How segmentation reshapes the way you capture value

In quite a few organizations, pricing continues to be treated as an operational consequence rather than a strategic decision. Price lists are built, margins are adjusted, you react to the competition and, in the best of cases, some segmentation criteria based on customer size or volume are incorporated, the typical thing you always see. However, by taking a closer look at how these decisions are made, it is possible to identify a recurring pattern: the price is defined from the product, not from the context in which that product generates value.

This approach, although functional in low-complexity scenarios, begins to show obvious limitations as organizations grow, diversify their portfolio, or serve heterogeneous markets. The same product is offered under the same conditions to customers who use it in radically different ways, at different times and under different levels of urgency. The result is not only a potential loss of margin, but a progressive distortion of the business model, where pricing no longer reflects the operational and commercial reality of the organization.

From this perspective, segmentation in pricing should not be understood as an additional technique within the commercial toolkit, but rather as a structural mechanism that allows the price to be aligned with the different ways in which value is manifested. It is not only a matter of identifying who the customer is, but of understanding how they buy, when they buy, how urgently they do so and what level of complexity they introduce into the operation. We will explain it later to make it clearer to you.

In this context, addressing segmentation from variables such as willingness to pay, purchasing behavior, urgency and maturity of the customer allows us to build pricing models that are more coherent with the reality of the business. What is relevant is not the existence of these dimensions, but the way in which they are integrated within a system that translates operational and strategic differences into price decisions, with a view to increasing the margin, otherwise it is no joke.

Willingness to pay as a structural variable of pricing

One of the most frequent mistakes in the construction of pricing models is to assume that the market has a homogeneous perception of value, which is not always true. Under this logic, price becomes an implicit average of multiple dispositions to pay, which inevitably leads to inefficiencies: customers willing to pay more receive low prices, while price-sensitive customers face unnecessary barriers to entry.

The willingness to pay, however, is not a directly observable variable. It manifests itself through behaviors, historical decisions and, in many cases, through the urgency or context in which the purchase is made. Therefore, segmenting according to this variable implies building approximations that allow the identification of consistent patterns.

In the courier transportation industry, for example, willingness to pay varies significantly between corporate customers who require critical deliveries and individual customers who ship packages without time restrictions. Both use the same logistics service, but the value they perceive is different. While for a company that relies on just-in-time deliveries the service is part of its core operation, for an occasional customer it may simply be a convenient means. Consequently, applying a uniform tariff does not reflect this structural difference.

Something similar occurs in the pharmaceutical industry, where certain products may be perceived as indispensable in hospital contexts, while in outpatient settings their relative value decreases. The willingness to pay is not determined solely by the product itself, but by the risk associated with not using it or replacing it. In this sense, pricing should capture that difference in criticality, beyond production or distribution costs.

From this perspective, segmenting by willingness to pay implies recognizing that price is not a property of the product, but a function of the context in which that product is used. This requires moving away from static models and moving towards structures that allow these variations to be captured consistently.

Purchasing behavior as an indicator of economic efficiency

Beyond the perception of value, purchasing behavior introduces an additional dimension that directly impacts profitability. Not all customers generate the same acquisition cost, nor do they require the same level of commercial effort, nor do they present the same predictability in their decisions.

In the appliance industry, for example, it is common to observe clear differences between customers who buy in a planned way, researching in advance and comparing options, and those who make impulsive decisions at physical points of sale. Both can purchase the same product, but the cost associated with closing the sale is different. The customer who comes in with an informed decision reduces the need for business model intervention, while the hesitant customer may require advice, demos, and follow-up.

In the dairy and retail sector, purchasing behavior also allows relevant patterns to be identified. A customer who buys products on a recurring basis in large quantities presents a different dynamic than one who buys sporadically in convenience stores. Frequency, average ticket, and loyalty influence demand stability and thus operational planning. Incorporating these variables into pricing not only improves value capture, but also contributes to greater efficiency in the supply chain.

This means that pricing is no longer an isolated variable and begins to be integrated with marketing, distribution, and operations decisions. Purchasing behavior, in this sense, is not only a commercial data, but an indicator of economic efficiency that should be reflected in the price.

Urgency as a determinant of economic value

The time dimension is probably one of the most underestimated variables in pricing models. In many cases, time is managed as an operational attribute, but not as a factor that alters the perception of value, and it does!

In the courier industry, this relationship is particularly evident. The same shipment can have radically different prices depending on whether it is delivered in a couple of days, in 24 hours, or on the same day. However, beyond express services, many organizations fail to fully capture the value associated with urgency, especially when it is not explicitly defined in the portfolio.

For example, a customer who requires urgent delivery to avoid a disruption to their operation is willing to pay significantly more than one who can wait. However, if the pricing model does not account for this difference, both customers end up paying the same, transferring the cost from urgency to operation without adequate compensation.

In the pharmaceutical industry, urgency also plays a critical role. The immediate availability of certain medications can be decisive in specific clinical situations. Although regulations limit flexibility in pricing, as well as the expiration of products, there are still spaces where logistics, distribution, and availability can be structured in a way that reflects the value of time.

In the context of appliances, urgency can manifest itself in the need to replace essential equipment, such as a refrigerator or washing machine. The customer who faces an unexpected failure has a different willingness to pay than the one who plans his purchase in advance. Incorporating this variable into pricing can translate into fast delivery services, immediate installation or guaranteed availability, each with a differentiated price.

From this perspective, time is not only a factor of service, but also an economic variable that modifies the willingness to pay. Ignoring this dimension implies leaving value on the table and, in some cases, distorting the perception of equity in price.

  

Customer maturity as a reflection of complexity and dependency

As organizations serve customers with varying levels of sophistication, one variable emerges that is often approached superficially: customer maturity. Not all customers use products or services in the same way, nor do they require the same level of accompaniment.

In the pharmaceutical industry, for example, there are significant differences between institutions with highly structured processes and those with a lower level of formalization. The former can integrate products within defined protocols, optimizing their use and reducing variability, while the latter may require greater support, training, and follow-up. This differential in complexity has a direct impact on the cost of serving, although it is rarely reflected in the price.

In the home appliances sector, customer maturity can be seen in their ability to evaluate technical characteristics, compare options and make informed decisions. A client with high maturity reduces the need for commercial intervention, while one with less experience can depend significantly on advice in store or in digital channels.

In the courier industry, maturity translates into the ability to integrate logistics services within broader operational processes. Companies with advanced systems can automate shipments, optimize routes, and reduce errors, while less sophisticated customers can generate greater operational friction. In this sense, pricing should consider not only volume or frequency, but also the level of integration and efficiency that the customer brings to the system, or in other words, how much it costs to serve each customer.

From this perspective, customer maturity is not just a simple attribute, but a variable that influences the cost structure and the stability of the business relationship. Incorporating it into the pricing model allows the price to be aligned with the real complexity of the service, avoiding implicit subsidies between segments.

The integration of variables as a pricing system

Looking at each of these dimensions in isolation can be helpful in understanding their individual impact, but the real value emerges when they are integrated into a coherent system. Customer willingness to pay, buying behavior, urgency, and maturity do not operate independently; they overlap and reinforce each other.

A corporate customer in the courier industry, for example, may have a high willingness to pay, recurring behavior, high urgency, and a high level of maturity. This set of variables defines a completely different profile than that of an occasional customer, even if both use the same service. Pricing, therefore, should be able to capture this combination of factors, rather than simplifying it into a single dimension.

In the dairy and retail sector, the integration of these variables makesit possible to differentiate between customers who buy in a planned and recurring way, with low urgency and high predictability, and those who buy impulsively, with high urgency and lower loyalty. Each of these profiles has different implications in terms of pricing, promotion, and inventory management.

This implies that the pricing model is no longer a static table and becomes a dynamic structure that responds to multiple variables. The complexity does not lie in the number of segments, but in the organization's ability to manage this information consistently and translate it into operational decisions that contribute to the margin that matters to us.

Final reflection on segmentation as a decision architecture

As organizations evolve, pricing is no longer an isolated function, relegated to the sales department, and becomes a reflection of the business decision architecture. Ideally, the way segmentation should be possible to measure and interpret customer behavior, which ultimately determines the ability to capture value in a sustainable way.

Persisting in homogeneous pricing models in heterogeneous contexts is not only a technical limitation, but a sign that the business model is not adequately incorporating the structural differences of the market. Segmentation, in this sense, is not just another analytical exercise, but a necessary and I would say indispensable condition for pricing to fulfill its function.

The relevant question is not whether it should be segmented, which everyone does, in their own way, of course, but how these variables are being integrated into a system that allows different pricing opportunities to be generated.