Customer Segmentation: Some Practical Examples

Customer segmentation is the foundation of customer intelligence, and essential for a good marketing strategy. In this text, I will update and expand on what was previously published about tactical segmentation and the segmentation dimensions.

Several years have passed, and there have been many projects to learn from.

Customer view and segmentation

Customers are different from each other and have different needs. Ultimately, each individual customer has unique needs and interests, which is the basis of one to one marketing. This personalized marketing has become a reality on the Internet with retargeting or remarketing – you’ve surely noticed it: after checking out an item or «almost buying» it, its ads «follow» you wherever you browse. This is a common technique used by Google and Meta advertising networks, and Amazon often does it too.

But when we need to define a customer strategy, full personalization is not useful. A strategy for each of the customers entails too much complexity. Of course, we can train models at the customer level – for acquisition, cross-selling, churn prediction – in a «black box» mode and use them as long as they are profitable. But this approach means losing the «explanatory» aspect of segmentation, which allows us to understand how different types of customers behave.

It’s better to assume that there are groups of customers similar to each other and different from others. This will allow us to define differentiated strategies for each group in the marketing plan. Segmenting is all about identifying these homogeneous groups and classifying customers into them.

Customer segmentation should not be confused with market segmentation. The latter usually describes a market based on the type of customers that compose it. But in market segmentation, customers are not identified, so it is not possible to use segmentation to establish a personalized relationship at the customer level.

For instance, market segmentation would inform us that the segment of single-family homes with children and an income above 3,000€ spends an average of 285€ on DIY products, which is four times the average household spending in that category. Customer segmentation would also tell us who those customers are and how to contact them.

Segmenting customers requires a database that captures, at a minimum, the transactional data, generated when purchasing goods or services. Typically, the tickets, where products, quantities, and prices are detailed. But also, a marketing database should capture other user information, such as demand potential, or market evolution and trends, or the online acquisition channel. And, in the case of B2B segmentation, information about the company’s activity, volume, geography, ownership, participations, and decision-maker characteristics.

Segmentation reaches its full potential with the use of multivariate statistical techniques, data mining or data science for data analysis.

Types of Customer Segmentation

There isn’t just one approach, I would suggest several classifications of segmentation techniques, based on different criteria:

• Objectives, purpose of segmentation: Strategic Segmentation -explained below- vs tactical segmentations
• Dimensions, or types of information used: value vs potential, lifetime customer value, relational and social dimensions… in this post I discuss types of segmentation by customer dimensions
• Mode of application, deferred versus immediate-automatic, online vs offline…
• Segmentation by customer lifecycle stage, throughout the Lifetime Cycle or Customer journey

Example of Customer Segmentation in Retail

Customer segmentation involves reducing all the complexity of shopper data, which can be thousands or millions of cases with hundreds of variables, to a single snapshot where:

• Customers are grouped into a limited number of segments

• Variables are reduced to a single segment label, which summarizes the vast wealth of data that has shaped the segment – receipts, sociodemographic features, distance to the point of sale, purchasing channels used…

Below we see an example of customer segmentation in retail, in a scatter plot based on two key variables: average spending and average purchase frequency.

It’s a stylized example, but based on a real case. Up to 100 variables of the type: total spending, frequency, regularity, categories purchased, channel, store format, leading brands vs first price vs own brand, household size and distribution by age and gender, distance to the nearest store, number of stores and channels used by the household… the data was rich, and allowed answering specific questions, but it was difficult to answer the most important one: in summary, what types of customers do we have, and what strategy can we pursue with each type? This is what strategic customer segmentation answers.

In the graph we see a segment, labeled as ‘Bulk shopping’, which visits the store very infrequently, but makes a very high ticket purchase on each visit. On the contrary, the ‘Daily shopping’ segment behaves oppositely: many low-value visits. The ‘Deal-seeking Families’ segment is around the average for both variables, but constitutes a homogeneous group, defined by high spending on promotions, first price, own brand, large household, with children… these indicators differentiate them from the other segments.

That is, each segment has specific behaviors and needs, and we should adopt a specific strategy, almost a marketing plan for each of them. Strategic segmentation makes the customer-centric vision a reality, as it is the only way to set goals by customer.

Examples of Applying Strategic Segmentation

Strategic segmentation enables strategic business decisions based on customer segments:

• Profitability goals by customer. If a financial institution seeks to «reduce the dropout rate of highly engaged passive customers by 5%», strategic customer segmentation will be necessary. It won’t be as necessary if the goals are set in product terms, for example, «increase product x’s business uptake by 5%».
• Customer vision in complex businesses. When trying to extend the customer’s vision across different divisions – an insurer has its own network, agents, online subsidiary, and wants to integrate customers. Which segments are more present in which channels? Is it of interest to provoke a change? Do we specialize the portfolio by segments – channels?
• Format strategy. A hypermarket chain wonders whether to grow with new hypermarkets, or with its proximity brand, or its discount one… it can base its decision on the types of customers that will shape its long-term results. It can also try to «migrate» the segment of customers at risk of leaving the hyper format to the discount format.
• Optimization of assortment. Defining the optimal assortment is a crucial decision for the retailer, it is known how Mercadona reduced its suppliers and references, as a means to reduce the average cart cost for its customers. That is, it bet on certain types of customers, of high volume and especially price sensitive.
• Delivery, home delivery and collection points. With the rise of e-commerce, last-mile and logistical optimization is critical, as delivery costs are significant and not any price can be charged to the customer. A customer segmentation allows estimating the current and, above all, future value of an area, so that we can assume delivering at a loss for a while knowing that the area has a high potential to contain high-value segment customers, which in the future will «fill» the truck and generate profitability.
• New products, services. The launch of an online supermarket has been mentioned, another could be which segments would benefit from home delivery? From a payment card with cash-back? From an app with personalized offers?
• Resizing Strategy. When, in a crisis environment, it’s necessary to reduce resources -stores, services, products…- what do we keep, what is our core? Let the types of customers who provide us the most margin answer that.

A strategic customer segmentation achieves success when used by business leaders, becoming the customer conceptual map that the organization systematically uses. Therefore, it’s key, in addition to purely statistical variables, to consider the intuitive visions of customer types that these decision-makers are used to handling.