With the vast amounts of data available today, marketers need to apply human analysis to extract strategy from the data presented. 

However, as pointed out by Fiona Lovatt, the Human Insights Director for Nutrition in Europe at the Coca-Cola Company, during a panel at the Market Research Society’s 2023 Insight Alchemy conference this month, the reality of a brand manager’s role is such that they often lack the capacity to act strategically.

“I think if you walked in the shoes of a brand manager, a lot of people would be shocked at how operational and short-term that role can be,” she said.

Data is an essential tool for marketers looking to make informed strategic decisions. However, with the vast amount of data available, it’s easy to get lost in the numbers and lose sight of the human aspect. Marketers get bogged down by the day-to-day, so they do not have the time to focus on long-term strategic thinking.

That’s why marketers must use human analysis and understanding when extracting insights from data for strategic decision-making. This blog post will discuss why this is important and how marketers can achieve it.

The importance of strategic thinking in a post-Covid world.

As the world begins to emerge from the chaos and uncertainty of the COVID-19 pandemic, it is important to start thinking strategically about how to move forward. Now more than ever, strategic thinking is critical to achieving long-term success.

For one, strategic thinking allows businesses to identify, prioritise and capitalise on new opportunities. A sound strategy allows business leaders to identify where and how to invest resources best to meet long-term goals. As businesses return to pre-pandemic productivity levels, it’s important to remain strategic and capitalise on potential opportunities.

Strategic thinking can also help businesses mitigate risks. A strategic approach helps leaders better anticipate and plan for possible disruptions and formulate plans to address any unexpected challenges that may arise quickly. Strategic planning can also help companies avoid pitfalls or setbacks caused by misdirected resources or efforts.

Most importantly, strategic thinking enables organisations to adapt to an ever-evolving landscape. With the impacts of the pandemic, many businesses are facing a whole new set of challenges and opportunities. With the help of strategic thinking, business leaders can stay agile and proactively develop new strategies to help their companies stay competitive in this rapidly changing environment.

The post-pandemic world is dynamic and unpredictable. While technology provides abundant data, it must be used to create the insights needed for strategic decision-making and long-term planning.  By embracing strategic thinking, business leaders can help ensure their organisation is equipped with the necessary tools to remain competitive and succeed in this new environment.

Philips —the health technology brand, is making a conscious effort to help its marketers become more strategic by ensuring they have time to think about long-term strategy. As head of marketing insights and analytics for personal health, Fenny Léautier puts it, “the human behind it.”

Léautier wants his team members to spend time speaking to the consumer directly and not just focus on internal matters.

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The importance of the human element in insights. 

As digital marketers, the ability to interpret and analyse data to help guide our marketing strategies is essential. But in this digital age, there’s no shortage of data. So how can we use this data effectively?

The key lies in applying human analysis. Data itself is just a set of facts. We must rely on human intelligence and instinct to make sense of these facts and draw meaningful insights from them. In this post, we’ll discuss the need to combine human analysis with data to develop more effective strategies.

We all know the importance of data. We use it to make decisions, track trends, and create powerful campaigns. However, simply collecting data is not enough to achieve successful outcomes.

Rather than looking at data as numbers or figures, it is important to interpret the data to understand the context of the information fully. Analysing data alone cannot provide a complete picture of what’s happening. The information must be combined with a more holistic approach incorporating subjective factors, such as customers’ feelings and perceptions and economic and cultural conditions.

Once we’ve assessed the data in the context of its wider environment, we can use the insights gained to form the foundation for an effective strategy.

To truly harness the potential of data, marketers need to create a dialogue between human intelligence and analysis and machine analysis. For example, it’s easy to use algorithms to spot trends and opportunities, but marketers must apply their own judgment to determine whether the opportunities should be pursued.

At the same time, human analysis of data must be supported by predictive analytics, AI, and other machine-driven approaches. By combining these elements, marketers can build on their human insights to make more informed decisions.

While data can provide valuable insights, it’s important to remember that data only tells part of the story. Human analysis and understanding can fill in the gaps and provide context to the data. For example, data might show a particular marketing campaign’s high conversion rate, but it doesn’t explain why. Using human analysis and understanding, marketers can identify the factors contributing to the campaign’s success and replicate them in future campaigns.

Additionally, data can be misleading if it’s not analysed correctly. It’s easy to make assumptions based on data without considering the human element. Human analysis and understanding can help marketers to avoid these pitfalls and make more informed decisions.

Steps to ensure marketers are using human understanding when extracting insights from data for strategic decision-making:

How to Achieve Human Analysis and Understanding

  • Put Yourself in Your Customers’ Shoes

Putting yourself in your customers’ shoes is essential to achieve human analysis and understanding. Understanding their needs, desires, and pain points can help you to make more informed decisions. Use data to identify trends and patterns in customer behaviour, but don’t forget to consider the reasons behind those behaviours.

  • Use Qualitative Data

Quantitative data, such as website analytics and sales figures, is valuable but doesn’t tell the whole story. Qualitative data, such as customer feedback and surveys, can provide insights into customers’ emotions, attitudes, and preferences. Use this data to better understand your customers and how they interact with your brand.

  • Collaborate with Other Departments

Marketing doesn’t operate in a vacuum, and it’s important to collaborate with other departments, such as sales and customer service, to gain a broader perspective. These departments can provide valuable insights into customers’ experiences and pain points that may not be evident from data alone.

  • Take a Holistic Approach

To achieve human analysis and understanding, it’s important to take a holistic approach to data analysis. Don’t rely solely on data to make decisions. When analysing data, consider the human element, such as emotions, cultural context, and social factors.

  • Use Data to Inform Decisions, Not Dictate Them

It is important to remember that data should inform decisions, not dictate them. Use data to identify trends and patterns, but don’t forget to consider the human element. Ultimately, marketing decisions should be based on data analysis and human understanding.

Data has become a valuable asset for marketers in today’s digital age. With the vast amount of data available, marketers can use it to make informed strategic decisions that can significantly impact their business’s success. 

How marketers can harness data for strategic decision-making.

  • Define your marketing goals.

Before you start gathering data, you need to define your marketing goals. Your goals will determine what kind of data you need and how you will use it. For example, if your goal is to increase website traffic, you must track metrics such as page views, unique visitors, and bounce rate. To increase sales, you need to track metrics such as conversion rate, average order value, and customer lifetime value.

  • Identify the right data sources.

Once you have defined your marketing goals, you must identify the right data sources. Various data sources are available, such as customer, social media, website analytics, and market research data. Choose the data sources that align with your marketing goals and provide relevant insights.

  • Collect and analyse data.

After identifying the data sources, you need to collect and analyse the data. There are various tools and software available that can help you collect and analyse data. Google Analytics is a popular tool for website analytics, while social media platforms have their own tools. Use these tools to gather data and extract insights to help you make informed decisions.

  • Use data to make informed decisions.


Once you have gathered and analysed the data, it’s time to use it to make informed decisions. Use the insights to optimise your marketing campaigns, personalise your messaging, and target the right audience. For example, if your data shows that your website has a high bounce rate, you can use it to improve your website’s user experience and reduce the bounce rate.

  • Monitor and adjust

Data is not static, and it’s essential to monitor and adjust your marketing strategies based on new insights. Use A/B testing to test different marketing strategies and track their performance. Monitor your data regularly to identify new trends and make adjustments accordingly.

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Additional steps customer-centric marketers take when using customer data for long-term decision-making. 

  1. Define your target market. Start by clearly understanding who the intended customers are and what their needs are.
  2. Analyse customer data. Look at existing data on customer interactions and behaviour to inform strategic decisions.
  3. Connect with customers to gather their opinions. Use feedback and survey data to uncover customer motivations, perceptions, and behaviours.
  4. Engage with competitors and benchmark performance. Study competitor data to understand market trends and uncover opportunities.
  5. Utilise predictive analytics to determine the probability of customer behaviours. Use advanced statistical techniques to inform decision-making.
  6. Use customer insights to develop customer personas. Break down data into customer segments and create stories about who your customers are and what drives their decisions.
  7. Identify customer segments for marketing activities. Utilise data insights to inform your customer segmentation strategy.
  8. Look for feedback in qualitative research. Combine both qualitative and quantitative research to assess the success of customer campaigns.
  9. Apply analytical techniques to assess customer experience. Collect customer feedback and apply techniques like focus groups and survey design to gain deeper insight into the customer experience.
  10. Constantly monitor customer behaviour. Follow customer behaviour trends closely and continually update analytics to identify new opportunities.

Best practices for using data to make strategic decisions

When using data to make strategic decisions, it is important to adhere to best practices.

Ensure you are working with high-quality data.

First and foremost, the data must be accurate and up-to-date. Poor data can lead to wrong or incomplete decisions, so ensure you source the data from reputable sources. Additionally, take steps to ensure the data you are using is up-to-date and valid.

Utilise different types of data when making decisions.

Not all data is equal in terms of reliability and accuracy, so consider multiple sources, such as surveys, financial reports, market research, customer feedback, etc.

Consider how you can make impactful, data-driven decisions.

Analysing the data should give you insights that you can use to inform strategy. Leverage the data to come up with creative solutions, as well as make evidence-based recommendations.

By adhering to these best practices for using data to make strategic decisions, you can ensure that your decisions are based on accurate and reliable data and are ultimately successful.

Combining human analysis with data is the key to driving successful marketing strategies. Data provides the structure, while the human factor brings an understanding of the real-world implications. With a balance of the two, marketers can generate strategies to achieve the best campaign outcomes.

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As a marketer, you’re constantly juggling multiple priorities. You need to develop compelling campaigns that resonate with your target audience, stay ahead of the competition, and demonstrate the value of your products or services. With so much to do, it can be tempting to skip the research phase and jump straight into execution mode. However, this can be a costly mistake. Your marketing efforts will likely fall flat without a solid understanding of your customer’s needs, preferences, and pain points.s

That’s where market research comes in. By conducting research, you can gather valuable insights into your target audience and use these insights to inform your marketing strategy. However, knowing when to conduct research and how to do it right can be challenging. 

In this article, we’ll explore some telltale signs that indicate it’s time to conduct research and provide practical tips on how to conduct research effectively. Whether you’re a seasoned marketer or just starting out, this article will help you navigate the marketer’s dilemma and make informed decisions that drive growth.

Signs that It’s Time to Conduct Research

Several telltale signs indicate it’s time to conduct research. If you’re experiencing any of the following issues, it may be time to consider conducting research:

  • Declining Sales: If you’ve noticed a decline in sales, it could be a sign that your marketing strategy is no longer effective. Conducting research can help you identify the root cause of the decline and develop a plan to turn things around.
  • Customer Complaints: Are you receiving a lot of complaints from customers? This could indicate that your products or services aren’t meeting their needs. Research can help you understand what’s causing the complaints and how to address them.
  • Lack of Customer Engagement: If your customers aren’t engaging with your brand or products, it may be time to conduct research to understand why. This can help you develop more effective marketing campaigns that resonate with your target audience.
  • New Competitors: If new competitors have entered the market and are gaining market share, it’s important to conduct research to understand what they’re doing differently and how you can stay ahead.

Changing Market Conditions: Markets constantly evolve; what worked yesterday may not work today. Conducting research can help you stay up-to-date on changing market conditions and adjust your strategy accordingly.

Steps to Take Before Conducting Research

Before conducting any research, you must take some preparatory steps to ensure you’re clear on what you want to achieve. Here are some steps to consider:

  1. Define the Problem: The first step is to define the problem you’re trying to solve. What questions do you need answers to? What insights are you hoping to gain? It’s essential to be clear on the problem before embarking on any research.
  2. Set Research Objectives: Once you’ve defined the problem, you must set research objectives to help you achieve your goal. These objectives should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, if you’re trying to understand why sales have declined, your research objective might be to identify the key factors contributing to the decline.
  3. Identify the Target Audience: Next, you must identify your research’s target audience. Who are you trying to reach? What characteristics do they have? It’s essential to define your target audience so that you can design research that will yield meaningful insights.
  4. Choose the Right Research Methodology: There are many different research methodologies available, such as surveys, focus groups, interviews, and observational research. Each method has pros and cons; the right choice will depend on your research objectives and target audience. Choosing the right methodology ensures you get the insights you need.
  5. Develop the Research Instrument: Once you’ve chosen your methodology, you need to develop the research instrument – the tool you’ll use to collect data. This might be a survey questionnaire, a discussion guide for a focus group, or an interview protocol. It’s important to design the research instrument carefully to ensure you collect high-quality data.
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Crafting the Right Research Question

Once you’ve defined the problem, set research objectives, identified the target audience, and chosen the right research methodology, the next step is to craft the right research question. The research question should be clear, concise, and focused on the problem you’re trying to solve. In addition, you can develop supplemental questions to provide more context and depth around the issue. Here are some tips for crafting the right research question and creating additional questions:

  1. Start with a Broad Question: Begin by crafting a broad research question that captures the main issue you’re trying to address. For example, if you’re trying to understand why sales have declined, your general research question might be, “What factors are contributing to the decline in sales?”
  2. Narrow the Question: Once you have a broad research question, you need to narrow it down to something more specific. This will help you focus your research and ensure you’re collecting the correct data. For example, you might narrow your research question to “What are the key drivers of customer churn?”
  3. Make the Question Measurable: It’s important to make your research question measurable so that you can collect data that will help you answer it. For example, you might ask, “What percentage of customers who churn cite price as a factor?”
  4. Ensure the Question is Relevant: The research question should be relevant to the problem you’re trying to solve and the research objectives you’ve set. Ensure that the question will yield insights to help you make informed decisions.
  5. Keep the Question Simple: Keep the research question simple and easy to understand. This will help ensure that participants can answer it accurately and that you can analyse the data effectively. Let’s say you’re conducting research to understand why customers are not using a new feature on your product. Instead of asking a complex question like, “How do you feel about the usability of the new feature compared to previous versions of the product?” which may confuse participants, consider asking a simple and direct question like “Are you currently using the new feature?” This question is easy to understand and can be answered with a simple “yes” or “no,” making it easier for participants to answer accurately and for you to analyse the data effectively. 
  6. Develop Supplemental Questions: Once you have the key question, develop supplemental questions that provide more context and depth around the issue. These questions should help you understand the nuances of the problem and provide a more comprehensive view of the issue. For example, suppose you’re trying to understand why sales have declined. In that case, you might develop supplemental questions such as “How has customer sentiment changed over time?” or “What are customers saying about our competitors?”

Conducting the Research

Once you’ve defined the problem, set research objectives, identified the target audience, chosen the right research methodology, and crafted the right research questions, it’s time to conduct the research. Here are some tips for conducting the research effectively:

  1. Recruit Participants: Depending on your research methodology, you’ll need to recruit participants who fit your target audience. This might involve contacting customers via email, social media, or in-person events. Make sure to screen participants carefully to ensure they meet your established criteria.
  2. Structure the Research: Once you’ve recruited participants, you must structure the research to yield meaningful insights. For example, if you’re conducting a focus group, you might structure the discussion around key topics or questions. If you’re conducting a survey, you must design the questionnaire carefully to ensure you’re collecting the data you need.
  3. Collect Data: The next step is to collect the data. This might involve recording the discussion in a focus group, administering a survey online or in-person, or conducting interviews. Make sure to collect the data in a way that is consistent with the research methodology you’ve chosen.
  4. Analyse the Data: Once you’ve collected the data, you must identify patterns and insights. This might involve coding the data, running statistical analyses, or using qualitative analysis techniques. Analyse the data rigorously to ensure the insights are accurate and meaningful.
  5. Draw Conclusions: Finally, use the insights you’ve gained from the research to draw conclusions and inform your marketing strategy. What did you learn from the study? How can you use these insights to address the problem you identified at the beginning of the research process?
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Interpreting the Results

Once you’ve researched and analysed the data, it’s time to interpret the results and use them to inform your marketing strategy. Here are some tips for interpreting the results effectively:

  1. Look for Patterns: As you review the data, look for emerging patterns and trends. Are there any common themes or issues that participants identified? What insights can you gain from the data?
  2. Compare Results: If you conducted multiple research methods, compare the results to determine any consistencies or discrepancies. This can help you triangulate the data and ensure accurate insights.
  3. Consider the Context: When interpreting the results, it’s essential to consider the context in which the research was conducted. What external factors might be impacting the results? How do the results align with what you know about the market and your target audience?
  4. Draw Meaningful Conclusions: Based on the insights you’ve gained from the research, draw meaningful conclusions that will inform your marketing strategy. What changes do you need to make to your strategy? What opportunities can you pursue based on the insights?
  5. Communicate the Results: Finally, communicate the research results to your organisation’s stakeholders. This might include senior leadership, sales teams, or product development teams. Communicate the results clearly and effectively, and emphasise how they can be used to drive business growth.

Key Takeaways

The marketer’s dilemma of knowing when to conduct research and how to do it right is a challenge many marketers and product marketing managers face

However, by following best practices and taking a structured research approach, you can gather valuable insights into your target audience and use these insights to inform your marketing strategy.

  • Defining the problem is the first step in conducting research, followed by setting research objectives, identifying the target audience, choosing the correct methodology, and crafting the right research question.
  • Signs that indicate it’s time to conduct research include declining sales, customer complaints, lack of customer engagement, new competitors, and changing market conditions.
  • Conducting research involves recruiting participants, structuring the research, collecting data, analyzing the data, and drawing conclusions.
  • Interpreting the results involves looking for patterns, comparing results, considering the context, drawing meaningful conclusions, and communicating the results to stakeholders.
  • By taking a strategic approach to research and using the insights gained to inform your marketing strategy, you can develop compelling campaigns, stay ahead of the competition, and drive business growth.

No matter your experience level, prioritising research and using it to inform your marketing strategy is crucial for driving business growth. Following the steps outlined in this article, you can conduct research that yields valuable insights and helps you make informed decisions. 

If you’re ready to take the next step and conduct a research project, consider working with a trusted partner like Kadence International. With 30 years of expertise and offices in 10 countries, Kadence is a leading and award-winning market research firm that can help you conduct research that delivers actionable insights. Contact us to learn more and get started on your next research project.

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Big data refers to the massive amount of structured and unstructured data generated by various sources in our digital world, such as social media, e-commerce transactions, and mobile devices. This data is characterised by its sheer volume, velocity, and variety, making it difficult to process using traditional methods.

“Big data will become the basis for competitive advantage, replacing the traditional competitive advantage of having the best resources, the best people, or the best strategy.” – Ginni Rometty, CEO of IBM.

The role of big data in market research is crucial in providing businesses with valuable insights into consumer behaviour, preferences, and market trends. Market researchers use big data to analyse consumer data and understand their purchasing habits, preferences, and opinions, which helps businesses make informed decisions about product development, marketing, and sales strategies.

Big data also helps identify potential market opportunities and challenges and understand the effectiveness of marketing campaigns. By leveraging advanced analytical techniques, such as machine learning and predictive analytics, market researchers can uncover patterns and relationships in consumer data, which can help businesses tailor their products and services to meet the needs and preferences of their target market.

The term “big data” was first popularised in the late 1990s and early 2000s, but the concept of handling large amounts of data dates back to much earlier. Here is a rough timeline of the history of big data:

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The outlook for big data is very positive, with demand for big data solutions expected to continue growing as brands seek to harness the value of their data and make more informed decisions.

Here are some of the key trends and factors that are shaping the future of big data:

  • Continued Growth of Data: The amount of data being generated is continuing to grow at an exponential rate, driven by the proliferation of connected devices, the Internet of Things (IoT), and the rise of new technologies such as artificial intelligence and machine learning.
  • Wider Adoption of Cloud Computing: The trend towards cloud computing enables companies to store and process large amounts of data more efficiently and cost-effectively, driving the adoption of big data solutions.
  • Increased Focus on Data Privacy: As consumers become more aware of the value of their personal data, there is a growing demand for solutions that allow them to control and protect their information.
  • Advances in Artificial Intelligence and Machine Learning: The continued development of AI and machine learning makes it possible to extract more value from big data, enabling companies to gain new insights and make more informed decisions.
  • Expansion into New Industries: Big data is no longer limited to tech-focused industries and is increasingly being adopted by a wider range of industries, including healthcare, retail, finance, and energy.

4 Ways Big Data is Changing Market Research

As previously mentioned, big data refers to large and complex datasets generated by various sources, including social media, e-commerce transactions, and mobile devices. The sheer volume, velocity, and variety of big data can make it difficult to process and analyse using traditional data processing techniques.

“Big data is more than just a matter of size; it is an opportunity to find insights in new and emerging types of data and content, to make your business more agile, and to answer questions that were previously considered beyond your reach.” – Tim O’Reilly, Founder, and CEO of O’Reilly Media.

Big data is changing the way market research is conducted in several ways. First, big data allows market researchers to gain insights into consumer behaviour and preferences at a scale that was previously not possible. With big data, researchers can track consumer interactions across multiple touchpoints, including online and offline behaviours, social media interactions, and purchase history.

Second, big data enables market researchers to gain more accurate and in-depth insights into consumer behaviour and preferences. With traditional market research methods, such as surveys and focus groups, it can be difficult to get a complete picture of consumer behaviour and preferences, as the sample size is often limited and the data is self-reported. With big data, researchers have access to a much larger and more diverse dataset, which can provide a more accurate and in-depth view of consumer behaviour and preferences.

Third, big data allows market researchers to conduct research in real-time, providing brands with insights into consumer behaviour and preferences as they happen. This will enable companies to respond quickly to changing consumer preferences and needs and make more informed decisions.

Finally, big data enables market researchers to use more advanced analytical techniques, such as machine learning and artificial intelligence, to gain deeper insights into consumer behaviour and preferences. With these techniques, researchers can analyse large and complex datasets, uncover patterns and correlations, and gain insights into consumer behaviour and preferences in a way that was previously not possible.

In conclusion, big data is changing the way market research is conducted by providing researchers with access to larger and more diverse datasets, enabling real-time research, and allowing for more advanced analytical techniques. As a result, companies can gain more accurate and in-depth insights into consumer behaviour and preferences and make more informed decisions.

The Benefits of Big Data

The use of big data in market research offers several benefits that can help brands gain a better understanding of their customers and make more informed decisions. Some of the key benefits of big data in market research include the following:

  • Ability to gather and analyse vast amounts of data: One of the biggest benefits of big data in market research is the ability to gather and analyse vast amounts of data. With traditional market research methods, such as surveys and focus groups, it can be difficult to collect enough data to make accurate and informed decisions. However, with big data, researchers can gather and analyse vast amounts of data from a wide range of sources, including social media, e-commerce transactions, and mobile devices, providing a much more complete picture of consumer behaviour and preferences.
  • Real-time insights: Another key benefit of big data in market research is the ability to gain real-time insights. Traditional market research methods can take weeks or even months to gather and analyse data, by which time consumer preferences and behaviours may have changed. With big data, researchers can gain real-time insights into consumer behaviour and preferences, allowing companies to respond quickly to changes in the market.
  • Improved accuracy: Big data also provides a more accurate picture of consumer behaviour and preferences than traditional market research methods. With traditional methods, the sample size is often limited, and the data is self-reported, leading to biases and inaccuracies. With big data, researchers have access to a much larger and more diverse dataset, which can provide a more accurate view of consumer behaviour and preferences.
  • Advanced analytical techniques: Finally, big data enables market researchers to use more advanced analytical methods, such as machine learning and artificial intelligence, to gain deeper insights into consumer behaviour and preferences. These techniques can help researchers uncover patterns and correlations in large and complex datasets, giving organizations a more in-depth understanding of their customers.

The Power of Predictive Analytics

Predictive analytics is a key component of big data and is increasingly used by companies to make informed business decisions. Predictive analytics involves statistical models, machine learning algorithms, and other techniques to analyse large and complex datasets and predict future events or trends.

In market research, predictive analytics can forecast consumer behaviour and preferences and predict the success of marketing campaigns, product launches, and other initiatives. By leveraging the power of predictive analytics, brandss can better understand their customers, make more informed decisions, and stay ahead of the competition.

One of the key advantages of predictive analytics is its ability to identify patterns and correlations in large and complex datasets. This allows brands to predict future consumer behaviour and preferences and identify key drivers of consumer behaviour. For example, predictive analytics can identify the factors influencing consumer purchasing decisions, such as brand loyalty, price sensitivity, and product quality.

Another advantage of predictive analytics is its ability to provide real-time insights. Traditional market research methods can take weeks or even months to gather and analyse data, by which time consumer preferences and behaviours may have changed. With predictive analytics, organisations can gain real-time insights into consumer behaviour and preferences, allowing them to respond quickly to changes in the market.

The Challenges of Big Data

Despite the many benefits of big data in market research, several challenges are associated with this approach. Some of the main challenges of big data include the following:

  • The need for advanced data management systems: One of the biggest challenges of big data is the need for advanced data management systems. Traditional market research methods typically collect data in a centralised and structured format, making it easier to manage and analyse. However, with big data, data is often collected from a wide range of sources and in a variety of formats, making it more challenging to manage and analyse. As a result, companies must invest in advanced data management systems, such as data warehouses, data lakes, and cloud computing solutions, to effectively manage and analyse big data.
  • The need for skilled data scientists: Another challenge of big data is the need for qualified data scientists. With big data, organisations must analyse vast amounts of data using advanced techniques, such as machine learning and artificial intelligence, which require a high level of expertise. As a result, companies must invest in training and development programs for their data scientists or partner with external firms with the necessary expertise to effectively leverage the power of big data.
  • Data privacy and security concerns: With the increasing use of big data, there are also concerns about data privacy and security. With big data, organisations must collect and store vast amounts of personal data, which raises concerns about data privacy and security. As a result, companies must implement strong security measures and comply with data privacy regulations, such as the General Data Protection Regulation (GDPR), to protect personal data.
  • Quality and accuracy of data: Another challenge of big data is the quality and accuracy of data. With big data, organisations must rely on data from a wide range of sources, including social media, e-commerce transactions, and mobile devices, which may only sometimes be accurate or up-to-date. As a result, companies must validate and clean the data they collect to ensure its accuracy and quality.

Big Data Gone Wrong

There are several examples of big data gone wrong that are worth mentioning. One such example is the Cambridge Analytica scandal, where the data analytics firm gained unauthorised access to the personal data of millions of Facebook users, which was then used to influence political elections. This scandal brought attention to the potential misuse of big data and the importance of ethical considerations in its use.

“Big data is not about the data. It’s about creating insights, making informed decisions, and driving outcomes.” – Tom Davenport, Professor of Information Technology and Management at Babson College.

Another example is the concept of “fake news,” which has become increasingly prevalent with the rise of big data. The vast amounts of information available through big data can make it difficult to distinguish between credible and non-credible sources, leading to the spread of false information and misleading insights.

Finally, big data can also perpetuate existing biases and discrimination if the data used to inform decision-making is not diverse and representative. For example, facial recognition technology has faced criticism for having higher error rates for people with darker skin tones due to a lack of diverse training data.

These examples highlight the importance of responsible and ethical use of big data in market research and the need for companies to consider the potential consequences of their actions when leveraging big data to inform business decisions.

Integrating Big Data with Traditional Research Methods

While big data in market research offers many benefits, it is also essential to integrate it with traditional research methods, such as surveys and focus groups, to achieve a comprehensive understanding of consumer behaviour. This integration can help organisations:

  • Validate big data findings: By combining big data with traditional research methods, brands can validate the findings of big data and ensure the accuracy of their results. For example, by conducting surveys or focus groups, companies can gain insights into consumer attitudes and behaviours, which can be compared with the data collected from big data sources, such as social media or e-commerce transactions.
  • Gain deeper insights into consumer behaviour: Integrating big data with traditional research methods can also help organisations gain deeper insights into consumer behaviour. For example, by combining big data with focus groups, brands can gain a complete understanding of consumer attitudes and motivations, which can help them make more informed decisions.
  • Fill gaps in big data: Big data sources, such as social media and e-commerce transactions, only sometimes provide a complete picture of consumer behaviour. By integrating big data with traditional research methods, brands can fill gaps in their data and gain a full understanding of consumer behaviour.
  • Enhance the reliability of results: Integrating big data with traditional research methods can also enhance the reliability of market research results. By combining multiple data sources, organisations can gain a more accurate and comprehensive understanding of consumer behaviour.

The Role of Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming an important part of big data in market research. These technologies are often used to automate the analysis of large amounts of data, making it easier and faster to gain insights into consumer behaviour. Some of the ways in which AI and ML are used in market research include:

  • Predictive modelling: AI and ML are used to create predictive models that can identify patterns and trends in big data. These models can be used to forecast consumer behaviour and make informed decisions.
  • Sentiment analysis: AI and ML can also be used to perform sentiment analysis on social media data, making it possible to gain insights into consumer opinions and attitudes.
  • Natural language processing: AI and ML are also used to perform natural language processing (NLP) on big data sources, such as customer reviews or surveys. NLP allows companies to analyse text data and gain insights into consumer behaviour.

In the future, AI technologies, such as ChatGPT, could play a significant role in market research. For example, ChatGPT could conduct virtual focus groups or customer interviews. This type of AI could provide a more natural and interactive experience for participants, making it easier to gain insights into consumer behaviour. Additionally, ChatGPT could automate customer feedback analysis, making it possible to gain insights into consumer behaviour in real-time.

Best Practices for Big Data Market Research

When conducting big data market research, it is essential to follow best practices to ensure the quality and accuracy of the data. Some of the best practices for big data market research include:

  • Focus on data quality: The quality of the data is critical for making informed decisions. Organisations should focus on collecting high-quality data from reliable sources, such as customer surveys or transactional data. Additionally, it is essential to clean and validate the data to ensure accuracy.
  • Ethical considerations: Big data market research raises significant ethical concerns like privacy and data security. Brands should be transparent about their data collection practices and obtain consent from participants. Additionally, it is crucial to secure and store data to protect sensitive information properly.
  • Integration with traditional research methods: While big data provides valuable insights into consumer behaviour, it is important also to integrate it with traditional research methods, such as focus groups or customer interviews, to gain a comprehensive understanding of consumer behaviour.
  • Data management and storage: The volume and complexity of big data requires advanced data management systems and storage solutions. Brands should invest in these technologies to ensure that they can efficiently store, manage, and analyse large amounts of data.
  • Collaboration with data scientists: Organisations may need to collaborate with data scientists or other experts to analyse the data and extract insights. It is vital to work with experienced professionals to ensure that the data is analysed accurately and effectively.

Big Data in Action

Big data has been used in various industries to inform business decisions and improve market research. Here are a few examples:

  • Retail: Big data has been used by retailers to analyse customer purchase patterns and improve inventory management. For example, retailers can use data on customer purchases to determine which products are in high demand and adjust their inventory accordingly.
  • Healthcare: The healthcare industry uses big data to improve patient outcomes and reduce costs. For example, healthcare providers use patient health records and medical procedures data to identify trends and make treatment recommendations.
  • Finance: Financial services companies use big data to improve risk management and fraud detection. For example, banks can use data on customer transactions to identify unusual patterns that may indicate fraudulent activity.
  • Marketing: Marketers use big data to gain insights into consumer behaviour and target advertisements more effectively. For example, companies can analyse consumer searches and social media activity data to determine which products and services interest consumers.

These are just a few examples of how big data can inform business decisions and improve market research. As technology evolves and the amount of data generated continues to grow, we will likely see even more innovative uses of big data in the future.

Final thoughts and Key Takeaways

It is worth mentioning that the role of big data in market research is constantly evolving. As technology advances and the amount of data generated continues to grow, the opportunities to leverage big data in market research are only increasing.

“Big data, if used correctly, has the potential to change the face of market research forever. By harnessing the power of advanced analytics, market researchers can uncover new insights and trends that were previously hidden in the data.” – Raj De Datta, CEO and Co-Founder of Bloomreach.

One key trend in using big data for market research is the rise of omnichannel data. Omnichannel data refers to collecting data from various sources, including online and offline interactions, to understand consumer behaviour comprehensively. With the rise of the Internet of Things (IoT) and the increasing use of mobile devices, the amount of omnichannel data available for analysis is snowballing.

Another trend in using big data for market research is the increased focus on data privacy and ethics. With the growing amount of data being collected and analysed, companies must ensure that they respect consumers’ privacy and adhere to ethical standards.

Key Takeaways

  • Big data refers to the vast amounts of structured and unstructured data generated by modern technologies, such as social media, online transactions, and IoT devices.
  • The benefits of using big data in market research include gathering and analysing vast amounts of data in real-time, gaining deeper insights into consumer behaviour, and making more informed business decisions.
  • Predictive analytics is a powerful tool in big data, as it can help brands identify trends and predict future behaviour.
  • The use of big data in market research is not without its challenges, including the need for advanced data management systems, skilled data scientists, and ethical considerations.
  • Integrating big data with traditional research methods, such as surveys and focus groups, can provide a comprehensive understanding of consumer behaviour and help companies make more informed decisions.
  • AI and machine learning play a significant role in big data, as they can help process and analyse vast amounts of data and improve market research.
  • Best practices for conducting big data market research include ensuring data quality, considering ethical considerations, and integrating big data with traditional research methods.
  • Real-life examples of big data in action include its use in personalised marketing, identifying consumer trends, and predicting future behaviour.
  • Despite the potential benefits of big data in market research, there are also possible consequences, including spreading false information, perpetuating existing biases and discrimination, and potential misuse of data. As such, market researchers must be aware of these potential consequences and ensure that they use big data in an ethical and responsible manner.

In conclusion, big data has already significantly impacted market research and is only becoming more important as technology advances, and the amount of data generated continues to grow. Market researchers who embrace big data and understand its potential benefits and challenges will be well-positioned to succeed in the future.

In today’s highly competitive business environment, brands must have a well-informed and effective product marketing strategy. One of the key ways to achieve this is by using data-driven insights to inform decision-making and guide marketing efforts. With the vast amounts of data available, it’s possible to gain a deep understanding of your target audience, track the success of your marketing efforts, optimise your marketing mix, personalise your marketing approach, and stay ahead of the competition.

A recent study found that companies that use data-driven insights to inform their product marketing strategies are 2-3 times more likely to report significant revenue growth than companies that do not. By leveraging data-driven insights, companies can make informed decisions about their product marketing strategies and achieve better results. For example, data can help companies understand their target audience’s preferences and behaviours, shaping the messaging and offers used in marketing campaigns. Data can also help companies track the performance of their marketing efforts, allowing them to make adjustments and improvements as needed.

This blog will explore how companies can use data-driven insights to improve their product marketing strategies and achieve better results.

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Understanding Your Target Audience

One of the key aspects of a successful product marketing strategy is understanding your target audience. By gaining insights into their preferences, behaviours, and pain points, you can tailor your marketing efforts to meet their needs better and drive better results. There are several ways to use data to gain these insights, including:

  • Customer Surveys: Conducting customer surveys is a great way to gather information about your target audience’s preferences and behaviours. This can include their buying habits, product usage, and pain points.
  • Web Analytics: Web analytics tools, such as Google Analytics, can provide valuable insights into your target audience’s online behaviours, such as the pages they visit, the time they spend on your site, and the devices they use to access your site.
  • Social Media Analytics: Social media platforms, such as Facebook and Twitter, offer built-in analytics tools that can help you understand your target audience’s preferences and behaviours on these platforms.
  • Customer Interviews: Conducting customer interviews can provide valuable insights into your target audience’s pain points and preferences. This can include insights into what they like, dislike, and want in a product.

Using these and other data sources, you can gain a deep understanding of your target audience and use that information to inform your product marketing strategy. This can include the messaging and offers you use in marketing campaigns, the channels you use to reach your target audience, and the products and services you offer. Understanding your target audience can create a more effective product marketing strategy that resonates with them and drives better results.

Measuring the Success of Your Marketing Efforts

To effectively improve your product marketing strategy, it’s essential to track the performance of your marketing efforts. This allows you to understand what’s working well and what needs improvement, so you can make data-driven decisions and achieve better results. There are several key metrics to track, including:

  • Website Traffic: Tracking website traffic is a good indicator of the overall reach of your marketing efforts. You can track the number of visitors to your site, the pages they visit, and the time they spend on your site.
  • Conversion Rates: Conversion rates measure the percentage of website visitors who take a desired action, such as making a purchase or filling out a form. This metric is important because it indicates how effectively your marketing efforts drive results.
  • Customer Engagement: Customer engagement metrics, such as likes, shares, and comments, can provide valuable insights into how your target audience interacts with your marketing efforts. This information can inform future marketing campaigns and improve customer engagement.
  • Return on Investment (ROI): ROI is a key metric that measures the return on your marketing investment. This metric can determine the effectiveness of your marketing efforts and make data-driven decisions about where to allocate resources in the future.

By tracking these and other metrics, you can better understand the performance of your marketing efforts and make data-driven decisions to improve your product marketing strategy. This can lead to increased customer engagement, higher conversion rates, and improved bottom-line results.

Optimising Your Marketing Mix

One of the key benefits of using data-driven insights to inform your product marketing strategy is the ability to optimise your marketing mix. This includes optimising the channels, messaging, and offers used in marketing campaigns. Using data to inform these decisions, you can achieve better results and create a more effective marketing mix. Here are some ways to use data to optimise your marketing mix:

  • Channels: Data can help you understand which channels are most effective at reaching your target audience and driving results. This can include information about the type of content that resonates best with your target audience on each channel and the times when they are most active.
  • Messaging: Data can inform the messaging used in marketing campaigns by providing insights into your target audience’s pain points and preferences. This can help you create messages that are more relevant and effective.
  • Offers: Data can inform the offers used in marketing campaigns by providing insights into the type of offers most appealing to your target audience. This can include information about discounts, promotions, and other incentives that drive the best results.

Using data to inform these decisions, you can create a marketing mix tailored to your target audience’s needs and preferences and drive better results. This can include increased customer engagement, higher conversion rates, and improved bottom-line results. You can stay ahead of the competition by continuously monitoring and optimizing your marketing mix and achieve long-term success.

Personalising Your Marketing Approach

Personalisation is becoming increasingly important in product marketing, as customers expect to receive relevant and tailored experiences. 

Using data to personalise your marketing approach, you can create more effective marketing campaigns that resonate with individual customers. According to a survey by Epsilon, personalisation can increase email open rates by 26% and click-through rates by 14%.

Here are some ways to use data to personalise your marketing approach:

  1. Customer Segmentation: Data can be used to segment your customer base into different groups based on common characteristics, such as demographics, behaviours, and preferences. This information helps create targeted marketing campaigns for each segment.
  2. Behavioural Tracking: Behavioral tracking can provide valuable insights into the actions and preferences of individual customers. This information is invaluable in helping to personalise marketing messages and offers, such as recommendations based on past purchases.
  3. Dynamic Content: Dynamic content technology can deliver personalised experiences to individual customers based on their behaviours and preferences. For example, you can use data to show different images or messaging to different customers based on their interests.

Using data to personalise your marketing approach, you can create more relevant and effective marketing campaigns that drive better results. Personalisation can lead to increased customer engagement, higher conversion rates, and improved customer satisfaction, ultimately resulting in improved bottom-line results.

Staying Ahead of the Competition

In today’s highly competitive business environment, staying ahead of your competition and staying up-to-date on industry trends and best practices is essential. You can gain a competitive advantage and create a more effective product marketing strategy using data-driven insights. Here are some ways to use data to stay ahead of the competition:

  • Competitive Analysis: Data can be used to analyze your competitors’ marketing strategies, including their channels, messaging and offers. This information can inform your marketing strategy and stay ahead of the competition.
  • Industry Trends: Data can be used to stay up-to-date on the latest industry trends and best practices. This can include information about emerging technologies, consumer behaviours, and marketing techniques.
  • Customer Feedback: Customer feedback is a valuable data source that can shape your product marketing strategy. Using customer feedback data, you can stay ahead of the competition by understanding what customers want and need and continuously improving your offerings.

By using data-driven insights to stay ahead of the competition and stay up-to-date on industry trends and best practices, you can create a more effective product marketing strategy that drives better results. This can lead to increased customer engagement, higher conversion rates, and improved bottom-line results.

Conducting Effective Market Research

Market research is vital for informing product marketing strategies and making data-driven decisions. By conducting market research, companies can gain valuable insights into their target audience and industry trends, helping to shape their product marketing strategies and achieve better results.

Defining Your Research Objectives:

The first step in conducting effective market research is to define your research objectives. This involves identifying the questions you want to answer through market research and prioritizing them based on their importance to your business. For example, you may want to understand the needs and preferences of your target audience or stay up-to-date on the latest industry trends. By defining your research objectives, you can ensure that your market research is focused and effective.

Choosing the Right Research Method:

Once you have defined your research objectives, the next step is to choose the right research method. There are several market research methods, including surveys, focus groups, and customer interviews. The best approach for your research objectives will depend on the type of information you are trying to gather and the resources available to you. For example, customer interviews may be the best method for gaining deep insights into customer pain points, while surveys may be the best method for gathering large amounts of data.

Analyzing and Interpreting Your Data:

Once you have collected your data, the next step is to analyze and interpret it. This involves looking for patterns and trends in the data and using those insights to inform your product marketing strategy. There are several tools and techniques that can be used to analyze and interpret market research data, including statistical analysis, data visualisation, and machine learning algorithms.

Communicating Your Findings:

Once you have analyzed and interpreted your market research data, the next step is communicating your findings to stakeholders. This can include senior management, marketing teams, and other departments involved in product marketing. To effectively communicate your findings, you must present the data clearly and compellingly, using visual aids such as charts, infographics, graphs, or even video to help illustrate your points.

Incorporating Your Findings into Your Marketing Strategy:

The final step in conducting effective market research is incorporating your findings into your product marketing strategy. This involves using the insights from your market research to inform your product marketing strategy and make data-driven decisions. For example, you may use your market research findings to create more targeted marketing campaigns or to develop new products and services that better meet the needs of your target audience.

Data-driven insights are becoming increasingly important in product marketing as companies seek ways to reach their target audience and drive better results. By using data to understand your target audience, measure the success of your marketing efforts, optimise your marketing mix, personalise your marketing approach, and stay ahead of the competition, you can create a more effective product marketing strategy. Additionally, conducting effective market research can provide valuable insights into your target audience and industry trends, helping you to make data-driven decisions and achieve better results.

In market research, the collection and use of data raise several ethical considerations, such as obtaining informed consent, protecting the privacy and confidentiality of participants, avoiding deceptive practices, and ensuring data accuracy. 

Ethical guidelines, such as the International Chamber of Commerce’s ICC/ESOMAR International Code on Market and Social Research, provide a framework for conducting market research responsibly and respectfully. Additionally, industry-specific regulations, such as the General Data Protection Regulation (GDPR) in the European Union, further regulate the collection and use of personal data. Brands and their market research teams must be aware of these ethical considerations and guidelines to ensure the validity and credibility of their research findings and maintain the trust of their participants.

The Importance of Ethical Data Collection

The ethics of data collection play a crucial role in the credibility and validity of market research findings. When data is collected ethically, participants can trust that their personal information is handled responsibly and securely. 

This trust is essential for accurate research results, as participants are more likely to provide honest and complete answers when they feel their privacy and confidentiality are protected.

“The right to privacy is a fundamental human right, essential for the protection of human dignity and autonomy.” – Justice Michael Kirby.

Additionally, ethical data collection practices help to maintain the reputation and credibility of the market research industry. Deceptive or unethical practices can damage the reputation of both the individual researcher and the industry as a whole, leading to a loss of trust from participants, clients, and stakeholders.

It is also a legal obligation for researchers to adhere to ethical standards and regulations, such as the GDPR. Failing to comply with these regulations can result in significant fines and legal consequences, damaging the reputation of the research company and potentially impacting its ability to conduct research in the future.

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Data Privacy Laws Around the World

Data privacy laws vary by country, but here is an overview of some of the most notable data privacy laws in different regions worldwide.

It is important to note that these laws are subject to change and that organisations should stay informed about their regions’ latest data privacy laws and regulations.

UK: The General Data Protection Regulation (GDPR) applies to organisations operating in the EU, including the UK. The GDPR requires organisations to obtain explicit consent from individuals before collecting and processing their personal data.

Europe: The General Data Protection Regulation (GDPR) applies to organisations operating in the EU. It sets out strict rules for collecting and processing personal data, including the right to erasure and data portability.

USA: The United States does not have a comprehensive federal data privacy law, but some states have enacted their own privacy laws, such as the California Consumer Privacy Act.

Canada: The Personal Information Protection and Electronic Documents Act (PIPEDA) governs the collection, use, and disclosure of personal data in Canada. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

Thailand: The Personal Data Protection Act (PDPA) became effective in May 2020 and governed the collection, use, and disclosure of personal data in Thailand. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

Philippines: The Data Privacy Act of 2012 governs the collection, use, and disclosure of personal data in the Philippines. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

Japan: The Act on the Protection of Personal Information governs the collection, use, and disclosure of personal data in Japan. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

Indonesia: The Personal Data Protection Law governs the collection, use, and disclosure of personal data in Indonesia. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

China: The Cybersecurity Law of the People’s Republic of China governs the collection, use, and disclosure of personal data in China. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

Singapore: The Personal Data Protection Act (PDPA) governs the collection, use, and disclosure of personal data in Singapore. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

India: The Personal Data Protection Bill, 2019, governs the collection, use, and disclosure of personal data in India. The bill requires organisations to obtain explicit consent before collecting personal data and to protect the privacy of the data they collect.

Vietnam: The Personal Data Protection Law governs the collection, use, and disclosure of personal data in Vietnam. Organisations must obtain explicit consent before collecting personal data and must protect the privacy of the data they collect.

Examples of Brands Fined for Violating Data Privacy

These are just a few examples of the many brands that have faced fines for violating data privacy laws. It is essential for companies to take data privacy seriously and to comply with the relevant laws and regulations to avoid similar consequences.

  1. Google was fined €50 million by the French data protection authority (CNIL) in January 2019 for violating the General Data Protection Regulation (GDPR).
  2. Facebook was fined $5 billion by the Federal Trade Commission (FTC) in July 2019 for violating its users’ privacy rights.
  3. Marriott International was fined £18.4 million by the Information Commissioner’s Office (ICO) in July 2019 for a data breach affecting approximately 339 million guests.
  4. British Airways was fined £20 million by the ICO in July 2019 for a data breach affecting approximately 500,000 customers.
  5. Equifax was fined £500,000 by the ICO in September 2018 for a data breach affecting approximately 15 million UK citizens.

The Ethics of Data Privacy

Data privacy is a critical aspect of ethical data collection in market research. The personal information of participants must be protected and kept confidential to maintain their trust in the research process and to prevent potential harm or abuse of their data.

One of the key ethical considerations in protecting consumer data privacy is obtaining informed consent. Participants must be fully informed about how their data will be collected, used, and stored, and they must give explicit consent for their information to be used in the research. This includes informing participants who will have access to their data, for what purposes it will be used, and for how long it will be stored.

“Data is the new oil, but privacy is the new gasoline.” – Unknown.

Another important consideration is data security. Market researchers must implement appropriate measures to secure the collected data, such as encryption and secure storage solutions, to prevent unauthorised access and to protect participants’ information from theft or breaches.

It is essential for market researchers to be transparent and honest about their data collection practices. Deceptive or misleading practices, such as collecting data without obtaining proper consent or using data for purposes outside of what was initially disclosed, can severely damage the trust of participants and harm the reputation of the market research industry.

The concept of data privacy has been a concern for individuals and organisations for many decades. Still, it has become increasingly relevant in recent years with the rapid growth of technology and the increasing amount of personal data collected and stored by organisations. Here is a timeline of some key events related to data privacy and notable data breaches by year:

  • 1970s: The first privacy laws, such as the US Privacy Act of 1974, are enacted in response to government data collection and storage concerns.
  • 1980s: The first computer viruses were discovered, and the threat of data breaches became more prominent.
  • 1990s: The rise of the internet and the increasing use of personal computers leads to concerns about online data privacy.
  • 2000s: The growth of social media and the increasing amount of personal data collected by organisations leads to increased privacy concerns.
  • 2005: The first large-scale data breach, involving the theft of millions of credit card numbers by one of the largest credit card processors in the United States, CardSystems Solutions, is reported. The breach was one of the first large-scale data breaches to receive widespread media attention and raised concerns about the security of personal data stored by organisations. The breach resulted in the loss of credit card information for 40 million individuals and prompted a number of major credit card companies to reissue their customers’ credit cards. The breach also led to increased scrutiny of data security practices by organisations and a call for stronger data privacy laws to protect consumers.
  • 2013: The first high-profile data breach involving the unauthorised access of personal data, such as names, addresses, and social security numbers, is reported. Hackers stole 40 million credit card numbers and 70 million other pieces of information, such as names, addresses, and phone numbers, from the retailer’s database. The breach was one of the largest data breaches to date and resulted in widespread media coverage and concern about the security of personal information stored by organisations. The breach also increased scrutiny of data security practices and calls for more robust data privacy laws to better protect consumers. This event highlighted the need for organisations to take data privacy and security seriously, implement strong security measures, and regularly review and update their practices to stay ahead of evolving threats.
  • 2018: The European Union’s General Data Protection Regulation (GDPR) goes into effect, setting new standards for data privacy and security in Europe.
  • 2019: The Capital One data breach, involving the theft of personal data of over 100 million individuals, is reported.
  • 2020: The Zoom video conferencing platform becomes widely used due to the COVID-19 pandemic, leading to concerns about the security of personal data being transmitted over the platform.

The Ethics of Data Use

The use of collected data is just as important as the collection process in terms of ethical considerations. Market researchers are responsible for using the data they collect in a manner that is respectful, non-discriminatory, and in line with the initial purpose for which it was collected.

One key consideration is avoiding discriminatory practices. Market research data must not be used to make decisions that unfairly impact or discriminate against particular groups based on race, gender, religion, or sexual orientation. Researchers must also ensure that their findings are not used to perpetuate negative stereotypes or to support biased viewpoints.

“Ethics is knowing the difference between what you have a right to do and what is right to do.” – Potter Stewart.

Another important consideration is maintaining the confidentiality of participants’ information. Researchers must not use collected data in a manner that violates participants’ privacy, such as by sharing it with third parties without proper consent. The data must be used only for the purposes for which it was collected and must be kept confidential to the extent required by law or ethical guidelines.

The Importance of Consent

Obtaining informed consent from consumers is crucial to ethical data collection in market research. It is essential for market researchers to respect the privacy rights of participants and to ensure that they fully understand how their data will be used and what they agree to when they provide it.

Informed consent means that participants clearly understand the purpose of the research, how their data will be collected, used, and stored, and the consequences of participating or not participating in the research. Participants must also be allowed to opt-out of the research or withdraw their consent at any time.

When participants provide their informed consent, it demonstrates their trust in the market research process and their willingness to participate. This trust is essential for accurate research results, as participants are more likely to provide honest and complete answers when they feel their privacy and confidentiality are protected.

However, obtaining informed consent also protects the rights of participants and ensures that their data is not being collected or used without their knowledge or permission. Market researchers must be transparent and honest about data collection and use practices to build trust and credibility with their participants.

Data Security and Protection

Data security and protection are crucial components of ethical data collection in market research. Market researchers are responsible for implementing appropriate measures to secure the collected data and prevent unauthorised access, theft, or breaches.

One key consideration is using secure storage solutions, such as encrypted databases, to store collected data. This helps to prevent unauthorised access to the data and to ensure that it is protected from potential breaches.

Another critical consideration is controlling access to the collected data. Market researchers must limit access to the data to only those who need it. They must have appropriate security measures, such as password protection, to prevent unauthorised access.

Additionally, market researchers must have procedures in place to detect and respond to data breaches if they occur. This includes regular monitoring of the security of collected data and having a plan to quickly address any breaches and take appropriate action to prevent future violations.

The Role of Industry Regulations

Industry regulations play a significant role in shaping the ethics of data collection in market research. Regulations such as the General Data Protection Regulation (GDPR) in the European Union and similar laws in other regions set standards for the collection, use, and storage of personal data and provide guidelines for protecting the privacy rights of individuals.

Market researchers must comply with these regulations and follow the established guidelines to ensure that their data collection practices are ethical and in line with the law. This includes obtaining informed consent from participants, protecting the privacy of collected data, and ensuring that data is not used in a discriminatory manner.

Industry regulations also set data security and protection standards, requiring market researchers to implement appropriate measures to secure collected data and prevent breaches. These regulations also give individuals the right to access their personal data and to request that it be deleted or corrected if it is inaccurate.

Ethical Considerations in the Use of Big Data

The use of big data in market research presents several ethical considerations, including data bias and algorithmic transparency. Market researchers must be aware of these considerations and take steps to ensure that their use of big data is ethical and in line with industry regulations.

Data bias refers to the inherent biases that exist in data sets, which can result in inaccurate or skewed results if not properly addressed. For example, suppose a data set used in market research predominantly consists of data from one demographic group. In that case, it may not accurately represent the experiences or opinions of other groups.

To address data bias, market researchers must be aware of their data sources and take steps to ensure that their data sets are representative and diverse. This may include sourcing data from multiple sources and using techniques such as oversampling to increase the representation of underrepresented groups.

Algorithmic transparency is another important consideration in using big data in market research. Algorithms used to analyze data can contain biases and make decisions that are not transparent or easily understood. To address this issue, market researchers must ensure that the algorithms they use are transparent and can be audited and that the decisions made by algorithms are easily explainable and free from bias.

Best Practices for Ethical Data Collection

Best practices for ethical data collection in market research include:

  • Having a clear privacy policy.
  • Obtaining informed consent.
  • Implementing appropriate data security measures.

By following these best practices, market researchers can ensure that their data collection practices are ethical, respectful of participants’ privacy rights, and in line with industry regulations.

Having a clear privacy policy is essential for ethical data collection. This policy should outline the type of data that will be collected, how it will be used, and who will have access to it. Participants should be able to understand the privacy policy easily and have the option to opt-out of data collection if they choose.

Obtaining informed consent is another key best practice for ethical data collection. Market researchers must inform participants about the data that will be collected and how it will be used and obtain their explicit consent before collecting any data. Participants should also have the option to withdraw their consent at any time.

Data security is also essential for ethical data collection. Market researchers must implement appropriate measures to secure collected data, such as encryption and secure storage, and take steps to prevent breaches and unauthorised access.

Checklist of Best Practices for Ethical Data Collection

By following this checklist of best practices for ethical data collection, market researchers can ensure that their data collection practices are responsible, honest, and in line with industry standards.

  1. Develop a clear privacy policy: Outline the data collection type, how it will be used, and who will have access to it.
  2. Obtain informed consent: Inform participants about the data that will be collected and how it will be used, and obtain their explicit consent before collecting any data.
  3. Implement data security measures: Encrypt collected data and store it securely to prevent breaches and unauthorised access.
  4. Respect the right to privacy: Allow participants to opt-out of data collection and allow them to withdraw their consent at any time.
  5. Avoid discriminatory practices: Ensure that collected data is used ethically and avoid discriminatory practices.
  6. Comply with industry regulations: Stay informed about industry regulations, such as GDPR, and ensure that your data collection practices align with these regulations.
  7. Consider the ethics of big data: Be aware of ethical considerations related to the use of big data, such as data bias and algorithmic transparency.
  8. Maintain transparency: Be transparent about your data collection practices and clearly communicate your privacy policy to participants.
  9. Conduct regular review: Regularly review your data collection practices to ensure that they are ethical and in line with industry standards.
  10. Educate yourself and your team: Stay informed about best practices for ethical data collection and educate yourself and your team on the importance of responsible data practices.

Using Market Research Agencies and Ethical Data Collection

By outsourcing market research to a trusted third-party firm, brands can have peace of mind knowing that experts in the field are handling their data collection practices and that appropriate measures are in place to protect consumer privacy. 

However, it is still crucial for brands to thoroughly vet and monitor the practices of their market research partners to ensure they meet their privacy and security standards.

Using a third-party market research firm can provide several benefits for brands regarding data privacy in market research. Some of these benefits include:

  1. Expertise: Market research firms often have specialised knowledge and experience in data privacy and security, which can help ensure that data collection and storage practices comply with applicable laws and regulations.
  2. Resources: Market research firms often have the resources and technology to implement robust security measures and respond to data breaches.
  3. Independence: Using a third-party market research firm can provide a level of independence and objectivity in data collection and analysis, which can help mitigate concerns about bias and privacy violations.
  4. Reputation: Market research firms have a reputation to maintain and are motivated to ensure that data privacy and security practices are of the highest standard.

Summary

The ethics of data collection in market research is an important and complex topic that must be carefully considered. By understanding the importance of ethical data collection, market researchers can ensure that they are protecting consumer data privacy, using collected data in an ethical manner, obtaining informed consent, and implementing appropriate data security measures.

The ethics of data collection is not only a matter of legal compliance but also a matter of maintaining the integrity of market research and respecting the rights of consumers. 

By following best practices for ethical data collection and staying informed about industry regulations and trends, market researchers can ensure that their data collection practices are responsible, honest, and in line with industry standards.

In summary, understanding the ethics of data collection in market research is essential for protecting consumer data privacy, maintaining the integrity of market research, and ensuring responsible data practices. Market researchers must be aware of the importance of ethical data collection and ensure that their data collection practices align with industry standards and best practices.

Big data and advanced analytics are hot. Voluminous sets of data can be processed automatically using technology. But the data becomes useful only when it is converted into meaningful information. While Big Data has become the buzzword today, it is of little use if it’s not profitably analysed.

The global Big Data and Analytics market is worth USD 274 billion. Around 2.5 quintillion bytes worth of data is generated each day. There are currently over 44 zettabytes of data in the entire digital universe.

So what is big data exactly, and how does it impact companies?

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Big data refers to large sets of data obtained from multiple sources, like medical records, government records, customer databases, mobile applications, search engines, business transactions, social networks, and other massive data sources. Big data may be structured or unstructured, allowing brands to manage large amounts of data more efficiently. Many organizations are moving away from legacy systems and consolidating data to make the research process seamless, cost-effective, and efficient. 

Technologies like text analytics help market researchers examine large amounts of information and data in real-time to track consumers’ sentiments and detect potential brand reputation issues before they become serious. 

Big data market research is invaluable for brands as it combines consumer and behavioural data with advanced analytics to enable faster decision-making that yields improved business outcomes. When big data and market research converge, everyone wins because it results in better, more relevant products and services for the consumer and a competitive advantage for the brand.

Big data and market research convergence allow brands to dig into data to uncover the “why” behind the numbers. Let’s say, for instance, a brand uses data mining to discover a sudden decline in the market share for a high-end product in a specific market. Using market research methodologies, it studies a sample of consumers that have exhibited a change in buying behaviour to unearth what led to the change. Was it a new product that entered the market, or did they reduce spending due to the economic climate?

These reasons are not presented in the data, and market research can help uncover the “why” behind a data set. 

Today, the digital consumption of information, products, and media makes everything measurable on a large scale. Social media analytics is an example of big data used on a massive scale globally. 

How does big data impact business?

A 2020 study showed that around 94 percent of organizations believe data and analytics are essential to growing their brand and supporting digital transformation. The study also found that the financial, hospitality, telecoms, and retail industries invest the most in big data and analytics. 

Big data in the Banking and Financial Services sector

The application of big data analytics has allowed financial services companies and banks to become more efficient, customer-centric, and competitive. This industry utilises big data to make transactions, trading, and financial activities seamless for their employees and customers.

Retail and eCommerce

The eCommerce and retail industries collect data through their Point of Sale (POS) systems, loyalty programs, and website browsing behaviour. It also helps with inventory replenishments. 

In the eCommerce industry, knowing your customers can unlock conversions and profits. Big data on real-time consumer behaviour, purchase history, and consumer preferences can help online stores recommend the most relevant products and offer them to consumers at the right time. Big data enables e-stores to conduct competitive analyses and pricing to lure consumers. Above all, technology allows online retailers to offer personalization, superior customer service, and experience.

While these industries invest heavily in big data, they are not the only ones. Many sectors like manufacturing, logistics, media, oil and gas, and healthcare are investing large sums of money in adopting this technology to manage their data efficiently. 

Big Data analytics for the healthcare industry is expected to reach USD79.23 billion by 2028. 

For most companies, data is fragmented, and brands are looking for people who can analyze and use data to optimise all business processes and functions. 

Big data impacts not only the private sector but also the public sector. For governments, big data has many applications, including health-related research, financial markets research, fraud detection, public safety, transportation, and environmental protection, to name a few. 

Advantages of Big Data 

Massive organizations like Google, Facebook, and Amazon have proved how big data can build big brands. These organizations have capitalised on big data mining and analytics to grow their brands and boost market valuations. 

One of the most significant advantages of big data is the ability to make informed decisions based on hard data and facts. 

Big data is valuable for consumers too. In the information age, the consumer can access ratings, product reviews, and an easier means of providing instant real-time feedback. This allows consumers to make informed choices. 

What are the challenges with big data and analytics?

As recently as last year, Facebook’s Mark Zuckerberg, Google’s Sundar Pichai, and Jack Dorsey of Twitter had to testify before Congress about the steps they have taken to deal with data privacy. 

Consumers have become more data savvy and are concerned with privacy issues and breaches. <add stats on #s ready to share data for more relevant messaging)

Business outcomes are only as good as the data; high-quality data (link) is of utmost importance. Researchers and brands must be cautious about the data sources and methodologies to obtain the most accurate, reliable, and relevant data. 

The big data market is poised for phenomenal growth in the coming years. With the development of technology penetration across all areas of life, digitization, and the widespread use of smartphones globally, large amounts of data are produced every second. This has led to the need for data analysis and big data. 

As brands apply big data, they make data-driven decisions faster and can respond quickly to market changes. This has a direct impact on their bottom line. But data is not enough; there has to be a fusion of data science with marketing science to help market research become more effective.

Kadence International helps leading brands make game-changing decisions. If you are looking for a research partner to help better understand your customers, we would love to help. Fill out our Request for a Proposal here.

According to the Global Research Business Network (GRBN), confidence in the market research industry has remained stable, and trust in data analytics has increased in 2022 compared with 2020. 

Still, market research as an industry needs to constantly work to improve the perceived value of research. The way to ensure this happens is by addressing the main challenges of obtaining high-quality data. 

The importance of data collection in market research cannot be emphasised enough. This blog post will analyze the main obstacles brands face in this area and provide guidance on how market researchers can tackle these challenges with the help of technology. 

The methods you use to collect and analyze data will significantly impact the quality of your market research report and its value in decision-making. The five best data collection tools for market research are surveys, interviews, focus groups, observation, and secondary sources. 

Understanding the best methodology to get the most accurate, error-free, and reliable data is essential. 

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What is data quality?

Data quality is a complex, multi-faceted construct. Quality data is data that is fit for its purpose and closely relates to the construct they are intended to measure. 

Let’s take the example of a brand like Amazon’s Audible and try to predict what type of books a person would be interested in based on his previous listening history. The data is likely high quality because the books subscribers have listened to in the past are a good predictor of what they would like to consume in the future. The books they have listened to in the past also have a close relationship with what you are trying to measure, in this case, book preferences, which makes the data high quality. 

Reliable data requires a high-quality sample with enough information to make conclusions that inform business decisions. For instance, in the same example of Audible, if a subscriber uses it only once in a while and has only listened to one book in six months, it fails to present a complete picture of the user’s preferences due to limited data or information available. 

In the example used above, the data is available in the app and is much easier to collect. However, this is not always the case. Many instances of market research involve collecting data from people taking surveys, user testing, or recollecting past experiences and feedback, which are much more challenging to measure. 

So how do you ensure you collect high-quality data that informs decision-making at every step of the organization? 

Utilise technology 

As the world has moved online, so have many market research methodologies. Many companies have been forced to move online quickly, which has been a blessing in disguise for them. Technologies like automation and Artificial Intelligence (A.I.) have allowed brands to obtain transparent, reliable, and accurate data more efficiently.

Technology can also be beneficial in identifying bad data. Automation helps select the best pool of candidates for a study and helps achieve a more balanced view of the respondents. It can help reduce subjectivity and bias, scale costs, and improve project speed and efficiency. 

Advanced profiling

To yield high-quality data, you must obtain a 360-degree view of the user or consumer. A good data scientist will study the consumer using all critical data points, like browsing history, purchase history, online behaviour, cart abandonment, geolocation, and other relevant data.

Proper Planning

Excellent outcomes need proper planning, which is valid for everything, including market research. The entire team must understand the research study’s objectives before doing anything else, including all the early actions, like identifying the right participants for the study. Researchers can then create a sample plan based on key objectives and participants. This will become the basis of the methodologies used and the survey designs. A good market research study also employs a screener to ensure they only include participants relevant to the study. 

Recruit the right people

At Kadence, we firmly believe your research is only as good as the people participating in your study. When carrying out a virtual study or focus group, it is vital to make sure people doing the testing or surveys are genuine and suitable for the particular study. Researchers must hunt down even the most difficult-to-reach audiences, as you need the right people for the research to yield unvarnished results. 

Ensure complete and active participation

Making surveys more engaging will always lead to higher participation in online surveys. A well-designed survey with clear instructions will ensure higher participation and more honest responses.

Throughout the survey, researchers can include questions to ensure participants are paying attention and potentially weed out those who are off-track and disengaged.

Screening dishonest participants

Researchers can go a step ahead to eliminate dishonest survey participants. Online surveys can identify potential red flags where people provide false demographic information so they can qualify for studies with high rewards. 

Researchers can selectively target participants who have been profiled in the past to avoid participants with false demographic information. 

Develop a system of efficient, consistent data quality checks throughout the process

Market researchers should always have an effective and efficient plan for weeding out bad data throughout the study. Automating and utilizing suitable technology can ensure you safely streamline the quality check process in real time.

A critical challenge with market research is the ethical collection and use of data. Discover why ethics are vital in data collection and how to ensure your data collection is always on the right side of law and ethics here:

The ultimate goal of market research is to obtain high-quality data that is accurate, relevant, and reliable. While well-planned and thoughtfully designed studies can yield effective results to inform decision-making, poorly planned and designed ones can lead to poor business outcomes.

The stakes are always high, so it is crucial for brands and researchers to constantly improve data quality and reliability to save time, money, effort, and resources and lead to better, more informed business decisions. 

Kadence International helps leading brands make game-changing decisions. If you are looking for a research partner to help better understand your customers, we would love to help. Simply fill out our Request for a Proposal here.

Data is at the heart of all research, and marketing research is no exception. It is the eyes and ears for a brand’s marketing initiatives. The data you gather — and its quality — will make a massive difference to how successful your research is, how accurate your findings are, and the impact on your business goals and strategies.

As a result, data collection is arguably the most critical market research stage. It can make or break the rest of the process, so it’s vital to do everything you can to make this stage run smoothly and successfully.

In this article, we’ll take a deep dive into why data collection matters in marketing research, the different types of data you should focus on, and all the options available to you when it comes to collecting that data. Let’s start by defining what data collection means.

What is data collection in market research?

Data collection entails gathering all the necessary raw information for your market research. Some people also extend the definition to include analysing that data to extract valuable insights for your research objectives.

It is a detailed, planned search process for all relevant data made by a researcher for a hypothesis.

The most critical purpose of data collection in market research is to ensure that reliable data is collected for statistical analysis so brands can make decisions backed by rich data. Therefore, your data must be high-quality, relevant, and plentiful enough to draw meaningful insights.

Why data collection is so important?

Data collection is a critical step in the research process, often the primary step. You can analyse and store essential information about your existing and potential customers when you collect data. This process saves your organisation money and resources, as you can make data-driven decisions. Data collection also allows you to create a library or database of customers (and their information) for marketing to them in the future or retargeting them.

Three main uses of data collection in market research:

  1. Data collection helps you make informed decisions and analyses, building complete and insightful market research reports that can drive future product launches, market-entry campaigns, marketing strategies, and more. Data collection is the foundational step for various activities that can lead to business growth.
  2. Data collection allows you to build a database of information about your market for future use. While your primary goal might be to create a research report with a specific objective, the data can still be helpful for future activities.
  3. Data collection allows you to target marketing and outreach more efficiently, thereby allowing your organisation to save money and do more with its resources.

The different types of data collection in marketing research

There are several different types of data to consider at this stage — let’s examine them more closely.

We can break down data into two main categories, which makes it easier to understand the types of data we want to focus on and helps us hone in on the research methods and channels that will be most useful.

Primary data

Primary data is collected directly by your researchers, specifically for your research purposes. This data is primarily collected from interviews, surveys, focus groups, and experiments. In other words, this data did not exist before your team collected it.

Secondary data

Secondary data refers to data that already existed before you started your research. Other researchers have already collected and compiled this data before. You can find this type of data in places like government reports, the analysis of other businesses, polls and surveys, and the work of NGOs. It’s typically cheaper and easier to obtain than your primary data, but it won’t be as relevant to your project.

Qualitative research

Qualitative research is usually the first step in data collection. It’s more textual than statistical and involves collecting non-numerical data like interview transcripts, video recordings, and survey responses.

Qualitative data is typically collected via first-hand observation through focus groups, interviews, and ethnography. It is a way of diving deep into ideas and concepts, allowing researchers to learn more about specific topics that may not be well understood.

Quantitative Research

Where qualitative research is relatively more text-based, quantitative research focuses on numbers and statistics. This data is expressed in charts, graphs, and tables and is typically used to test initial findings.

Methods used to collect quantitative data include more closed-ended survey questions, mobile surveys, and Likert scales. The main benefit of this type of data is that it allows researchers to make more broad generalisations and predictions, but it’s not well-suited for diving deep into particular questions.

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How data collection in marketing research works

There are many steps involved in the data collection process. Some of these steps begin even before you start collecting data.

Prior steps

There are several steps you should take before your data collection begins, such as:

・ Make sure you have all the necessary permission to collect your data. Today, data privacy laws are stronger than ever, so researchers need to take extra care to comply with regulations and have the full consent of their subjects and participants. It’s best to work with a legal compliance team to draft all the required documents, forms, and contracts to share with your research participants from the very beginning.

・ Make sure you have the support of any company decision-makers and stakeholders. It may be helpful at this stage to prepare a preliminary report informing any higher-ups of your plans, goals, sources, and any methods you plan to use.

・ Try to predict and pre-empt any possible challenges or problems, such as privacy regulations, collection methods, infrastructure, or budget. Anticipating any issues now will help you avoid costly problems and make the whole process run more smoothly.

・ Put together a team of skilled and qualified researchers and analysts. Data collection can be a difficult task, and you need to have the right experience and skillsets on your team.

Decide on your data collection methods.

The next stage is to decide which data collection methods you will use to collect data for your marketing research report. You will likely employ various methods here, as each has unique pros and cons. Here are the main methods you should consider:

・ Surveys

There are many ways to conduct surveys — in-person, online, post, email, mobile message, others. Surveys differ in content and structure — from simple Likert scales with just five possible numerical responses to more qualitative open-ended questions.

・ Focus groups

Focus groups allow you to bring multiple participants together to discuss the subject of your research and share their opinions. This format can be a great way to brainstorm ideas, and people can often bring good ideas out of each other. To get the best results, everyone should get a chance to speak, and no one person should dominate the group.

・ Interviews

One-to-one interviews are the best ways to dive deep into a person’s opinions about your brand or a specific product. However, they can be time-consuming and may require much planning.

・Observation and experimental research

This type of data collection involves observing individuals as they interact with specific products or services. It helps get around certain biases that people might have in interviews and surveys and cut right through to their true thoughts. However, it isn’t easy and requires an expert touch to get it right.

Identify and prepare for common challenges with data collection.

During the data collection process, you’re likely to encounter several challenges. The good news is that you can avoid these challenges and mitigate any impacts on your research report with proper preparation.

Here’s what to look out for:
・Bad methodology results in poor quality data

A lot can go wrong with your data collection methods — badly identified participants, poorly designed questions, and choosing the wrong methods are just a few examples. This can result in poor quality data, leading to erroneous conclusions and an unsuccessful research report. Take the time to work with experienced researchers and build the right data collection strategy for your needs.

・Logistical challenges

You will also come across many logistical challenges. For instance, you’ll need a big venue to hold everyone if you’re running a focus group. If you want to conduct a stream of interviews, you’ll need to hire a space for a particular time. You may need to arrange transport, refreshments, and a wide range of other logistical demands. If you fail to plan this properly in advance, your team could find itself in a highly stressful situation.

・Using the proper channels

The channels you use to connect with your audience are consequential — what works well for one demographic might completely fail for another. If you choose the wrong media (like Twitter to send surveys to an older demographic), you could have a poor response rate and lack usable data.

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How to collect data effectively

Get to know your audience.

You will need to have an intimate and deep understanding of your audience and the people you collect data from. This will ensure you target the right people, ask the appropriate questions, choose the correct methods and channels, and analyse the data in the proper contexts.

There are many ways to get to know your audience better in advance of data collection:

・ Use social media to spend time in the same spaces and groups as your audience members, chat with them, and find out who they are and what makes them tick.

・ Work with your sales and marketing teams — it’s their job to understand your audience, and they’ll have access to valuable insights.

・ Look at who is using your competitors’ brands and products.

Once you understand whom you target, it often helps create detailed user personas, outlining details about your typical audience members like their age groups, income brackets, and education levels. You can then use this information to tailor your data collection strategy to be relevant and valuable.

Prepare for the analysis of your data.

Collecting data is one thing, but you should always have an eye on the analysis of that data. This is where you extract insights and draw tangible value from the data — allowing you to make informed business decisions and create a valuable and applicable market research report.

When planning your collection methods and recording the results, always remember that someone will be analysing this data. Be organised, clear, and detailed, and work with your analysts to ensure they are aligned with your approach.

Use a wide range of methods and channels.

The best data collection relies on various tools and channels instead of focusing on just one or two. By combining a number of the approaches mentioned in this article, you will connect with a broader part of your market, gaining a better understanding of how different demographics feel and leading to a more valuable and insightful analysis.

For example, if you focus solely on digital channels like social media and online surveys, your responses may skew heavily towards younger people. Some in-person interviews, focus groups, and postal surveys help target a broader range of age groups and accurately reflect your market and their views.

Data collection is a critical part of market research. It serves many important purposes, and it is essential to get it right to create effective research reports and complete a vast range of different business objectives.

At Kadence, we help companies worldwide fine-tune their data collection, laying the foundations for informed and effective market research.

Contact us to learn more about how we can help you do the same.

Every market research report begins with data collection, and this stage of the process influences how everything else goes. If you collect high-quality data from relevant sources and use the proper channels, you’ll boost your chances of creating a clear, accurate, and valuable report.

Data collection is at the heart of market research. If you do data collection wrong, the result could be an essentially useless market research report, wasted money, and poorly informed business decisions. Therefore, you need to use the right data collection tools.

The methods you use to gather your data in the early days of the market research will majorly impact the quality of the data and the effectiveness of your research report. This article will look at the best data collection tools available for market research and why they’re so helpful.

Five essential data collection tools for Market Research

1. Surveys

Surveys are one of the most versatile and established ways of collecting data. They come in all shapes and sizes but typically follow the same rough pattern — a series of questions aimed at gathering opinions and experiences around a specific thing like a product, marketing campaign, or brand.

One of the best things about surveys is the number of channels they can be shared through:

  • In-person paper surveys
  • E-mail
  • Social media
  • Your website
  • Postal
  • Mobile message
  • In-app surveys

The list is almost endless. You’ve probably encountered the series of buttons in public toilets and areas like airports asking you to rate your experience quickly — that’s a fundamental type of survey aimed more at measuring customer satisfaction than market research.

Surveys can be designed in several ways. More qualitative surveys ask open-ended questions like, “What did you like about this product?” They encourage extended, detailed answers to allow deep dives into the data.

On the quantitative side, surveys may use a Likert scale — a series of points (for example, Strongly Disagree, Disagree, Neither Agree nor Disagree, Agree, Strongly Agree). These types of surveys are much more restrictive for the respondent but allow you to gather more numerical data to prove existing hypotheses and create charts and graphs.

2. Interviews

Like surveys, interviews are another way of gaining a deep and personal insight into an individual’s experiences and opinions on a topic. Interviews are incredibly qualitative and the only reliable way of getting an individual’s uninterrupted views on a topic in real-time. Interviews allow for the most profound and unfiltered responses of all the data collection methods listed here.

There are many ways to conduct an interview. Some methods are highly structured with a clear set of questions and the interviewer firmly guiding the conversation. Others can be more informal, with the interviewee free to talk about their experience at length without much input. Interviewers need to ensure they don’t nudge the respondent towards specific answers or encourage bias.

In the past, interviews could only be conducted face-to-face, introducing challenges around finding the time, space, and staff to carry them out. Today it’s possible to conduct interviews via phone call or video chat, making it much more manageable. However, these methods risk missing out on the body language cues and subtle gestures that can spark further questions.

3. Focus Groups

Focus groups bring multiple people together to discuss a particular topic (for example, a new product) and share their experiences and thoughts.

Focus groups can be helpful for several reasons — they help you gather multiple opinions at once, promote healthy discussion, and allow you to be more economical with your time and space. The best focus groups bring together people from diverse demographics and backgrounds.

It is vital to make sure one or two more assertive people don’t dominate your focus group. To prevent this, make sure to moderate the group effectively and allow everyone to have their say. At the same time, be mindful of people adapting their opinions to fit the overall group consensus.

4. Observation

Observation is a time-tested method of data collection that, when done right, allows researchers to gather large amounts of unbiased and unfiltered feedback. It works by giving the participant a series of questions or asking them to share their thoughts on something (like a product) in real-time.

During this process, the observer does not interfere with the participant. They watch closely and note the participant’s non-verbal reactions like facial expressions and body language. The idea is that participants’ verbal responses can be influenced by bias and tailored by the person. However, nonverbal behaviour is much less easy to control and may reflect a more honest reaction.

Observation can be an advantageous way of cutting to the root of what a person believes about a product. You should attain your participant’s full consent before the process begins. You should also be careful not to draw overly firm conclusions from the interpretation of their body language — which should be viewed as a guide.

5. Secondary sources

There are several options here, and depending on your market and research purposes, there may be a great deal of data already available. In addition to the primary methods discussed above, researchers can also look at data that others have already collected. Here are some examples of secondary data for market research:

  • Government reports. While these are not usually specific to any business needs, they can still be beneficial. Government surveys and reports contain data about income brackets, spending behaviour, customer attitudes, and more. Combined with other data collection activities, this can help you better understand your target market, build more accurate customer profiles, and improve your marketing, among other benefits.
  • NGO resources. Non-governmental organisations frequently research a range of subjects. Much of the data they collect is relevant to marketers for similar reasons as government reports.
  • Business reports. Other companies, industry groups, and market research organisations regularly create detailed research reports that you may be able to access and use. These often don’t come cheap, but they can provide valuable insights into your target market — essentially doing a lot of your work.
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Data collection is one of the most important tasks you’ll carry out in your market research efforts. At Kadence, we help companies worldwide with every stage of the research process, including collecting and analysing precisely the correct data. Contact us to find out how we can help you do the same.

Businesses strive daily to provide what customers want. Their success depends mainly on how well they understand the needs and motivations of their target audience. 

In the past, this frequently translated into a scattershot approach to meeting customer demands—build more products, design more features, and so on—with, at best, a goal of growing sales. 

But this slapdash strategy occasionally resulted in overspending, overcommitment of resources, and other strains on business operations that could threaten the business’s existence. 

The organised process of data collection in market research has changed all that. Now the focus is on collecting and analyzing high-quality data—information relevant to meeting customer demands—and how this data is obtained. The goal is the “systematic method of collecting and measuring data gathered from different sources of information,” as Medium notes, adding that an “accurate evaluation of collected data can help researchers predict future phenomenon and trends.”

Broadly speaking, there are two chief forms of data:

  • Primary data refers to first-hand information gathered straight from a primary source. 
  • Secondary data encompasses information found in public records, trend reports, market statistics, etc. 

Armed with high-quality data, businesses can better understand their prospective customers—what they want, what they already like, where they conduct their research, and much more. Companies come away with a deeper grasp of their markets, how their products will benefit that market, and the potential challenges they may face later. 

At its best, market research offers a blueprint of how a brand can move forward while avoiding the pitfalls it might otherwise encounter (without the benefit of high-quality data). 

It’s helpful to remember that a wealth of relevant data may already exist in your company. Information gleaned from business analytics and customer service scores offer vital insights into why consumers act the way they do. It’s an excellent place to begin research and avoid any duplication in data mining. 

What sources of data collection work best? What should brands know about the methodologies employed to acquire and measure such data?

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The Value of Quantitative and Qualitative Data

Within the broader scope of primary and secondary data, there are other aspects of data collection worth noting:

  • Quantitative research relies on hard facts and numerical data to gain an objective view of consumer opinion. In general, this approach focuses on uncovering insights about large groups of consumers or the population as a whole. It enables brands to easily compare purchasing and other behaviours of different groups (age, gender, market) and to identify potential buying trends on the horizon. 
  • Qualitative research is less concerned with statistics and trends and more focused on the “human” aspect of buying. This research digs deep into the more intangible and subjective reasons why customers behave the way they do. 

As we have noted before, “People are complex and often unpredictable,” so qualitative research “means getting to know your customers and their motivations better.” As a result, brands can more effectively study customer pain points and barriers to consumer use while also guiding the way to a more personalised approach to marketing.

Where Qualitative Data Comes From

So, what are the sources of data collection? Here’s a quick rundown:

Focus groups. A group consisting of a small number of customers (usually no more than 15) meets to discuss a specific issue. Information derived from this approach often leads to rich insights around consumer attitudes and behaviours, underlying motivations, and perceptions about a brand. 

One-to-one, in-depth interviews. Researchers talk to consumers directly, seeking to understand participant opinions better. This method can be in the form of face-to-face interviews and phone or online interviews. 

Expert interviews. Industry experts are another rich source of data collection. Leveraging their knowledge through expert interviews can help brands explore the impact of emerging trends, thus helping to “future-proof” their business. 

Ethnography. In this realm, researchers immerse themselves in customers’ worlds to learn more about the role brands and products play in their daily lives. This can entail visiting consumers and accompanying them as they go about their day or through self-ethnography, where consumers take on video tasks to show us how they live. 

Online communities. Through an online platform, consumers undertake individual or group tasks that enable researchers to explore potentially sensitive issues and better grasp the attitudes and values that lead to that all-important decision to purchase a product or service. 

The personalized focus of qualitative research goes hand-in-hand with more quantitative research methods, adding context and depth to more numerical and data-based metrics.  

Survey Research Plays a Key Role

Sending out surveys is another key method for drawing insights to understand target customers or explore a new market. Surveys can be conducted in a variety of ways, including:

  • Email. This approach offers the benefit of reaching many people at an affordable cost.
  • Phone. Phone surveys are helpful for researchers seeking feedback from a particular demographic, i.e., older consumers who may not use online resources. 
  • Post. Postal surveys are another option, though of increasingly limited use. Prohibitive costs and a long time lag for responses often rule out this approach.
  • In-person. This method is useful when researchers want to know more about how consumers physically interact with a product or a similar situation. Again, the costs and logistics of this approach make it a less appealing process in general.  

These days, online surveys are often the primary method for collecting quantitative data. Existing customers can complete online surveys or respondents sourced from online panels (groups of people matching a brand’s target market who agree to participate in online research). Based on the results, brands can build accurate representative samples and extrapolate findings to the broader population. 

When it comes to quantitative research, survey questions usually include closed rather than open questions. For example, a survey participant being asked, “How satisfied are you with our delivery policy?” would be restricted to answers such as “Very satisfied/Satisfied/Don’t Know/Dissatisfied/Very Dissatisfied.” This method generates data that can be categorized and analyzed in a quantitative, numbers-driven way. 

How Technology Facilitates Data Collection  

Social media has emerged as a valuable source for insights into consumer perceptions and behaviours. Platforms like Facebook, Twitter, Instagram, and others have potentially vast data reservoirs on a target audience. 

On social media, consumers provide direct, unfiltered feedback about their needs, emotions, pain points, and hopes for the future. These platforms offer a relatively easy and inexpensive way to share surveys and questionnaires and enlist participants for upcoming focus groups.

In this respect, “social listening” offers an expedient method of gauging customer sentiment—what they like and don’t like about the buying experiences, preferences regarding how a purchase is made, and so on. 

Technology also makes it possible for researchers to dramatically expand their horizons, connecting with audiences in far-flung areas of a brand’s home country and around the world. Researchers can conduct real-time interviews and focus groups with consumers in multiple time zones using tools like Zoom and Skype. In this way, data collection for international research often yields a more powerful and richer understanding of consumer behaviour. 

Working with a Research Partner

It’s crucial to remember that every customer group is different. Some brands have a strong command of their markets and may conduct research on their own.

For many other brands, partnering with a professional research firm is the best approach to broad-based marketing research. At Kadence, we draw upon our extensive toolkit of qualitative and quantitative methodologies for a deep understanding of the needs of these under-served communities. The result is:

  • More productive research
  • Valuable insights into different demographics
  • Gaining a step on the competition 

By bringing companies closer to their customers, a third-party research firm can embed rich understanding across your organisation and promote more effective, customer-centric decision-making. This understanding often leads to more informed marketing strategies and greater success with untapped consumer populations.