better plan their operations and resources to meet future demands. For example, a hotel chain can use predictive analytics to forecast room occupancy rates, allowing them to adjust staffing and inventory levels accordingly.
  2. Identifying trends: Predictive analytics can help businesses identify trends in customer behaviour, market conditions, and more. By identifying these trends, companies can adapt their strategies to meet changing market conditions. For example, a retail business can use predictive analytics to identify emerging customer purchasing behaviour trends, allowing it to adjust its inventory accordingly.
  3. Predicting customer behaviour: Predictive analytics can help businesses predict customer behaviour, such as buying patterns, preferences, and responses to marketing campaigns. This can help companies to tailor their marketing efforts and improve customer engagement. For example, an e-commerce business can use predictive analytics to identify customers who are most likely to make a purchase, allowing them to target these customers with personalised offers.
  4. Optimizing pricing strategies: Predictive analytics can help businesses optimise their pricing strategies by identifying the optimal price point for products and services. Using predictive analytics, brands can adjust their pricing strategies to maximise profits and stay competitive. For example, an airline can use predictive analytics to adjust ticket prices based on demand, maximizing revenue while ensuring seats are filled.

Use Cases of Predictive Analytics Around the World

Brands across the globe are increasingly using predictive analytics to gain insights into market trends and customer behaviour. Here are some examples of how businesses have used predictive analytics:

  • Tesco – a leading UK-based grocery retailer, used predictive analytics to identify the most profitable products and services for their customers. By analyzing customer data, Tesco was able to tailor its offerings to meet the specific needs of its customers, resulting in increased sales and customer loyalty.
  • Amazon – the world’s largest online retailer, uses predictive analytics to provide personalised recommendations to customers. By analyzing customer data, Amazon can recommend products and services most relevant to each customer, increasing sales and customer satisfaction.
  • Alibaba – one of China’s largest e-commerce companies, uses predictive analytics to identify products likely to be popular with customers. By analyzing search and purchase data, Alibaba can recommend products and services that are most likely to become successful, leading to increased sales and revenue.
  • Toyota – a leading automobile manufacturer, uses predictive analytics to identify the most profitable sales channels and to optimise pricing strategies. Toyota can adjust its pricing strategies by analyzing sales data to maximise profits and stay competitive.
  • Tokopedia – a leading e-commerce platform in Indonesia, uses predictive analytics to identify popular products and optimise pricing strategies. By analyzing customer data, Tokopedia can adjust its pricing strategies to meet customer demand, leading to increased sales and revenue.

These examples show how businesses in various countries leverage the power of predictive analytics in market research to achieve their goals, such as increasing sales, improving customer satisfaction, and staying ahead of the competition.

Challenges of Predictive Analytics

While predictive analytics can be a powerful tool for brands, it’s essential to understand the challenges associated with using this technique. 

According to a study by McKinsey & Company, many businesses struggle with these challenges when implementing predictive analytics. For example, the study found that only 19% of companies are very confident in the accuracy of their predictive models.

Here are some of the challenges of using predictive analytics in market research:

  • The need for large amounts of data: To accurately predict future outcomes and trends, businesses need large quantities of high-quality data. This can be a challenge for companies that don’t have access to the necessary data or that struggle with data quality issues.
  • Potential for biases in data analysis: Predictive analytics is only as good as the data used to train the models. If the data used to train the model is biased, the predictions made by the model will also be biased. For example, a predictive model that is trained using only data from a specific demographic may not accurately predict behaviour for other demographics.
  • Difficulty in interpreting results: Predictive analytics can provide businesses with a large amount of data and insights, but it can be challenging to interpret these results and turn them into actionable strategies. Companies need the necessary skills and expertise to interpret the data and make informed decisions.
  • Data privacy and security concerns: As businesses collect more data for predictive analytics, there are concerns about data privacy and security. Companies must comply with data protection regulations and take appropriate measures to secure their data.

As Dr. Michael Wu, Chief AI Strategist at PROS, said, “The biggest challenge in predictive analytics is not the algorithm, but the data.” To overcome the challenges of using predictive analytics in market research, businesses must invest in data quality and security and ensure they have the necessary skills and expertise to interpret the data and make informed decisions.

Best Practices for Implementing Predictive Analytics

To successfully implement predictive analytics, businesses must follow best practices to ensure they get the most out of this powerful tool. Here are some tips and best practices for companies looking to implement predictive analytics in their market research:

  1. Choose the right software tools: Many software tools are available for predictive analytics, and businesses must choose the one that best meets their needs. This can include tools that provide data visualisation, machine learning algorithms, and data cleaning and preprocessing.
  2. Ensure data quality: As discussed earlier, data quality is critical for accurate predictions. Businesses must ensure they have high-quality data, free from errors and biases. This can include data cleaning, normalisation, and validation.
  3. Involve domain experts: Domain experts, such as market research analysts, can provide valuable insights and context for the data used in predictive analytics. By involving these experts in the process, businesses can ensure that their predictions are relevant and actionable.
  4. Use historical data: Predictive analytics relies on historical data to make predictions about the future. Businesses need to have access to historical data, which should be relevant to the problem being addressed.
  5. Test and refine the model: Predictive models should be tested and refined to ensure accuracy and reliability. This can involve using different algorithms, adjusting parameters, and comparing the results to actual outcomes.
  6. Monitor and update the model: Predictive models should be monitored and updated over time to remain relevant and accurate. As market conditions change, the model may need to be updated to reflect these changes.

According to a study by the International Institute for Analytics, businesses that follow best practices for implementing predictive analytics are more likely to succeed. For example, the study found that brands involving domain experts in the process are more likely to see positive results.

By following these best practices, businesses can ensure they make the most of predictive analytics in their market research efforts.

In conclusion, predictive analytics is a powerful tool for businesses seeking insights into market trends and customer behaviour. Companies can use historical data and machine learning algorithms to predict future outcomes and adjust their strategies accordingly. However, there are challenges associated with using predictive analytics, such as the need for large amounts of high-quality data and the potential for biases in data analysis. 

To successfully implement predictive analytics in market research, businesses must follow best practices, such as choosing the right software tools and involving domain experts.

Kadence International, a market research agency, can help businesses navigate market research challenges and leverage the power of predictive analytics. With data collection, analysis, and interpretation expertise, we can provide valuable insights and help brands make data-driven decisions that lead to success. Contact us today to learn how we can help your business with market research.

Imagine this: You’re scrolling through your social media feed and come across a product ad that catches your attention. The ad tells a story that speaks to your heart, making you want to learn more about the product and even consider buying it. This is the power of storytelling in product marketing.

In today’s crowded marketplace, it’s becoming increasingly difficult for brands to stand out and connect with their target audience. Storytelling provides a way for companies to create a lasting emotional connection with their customers by tapping into their hopes, fears, and desires.

Many companies and brands have successfully used storytelling in their product marketing. Take Nike, for example, whose “Just Do It” campaign tells stories of athletes overcoming challenges to achieve greatness. 

And there’s Coca-Cola, whose “Share a Coke” campaign tells the story of a simple act of sharing a Coke with friends and family, highlighting the brand’s values of happiness and togetherness.

But how can companies effectively use storytelling in their product marketing? In this article, we will explore the art of storytelling in product marketing, providing tips and guidance on creating compelling brand stories that engage customers and drive sales. We will also discuss the importance of understanding your audience, choosing the right channels for sharing your story and measuring the success of your storytelling efforts. So, let’s get started and discover the art of storytelling in product marketing.

The Power of Storytelling

In the world of marketing, storytelling is a powerful tool that brands can use to connect with their customers on a deeper, emotional level. By telling relatable and inspiring stories, companies can create a connection with their audience that goes beyond the product or service they offer.

Successful companies understand the value of storytelling. Apple’s “Think Different” campaign tells the story of how it differs from other technology companies, highlighting its innovation and creativity. This story inspires customers to see themselves as part of a community of people who are also “different.”

Dove’s “Real Beauty” campaign tells the story of how women should embrace their natural beauty. The campaign uses real women with diverse body types and skin tones and focuses on their stories and struggles. This story resonated with customers and helped Dove become a leader in the beauty industry.

Storytelling is a powerful tool in product marketing because it evoles emotions, connects with customers on a deeper level.

To quote Maya Angelou, “I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.” This is the essence of storytelling in product marketing: create an emotional connection with customers that lasts beyond the transaction.

Understanding Your Audience

To create a compelling brand story, it’s crucial to understand your target audience and their needs and interests. This knowledge allows you to tailor your storytelling to resonate with them and create a strong emotional connection.

Customers are looking for brands that align with their values and beliefs. They are more likely to engage with content that speaks to those values. A great example of this is TOMS Shoes, a company that donates a pair of shoes to someone in need for every pair purchased. TOMS promotes its ethos and tells a story of social responsibility and giving back. This story resonates with customers who value social impact and has helped TOMS become a leader in the ethical fashion industry.

Another example is Airbnb, a company that tells the story of “belonging anywhere.” The brand’s storytelling focuses on the unique and authentic experiences that customers can have when they use Airbnb, catering to the needs and interests of travellers who seek immersive and personalised travel experiences.

To understand your target audience and their needs and interests, it’s important to gather data and insights about their demographics, psychographics, and behaviours. This information can be collected through market research, customer surveys, and social media analytics.

Once you deeply understand your target audience, you can tailor your storytelling to meet their needs and interests. This can include incorporating their values and beliefs, using language and visuals that resonate with them, and telling relatable and inspiring stories.

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Creating Your Story

Creating a compelling brand story is an art that requires careful planning and execution. A strong brand story can engage customers, create an emotional connection, and differentiate your brand from competitors.

Here are some tips and guidance on how to create a compelling brand story:

Develop a relatable character 

Your story’s protagonist should be someone your target audience can relate to. This character should have struggles and challenges that they can identify with.

For example, the clothing brand Patagonia tells the story of Yvon Chouinard, the company’s founder, as a relatable character who embodies the brand’s values of sustainability and environmentalism.

Create conflict

A compelling story needs conflict to create tension and keep the audience engaged. This conflict could be anything from a problem your target audience faces to a challenge your company overcame.

The shoe company Allbirds tells the story of how they discovered a sustainable material to make their shoes, overcoming the challenge of finding an environmentally-friendly option in the fashion industry.

Provide a resolution

A resolution is the story’s conclusion, where the conflict is resolved. This resolution should satisfy the audience and reinforce your brand’s values.

The car company Volvo tells the story of how their cars prioritise safety, resolving the conflict of fear and danger on the road.

Use visuals and language

Your language and visuals should be consistent with your brand’s values and personality. This includes everything from the tone of your language to the colours and imagery you use.

The makeup brand Glossier uses playful and colourful imagery in its storytelling to reflect the brand’s personality and appeal to a younger demographic.

Choosing Your Channels

Once you’ve developed a compelling brand story, it’s time to share it with the world. Choosing the right channels for sharing your story can help you reach your target audience and create a lasting impact. 

Here are some of the channels you can use to share your brand story:

Social media

Social media platforms such as Facebook, Instagram, TikTok and Twitter are great for sharing visual and engaging content. According to Hootsuite, social media users spend an average of 2 hours and 24 minutes per day on social media. This presents a huge opportunity for brands to connect with their target audience and share their brand story.

The sportswear brand Lululemon uses Instagram to showcase their products and tell the story of their brand’s values and lifestyle. They also use influencer partnerships and user-generated content to create a community around their brand.

Email marketing

Email marketing is an effective way to reach customers directly and share your brand story.

According to Hubspot, email marketing has an average ROI of 38:1, making it a highly effective marketing channel.

The cosmetics company Sephora uses email marketing to share its brand story and promote its products. They send personalised emails based on customers’ purchase history and preferences, using language and visuals that resonate with their target audience.

Content marketing

Content marketing involves creating valuable, educational content that provides value to your target audience. This content can be shared on your website, blog, or social media platforms.

The furniture retailer West Elm uses content marketing to educate customers on interior design trends and share their brand story. They create blog posts and social media content that features their products in real-life settings and offers design tips and inspiration.

Measuring Success

Measuring the success of your storytelling efforts is essential to understand the impact of your brand story on your target audience. 

By tracking metrics such as engagement, conversions, and sales, you can evaluate the effectiveness of your storytelling and optimise your strategy accordingly.

Here are some metrics you can use to measure the success of your storytelling efforts:

Engagement

Engagement metrics include likes, comments, shares, and followers on social media platforms. These metrics can help you understand how well your target audience connects with your brand story.

Conversions

Conversions refer to your target audience’s actions after engaging with your brand story. This can include signing up for a newsletter, downloading a resource, or making a purchase.

Sales

Sales metrics include revenue, order value, and customer retention. By tracking these metrics, you can understand the direct impact of your brand story on your bottom line.

The role of Market Research and Storytelling

Market research is crucial in creating a compelling brand story that resonates with your target audience. By understanding your target audience’s needs, preferences, and pain points, you can create a brand story that is relatable and engaging.

Here are some ways that market research can help product marketers create a compelling story for their product:

Identify customer pain points

Market research can help you identify your target audience’s problems and pain points. By understanding their challenges, you can create a brand story that addresses these issues and provides solutions.

Determine brand values

Market research can help you identify the values and beliefs that your target audience cares about. By incorporating these values into your brand story, you can create an emotional connection with your audience.

Test messaging

Market research can help you test different messaging and brand story concepts with your target audience. By getting feedback from your audience, you can optimise your brand story and ensure that it resonates with your customers.

Storytelling is a powerful tool that product marketers can use to create a lasting emotional connection with their customers. By tapping into their hopes, fears, and desires, companies can tell compelling brand stories that engage customers and drive sales.

As competition in the marketplace continues to grow, the brands that can tell a compelling brand story will be the ones that stand out and succeed. 

A small startup named Gymshark partnered with a group of fitness influencers on Instagram to promote their fitness apparel line. The influencers shared photos and videos of themselves wearing Gymshark clothing with their followers, and the results were nothing short of astounding. Gymshark grew into a multi-million-dollar company in just a few years, largely thanks to its influencer marketing strategy.

This is just one example of the power of influencer marketing in product promotion. As consumers increasingly turn to social media to discover and purchase products, partnering with influencers has become a popular and effective way for brands to reach new audiences and leverage the trust and credibility that influencers have built with their followers.

In this article, we’ll explore the world of influencer marketing, and show you how to use this powerful strategy to drive sales and boost brand awareness. Whether you’re a product marketing manager, marketing executive, or head of market research, this article will provide you with a comprehensive guide to the ins and outs of influencer marketing and show you how to incorporate it into your product promotion strategy.

What is Influencer Marketing? 

Influencer marketing is a marketing strategy that involves partnering with individuals with a significant social media following. These individuals, known as influencers, can be bloggers, vloggers, celebrities, or simply social media users with a large following. 

Brands partner with these influencers to promote their products or services, leveraging the trust and credibility that the influencers have built with their followers.

One of the main differences between influencer marketing and other marketing strategies is how it relies on social proof and authenticity. Unlike traditional advertising, where the brand is the primary focus of the message, influencer marketing is all about the relationship between the influencer and their followers. 

By partnering with an influencer, a brand can tap into the trust and credibility the influencer has built with their audience.

The term “influencer” was first used in 2006 in a blog post by marketer Duncan Watts. Since then, the concept has exploded in popularity, and influencer marketing has become crucial to many brands’ marketing strategies.

 

Why Influencer Marketing Works. 

Influencer marketing has become a popular marketing strategy because it leverages the psychology of social proof, trust, and authenticity to drive sales and brand loyalty. 

Social Proof

Social proof is the idea that people are more likely to follow the actions of others when making decisions. In the case of influencer marketing, when an influencer promotes a product, their followers are more likely to trust and try that product. This is because the influencer serves as social proof, indicating that the product is valuable and worth trying.

Trust 

Influencers can build trust with their followers over time by consistently providing value and building relationships. This trust is important because it allows the influencer to promote products that feel authentic and genuine rather than pushy or salesy.

Authenticity

Influencers can create a sense of authenticity in their content by sharing their personal experiences and opinions. This authenticity is essential because it allows the influencer to connect with their followers on a deeper level, and build a sense of community around shared values and interests.

The influencer marketing industry is estimated to be worth $15 billion and is expected to grow to $84 billion by 2028, a clear indication of its effectiveness. 

The power of influencer marketing lies in its ability to tap into the psychology of social proof, trust, and authenticity and create a sense of community around shared values and interests. By partnering with the right influencers, brands can drive sales, boost brand awareness, and create a loyal customer base.

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Identifying the Right Influencers. 

When it comes to influencer marketing, it’s essential to find the right influencers for your brand and product. Choosing the right influencer can make all the difference in the success of your campaign. 

Here’s why it’s important to identify the right influencers and some tips for how to do so.

  • Audience Relevance: Audience relevance is one of the most important factors to consider when choosing influencers. You want to ensure the influencer’s audience fits your brand and product well. This means looking at factors like the influencer’s follower demographics, interests, and values.
  • Engagement Rates: Engagement rates are another essential factor when choosing influencers. High engagement rates indicate that the influencer’s followers actively engage with their content, meaning your brand’s message is more likely to be seen and heard.
  • Brand Alignment: It’s important to consider how the influencer aligns with your brand’s values and message. Choosing an influencer who shares similar values and aesthetics as your brand can help to create a more authentic and effective campaign.

In the UK, the beauty brand Glossier has also successfully identified the right influencers. The brand has worked with a range of beauty influencers who align with its message of natural, effortless beauty. This has helped the brand to build a loyal following and achieve massive success in the UK market.

Identifying the right influencers is crucial to the success of your influencer marketing campaign. By considering factors like audience relevance, engagement rates, and brand alignment, you can choose influencers who are more likely to connect with your target audience and drive results for your brand.

Creating an Influencer Marketing Campaign

Once you’ve identified the right influencers for your brand and product, developing a successful influencer marketing campaign is next. Here’s how to do it:

  1. Create a Clear Message: The first step in creating an effective influencer marketing campaign is to create a clear and compelling message. This message should align with your brand values and goals and resonate with the influencer’s audience.
  2. Set Goals and Metrics: Next, you’ll want to set clear goals and metrics for your campaign. This could include things like increasing brand awareness, driving website traffic, or boosting sales. By setting clear goals and metrics, you’ll be able to measure the success of your campaign and make adjustments as needed.
  3. Provide Guidelines and Content: It’s also vital to provide influencers with clear guidelines and content to share. This could include things like product photos, videos, or copy. By providing influencers with these assets, you’ll be able to ensure that the message is consistent and on-brand.

Clothing brand PrettyLittleThing launched an influencer marketing campaign to promote its festival line. The brand worked with various influencers attending music festivals that summer, creating a clear and consistent message around festival fashion. The campaign was a huge success, with PrettyLittleThing reporting a 15% increase in sales during the festival season.

Creating a successful influencer marketing campaign requires a clear message, clear goals and metrics, and clear guidelines and content for influencers to share. By following these steps, you can develop a campaign that resonates with your target audience and drives results for your brand.

Measuring the Success of Your Influencer Marketing Campaign

Measuring the success of your influencer marketing campaign is crucial to understanding what’s working and what’s not. By tracking key metrics, you’ll be able to see how your campaign is performing and make adjustments as needed. Here’s what to track:

  • Engagement Rates: Engagement rates are one of the most important metrics to track in an influencer marketing campaign. This includes things like likes, comments, and shares. High engagement rates indicate that the influencer’s followers actively engage with the content and are more likely to take action.
  • Click-Through Rates: Click-through rates are another important metric to track. This measures how many people clicked on a link in the influencer’s content and visited your website. High click-through rates indicate that the influencer’s followers are interested in your product and are more likely to become customers.
  • Conversion Rates: Conversion rates are the ultimate metric to track, as they measure how many people who clicked through to your website actually made a purchase. By tracking conversion rates, you’ll see how effectively your influencer marketing campaign drives sales and revenue.

It’s important to track these metrics consistently throughout your campaign and to make adjustments as needed. For example, if you’re not seeing the engagement rates you were hoping for, you may need to adjust your message or content to resonate better with the influencer’s audience.

Measuring the success of your influencer marketing campaign is crucial to understanding what’s working and what’s not. By tracking metrics like engagement, click-through, and conversion rates, you’ll see how effective your campaign is at driving sales and revenue. This will allow you to adjust as needed and ensure that your campaign delivers the results you need to achieve your business goals.

Legal Considerations

When working with influencers, ensuring that your campaign complies with relevant laws and regulations is important. In the US, the Federal Trade Commission (FTC) has issued guidelines for influencer marketing, requiring influencers to disclose sponsored content. This means influencers must clearly state when they are being paid or compensated to promote a product. Failure to comply with these guidelines can result in legal repercussions for the brand and the influencer.

In addition to following disclosure guidelines, brands should also ensure that they have clear contracts with influencers that outline the terms of the partnership, including payment, deliverables, and any exclusivity clauses.

Micro-Influencers

While many brands focus on partnering with macro-influencers with millions of followers, micro-influencers with smaller, more niche followings can be just as effective. Studies have shown that micro-influencers often have higher engagement rates and can be more cost-effective than macro-influencers.

Influencer Platforms

Various influencer platforms and tools are available to help brands find and work with influencers. These platforms can simplify the influencer marketing process, providing access to a wide range of influencers and tools for managing partnerships and tracking metrics.

One popular influencer platform is AspireIQ, which allows brands to easily search for influencers based on factors like audience demographics and engagement rates. The platform also provides tools for managing partnerships and tracking metrics like engagement and sales.

Influencer Marketing Trends

Finally, staying up-to-date on the latest influencer marketing trends and predictions can help brands stay ahead of the curve and create more effective campaigns. Some of the latest trends in influencer marketing include:

  • The rise of TikTok influencers: As TikTok continues to grow in popularity, brands are partnering with influencers on the platform to reach a younger audience.
  • The focus on authenticity: Consumers are becoming increasingly wary of inauthentic or forced influencer partnerships, and brands are responding by focusing on more genuine, long-term relationships with influencers.
  • The shift towards performance-based metrics: Brands are increasingly focused on measuring the ROI of influencer marketing campaigns and are using metrics like conversion rates and sales to evaluate the effectiveness of their campaigns.

Keeping up with these trends and others can help brands create more effective influencer marketing campaigns and stay ahead of the competition.

The role of Market Research in Influencer Marketing

Market research is a critical component of any successful influencer marketing campaign. By conducting thorough research, product marketing managers can gain insights into their target audience, identify the right influencers to partner with, and track the success of their campaign. Here’s a closer look at the role of market research in influencer marketing:

Before the Campaign

Before launching an influencer marketing campaign, it’s essential to conduct market research to identify your target audience and the influencers who are most likely to resonate with that audience. This could include analyzing audience demographics, social media behavior, and content preferences and conducting surveys and focus groups to gain deeper insights.

During the Campaign

Market research can also be valuable during the course of an influencer marketing campaign, as it allows you to track metrics like engagement rates, click-through rates, and conversion rates. This data can be used to adjust your campaign in real time, improving its effectiveness and ensuring you get the best ROI possible.

Post Campaign

After the campaign has concluded, market research can be used to evaluate its success and identify areas for improvement. This could include analyzing data on sales, brand awareness, and customer loyalty, as well as conducting surveys and focus groups to gain feedback from customers and influencers.

Who doesn’t want to learn from their hero? Nike celebrates their influencers and showcases them as inspirations. Their top posts featured the likes of Cristiano Ronaldo, Roger Federer, Rafael Nadal, and artists like Kendrick Lamar, Travis Scott, and Kevin Hart. According to Swaymap, over 1000 influencers tag @nike on Instagram each month. Nike has successfully used market research to inform its influencer marketing campaign is Nike. The company researched its target audience extensively and identified a group of influential sneakerheads on social media. Nike then partnered with these influencers to create content around its new product releases, significantly increasing sales and brand awareness.

Market research plays a critical role in influencer marketing, helping product marketing managers identify their target audience, choose the right influencers, and track the success of their campaign. By conducting thorough research before, during, and after the campaign, brands can ensure they get the best ROI possible and build a loyal customer base.

Influencer marketing is a powerful tool for product marketing managers and marketing executives looking to promote their products and build a loyal customer base. By partnering with the right influencers, creating a clear and compelling message, and tracking metrics throughout the campaign, brands can tap into the psychology of social proof and authenticity to connect with their target audience and achieve their business goals.

Key takeaways from this article include:

  • The importance of identifying the right influencers.
  • Creating a clear message.
  • Measuring the success of the campaign.
  • Leveraging market research to inform the campaign at every stage. 

Following these best practices, brands can create influencer marketing campaigns that drive sales, boost brand awareness, and build a loyal customer base.

Influencer marketing is an exciting and rapidly growing area of marketing with the potential to drive significant business results. By following the best practices outlined in this article, product marketing managers and marketing executives can successfully leverage the power of influencer marketing to promote their products and build a strong brand.

At Kadence International, we specialise in market research and can help you gain the insights you need to create a successful influencer marketing campaign. Contact us today to learn more about our services and how we can help you take your marketing strategy to the next level.