Emerging technological advancements are transforming market research forever. As many consumers move online, the way brands identify and understand consumer needs is being reimagined.

Many technology trends disrupt the market research industry —from data collection and new product launches to tracking brand performance. This blog post will focus on the breakthroughs in technology impacting brand tracking and product performance tracking.

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Brand and performance tracking refers to the process of continually measuring brand health over a period within the target audience. It allows brands to measure the performance of a product in relation to its competition. After a new product is launched, market research helps brands gauge performance to stay competitive. 

With consumers increasingly moving online, brands can tap into new, vast, and reliable consumer behavior data in real-time. This has also made Direct to Consumer marketing much more common. Brands like Happy Human (Singapore), Dime Beauty (U.S.A.), Joi (Malaysia), Sleepy Owl (India), Recess (Philipines), Adopt a Cow (China), and Knot (Japan) have eliminated the middleman to create, develop, sell, and distribute their products directly to the end-user. The absence of middlemen and brick-and-mortar stores allows them to maintain quality and reduce prices. But this is not all. These brands also have the added advantage of measuring performance directly without employing market research across several retail outlets. They can discover brand sentiment directly, making them more agile, nimble, and competitive. 

While there is still a place for traditional research methodologies, technologies like machine learning, Artificial Intelligence, Virtual Reality, and chatbots continue to reinvent the market research industry. 

Let’s look at the primary technologies in brand tracking and competition analysis that are changing the face of market research. 

E-commerce brands utilize price monitoring software technology to track competitor pricing.

In the fiercely competitive E-commerce world, the key to outperforming the competition is tracking and monitoring the price competing brands charge for similar products and services. Brands need to keep a keen eye on their competitor’s pricing strategy and price changes over several products to stay competitive, and that’s not an easy task even for larger companies. 

This is where e-commerce price monitoring technology comes into play. 

Ecommerce price monitoring software allows brands to track their competitor’s price changes and dynamically adjust their pricing. 

By employing this type of software, brands can stay abreast with competitor pricing and adjust pricing based on demand, competition, and inventory levels. 

Many such tools are available in the market, including Minderest, Price2Spy, and Prisync, with sophisticated matching technology and high levels of accuracy. 

Market research utilizes machine learning and A.I. for brand and performance tracking to revamp advertising and messaging. 

While some grey areas are associated with A.I. in other fields, the market research industry has embraced this technology.

One of the things brands need to track constantly is how their messaging is resonating with the target audience and how the market perceives their brand. This is because a brand is not just the logo and tagline. It is a sum of all parts and is an overall feeling that tells a narrative and evokes sentiment and emotion in the audience. 

Technology helps brands better understand brand performance and perception to inform better decision-making. It allows brands to measure and bridge the gaps between their intent and how the audiences interpret and perceive their message.

The use of A.I. in brand tracking has allowed market researchers to analyze qualitative surveys at a fraction of the time taken by manual data collection methods. Furthermore, this enables them to ask more open-ended and follow-up questions, find the right panelists faster, eliminate bias, write reports quickly, and significantly improve the quality of their surveys and reports. 

In today’s dynamic digital marketplaces, A.I. is powering brand tracking to gauge the changing consumer perceptions. 

Sentiment analysis is a sub-category of A.I. and N.L.P., which automatically uncovers feelings, emotions, and sentiments behind plain blocks of text. It is extensively used in brand tracking because it is efficient, reliable, and accurate. 

Over 45 percent of the world is on social media. There are about 500 million tweets per day, and about 1.96 billion people worldwide use Facebook every day. Consumers constantly call out brands on these social media platforms and review sites. It would be overwhelming and near impossible to collect data manually. Brands can effectively gauge overall brand sentiment across platforms and channels online using automated tools. 

For instance, when the popular ride-sharing service, UBER, launched a new version of its app, it used social media monitoring and text analytics to measure user sentiment about the new version of the app. Eye-tracking technology works similarly and can track users’ engagement scores and emotions on a website. 

There are several brand tracking tools available for brands. Candymaker Mars used one such tool that combines the standard digital video metrics, like view-through rates and skip rates, with facial expression tracking of the viewers while watching the ad using an A.I. algorithm.

While the tool measures digital behaviors, it puts enormous weight on gauging emotion and sentiment. This technology is essential to track brand performance in a world plagued with minuscule attention spans. It allows brands to obtain a complete picture of consumer perception. 

Many technologies use participants’ webcams to track their facial and emotional responses while viewing ads, providing invaluable data used to inform sales forecasts. 

Chatbots are aggregating vast amounts of consumer data.

The usage of chatbots as a communication channel between brands and consumers has increased by 92 percent since 2019. 

As many consumers shop online, they engage with chatbots, making them the fastest-growing brand communication channel.  

A survey found that up to 80 percent of users answered questions, three times higher than responses from email surveys. 

Brands like IKEA are using chatbots to gather valuable consumer feedback. Companies use Whatsapp and Facebook messenger to measure consumer sentiment and feedback efficiently. 

The use of brand tracking cannot be overemphasized. It allows brands to understand how their current audience perceives the brand. It can also lead brands to uncover until now undiscovered target audiences. 

With brand tracking software, brands can see the true impact of their campaigns. Brand tracking holds the key to insights any brand needs to thrive. Using the right tools and technology, brands can obtain actionable information about the brand perception among the target audience and how it scores against the competition.

A brand is one of the most valuable assets of an organization. It is, therefore, critical to continually measure satisfaction, awareness, and perception. Incorporating brand tracking into their marketing strategy can help brands understand their target audiences and consumer needs and make more profitable marketing decisions. Technology has made it easier to uncover massive data sets to monitor a brand effectively and accurately. By combining this technology with digital metrics, brands can increase their competitive advantage.

Just like we need a GPS to take us from point A to Point B, businesses need to intuitively map their customer’s journey to ensure they are moving through the process. But instead of plotting it physically on a map, brands need to use technology to visualize each touchpoint the customers interact with when they engage with them. 

Today, customers interact with brands multiple times on various platforms, and brands need to funnel them to continue moving forward. 

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What is customer journey mapping?

A customer journey map is a visual plotting or representation of customers’ experiences and touchpoints with a brand. It tells the complete story of a brand’s relationship with a customer, starting with the first engagement and moving toward a path to purchase and becoming a loyal customer. 

Journey mapping is not a single instance or solution; it is a process that integrates every facet of an organization, from marketing to sales to customer service.

Why Customer Journey Mapping is Invaluable for Brands

Today, customers expect a lot from each interaction with a given brand. Personalization, consistency at each touchpoint, and relevance are not just “good to have” anymore; they are necessary to drive conversions and brand loyalty. 

Customer Journey Mapping is beneficial not only for sales and marketing but also for the creative team. Armed with this information, content creators can develop timely, relevant, personalized copy and speaks to the customer at each touchpoint. Designers can derive context from this information and design an elevated customer experience. 

Customer Journey Mapping is helpful for many reasons, and it primarily helps with the following three steps:

1. Identify all touchpoints to understand the customer experience better.

Customer Journey Mapping helps you construct a seamless and intuitive customer experience through every touchpoint. This is often missed by quantitative research.

For instance, a journey map may uncover a tremendous amount of online research in the discovery phase of a particular product or service. This would lead a brand to question how it appears on search engines and the content customers find when researching the product online. 

2. Get in tune with your customers at every step of the way.

Customer Journey Maps are visual aids that help understand the customers better at each touchpoint. It visually reveals patterns in customer behavior and emotions, and once these are identified, brands have an account of the steps that are working and those with gaps.

3. Identify gaps in your CX and lead your customers intuitively through the funnel.

Customer Journey Mapping aims to understand each touchpoint and ensure measurement tools are in place to help monitor each customer interaction. 

For instance, for a travel website, a customer’s journey starts when they search for airline tickets and cover all the steps through research, queries, finding tickets, booking them, making a payment, and receiving confirmations and other travel-related information. It includes signing up for a newsletter, recommendations to book hotels, prompting the user to check-in, and offering additional information. In a retail setting, Customer Journey Mapping would include the signage, lighting, store layout, temperature, smell, comfort, and other physical elements in addition to interactions with the employees. 

Customer Journey Mapping helps you fill gaps and focus on areas that need improvement for an intuitive and seamless customer experience. 

How to Get the Most out of Your Customer Journey Map

The ultimate goal of a Customer Journey Map is to improve the customer journey and move prospects through the funnel. This is because inefficient systems and interactions cause frustration amongst users and prospects, impeding conversions and sales. 

Below are a few tips to keep in mind when researching your customer journey.

  • Some brands do a great job acquiring customers but are not good at activating. Therefore, brands should include every touchpoint, like packaging, labels, messaging and ads, and social voice.
  • A Customer Journey Map should be a combination of analytics and customer feedback. Therefore, brands must gather quantitative data from multiple sources, including call center and CRM software, QR codes scanned, website and social media analytics, and other metrics.
  • It is essential to include post-purchase components into the Customer Journey Map. The relationship with the customer continues long after they purchase something. This helps you get repeat business, loyal customers, favorable reviews, and raving fans who will refer the product or service to others. 

How Market Research can help brands build Customer Journey Maps

So how do you use market research to help improve the customer experience? 

Let’s examine this with the example of a retail shoe store. You identified the salesperson as a critical touchpoint. You can use a focus group to experience the store just as they would if shopping for shoes. 

Ask them to identify the experiential element of each touchpoint, including what they see, smell, hear, and feel. The focus group will then prioritize what parts of the journey need improvement. They will provide insights on how easy it was to find what they were looking for, the annoying details, how the store stacks up to a competitor, and the customer satisfaction score. The brand can then build an action plan to improve the customer experience at their store. 

This is how the brand identifies gaps, determines development priorities, builds a plan to remedy the issues and bottlenecks, and allocates funds to optimize sales and Return on Investment (ROI). 

Customer Journey Mapping should be a combination of quantitative and qualitative methods. 

Market research and building Customer Journey Maps allow brands to compare what they believe the customer journey looks like and what it is like in reality. When you combine the metrics and data with sensory components, you can experience the journey through your customer’s eyes. This “outside looking in” approach will significantly improve the customer experience and revenues.

How is your product received by consumers or business decision-makers? What are the pros and cons of a change in an existing product feature or new varieties of your current big-sellers? Why is a product failing to perform? You want to tweak a formulation, messaging or packaging to cut costs or reach new audiences – but will that scupper or supercharge your sales? The answers to all these questions can be found in product testing research.

What is product testing?

With product testing, we’re not looking to establish general consumer attitudes or behaviours. Nor is this about standing up a new concept or looking for gaps in the market. The primary job of product testing is to tell us how people respond to an actual product – including how they use it and what they think its qualities are – allowing brands to decide whether and how to market it.

When should I do product testing research?

Let’s look at the natural marketing life-cycle to explain how product testing research can support the emergence, and successful exploitation, of a product – and place it in the context of a wider field of market research:

  • Ideation – dreaming up an idea worth pursuing. Research helps identify unmet consumer needs, value-chain opportunities, potential applications of innovation and new markets.
  • Screening – a rigorous approach to deciding which ideas are worth pursuing, again drawing on research and feasibility work, and assessing potential audiences.
  • Concept testing – seeing how the manifestation of ideas might work in the market, leading to additional screening out of less viable concepts.
  • Prototyping – designing products to prove mass production feasibility, form factors and feature sets.
  • Early product testing – evaluating consumer attitudes to the product itself, either in controlled settings, in the field or in everyday contexts; typically unbranded or with highly simplified packaging.
  • Late product testing – which might include feedback from earlier tests to refine messaging, packaging and final form factor.
  • Testing iterations of a product to forecast the impact (on sales and usage) of changes to features, formulations, targeting and marketing – often in response to changes in sales patterns or negative customer feedback.

In other words, product testing is distinct from concept testing. It’s all about refining the delivery of something that is (or soon will be) a finished product. This might include changes to feature sets, the marketing pitch, pricing, ideal target audience and other details. It’s not so much whether the product works – it’s how the product will work best.

Use cases for product testing research

In summary, then, you can apply product testing to:

  • Find out how a close-to-final version of a new product might perform.
  • Tweak that product to optimise its performance on launch.
  • See how a new product is performing post-launch.
  • Test the effect of changes to product design or presentation.
  • Evaluate or explore how a product is marketed.
  • See how well consumers in a new market will accept an existing product.
  • Undertake ‘penalty analysis’ to see which qualities, when changed, alter consumer options about a product.

Product testing in action – a case study

One way to think about the value of product testing is as a way to optimise the introduction or evolution of a particular product. A good example is work we’ve done with a beverage brand to launch a new range of iced teas. The client wasn’t launching a brand-new product – they had worked up some new flavours and wanted to know which they could launch successfully, how these might affect brand perception and what consumers made of them.

There were eight formulations to be tested. We added in an established variant to act as a benchmark, giving us a way to test the relative strength of the products. We measured various metrics with consumers to provide comparable scores as a key insights.

The product isn’t necessarily going to change in this case. It’s a chance for the client to check which variants might work best, to optimise the roll-out and then make minor refinements if the research delivers consistent feedback on particular elements. How research subjects describe the teas might also help shape marketing and packaging for instance.

In other cases, clients might test out product names and straplines on consumers while they’re testing the product to create a range of possibilities, not just on the branding and marketing, but also on likely target audiences and even pricing. Does the product live up to a premium positioning? Or will it chime with more down-to-earth messaging?

The role of product testing guidelines

We work with many clients that have well-established product testing guidelines – a set of standards that enable them to better evaluate products over time and give them clearer benchmarks for making decisions. For large corporates in particular, the product testing research project is an exercise in generating fairly standardised numbers – data that fits a well-established, almost algorithmic approach to evaluating potential product performance.

When this isn’t the case – or if the guidelines are relatively basic – it’s a good idea to establish some clear ground rules at the start of a project to ensure it delivers insights that will shape client decisions. You might need to agree:

  • How the product should be stored, prepared, presented and used.
  • The audience it should be tested with, and how to recruit them.
  • Where to conduct tests with participants (see below).
  • What metrics to record.
  • How to record their experiences – and other feedback.
  • The research methodologies that will work best.
  • Whether to use a control product for comparative purposes.

Introducing a framework helps everyone understand what success looks like for a product: for it to go forward, what will the research need to show? Is it being on par with an existing or rival product on overall performance? Does it need to be statistically stronger on key metrics?

In some ways, it’s akin to a science experiment: you outline the aim (proving it’s better than the existing product) based on your prediction (the product design); we provide a sound, rigorous methodology to test that assumption (the research); and the result gives conclusive result to tell you how to proceed.

How does product testing work? Where and how to test

Different objectives of product testing will suit different methodologies. A lot depends on what brands already know about the product and the way it’s perceived; on what they want to learn from the tests (see below); and the type of product under review.

There are broadly three environments to conduct product tests. Let’s look at the use-cases and the pros and cons.

1. Central Location Testing (CLT)

This is where participants are invited to a facility to undertake the test. This is ideal for evaluating products in controlled conditions, especially when testing a variety of use cases. It’s also suited to products that won’t be used as much in the home – especially in food outlets, for instance.

A good example would be a new foamless cappuccino we tested. To get comparable results, the same machine was used in different central testing centres, with the client providing an expert barista to produce the same product every time.

CLT is ideal for evaluating products under controlled conditions – testing different fragrances, say, is hard if the conditions allow for cross-contamination of scents. But it’s also very useful where confidentiality is important. We set up a CLT in a hotel, for example, so that a new tech product could be tested by invited consumers without the design leaking. Non-disclosure agreements might be a feature of any product test, but for this kind of commercially or technologically sensitive research, the controlled setting can be helpful too.

The other advantage of CLT is liability management. Some products – foodstuff and cosmetics, in particular – might cause adverse reactions with test subjects, and it’s easier to screen and monitor them on-site.

You can find out more about central location testing in our guide.

2. Street Intercept Testing (SIT)

This is literally grabbing participants in an ambient setting for a few minutes to get them to try something and test their reactions. This works well for relatively simple research – the questionnaire will need to be relatively quick in a supermarket or street setting – and for targeting particular participants. Testing a new cheese at the deli counter in a supermarket would be one application.

It’s also ideal for capturing insights within specific use locations – when a central facility would be a little abstract. We worked with a sports beverage brand to test a special protein-rich drink. The use-case is post-exercise, so intercepts with the target market in a gym setting yielded much more insights than a central location could have.

3. In-Home Testing (IHT)

For many products, the consumer’s home (or, in some cases, their workplace) will be the usual usage location. Getting the products into the home for a period of use, then running online, telephone or face-to-face follow-up questionnaires is a great way to see how they work ‘as intended’.

In-home testing tends to be ideal for more sustained testing. The taste of a new iced tea or reformulated cheese can be tested fairly immediately. But a toothpaste, cleaning product, in-home device or even lightbulb, will only reveal itself properly over a few days’ use. Out of the control conditions, we can learn more about how good instructions for use are; we can see how consumers might use the product in their daily lives or in combination with other products; and we can monitor evolving opinions about the product as they get used to it.

Obviously in-home testing has been popular during Covid-19 lockdowns – not least because many products are now being consumed or used in the home that might otherwise have been ambient products; but also because centralised or street intercept tests have been harder to run for biosecurity reasons.

Note also that IHT allows for different research methodologies. As well as post-use surveys, we can get consumers to keep diaries of use, highlighting a wider variety of situations and providing more qualitative inputs.

Woman scanning food in her fridge with her phone

How to do product testing

Where to start

For many companies, product testing isn’t the start of their journey with us. This kind of research is often part of a much bigger engagement process around a brand or product line; or it might be commissioned by a brand we already do different kinds of work with. So the starting point is rarely a cold introduction to a product.

But even with some engagement beforehand, the first step in product testing is to look at the product and the client’s requirements, and then design an approach that will answer their key questions.

For some, those questions will be extremely precise. For example, one detergent brand asked us to test out a new toilet cleaning product. They knew exactly what segment they wanted to target – ABC1 consumers in their 30s and 40s who were already familiar with the brand – and even the methodology they wanted (in-home testing).

That’s largely a logistical challenge – getting the product and a control cleaner into their homes, in plain packaging, so they can be tested side-by-side; then running an online survey to generate some quantitative data and some qualitative feedback comparing the product to a known comparator.

Another example might be a commercial-kitchen mayonnaise we tested. The client was keen to assess not just how the product performed against other formulations of mayo, but also what professional chefs thought of it in different applications. Will it be at least on par with the existing product? What recipes or dishes did it suit? And how did it compare commercially?

One thing to bear in mind is that you should be testing for things you might change as a result of the insights we generate. Knowing what can change (from packaging and marketing, to cosmetic attributes or even key design features) as a result of the research findings – and what you definitely can’t alter – will ensure the tests are focused and useful.

Methodology reflections

We find that CLT is generally better for ‘sequential monadic’ testing. ‘Monadic’ means the consumer is evaluating a single product, and this is obviously possible in any environment. Even ‘paired comparison’ testing – head-to-head – can be done in-home. But sequencing the comparisons scientifically (standalone, then in head-to-head, for example) often generates more reliable data.

In terms of participant recruitment, clearly targeting the audience accurately – whether in the field, via lists of consumers, or panels – is key. They are often motivated because they receive free products. But in some cases, especially with the more in-depth or time-consuming studies, the chance to earn money is also a motivator.

Hard and soft questions

Product testing can answer a lot of questions. For seasoned clients, they’re often very precise ones – they’re seeking standardised data on usage and performance that will help them contextualise the product within a portfolio.

A good example of a ‘hard’ question might be pricing. Using techniques such as the Gabor–Granger method (to understand price elasticity) or the Van Westendorp Price Sensitivity Meter (which creates an optimal price point for a given audience), research can reveal a lot about the economics of a product.

Hard questions like that are often central, even when we’re working with smaller companies that are looking to take a product from prototype to production and need to calculate the risk/rewards involved.

Smaller companies, however, are more likely to be asking ‘soft’ questions, too, where quantitative surveys are augmented with qualitative insights. They might be trying to learn more about consumer attitudes to the category as well as the product; or develop a deeper understanding either to tweak the product being researched – or inform future innovations.

A good market research agency can really help with this part of the process. For example, in some companies there might not be rigid product testing guidelines in place. But by explaining what they need to know to market the product, what they might be able to change about it and what they’re not sure about, we can help companies come up with fieldwork that will deliver clear metrics and provide answers to their key questions.

What’s the outcome?

A well-planned, well-run product testing project is rarely just looking for a blunt ‘go/no-go’ answer to a product roll-out or adjustment. Although many big brands have a well-established formula for conducting product tests – designed to plug data into their tried-and-tested algorithms – even these clients will often use the test as an opportunity to learn more about the product in different dimensions.

Sometimes that’s just a by-product of a sufficiently expert and thoughtful product test. As market research professionals, we learn a lot more about products during tests than the raw data suggests. Often it’s the degree of flexibility the market research team brings to the product test that makes it most valuable.

That’s true whether the primary objective is standardised data on product attributes – or semi-quantitative work with a healthy dose of qualitative inputs to shape decisions. By making sure the parameters for the product’s adaptation are clear and the questions about it well framed, we can ensure the right blend of methodology and insights meet the client needs.

A good example of that would be taste tests for a new formulation at a chocolate brand. ‘Super tasters’ working at the client will arrive at some finely calibrated formulation, created to be aligned to brand values and differentiate the product. But it’s ordinary consumers whose verdict will shape its ultimate success.

Looking to embark on product testing research?

With experience in product testing research, we can combine the inputs and recommend robust methodologies to make sure the product hits the sweet spot in the market. Find out more about our product testing capabilities or get in touch to discuss with our team.

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At a time when there is concern that news outlets are feeding coronavirus panic and confusion, it may have been easy to miss some of the more positive news stories emerging in the last few weeks.

Chief among them is the impact that digital technology has had across Asia, as parts of China in particular have gone into lockdown, and the implications of this.

Across China, as The Economist reported earlier this week, subscriptions to digital health services have increased exponentially – a shift in consumer behavior that previously had been expected to take five whole years. Similarly, we have seen reports that mobile, social media and streaming services are experiencing a strong uptick in usage whilst people are stuck indoors. Schooling has also moved online, with students taking classes through grade-specific TV channels, and the internet.

Above all, we’ve seen people using digital resources to overcome the loneliness of isolation. Gyms are offering sessions via WeChat, clubs are hosting club nights online, and gamers are congregating online to play together in increasing numbers, with Tencent’s Honor of Kings game reaching a peak in average daily users.

So will there be in any digital silver linings for the market research industry?

Non face-to-face methodologies are hardly new in our industry, but a shift towards online – particularly when it comes to qualitative research – now feels unavoidable. Where once a traditional focus group or face-to-face interviews may have sufficed, we’ll undoubtedly see digital techniques coming in to play more and more.

But herein lies a word of caution: because not all digital techniques are created equally, and not all solutions are suitable for certain projects: the most appropriate methodology will always depend on a study’s objectives.

There are plenty of digital options available to researchers: online focus groups, skype depth interviews, mobile diaries, and online communities to name but a few, but how do you work out which methodology is best suited to your study?

First of all, it’s important to start your thinking with your objectives, not your methodology. Just because you might have once used focus groups or face-to-face depth interviews in the past, doesn’t necessarily mean an online focus group or skype interview are the best ways to meet your objectives using digital tools. Start by asking:

  • Are you looking for breadth, or depth of insight?
  • Who are you looking to influence with your findings? What kinds of asset are most likely to have impact and support real change across your organization? How quickly do your stakeholders need access to your insights?
  • How important is it to observe discussion and interaction between respondents – are you looking to compare different points of view?

How you answer these questions will heavily impact the methodology that’s right for you.

For instance, say you are conducting a concept or product test. Typically, you’d use a focus group setting so your product and design team could observe respondent reactions, and make on-the-spot changes to your product.

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If you’re looking for breadth, speedy insights, and discussion between respondents to understand how views differ, you might automatically think that an online focus group session, with respondents and stakeholders logging in from separate locations is your answer. However, while online focus group technology mimics the experience of a focus group setting, in practice, it is much harder for respondents to communicate with one anyone other than the moderator – you’re unlikely to meet your ‘discussion between respondents’ objective.

Instead, an online community would allow you to hit the nail on the head of all three of your objectives and then some. The key difference versus an online focus group is your ability to nurture and observe conversations between respondents in the community in a much more natural environment.

You can even use the platform to segment different audiences together, or keep the community broad to observe discussions across the whole group. Stakeholders are able to log on at any time they choose, to observe conversations, and input suggestions for additional questions to the moderators. And say you have one or two topics you’d like to explore in more depth? You can always set up private questions, to conduct one-to-one research as part of the community. And when it comes to final assets, online communities are really unrivalled when it comes to video and photo content that can be used to help land insights with your stakeholders.

If, however, observing interaction between respondents really isn’t a key necessity, and you’re looking for depth of insight, you may want to consider depth Skype interviews instead of your traditional focus group. Digital depth interviews work beautifully for concept and product testing as part of a staged program of research, especially when you meld multiple touch-points together. You could consider following an initial Skype interview with a selfie-style filmed product review in-home for example, to really dig into consumer views.

Ultimately, while all of these methodologies have been around for some time, it’s likely that a reduction in face-to-face research will see us being far more creative with the digital options available to us. It will be fascinating to see whether or not these changes result in a long-term shift towards digital methodologies. Back in 2014 during London’s tube strikes, commuters were forced to find alternative routes to get travel around the city. Following the strikes, Transport for London reported that one in 20 commuters actually stuck with the new route they’d discovered. Will the research industry see a similar permanent shift? Time will tell.

Kadence has a wealth of experience in using digital research methodologies to help answer critical questions for brands and businesses. If you’re looking for support to help you find the best approach to meet your business objectives, please get in touch.  

In B2B, growth hinges not merely on expanding reach but on the precision of that expansion. Imagine a software company doubling its sales by targeting only those businesses poised to benefit most from its solutions. This is the power of strategic market segmentation in action.

Globally, brands operating in diverse markets have harnessed market segmentation to unlock unprecedented growth. These companies have moved away from the outdated “one-size-fits-all” approach, opting for precision targeting aligning with each region’s unique dynamics.

According to a Harvard Business Review study, personalized marketing can deliver five to eight times the ROI on marketing spend and lift sales by 10% or more. Yet, many organizations struggle to implement effective segmentation strategies, often resulting in wasted resources and missed opportunities.

What is B2B Market Segmentation?

B2B market segmentation is the strategic practice of dividing business-to-business targets into distinct groups of clients that share similar needs, characteristics, or behaviors. This nuanced approach allows companies to tailor their marketing and sales strategies to address the specific demands of each segment, thereby enhancing both efficiency and effectiveness.

Segmentation is integral to modern B2B marketing, enabling companies to deliver personalized experiences and targeted campaigns that resonate with specific audience groups. The integration of technology, such as AI and machine learning, has further refined segmentation techniques, allowing for more precise and dynamic segmentation models that adapt to changing market conditions.

Types of B2B Market Segmentation

Here are the primary segmentation types used by leading B2B companies:

  • Firmographic Segmentation

Dividing the market based on organizational characteristics such as industry, company size, revenue, and geographic location.

  • Decision-Maker Type Segmentation

Segmenting based on the roles and preferences of individuals within organizations, such as IT directors, finance managers, or procurement officers.

  • Profitability or Potential Segmentation

Tiering customers based on lifetime value, profitability, and sales potential.

  • Needs and Attitudes Segmentation

Segmenting based on the psychological attributes of organizations, including values, motivations, and pain points.

  • Behavioral Segmentation

Grouping companies based on their purchasing behavior, spending habits, and interaction with your brand.

  • Jobs-to-Be-Done (JTBD) Segmentation

Focusing on the specific outcomes or “jobs” customers aim to achieve with your products or services.

Research-brief

Why B2B Segmentation Matters

Market segmentation is a critical component of successful B2B marketing and sales strategies. Here’s why segmentation is indispensable for driving growth:

Targeting Prospects

Not every prospect holds equal value for your business. Segmentation enables companies to identify and focus on the most attractive prospects—those that align closely with their ideal customer profiles and exhibit higher conversion probabilities. For example, Salesforce employs advanced segmentation techniques to identify high-potential accounts within various industries, allowing them to allocate resources more effectively and achieve significant increases in their sales pipeline.

Prioritizing Customers

Understanding which customers are more profitable or exhibit higher retention rates allows businesses to prioritize their efforts effectively. MailChimp, for example, uses segmentation to identify its most profitable customer segments, enabling them to tailor their services and support to enhance customer loyalty and lifetime value. This strategic focus ensures resources are directed toward maintaining and growing relationships with the most valuable clients.

Refining Marketing Messages

Tailored marketing messages resonate more deeply with specific segments. Instead of deploying broad, generic messages, companies can craft communications that speak directly to each segment’s unique pain points and needs. HubSpot leverages segmentation to deliver personalized content that addresses the specific challenges faced by different industries, resulting in higher engagement and conversion rates. For example, messaging focused on lead generation might appeal to marketing teams, while content highlighting sales automation features could attract sales professionals.

Optimizing Channel Strategy

Different segments prefer different communication channels. Segmentation ensures marketing efforts are directed through the most effective channels for each group. IBM utilizes segmentation to determine the preferred channels of various customer segments, such as digital marketing for tech-savvy clients and industry-specific conferences for traditional sectors. This targeted approach ensures marketing messages reach the right audience through the most impactful channels.

Developing the Right Content

Knowing each segment’s unique needs helps create relevant content that addresses specific challenges and interests. Whether whitepapers, webinars, or case studies, targeted content enhances engagement and positions your company as a valuable resource tailored to each segment’s requirements. Microsoft employs segmentation to develop specialized content for different industries, ensuring their marketing materials are relevant and impactful for each target group.

Allocating Budget and Resources

Efficiently distributing marketing budgets and resources based on the potential and profitability of each segment ensures optimal return on investment. For instance, Adobe found segmented campaigns achieved a 14% increase in email opens and a 101% increase in clicks compared to non-segmented campaigns. By focusing its marketing spend on high-potential segments, Adobe was able to maximize the effectiveness of its campaigns and achieve better overall results.

Building Your B2B Target Account List

A well-crafted Target Account List (TAL) is the foundation of any successful B2B market segmentation strategy. It ensures your marketing and sales efforts are focused on the accounts with the highest potential for growth and profitability. 

Here’s how to effectively build and manage your TAL.

Start with Your Existing List

Begin with the accounts you already have. These are businesses you have established relationships with and understand well. Leveraging your existing accounts provides a solid starting point for your TAL and offers insights into the characteristics of your most valuable customers.

Key Steps:

  • Analyze Current Customers: Identify common traits among your top-performing accounts, such as industry, company size, revenue, and geographic location.
  • Identify Patterns: Look for patterns in purchasing behavior, engagement levels, and product usage to understand what drives success within your existing customer base.
  • Segment Accordingly: Use these insights to create initial segments within your TAL, focusing on accounts that mirror your best customers.

Methods for Identifying Target Accounts

Building a robust TAL involves several methods to ensure you are targeting the right accounts. Here are three proven methods:

1. Ideal Customer Profiling (ICP)

  • Definition: ICP involves defining the characteristics of your best customers based on firmographics, behavioral traits, and strategic priorities.
  • Implementation: Incorporate data from CRM systems, sales feedback, and market research to create a detailed profile of your ideal customer.
  • Benefits: Helps identify and focus on accounts most likely to convert and deliver long-term value.

2. Predictive Analytics

  • Definition: Predictive analytics leverages historical data and machine learning algorithms to forecast which accounts are most likely to convert.
  • Implementation: Integrate predictive analytics tools with your CRM to analyze patterns and predict future outcomes.
  • Benefits: Enhances the accuracy of your TAL by identifying high-potential accounts that may not be immediately obvious.

3. Behavior-Based Targeting

  • Definition: This method segments accounts based on their real-time behavior and engagement signals, such as website visits, content downloads, and interaction with marketing campaigns.
  • Implementation: Use marketing automation platforms to track and analyze account behavior, allowing for dynamic segmentation.
  • Benefits: Facilitates timely and relevant interactions, increasing the chances of converting engaged prospects.

Checklist for Identifying Target Accounts

To ensure that your TAL is comprehensive and effective, use the following checklist:

  • Firmographic Fit:
    • Company size (number of employees, revenue)
    • Industry sector
    • Geographic location
  • Strategic Alignment:
    • Business objectives align with your offerings
    • Potential for long-term partnership
  • Engagement Levels:
    • Interaction with your brand (e.g., website visits, content downloads)
    • Participation in webinars or events
  • Purchase Intent:
    • Indicators of readiness to purchase (e.g., specific content consumption)
    • Behavioral signals showing interest in your products or services
  • Profitability:
    • High lifetime value potential
    • Lower acquisition costs compared to other segments
  • Additional Considerations:
    • Decision-Maker Access: Ensure you have access to key decision-makers within the target accounts.
    • Competitive Landscape: Assess the presence and strength of competitors within each target account.
    • Technological Fit: Evaluate whether your solutions integrate well with the target account’s existing technology stack.

Creating and Prioritizing Segments

Effective market segmentation is about creating meaningful segments that align with your business objectives and drive substantial growth.

Effective Segmentation Approaches

B2B companies employ various approaches to create effective segments:

  1. Simple Segmentation
    • Description: Using a single criterion, such as industry or company size, to categorize accounts.
    • Benefit: Easy to implement and understand.
  2. Multi-Attribute Segmentation
    • Description: Combining multiple criteria, such as industry, company size, and geographic location, for more precise targeting.
    • Benefit: Enhances targeting accuracy by considering multiple dimensions.
  3. Advanced Segmentation
    • Description: To create highly refined segments utilizing complex data points, including predictive analytics and machine learning.
    • Benefit: Allows dynamic and real-time segmentation that adapts to changing market conditions.

Prioritizing Segments

Not all segments offer the same potential for growth and profitability. Prioritizing segments ensures that your marketing and sales efforts are focused on the most valuable opportunities. Here’s how to effectively prioritize your segments:

  1. Revenue Potential

Focus on segments with the highest potential for revenue generation, either through initial purchases or upselling opportunities.

  1. Engagement Levels

Target segments that show strong engagement with your brand, such as frequent interactions, high content consumption, and active participation in campaigns.

  1. Resource Allocation

Allocate resources to segments that can be effectively managed with your available resources, ensuring sustainable and scalable growth.

  1. Profitability

Prioritize segments that offer high lifetime value and lower acquisition costs, enhancing overall profitability.

Checklist for Vetting Segmentation Approach

To ensure your segmentation approach is robust and effective, use the following checklist:

  • Are the segments differentiated from one another?
  • Can each segment be effectively managed with your current resources?
  • Do the segments align with your overall business goals and objectives?
  • Are the segments sustainable and capable of growing over time?
  • Do the segments make sense and are easily understandable by your team?
  • Is there minimal overlap between segments, ensuring each account fits neatly into one segment?

By adhering to this checklist, you can validate the effectiveness of your segmentation strategy and ensure that it supports your business objectives.

Implementing Segmentation Strategies

Once you have built and prioritized your Target Account List (TAL) and created meaningful segments, the next crucial step is implementing your segmentation strategies effectively. This involves meticulous data collection and analysis, developing precise segmentation criteria, and crafting segmented marketing strategies that resonate with each distinct group.

Data Collection and Analysis

Accurate and comprehensive data collection is the backbone of effective segmentation. Your segmentation efforts can lead to misguided strategies and wasted resources without reliable data. 

Here’s how to ensure your data collection and analysis are robust:

Gathering Data from Various Sources

To create well-defined segments, gather data from multiple sources to gain a holistic view of your target accounts. Key data sources include:

  • CRM Systems: Centralize customer information, including firmographics, purchase history, and interaction records.
  • Google Analytics: Track website behavior, such as page visits, time spent on the site, and conversion rates.
  • Customer Feedback: Utilize surveys, feedback forms, and Net Promoter Scores (NPS) to understand customer satisfaction and pain points.
  • Marketing Automation Tools: Monitor engagement metrics like email opens, click-through rates, and webinar attendance.
  • Social Media Analytics: Analyze engagement and sentiment on platforms like LinkedIn and Twitter to gauge brand perception and interests.

Utilizing Qualitative and Quantitative Research Methods

A balanced approach using both qualitative and quantitative research methods provides deeper insights:

  • Quantitative Research: Employ statistical analysis to identify patterns and correlations within large datasets. Techniques such as cluster analysis and regression analysis can reveal significant segmentation criteria.
  • Qualitative Research: Conduct in-depth interviews, focus groups, and case studies to understand the motivations, challenges, and preferences of your target segments. This approach adds depth to your segmentation, uncovering the ‘why’ behind the numbers.

Developing Segmentation Criteria

Once data is collected, the next step is to establish clear and actionable segmentation criteria. These criteria should align with your business objectives and provide a framework for differentiating your target segments.

Establishing Firmographic, Behavioral, and Psychographic Criteria

  • Firmographic Criteria:
    • Industry: Categorize accounts based on the sectors they operate in, such as healthcare, finance, or technology.
    • Company Size: Segment by the number of employees or annual revenue to tailor solutions that fit their scale.
    • Geographic Location: Consider regional differences that may affect purchasing behavior and preferences.
  • Behavioral Criteria:
    • Purchase History: Analyze past purchases to predict future needs and identify opportunities for upselling or cross-selling.
    • Engagement Levels: Track interactions with your brand to determine the readiness of an account to make a purchase.
    • Content Consumption: Understand what types of content (e.g., whitepapers, webinars) resonate most with each segment.
  • Psychographic Criteria:
    • Values and Beliefs: Segment based on the core values and beliefs of the organization, such as a commitment to sustainability or innovation.
    • Motivations: Understand what drives your customers, whether it’s cost-efficiency, technological advancement, or market expansion.
    • Pain Points: Identify the specific challenges each segment faces and tailor your solutions to address these issues.

How to Align Criteria with Business Objectives

Ensure your segmentation criteria are directly linked to your business goals. For instance, if your objective is to increase market share in the healthcare sector, your segmentation should prioritize healthcare organizations and tailor your strategies to meet their specific needs. For example, LinkedIn aligns its segmentation criteria with its business objective of expanding its enterprise solutions by focusing on large organizations in the technology and finance sectors, delivering tailored LinkedIn Learning and Sales Navigator offerings.

Creating Segmented Marketing Strategies

With well-defined segments and clear criteria, you can now develop targeted marketing strategies that resonate with each group. Personalized strategies enhance engagement, foster stronger relationships, and drive higher conversion rates.

Designing Personalized Campaigns for Each Segment

  • Customized Messaging: Craft messages that address each segment’s specific needs and pain points. Use language and terminology that resonate with their industry and organizational culture.
  • Tailored Content: Develop content relevant to each segment’s stage in the buyer’s journey. Provide educational resources for early-stage prospects and detailed product information for those closer to making a purchase.
  • Channel Optimization: Based on each segment’s preferences and behaviors, choose the most effective channels. This ensures that your messages reach your audience where they are most receptive.

Implementing Multi-Channel Marketing Strategies

A multi-channel approach ensures that your segmented messages are consistently delivered across various touchpoints, enhancing brand visibility and reinforcing your value proposition.

  • Email Marketing: Use personalized email campaigns to deliver targeted messages and nurture relationships with specific segments.
  • Content Marketing: Create and distribute content, such as blogs, whitepapers, case studies, and videos, specifically designed for each segment’s interests and needs.
  • Social Media: Engage with segments on platforms they frequent, using tailored content and targeted advertising to increase engagement and reach.
  • Events and Webinars: Host events and webinars that cater to each segment’s interests and needs, providing valuable insights and fostering direct engagement.

Case Study: Adobe

Image credit: Adobe

Challenge: Adobe sought to optimize its marketing campaigns by delivering highly personalized content to different segments based on their engagement levels and needs.

Segmentation Strategy: Adobe implemented a multi-channel marketing strategy that leveraged behavioral segmentation to tailor content delivery across various touchpoints.

Implementation:

  • Personalized Email Campaigns: Sent targeted emails with content relevant to each segment’s engagement level and interests.
  • Dynamic Content Creation: Developed tailored whitepapers, webinars, and case studies for different segments.
  • Channel-Specific Strategies: Utilized social media and digital advertising to reach tech-savvy segments while engaging traditional sectors through industry conferences and trade shows.

Results:

  • Increased Engagement: Achieved a 30% increase in engagement through personalized marketing efforts.
  • Higher Conversion Rates: Improved conversion rates by focusing on high-potential segments with relevant content.
  • Sustained Revenue Growth: Maintained steady revenue growth by continuously refining and optimizing segmented campaigns.

Leveraging Technology for Enhanced Segmentation

In the rapidly evolving B2B landscape, technology plays a pivotal role in refining and enhancing market segmentation strategies. Leveraging advanced technologies not only streamlines the segmentation process but also provides deeper insights, enabling more precise and effective targeting.

Advanced Analytics and AI

Advanced Analytics and AI offer sophisticated tools to analyze vast data and uncover actionable insights, enabling more nuanced and predictive segmentation models.

Key Components:

  • Predictive Analytics: Uses historical data and statistical algorithms to forecast future behaviors and trends, helping identify high-potential segments.
  • Machine Learning: Employs algorithms that learn from data patterns to improve segmentation accuracy over time, dynamically adjusting criteria based on new data.
  • Natural Language Processing (NLP): Analyzes unstructured data, such as social media interactions and customer feedback, to gain deeper insights into customer sentiments and preferences.

Automation Tools

Automation tools streamline the segmentation process, making it more efficient and scalable by handling repetitive tasks, managing large datasets, and ensuring consistent application across all marketing and sales activities.

Key Components:

  • Marketing Automation Platforms: Integrate with CRM and other data sources to automate the segmentation process, ensuring target segments are always up-to-date and accurately defined.
  • Customer Data Platforms (CDPs): These platforms centralize customer data from various sources, providing a unified view of each account and facilitating seamless data integration for comprehensive and up-to-date segmentation.
  • AI-Powered Segmentation Tools: Leverage AI to automatically identify and create segments based on complex data patterns and predictive indicators.

Final Thoughts

Effective B2B market segmentation is not just a strategy; it is a necessity in today’s competitive and dynamic business environment. By understanding and implementing strategic segmentation, businesses can achieve:

  • Sustainable Growth: Focused efforts on high-potential segments drive consistent and scalable growth.
  • Enhanced Customer Satisfaction: Tailored marketing and sales approaches meet the specific needs of each segment, fostering stronger relationships and loyalty.
  • Optimized Resource Allocation: Efficiently distribute marketing budgets and resources based on the potential and profitability of each segment, maximizing return on investment.

As the global market evolves, embracing strategic market segmentation will be pivotal in navigating complexity, addressing diverse customer needs, and maintaining a competitive edge. Senior leaders in market research and branding must prioritize segmentation as a core component of their growth strategies, leveraging data-driven insights and advanced technologies to unlock unparalleled opportunities and drive their businesses toward greater success.

Imagine you’re a digital marketer for an online retailer specializing in fitness gear. You’ve just launched a new line of eco-friendly yoga mats, and you’re tasked with maximizing sales through your website. You test two different product page versions to see which drives more purchases. 

Version A features a prominent “Limited Time Offer” banner at the top, while Version B includes a series of customer testimonials right beneath the product title. The results of this A/B test could significantly affect your sales figures and offer deeper insights into what motivates your customers to buy.

Such is the power of A/B testing, a method companies of all sizes use to make data-driven decisions that refine user experiences and improve conversion rates. 

A/B testing provides a data-driven solution to optimize website effectiveness without the guesswork. By comparing two versions of a page or element directly against each other, brands can see which changes produce positive outcomes and which ones do not, leading to better business results and a deeper understanding of customer behavior.

Whether you’re looking to increase conversion rates, enhance user engagement, or drive more sales, effective A/B testing is the key to achieving your goals precisely and confidently.

A/B testing, or split testing, is a method in which two versions of a webpage or app are compared to determine which performs better. Imagine you’re at the helm of a ship; A/B testing gives you the navigational tools to steer more accurately toward your desired destination—increased sales, more sign-ups, or any other business goal. It involves showing the original version (A) and a modified version (B), where a single element may differ, such as the color of a call-to-action button or the layout of a landing page, to similar visitors simultaneously. The version that outperforms the other in achieving a predetermined goal is then used moving forward.

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The Importance of A/B testing and ROI

The compelling advantage of A/B testing is its direct contribution to enhancing business metrics and boosting return on investment (ROI). 

A recent study by eConsultancy shows that companies that use A/B testing are twice as likely to report significant improvements in conversion rates. 

Online retailers frequently use A/B testing to optimize website leads and increase conversion rates. This includes split testing product pages and online advertisements, such as Google Shopping Ads. By A/B testing different product page layouts, retailers can identify a version that increases their sales, impacting annual revenue. Similarly, SaaS providers test and optimize their landing pages through A/B testing to find the version that increases user sign-ups, directly improving their bottom line.

A/B testing is less about guessing and more about evidence-based decision-making, ensuring every change to your interface is a strategic enhancement, not just a cosmetic tweak.

Preparing for A/B Testing

1. Setting Objectives

Before launching an A/B test, defining clear, measurable objectives is critical. These objectives should be specific, quantifiable, and aligned with broader business goals. Common goals include increasing conversion rates, reducing bounce rates, or boosting the average order value. The clarity of these objectives determines the test’s focus and, ultimately, its success.

2. Identifying Key Elements to Test

Choosing the right elements on your website for A/B testing can significantly affect the outcome. High-impact elements often include:

  • CTAs: Testing variations in the text, color, or size of buttons to see which drives more clicks.
  • Layouts: Comparing different arrangements of elements on a page to determine which layout keeps visitors engaged longer.
  • Content: Tweaking headlines, product descriptions, or the length of informational content to optimize readability and conversion.
  • Images and Videos: Assessing different images or video styles to see which leads to higher engagement or sales.

3. Understanding Your Audience

Effective A/B testing requires a deep understanding of your target audience. Knowing who your users are, what they value, and how they interact with your website can guide what you test and how you interpret the data from those tests.

Data Analytics Snapshots:

Utilizing tools like Google Analytics, heatmaps, or session recordings can provide insights into user behavior. Heatmaps, for example, can show where users are most likely to click, how far they scroll, and which parts of your site draw the most attention. These tools can highlight areas of the site that are performing well or underperforming, guiding where to focus your testing efforts.

Importance of Audience Insights:

Understanding user behavior through these tools helps tailor the A/B testing efforts to meet your audience’s needs and preferences, leading to more successful outcomes. For instance, if heatmaps show that users frequently abandon a long signup form, testing shorter versions or different layouts of the form could reduce bounce rates and increase conversions.

These preparatory steps—setting objectives, identifying key elements, and understanding the audience—create a strong foundation for successful A/B testing. By meticulously planning and aligning tests with strategic business goals, companies can ensure that their efforts lead to valuable, actionable insights that drive growth and improvement.

Designing A/B Tests

Developing Hypotheses

A well-crafted hypothesis is the cornerstone of any successful A/B test. It sets the stage for what you’re testing and predicts the outcome. A strong hypothesis is based on data-driven insights and clearly states what change is being tested, why, and its expected impact.

Guidance on Formulating Hypotheses:

  • Start with Data: Analyze your current data to identify trends and areas for improvement. For instance, if data shows a high exit rate from a checkout page, you might hypothesize that simplifying the page could retain more visitors.
  • Be Specific: A hypothesis should clearly state the expected change. For example, “Changing the CTA button from green to red will increase click-through rates by 5%,” rather than “Changing the CTA button color will make it more noticeable.”
  • Link to Business Goals: Ensure the hypothesis aligns with broader business objectives, enhancing its relevance and priority.

Examples:

  • Good Hypothesis: “Adding customer testimonials to the product page will increase conversions by 10% because trust signals boost buyer confidence.”
  • Poor Hypothesis: “Changing things on the product page will improve it.”

Creating Variations

Once you have a solid hypothesis, the next step is to create the variations that will be tested. This involves tweaking one or more elements on your webpage based on your hypothesis.

Instructions for Creating Variations:

  • Single Variable at a Time: To understand what changes affect outcomes, modify only one variable per test. If testing a CTA button, change the color or the text, but not both simultaneously.
  • Use Design Tools: Utilize web design tools to create these variations. Ensure that the changes remain true to your brand’s style and are visually appealing.
  • Preview and Test Internally: Before going live, preview variations internally to catch potential issues.

Choosing the Right Tools

Selecting the appropriate tools is crucial for effectively running A/B tests. The right tool can simplify testing, provide accurate data, and help interpret results effectively.

By following these steps—developing a strong hypothesis, creating thoughtful variations, and choosing the right tools—you can design effective A/B tests that lead to meaningful insights and significant improvements in website performance. This strategic approach ensures that each test is set up for success, contributing to better user experiences and increased business outcomes.

Implementing A/B Tests

Effective implementation of A/B tests is critical to achieving reliable results that can inform strategic decisions. 

Test Setup and Configuration

Setting up an A/B test properly ensures that the data you collect is accurate and that the test runs smoothly without affecting the user experience negatively.

Step-by-step Guide on Setting Up Tests:

  • Define Your Control and Variation: Start by identifying your control version (the current version) and the variation that includes the changes based on your hypothesis.
  • Choose the Type of Test: Decide whether you need a simple A/B test or a more complex split URL test. Split URL testing is useful when major changes are tested, as it redirects visitors to a different URL.
  • Set Up the Test in Your Chosen Tool: Using a platform like Google Optimize, create your experiment by setting up the control and variations. Input the URLs for each and define the percentage of traffic directed to each version.
  • Implement Tracking: Ensure that your analytics tracking is correctly set up to measure results from each test version. This may involve configuring goals in Google Analytics or custom-tracking events.

Interactive Checklists or Setup Diagrams:

A checklist can help ensure all steps are followed, such as:

  • Define control and variation
  • Choose testing type
  • Configure the test in the tool
  • Set traffic allocation
  • Implement tracking codes

Best Practices for Running Tests

Once your test is live, managing it effectively is key to obtaining useful data.

Tips for Managing and Monitoring A/B Tests:

  • Monitor Performance Regularly: Check the performance of your test at regular intervals to ensure there are no unexpected issues.
  • Allow Sufficient Run Time: Let the test run long enough to reach statistical significance, usually until the results stabilize. You have enough data to make a confident decision.
  • Be Prepared to Iterate: Depending on the results, be prepared to make further adjustments and rerun the test. Optimization is an ongoing process.

Visual Dos and Don’ts Infographics

To help visualize best practices, create an infographic that highlights the dos and don’ts:

  • Do: Test one change at a time, ensure tests are statistically significant, and use clear success metrics.
  • Don’t Change multiple elements at once, end tests prematurely, and ignore variations in user behavior.

Statistical Significance and Sample Size

Understanding these concepts is crucial for interpreting A/B test results accurately.

Explanation of Key Statistical Concepts:

  • Statistical Significance: This measures whether the outcome of your test is likely due to the changes made rather than random chance. Typically, a result is considered statistically significant if the probability of the result occurring by chance is less than 5%.
  • Sample Size: The number of users you need in your test to reliably detect a difference between versions. A sample size that is too small may not accurately reflect the broader audience.

Graphs and Calculators:

  • Provide a graph showing how increasing sample size reduces the margin of error, enhancing confidence in the results.
  • Link to or embed a sample size calculator, allowing users to input their data (like baseline conversion rate and expected improvement) to determine how long to run their tests.

By following these guidelines and utilizing the right tools and methodologies, you can implement A/B tests that provide valuable insights into user behavior and preferences, enabling data-driven decision-making that boosts user engagement and business performance.

Analyzing Test Results

Once your A/B test has concluded, the next crucial step is analyzing the results. This phase is about interpreting the data collected, understanding the statistical relevance of the findings, and making informed decisions based on the test outcomes.

Interpreting Data

Interpreting the results of an A/B test involves more than just identifying which variation performed better. It requires a detailed analysis to understand why certain outcomes occurred and how they can inform future business decisions.

How to Read Test Results:

  • Conversion Rates: Compare the conversion rates of each variation against the control. Look not only at which had the highest rate but also consider the context of the changes made.
  • Segmented Results: Break down the data by different demographics, device types, or user behaviors to see if there are significant differences in how certain groups reacted to the variations.
  • Consistency Over Time: Evaluate how the results varied over the course of the test to identify any patterns that could influence your interpretation, such as a weekend vs. weekday performance.

Statistical Analysis

A deeper dive into the statistical analysis will confirm whether the observed differences in your A/B test results are statistically significant and not just due to random chance.

Understanding Statistical Significance and Other Metrics:

  • P-value: This metric helps determine the significance of your results. A p-value less than 0.05 typically indicates that the differences are statistically significant.
  • Confidence Interval: This range estimates where the true conversion rate lies with a certain level of confidence, usually 95%.
  • Lift: This is the percentage increase or decrease in the performance metric you are testing for, calculated from the baseline of the control group.

Making Informed Decisions

With the data interpreted and the statistical analysis complete, the final step is to decide how to act on the insights gained from your A/B test.

Guidelines on How to Act on Test Results:

  • Implement Winning Variations: If one variation significantly outperforms the control, consider implementing it across the site.
  • Further Testing: If results are inconclusive or the lift is minimal, running additional tests with adjusted variables or targeting a different user segment may be beneficial.
  • Scale or Pivot: Depending on the impact of the changes tested, decide whether to scale these changes up to affect more of your business or to pivot and try a different approach entirely.

Decision Trees or Flowcharts:

Create a decision tree or flowchart that outlines the decision-making process following an A/B test. This could include nodes that consider whether the test was statistically significant, whether the results align with business goals, and what follow-up actions (like further testing, full implementation, or abandonment of the change) should be taken based on different scenarios.

By thoroughly analyzing A/B test results through data interpretation, statistical analysis, and strategic decision-making, organizations can ensure that they are making informed decisions that will enhance their website’s user experience and improve overall business performance. This data-driven approach minimizes risks associated with website changes and ensures that resources are invested in modifications that provide real value.

Beyond Basic A/B Testing

Once you have mastered basic A/B testing, you can explore more sophisticated techniques that offer deeper insights and potentially greater improvements in user experience and conversion rates. This section delves into advanced testing strategies and the importance of ongoing optimization through iterative testing.

Advanced Testing Techniques

Advanced testing methods allow you to explore more complex hypotheses about user behavior and website performance, often involving multiple variables or entire user journeys.

Multivariate Testing (MVT):

  • Overview: Unlike A/B testing, which tests one variable at a time, multivariate testing allows you to test multiple variables simultaneously to see which combination produces the best outcome.
  • Application: For example, you might test different versions of an image, headline, and button on a landing page all at once to determine the best combination of elements.
  • Benefits: This approach can significantly speed up the testing process and is particularly useful for optimizing pages with multiple elements of interest.

Multipage Testing:

  • Overview: Also known as “funnel testing,” this technique involves testing variations across multiple pages that make up a user journey or funnel.
  • Application: You might test variations of both the product and checkout pages to see which combination leads to higher conversion rates.
  • Benefits: Multipage testing helps ensure consistency in messaging and user experience across multiple stages of the user journey, which can improve overall conversion rates.

Continuous Improvement and Iteration

The goal of A/B testing is not just to find a winning variation but to continually refine and enhance your website based on user feedback and behavior.

Importance of Ongoing Optimization:

  • Iterative Process: Optimization is an ongoing process that involves continually testing and refining website elements based on user data and business objectives.
  • Learning from Each Test: Each test provides valuable insights into whether a variation wins. These insights can inform future tests, leading to better user experiences and higher conversion rates.

Iterative Testing Strategies:

  • Start with Broad Tests: Begin with broader tests to identify which elements have the most significant impact on user behavior.
  • Refine and Repeat: Use the insights gained to refine your hypotheses and test more specific variations.
  • Expand Testing: Once you’ve optimized major elements, expand your testing to less prominent components that could still affect user experience and conversions.

Timelines and Case Studies:

  • Timeline Example: Show a timeline that outlines an annual testing strategy, with phases for broad testing, refinement, and expansion.
  • Case Study: Present a case study of a company that implemented continuous testing. Highlight how iterative testing helped them achieve a significant, sustained increase in conversion rates over time. For instance, a tech company could use iterative testing to fine-tune its sign-up process, resulting in a 50% increase in user registrations over a year.

By advancing beyond basic A/B testing and embracing more complex and continuous testing strategies, companies can optimize their websites more effectively and foster a culture of data-driven decision-making. This approach leads to improvements that align with user preferences and business goals, ensuring sustained growth and a competitive edge in the market.

Common Pitfalls and How to Avoid Them

A/B testing is a powerful tool for website optimization, but common pitfalls can undermine its effectiveness. This section explores typical errors that occur during the testing process and provides strategies to ensure the validity and reliability of your tests.

List of Common Mistakes

Identifying Errors and Solutions:

  • Testing Too Many Changes at Once: It can make determining which change affected the outcome difficult.
    • Solution: Focus on testing one change at a time or use multivariate testing for simultaneous changes and analyze the impact of each element separately.
  • Not Allowing Enough Time for the Test to Run: Ending a test too soon can lead to conclusions that aren’t statistically significant.
    • Solution: Ensure each test runs long enough to collect adequate data, reaching statistical significance before making decisions.
  • Testing Without a Clear Hypothesis: Starting tests without a clear, data-backed hypothesis leads to unclear outcomes.
    • Solution: Develop a precise hypothesis for each test based on thorough data analysis and clear business objectives.
  • Ignoring User Segmentation: Different segments may react differently to the same change.
    • Solution: Segment your audience and analyze how different groups respond to each variation.

Visuals of Pitfalls vs. Best Practices:

  • Create side-by-side infographics showing examples of these mistakes versus best practices. For example, visually compare the outcome of a test that changed multiple elements simultaneously against one that tested a single change.

Ensuring Validity and Reliability

Maintaining the integrity of your A/B tests is crucial for obtaining reliable, actionable insights.

Tips on Maintaining Test Integrity:

  • Use Proper Randomization: Ensure that the distribution of users between the control and test groups is random to avoid selection bias.
    • Tool Tip: Utilize tools that automatically handle randomization to avoid manual errors.
  • Control External Factors: Holidays, marketing campaigns, or significant news events can skew test results.
    • Solution: Monitor external factors, adjust the testing period, or filter the data to account for anomalies.
  • Ensure Consistent Test Conditions: Changes in the testing environment or platform during the test can invalidate results.
    • Solution: Keep the testing conditions consistent throughout the test period and verify configuration settings regularly.
  • Validate Test Setup Before Going Live: A misconfigured test can lead to incorrect data interpretation.
    • Solution: Run a smaller pilot test or use a checklist to ensure every test element is correctly set up before full deployment.

Troubleshooting Guide with Graphic Aids:

  • Develop a troubleshooting guide that includes common scenarios where A/B test integrity might be compromised. Include flowcharts or decision trees that help identify and resolve issues such as data discrepancies, unexpected user behavior, or sudden changes in conversion rates.
  • Example Graphic Aid: A flowchart that helps determine actions when test results seem inconsistent with historical data or benchmarks. Steps might include checking configuration settings, reviewing segmentation criteria, or extending the test duration.

By understanding and avoiding these common pitfalls and maintaining rigorous standards for validity and reliability, organizations can ensure that their A/B testing efforts lead to meaningful improvements and robust data-driven decisions. This approach not only enhances the effectiveness of current tests but also builds a foundation for future testing strategies that are even more successful.

A/B Testing Case Studies

A/B testing has proven to be a critical tool for businesses aiming to optimize their online presence based on data-driven decisions. Here, we delve into some specific real-life case studies from different industries, highlighting the successes and lessons from A/B testing.

Success Stories

E-commerce: Humana

  • Overview: Humana, a well-known health insurance company, conducted an A/B test to increase click-through rates on one of their primary campaign landing pages. They tested the simplicity and message of their banner and CTA.
  • Changes Tested: The original banner had a lot of information and a standard “Shop Medicare Plans” button. The test variation simplified the message and changed the button text to “Get Started Now.”
  • Results: The variation led to a 433% increase in click-through rates to the insurance plans page.

B2B: SAP

  • Overview: SAP, a leader in enterprise application software, tested the copy of their CTA on a product page. The hypothesis was that a more action-oriented CTA would increase engagement.
  • Changes Tested: The original CTA read “Learn more,” which was changed to “See it in action” in the variation.
  • Results: This simple change in wording resulted in a 32% increase in clicks.

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Digital Media: The Guardian

  • Overview: The Guardian tested different wordings for their support and donation CTAs to determine which would more effectively encourage readers to contribute financially.
  • Results: The test revealed that a direct ask for contributions using emotive language resulted in a higher click-through rate than a more generic request for support.
  • Lesson: This A/B test highlighted the importance of emotional resonance in messaging, especially for non-profit or cause-based initiatives.

Travel Industry: Expedia

  • Overview: Expedia conducted A/B testing to optimize hotel booking conversions on their site by altering the display of discount offers.
  • Changes Tested: They tested the visibility and presentation of savings messages (e.g., showing a percentage off versus a specific dollar amount saved).
  • Results: Showing the amount of money saved led to a slight decrease in conversion rates, contrary to expectations.
  • Lesson: The test underscored the potential for “over-optimizing” to backfire and the need to balance how offers are presented to avoid overwhelming customers.

Final Checklist of A/B Testing Steps

To help ensure your A/B testing journey is structured and effective, here is a visual checklist encapsulating the process:

  1. Define Objectives: Clearly state what you aim to achieve.
  2. Formulate Hypotheses: Base your assumptions on data and prior insights.
  3. Select the Testing Tool: Choose a platform that suits your scale and complexity needs.
  4. Design the Test: Create variations based precisely on your hypotheses.
  5. Run the Test: Ensure the test is long enough to gather meaningful data.
  6. Analyze Results: Use statistical analysis to interpret the outcomes.
  7. Implement Changes: Apply successful variations or further refine and test.
  8. Repeat: Use insights gained to continuously improve further testing.

Regardless of the outcome, every test is a step forward in understanding your users better and refining your digital offerings to meet their needs more effectively. The journey of optimization is continuous, and each effort builds upon the last, opening new doors to innovation and growth.

Harness the power of A/B testing to start making informed decisions that propel your business forward. Your next breakthrough could be just one test away.