Tailoring Customer Value for Different Segments

Last updated on:

March 23, 2025

Walk into any store and you will notice two very different kinds of shoppers. One person knows exactly what they need, grabs it, and heads straight to the billing counter. Another lingers, browses, touches products, and treats the visit as an outing in itself. These two shoppers may buy from the same aisle, but they are driven by completely different motivations. Understanding what each one actually wants from the purchase is the foundation of segmenting customers by value, and it is what allows a retailer to deliver the right experience to the right person.

Utilitarian and hedonic needs
Functional and economy needs
Esteem and experiential needs
Benefit segmentation: grouping customers by what they want
How benefit segments look in practice
Advanced segmentation: RFM, RFA, and beyond
From RFM to RFA
Building a complete customer picture
Turning customer insight into store design
Why the framework holds together

Utilitarian and hedonic needs

At the broadest level, every shopping trip satisfies one of two needs. Utilitarian needs are about getting a job done. The customer wants to accomplish a specific task, such as buying a calculator, replacing a broken charger, or restocking groceries. The purchase is a means to an end. Hedonic needs are about pleasure, entertainment, and emotional experience. Here the shopping itself is the reward, not just the product that comes out of it.

This distinction matters because the same product can be sold in two very different ways depending on which need is in play. Research published in the Journal of Service Research shows that hedonic and utilitarian contexts differ on fundamental features such as how emotional the experience is, the customer’s motivation, and how risk is perceived. A retailer who recognises these differences can shape store layout, messaging, and service to match.

Functional and economy needs

Utilitarian needs split further into two sub-categories. Functional needs are about solving a consumption problem. The customer wants a product that performs reliably and does what it promises, like a kettle that boils water quickly or a school bag that lasts the year. Economy needs are about getting the lowest possible cost. Here the customer is highly price-sensitive and will choose the cheapest option that meets a minimum standard.

A study by the Marketing Science Institute found that monetary promotions such as straight price cuts and coupons work better for utilitarian products. This makes sense: a shopper buying for functional or economy reasons responds most strongly to a clear, tangible saving.

Esteem and experiential needs

Hedonic needs also divide into two parts. Esteem needs relate to self-enhancement and social position. A customer buys a premium handbag or a well-known brand partly because of how it reflects on them and the role they want to project. Experiential needs are about sensory pleasure, variety, and mental stimulation. The shopper enjoys the colours, the textures, the discovery of something new, and the sheer fun of browsing.

For these hedonic motivations, the same Marketing Science Institute research found that non-monetary promotions, such as free gifts and contests, are more effective than simple discounts. A shopper seeking pleasure or status is moved more by added experience than by a few rupees off.

Benefit segmentation: grouping customers by what they want

Once you understand these needs, the next step is to group customers who seek similar benefits into distinct segments. This idea is known as benefit segmentation, and it was first introduced by marketing scholar Russell Haley in a 1968 Journal of Marketing article. Haley argued that the benefits people seek from a product are the real reason behind their choices, and therefore the most useful basis for dividing a market.

What made his approach powerful was its focus on causal rather than descriptive factors. Traditional segmentation tools sorted people by age, gender, or location, but Haley showed these merely describe customers after the fact and do a poor job of predicting what they will actually buy. As later analysis of his work explains, benefit segmentation groups customers by the benefits they pursue in consumption, which gives a deeper insight into motivation and behaviour.

The practical insight is striking. As one explanation of the method puts it, customers with no demographic similarities at all may buy a product for the exact same reason, while two people who look identical on paper may want entirely different things from it. Benefit segmentation asks the more useful question: what problem does this customer actually want solved?

How benefit segments look in practice

Consider clothing. A fashion segment places high importance on style and design but low importance on price. These shoppers are driven by esteem and experiential needs. A price segment prioritises low cost above almost everything else, driven by economy needs. Both groups buy clothes, but a single store strategy cannot serve both well. The retailer must decide which segment to target and build its value proposition around it.

This is exactly how India’s organised retailers positioned themselves. Big Bazaar built its entire identity around functional and economy needs, with low price as its core value proposition. From its launch in 2001, the chain offered the best price proposition to customers, focusing on affordable, unbranded products of comparable quality. Its founder famously said the business was not about selling ambience but about giving customers the best possible deals. Campaigns such as “Sabse Saste Din” reinforced a “value for money” promise aimed squarely at budget-conscious families.

Shoppers Stop took the opposite route, serving esteem and experiential needs. It positioned itself at the aspirational premium end of the market, where the value proposition is quality and experience for the price rather than the lowest price. The two retailers did not really compete for the same customer. They served different benefit segments with different needs.

Advanced segmentation: RFM, RFA, and beyond

Needs and benefits explain why customers buy. To act on this at scale, retailers turn to transactional data. The most widely used approach is RFM analysis, which scores each customer on three dimensions: recency, frequency, and monetary value. As described by TechTarget, RFM ranks and groups customers based on how recently they bought, how often they buy, and how much they spend, so that marketing effort can be focused where it pays off most.

The logic behind each dimension is simple. A recent purchase signals an active, receptive customer. High frequency suggests loyalty and satisfaction. High monetary value identifies the big spenders who deserve different treatment from occasional buyers. Customers are typically scored from one to five on each factor, and the combined scores sort everyone into segments such as high-value, loyal, at-risk, and lost customers.

From RFM to RFA

RFM is useful but not perfect. A newer refinement replaces monetary value with average order value, giving recency, frequency, and average order, or RFA. The reasoning is that total spend can be skewed by one large purchase, whereas looking at monetary divided by frequency reveals the typical size of each transaction. For customers who buy repeatedly, average order value gives a sharper picture of behaviour and allows for more precise targeting than a single lump-sum figure.

Building a complete customer picture

The most sophisticated organisations do not stop at transactions. They combine several layers of information to understand customers fully. These include attitudes and stated preferences, soft behaviours such as browsing and engagement, hard transactional behaviours captured by RFM or RFA, geodemographic data about where customers live, and long-term financial value such as expected customer lifetime value.

This matters because RFM alone has clear limits. It does not account for demographics or psychographics, and it works poorly for products bought infrequently. Analysts therefore recommend integrating RFM with other methods such as cluster analysis and lifetime value modelling. Layering these inputs together turns raw data into a rounded view of who the customer is and what they are worth over time.

Turning customer insight into store design

Segmentation is only valuable if it changes what a retailer actually does. One of the clearest examples comes from Crossword, India’s largest bookstore chain, founded in Mumbai in 1992. Crossword built its reputation by moving away from the cramped, dusty bookshops of the past toward spacious, well-organised stores with clear signage and an inviting atmosphere, with the customer at the centre of its design decisions.

Crossword studied how Indian shoppers actually moved through and used its stores, then redesigned its racks accordingly. It lowered the height of its shelves to match the average height of Indian customers. This had two effects: shoppers could reach and browse books more easily, and the lower racks made the store feel more open and spacious, which improved the overall shopping experience. The lower shelves were also inclined, or angled, so customers could read book spines and covers clearly without having to bend or squat down.

These were not cosmetic tweaks. They were direct responses to the value drivers of Crossword’s customers, who came not just to buy a specific title but to browse, discover, and enjoy the experience. A book chain serving experiential needs has to make browsing comfortable and pleasant. By matching its physical layout to how its shoppers behaved, Crossword reinforced exactly the value it was trying to deliver. The approach clearly worked: the chain has grown into India’s leading bookstore retailer, operating over 120 stores across 40 cities.

Why the framework holds together

Each layer of this approach reinforces the others. Identifying whether customers have utilitarian or hedonic needs tells you what kind of value to offer. Benefit segmentation groups those customers into actionable segments. RFM and RFA data identify your most valuable customers within each segment and guide where to spend your effort. And insights about behaviour feed directly into decisions about pricing, promotion, and even the height of a shelf. A retailer who works through all of these is far better placed to serve customers than one who treats every shopper the same.

What do you think? If you ran a store, would you focus on serving one benefit segment really well, or try to satisfy several at once? And looking at a shop you visited recently, can you tell which customer need its layout and pricing were actually designed to meet?

References

PDF 📄

Tailoring Customer Value for Different Segments

Table of Contents

Utilitarian and hedonic needs 🔗

Functional and economy needs 🔗

Esteem and experiential needs 🔗

Benefit segmentation: grouping customers by what they want 🔗

How benefit segments look in practice 🔗

Advanced segmentation: RFM, RFA, and beyond 🔗

From RFM to RFA 🔗

Building a complete customer picture 🔗

Turning customer insight into store design 🔗

Why the framework holds together 🔗