Market segmentation definition

Market segmentation (also called marketing segmentation) is the process of dividing up the customer (and prospective customer) base into groups that have common characteristics and are different from each other in some meaningful way. Market segmentation enables you to identify the most attractive customers to target and to develop a differentiated marketing strategy for each segment.  

Benefits of market segmentation

Why do you need market segmentation? Segmenting the market will enable you not only to better understand your customers but also to decide which groups you can serve most effectively. You may decide to develop a different offer for each segment of the market or to disregard some segments entirely, because they would not be the most profitable, easy to reach, or aligned with your wider strategy.  

Uncovering opportunities

Marketing segmentation can also help you to identify unmet needs within different customer segments and uncover opportunities to address new groups of customers. You can also use market segmentation research to better understand a topic or issue of importance, in social or political research, for example, and how that issue plays out amongst different groups of people. 

Tailoring your marketing strategy

Once you have conducted your marketing segmentation research and chosen your target segments, you can tailor your marketing strategy and tactics for each segment. This can include having different approaches for any or all of the following: 

• Products
• Pricing
• Packaging
• Positioning
• Messaging
• Advertising
• Media
• Sales channels

Types of market segmentation

There are four types of market segmentation: demographic, geographic, behavioural and psychographic.  

Demographic market segmentation

This is a simple and logical way of dividing up the market. Factors such as age, gender, income, education and life stage all have an impact on how people buy products and respond to marketing messages and approaches. Examples of market segmentation using demographics include the following:

• Segmentation by age: motor brands target younger customers with smaller, cheaper models and use messaging focused on how fun the car is to drive, whereas older customers are targeted with higher-end specifications and messages about comfort and luxury
• Segmentation by income: holiday companies often have different brands for high-end luxury holidays, aimed at higher income travellers, and budget breaks for those with lower incomes. 
• Segmentation by life stage: financial services firms aim pensions at younger working adults and equity release schemes at older, retired people.

Geographic segmentation

Another simple and logical way of dividing the market – by region, country or continent, or by type of geography such as urban vs. rural.

Cultural segmentation

Consider the fast-food chain with restaurants in every country around the world. Cultural differences mean that the same beef burger that sells so well in western Europe and the US would be unlikely to sell at all in India, for example. McDonald’s use of geographic segmentation has led them to create a local version of the Big Mac for the Indian market, a chicken or veggie burger known as the Maharaja Mac which are now “some of the most sought-after burgers on the McDonald’s India menu.”

Geographical features in segmentation

As well as taking cultural issues into account, geographic segmentation can also be based around features such as weather or the physical geography of the region. People living in tropical regions probably don’t buy snow chains for their cars; those living in urban settings probably aren’t interested in tractors. This can optimally be achieved through an in-depth global market study targetted to your brand.

Geographic B2B segmentation

Geographic segmentation can be useful even when there are a lot of similarities between regions – B2B sales teams, for example, often create B2B segmentations by dividing customers up by territory, simply to make them easier to reach.

Behavioural segmentation

Behavioural segmentation splits customers according to how they act – and also how they act in relation to your product.

So, for example, you may want to split your customer base into light and heavy users of your product. It is likely that you will want to reward the heavy users for their regular use, potentially with loyalty bonuses. You will probably want to encourage the light users to become heavier users, perhaps by offering discounts or other incentives for bulk or repeat purchases.

You may also want to split your customers by how they interact with you. Do you have some customers who only interact with your brand online? And others who buy your product in stores or prefer to place orders by phone?

Online customer data can be invaluable in understanding how customers interact with your brand and use your products.

Psychographic segmentation

Psychographic segmentation divides people according to personality characteristics such as their  opinions, attitudes, needs and preferences.

Price sensitivity

For example, rather than targeting customers according to income, a more nuanced and effective approach may be to understand whether they are price sensitive. This may or may not be related to their income level.  You can then create a basic offering for price-sensitive customers and a feature-rich version for those who want quality at any price.  Supermarket own brands are a great example of this type of segmentation in action.

Attitudes in segmentation

A chain of gyms may want to understand customers’ attitudes so that they can keep them motivated to use their membership – are they reluctant couch potatoes who need frequent nudges to work out, or are they eager fitness freaks who would benefit from finding out about any new gym classes or offers?

Motivations in segmentation

Or a charity may want to understand what motivates people to donate – are they driven by empathy with the cause, by knowing someone who would benefit from the help the charity offers, or by a general sense of altruism? 

Psychographic segmentation research can uncover a wide variety of different ways to group and make sense of the customer base, sometimes in unexpected ways that can create new marketing opportunities.

A practical example of market segmentation

It is possible – desirable in fact – to combine some of these forms of segmentation. We conducted a segmentation for a protein shake manufacturer that combined elements of behaviour and attitudes. This enabled us to create a segmentation of five personas based on based on lifestyles, fitness regimes and consumption patterns. The new segmentation model has given our client the ability to develop and target marketing communications, promotions and new product development strategies with a degree of precision that wasn’t previously possible.

Contact us

If you want to better understand your market and develop a customer segmentation that enables you to uncover new opportunities, improve your communications and create competitive advantage, please get in touch with Jeremy@brandspeak.co.uk or contact us via enquiries@brandspeak.co.uk .

How to conduct market segmentation research

There are two main ways to conduct a market segmentation study: a priori segmentation and post-hoc segmentation.

What is a priori market segmentation?

A priori translates from the Latin as ‘from the former’, and relates to an argument or line of reasoning that is deductive, based on logic rather than observation or experience. However, in the context of market segmentation, it means grouping people according to predetermined factors.

For example, FMCG brands may find that grouping customers according to how often they use their products is the most meaningful way of addressing their needs. Someone who drinks Coke every day and at every meal is likely to have an entirely different relationship with the brand to someone who has one can a week as a treat.

Demographic differences such as age can also be helpful a priori methods of segmenting a market. For example, marketing children’s clothes to parents with toddlers requires one approach; the same brand appealing directly to teenagers will need a very different marketing strategy. Similarly, vitamins and health supplements are often explicitly targeted at specific age groups or life stages.

How to conduct a priori segmentation research

Most segmentation research uses quantitative surveys, which are usually carried out online nowadays. For some types of a priori segmentation, you don’t need to conduct a separate survey or to use any complex analysis. If the segmentation variable can be asked directly of the respondent, such as age, gender or even frequency of use, you can create segments from a simple crosstab of that variable, and you can use existing research such as brand tracking studies or customer satisfaction studies to create your segments.

However, you may want to conduct additional analysis to get a more detailed picture. For example, if you want to divide your customers up according to how loyal they are to your brand, you could use one simple measure of loyalty, such as the NPS. However, you may decide that it is a bit more complicated than that. You can then conduct a survey designed specifically to understand loyalty and use multivariate analysis techniques to create your segments.

What is post-hoc market segmentation?

Post hoc means ‘after the event’. In the context of market segmentation research, this means analysing research data to derive segments, without having decided on them in advance.

As an aside, not to be confused with post-hoc fallacy. Sometimes the term ‘post hoc’ is used as shorthand for ‘post hoc, ergo propter hoc,’ a Latin phrase meaning ‘after this, therefore because of this.’ The phrase expresses the logical fallacy of assuming that one thing caused another merely because the first thing preceded the other.  For example, if the doorbell rings, and immediately afterwards, your microwave dinner pings, the fallacy would be to assume that the doorbell had caused your dinner to finish cooking. Not only is this ridiculous, but it also has nothing to do with market segmentation.

When to use post-hoc market segmentation

You would be more likely to use post-hoc market segmentation when grouping people according to their attitudes, to a particular product or category, their opinions about a particular issue or their motivations towards a particular behaviour. The resulting segments are typically more descriptive and richer than those created using a priori techniques.

For example, a supermarket chain may want to understand how to group customers in a more detailed and nuanced way, according to a variety of variables, not just how often they shop or how much they spend.

How to conduct post-hoc segmentation research

The first step is to design a questionnaire that will cover all the areas that will be useful in differentiating customer types, and in meeting your objective for the segmentation.

Exploratory qualitative research

We would always recommend starting with some qualitative research – focus groups or depth interviews – to guide questionnaire design. Using our supermarket example, we might suggest an initial qualitative stage, interviewing customers of different ages, family structures, incomes and genders. Having done so, we can be sure that the survey design hasn’t missed anything and uses language that will resonate with customers.

Analysis

Once the survey data is collected, we can conduct analysis to create your segments. We use techniques such as factor analysis and cluster analysis to determine how different variables relate to each other and how customers are grouped according to multiple dimensions.

The goal of the analysis is to create segments that contain people who are as similar as possible within each segment, and as different as possible to people in other segments, be it for a study of local markets or global market research. Segments also need to be useful – so they must be capable of being targeted in the real world, using demographic identifiers or other ways of finding segment members.

As such, segmentation is as much an art as it is a science. It is not unusual for the analysis to create several different options for how to divide the data. In such a case, you would look at how useful each option is, the relative size of the segments and the degree to which they all differ.

Number of segments

Most studies yield somewhere between four and eight segments, with five or six being ideal. Any less, and the segments are likely to be too big, with too many differences to make the exercise useful; any more and the segments might be too small and only slightly different from each other. Having more than eight segments, which all need separate marketing strategies, can also be unmanageable in a practical sense.

Sample size

It is critically important that the sample size for the survey is large enough to conduct the segmentation and to enable analysis within the segment. Best practice is to have segments of at least n= 200. If you have six segments, that is a minimum of 1,200 respondents – although you actually need more as segment sizes are likely to differ.

Naming the segments

Once you have derived your segments, it is important to name them so that you and your team can easily understand what they mean. Using the supermarket example, you might have a segment that always buys the own-brand budget items wherever possible and is very price sensitive. Rather than just calling them ‘segment one’ or describing them, if you call them ‘low-end budgeters’, it creates a mental picture. Similarly, you may have another segment that shops every day for a small amount of goods and likes to browse. These could be the ‘basket browsers’.

In summary

Market segmentation research ranges from simple analysis of existing data to more complex analysis requiring dedicated qualitative and survey research. Either way, a unique segmentation that aligns with your business objectives will help you to understand your customers and prospects, uncover more opportunities to serve your market and meet customers’ needs in a way that feels personal to each of them.

We are specialists in customer segmentation research and delivery in both B2B and consumer markets.

What is Ad campaign measurement research?

Ad campaign measurement research is also known as ad tracking or ad testing research. The goal is to track ads during the time that the campaign is live so that you can understand the impact it is having on the market. Ad tracking should be conducted across all the media channels where your ads appear, which can also help you to establish the effectiveness of one channel versus another.

Most advertising tracking research also uses a pre-campaign wave as a benchmark. This is particularly important when you are looking at the impact of the campaign on your brand – how can you know whether metrics such as brand awareness have changed if you don’t conduct research before it is launched?

Why conduct Ad campaign measurement research?

The overarching reason to conduct campaign measurement research is to understand the efficacy of your approach. Within this, there are a range of specific evaluation goals. You may want to assess how different creative executions within the same ad campaign are cutting through, to explore the mix of media channels and see which are most effective or to see whether you are reaching your desired target audience. 

For example, if you are targeting young mums but find that the people who are most likely to remember seeing the ad are men in their 60s, who aren’t so likely to be buyers of the mother-and-baby yoga products you are selling, you know that there is a problem.

Alternatively, you may find that one execution is widely remembered but the messaging isn’t cutting through effectively, whereas another is communicating clearly but is less well remembered. 

Once you have identified these types of issues, you can start to diagnose the problem. Are you on the right websites for young mums? Or is your messaging confusing? Is there an issue with relevance? What about images – does the ad with the dog do better than the one with the cat?

Over time you can start to build up a library of insight about what works and what doesn’t for your audiences and your brand, making future campaigns more likely to be successful. 

Depending on the length and volume of your campaign, as well as using ad-tracking research to learn for future ad campaigns, you can also diagnose and fix underperformance while the campaign is live. In particular, if your advertising is largely digital, it is much easier to alter the content of ad executions or adjust the media while the ads are live.

How to conduct Ad campaign measurement research

Campaign measurement research is typically conducted via online surveys – the most cost-effective method for larger-scale quantitative research. The online medium offers many different options to provide your participants with stimulus materials that enable them to experience the ads that you are testing; you can ask survey respondents to play audio clips to simulate radio advertising, show them video clips of TV ads, or still images from magazine, outdoor or online advertising.

Before showing or playing the stimulus, you can ask unprompted questions about the brand and about advertising recall to see how well the ads are cutting through without the memory jog of seeing or hearing them.

You can choose whether to conduct the research purely about the campaign itself or to put ad tracking questions into a brand tracking study. In the latter case, you can conduct a more granular analysis, including metrics such as the NPS, to look at the relationship between the campaign and how it is received by target customers.

What is most important about your ad tracking methodology is that it must fit with your objectives for the campaign – you must be sampling the right audience, asking the right questions and timing the research correctly – ideally before and during the campaign, as discussed earlier. You can also conduct research after the campaign has finished to see how well it has been recalled and whether there has been a lasting impact on brand equity.

Five key considerations

1. Develop a clear definition of success. How will you know if the campaign has been successful unless you know what success looks like? The definition of success should be linked to your campaign objectives. If the campaign is intended to raise awareness of your brand or of a particular product or service, the measurement of success will be very different from a campaign intended to increase sales or promote a specific offer.
2. Avoid hothousing. By ‘hothousing’ we mean giving the survey participant an unrealistically intense exposure to your ad. In real life, people don’t pay very much attention to ads, but all surveys are artificial situations and your participants are inevitably directed to look at the ads in more detail than normal. There is a lot you can do with survey design to combat this and make the experience a little closer to real life, such as hiding the ad amongst other ads and content, using limited time exposure or using timed responses to target System 1 automatic responses.
3. Take media spend into account. A low-spend campaign is unlikely to perform as well as a high-spend campaign, regardless of how good the creative idea or execution. This is particularly important to remember as you start to build up a library of campaign research so you can learn for the future – if you don’t know the spend, you can’t effectively evaluate the performance and calibrate your benchmarks.
4. Consider ‘always on’ reporting. If your goal is to tweak the campaign while it is live, it is helpful to be able to access ‘always-on’ results via a dashboard of key metrics as the research unfolds. This is also helpful over the course of a longer campaign as you would hope that recall and positive impact would build over time as there are more opportunities to see the ad, and the messages are reinforced through repetition.
5. Look for a flexible approach, not a ‘black box’. There are advantages to standardising your ad-tracking research. In particular, the ability to compare one campaign with another is particularly valuable. However, you must be able to customise the research design to fit your objectives – timing, KPI metrics, success metrics, additional brand questions, target audience etc. A flexible approach to campaign measurement research will enable you to keep some standardised questions whilst ensuring that the survey design is right for that particular ad campaign.

Advantages and disadvantages of Net Promoter Score

Across the research industry, NPS has many strong advocates and an equal number of opponents.

Disadvantages of Net Promoter Score

The question poses an unrealistic real-world scenario. People don’t often go around recommending brands to friends and colleagues. This argument is strengthened when considering brands in low-engagement sectors such as pensions, insurance, or even toothpaste. As a personal example, I’ve been asked by my GP Practice to give them an NPS rating after a recent visit. I ignored it as it felt at odds with reality.

The Detractors definition is too high. Is it fair to count someone who gives a brand a recommendation score of 6 out of 10 a ‘detractor’? If not, then the calculation and therefore the NPS score is erroneous.

It’s open to and suffers from abuse. There are numerous accounts of people being encouraged to give a higher score than they normally would. This is prevalent across many industries and is common in the hospitality and retail sectors, to name a few. The driving force behind this seems to be that frontline staff are sometimes measured against NPS score results, which encourages coercion.

As another personal example, after making a purchase in an electrical goods store, the sales assistant asked me to complete an NPS customer satisfaction survey. She then followed with a rhyme she (or her colleagues) had come up with; ‘an eight’s OK, but a nine would be fine’. I enjoyed the ingenuity but didn’t appreciate the coaxing to give a high score. Again, I ignored the survey.

It doesn’t pick up the views of the silent majority. Those that had neither a good nor poor customer experience and are less inclined to give any feedback. And if they did, they would have likely given a middle-ground score of 7 or 8 (named as Passives), diluting the percentage scores of Detractors and Promoters. Again, making the NPS score inaccurate.

It also dissuades people that can’t relate to the question and see it as a silly or pointless. Just like me in the two examples I gave earlier.

It’s too blunt a measure. It’s not wise to rely on one simple question as a barometer of brand health, considering all the nuance and subconscious relationships people have with brands.

Advantages of Net Promoter Score

Its simplicity is a strength. The fact that NPS is derived from one straightforward question offers the ability to distribute easily and widely, building a substantial and accessible data set quickly. It’s also easy to understand – it doesn’t take a statistician to explain how the score was derived or what it means.

It provides a universal and comparable measure of brand health. It allows us to provide a head-to-head comparison, either between competitor brands (for example, with a brand tracking study), or over time, in the case of an ongoing customer satisfaction study.

Relative comparability cancels out any calculation anomalies. As argued above, NPS is open to challenge in terms of how the calculation is run and the score derived. The counter argument to this is that even if you don’t agree with the absolute scores, the fact that the same perceived anomalies are consistent means that the relative comparison remains stable and is therefore a valid comparison.

Is Net Promoter Score Useful? – The Brandspeak position on NPS

As researchers we are by design and instinct, purists. We care deeply about asking the right questions in the right way and treating data with due reverence. As such, the opposing arguments might be a natural position for us to take.

That said, we’re also pragmatic and see the value of NPS. Accepting that the question itself acts as a fair proxy for brand sentiment, even if people wouldn’t actually ‘recommend the brand’ we do believe that respondents are able to make the cognitive leap between the literal question and the underlying sentiment.

We’re also on-board with the relative comparison argument. This is something we often bring into our analysis when comparing other data sets, whether that’s purchase frequency or money spent. We know that the absolute scores may not fully reflect reality, but it’s the relative differences we look for and trust.

We’re also glad of the simplicity and how that enables us to get across to our clients, a key metric easily and succinctly.

We will always make the call for NPS to be delivered free of coercion or bias. If frontline staff feel the need to try and influence the outcome, then we’d recommend disconnecting their performance from the measure. It might make for a stronger score, but becomes a self-defeating exercise, stripping away any real value that NPS can provide.

Summary – Where we net out.

We’re fully behind NPS but will always report the results in context, looking at the other data collected to understand what might be driving the score and what it means for the brand … and where a brand can take action to improve the score.

Introduction

A new product development (NPD) research programme is a pre-requisite for the successful development of new products and services. Within that programme, it is also essential that the most appropriate research approach is selected at each stage. In this article we consider this issue in detail, and specifically, whether it is more appropriate to use a sequential or monadic concept evaluation approach.

High rates of new product failure

Maximising the chance of new product success

Based on our experience of having helped to many successfully to market, we find that the chance of launch success is dramatically increased if the new product development team;

• Doesn’t put all its faith in just one concept, but instead progresses with a number of concept candidates at the outset. These concepts may be different in kind, or different expressions of the same, big idea.

When this is the case, even if the concept favoured by the development team ends up being the one that is ultimately selected for launch, it will have benefited enormously from the learning provided by the other concepts along the way.

• Includes market research at key stages of a stop / go, gated development process – if only to challenge internal preconceptions.

The benefits of a market research-led new product development process

A properly integrated programme of market research will ultimately enable the new product development team to identify:

• The ‘must succeed with’ target audience(s)
• The target audience needs, attitudes and behaviours that must be addressed
• The concept’s commercial potential
• The strengths and weaknesses of the preferred concept candidate, and the extent to which any weaknesses can be overcome
• Key competitors, and their own, perceived strengths and weaknesses
• The optimal price point for the new product or service
• Potential sales volumes
• Branding and packaging pre-requisites

Different types of new product development research

There are two main types of new product development research – qualitative and quantitative – and each has its place within the development process.

Qualitative research is more exploratory in nature and is typically used towards the beginning of the new product development process, to identify the ‘unknowns’ in relation to the target audience, and to generate initial hypotheses relating to the new concept(s) that can be substantiated through quantitative research.

It may involve interviews, focus groups, co-creation or ethnographic studies.

Quantitative research is typically used to map the market landscape, substantiate key findings from the qualitative research and identify the concept(s) with the greatest commercial potential.

It is most often undertaken in the form of surveys, which may be completed online or face-to-face, sometimes in the form of hall tests.

Concept Testing

There are 2 different approaches for concept testing:

• Monadic
• Sequential monadic

Both have different strengths and weaknesses, and it is important to be clear about these before deciding on which testing approach to take.

Scenario

Imagine a chocolate bar manufacturer wishes to test 4 new chocolate bar concepts, in order to identify the one with the greatest potential for launch.

Concept testing could be undertaken using either qualitative or quantitative research – or a combination of the two.

In addition, testing could be undertaken using either a monadic approach, or a sequential monadic one.

However, the results would provide very different levels of insight and understanding.

Monadic vs sequential monadic concept testing

Monadic concept testing  

To recap, In the case of monadic testing, each member of the sample is exposed to just one of the new concepts – and a single set of common questions.

Of course, with 4 chocolate bars to test, this means that 4 matching samples (or ‘sets’ of research participants) are required.

The questions that are asked of each sample set in relation to each bar must be exactly the same – and asked in exactly the same way and order.

The test provides an absolute, rather than relative evaluation of each concept. As an outcome, comparable analysis of the data between the 4 sample sets can reliably determine overall preference, as well as provide rich diagnostic feedback on each.

Advantages of monadic concept testing

This is the purer, more realistic approach. After all, how often does a person eat more than one type of chocolate bar in one sitting!

Additionally, with only one bar being evaluated by each person:

• There is no order bias
• There is more time to deep-dive on the reactions to that bar – to find out why individual respondents have reacted as they have, how the concept can be improved, and so on.
• There is less chance of respondent fatigue, that could lead to a lack of concentration, or ‘speeding’, whereby respondents start rushing their answers, in order to get to the end of the test

Disadvantages of monadic concept testing

A far larger, overall sample is required. In this example it would be 4 times larger for the monadic test than a sequential monadic approach, assuming that in the latter approach each respondent tests all 4 chocolate bars.

The monadic test is also likely to take longer to complete, because of the number of matching samples and testing sessions that are required.

Sequential monadic concept testing

In the case of the sequential monadic concept testing approach, either the whole respondent sample is exposed to all the concepts, or different subsets of the respondent sample are exposed to different subsets of the stimulus material.

In the case of our chocolate bar concept test, the approach would mean that either the whole sample tests all 4 chocolate bars, or the total sample is divided in two, and each subset tests two bars.

Advantages of Sequential Monadic Concept Testing 

The approach would result in a relative, rather than absolute evaluation of each concept, enabling us to determine which bar is preferred.

And, because sequential monadic testing typically involves smaller sample sizes overall, this can make the approach both more cost effective and faster.

Disadvantages of sequential monadic concept testing

On the other hand, with 4 bars to test instead of just one, there is far less time to gain wider contextual insights in relation to each one.

Another issue is that sequential monadic testing can give rise to concerns about order bias and respondent desensitisation as a result of being exposed to more than one concept.