ValueCreator Segmentation

Segmentation based on "the drivers of choice" by Dirk Huisman

In essence, market segmentation distinguishes groups of consumers with similar characteristics. These characteristics, such as product choice behaviour or price sensitivity, differentiate them from other consumers. A meaningful segment is readily identifiable and their specific behaviour or sensitivities can be targeted accordingly. Directing policy towards a segment is only useful if it results in a higher turnover, a larger profit margin, or greater customer loyalty.

Primary market segmentation
Primary market segmentation is based on socio-economic and demographic characteristics – characteristics that largely explain behaviour and which all suppliers take into account when designing their product range. Primary segmentation alone is no longer sufficient to gain a competitive advantage. In addition, the globalisation of communication and information means that the consumers and industrial buyers of today are confronted by a multitude of information sources when making a (conscious or unconscious) purchasing choice. How the individual consumer is informed and conditioned is much less clear than two decades ago. As a consequence individual behaviour is more and more difficult to predict using primary segmentation alone.
Secondary segmentation

To overcome the shortcomings of primary segmentation, secondary segments are distinguished. Multivariate analyses of attitudinal questions enable the identification (and convenient labelling) of secondary segment groups. These groups display similar attitudes, motives and life styles. The attraction of secondary segmentation lies largely in the convenient labelling and "story" constructed around the defined segments. It sounds plausible: the marketing or communication specialist can recognise and believe in the groups. This familiarity factor often leads to a positioning strategy based on those secondary segments. However, when the survey is repeated in a different time or context, with a different sample, or in a neighbouring country, the stability, validity and reproducibility often turn out to be limited. This is, of course, not surprising given that the segmentation is based on attitudinal scales and reactions to general statements. The scales used have at best only an indirect relationship to actual choice behaviour or underlying choice motives. Just like actual buying behaviour, an individual reaction to a statement such as "I live without financial worries" or "I love luxury" depends very much on the moment and can be inconsistent. For example, when responding to the same question in different situations I might react positively seven times out of ten. These seven times I would be classed as an "achiever" or a "new hedonist", but the remaining three times as "green and pure" or "socialist". In fact everyone belongs in some degree to multiple segments. Segments themselves can be stable in behaviour, but people shift between the segments, causing noise in predictions. As with primary segmentation, the stability and predictive value of secondary segmentation is further diminished by the multitude of stimuli and diversity of information sources affecting the consumer.

Today the lines of communication between producer and consumer have become more direct. A producer or wholesaler can easily and quickly get an insight into the wants and needs of the individual through, for example, customer loyalty cards or internet contact. One does not need a complete picture of the individual in order to be able to anticipate what that consumer is likely to want. To some degree this reduces the need for secondary market segmentation, but segmentation is still important. One individual may prefer a malt whiskey, another a blend and it is a waste for the producer to sell a malt for the price of a blend. From a business point of view it is essential to limit the variation in supply and focus closely on these differentiating sensitivities.

The ultimate aim of the producer is to increase turnover, profit and loyalty by anticipating the elements that determine the choice of the individual. So current and prospective clients must be segmented according to their differentiating sensitivities and/or by the elements that determine their choice behaviour.
Value segmentation

Segmentation based on elements that determine choice because they add or subtract value to the product or service is called "value segmentation" or "benefit segmentation". This form of segmentation has an advantage over primary or secondary segmentation – it can be linked directly to product or brand policy and market positioning. Optimising and co-ordinating policy decisions in this way leads to a maximisation of turnover and profit 1. A second key advantage of value segmentation is that choice sensitivities are measured in a specific context or situation. Unlike when using secondary segmentation, we now know which segment an individual will belong to in a different situation.

Value segmentation does have a disadvantage: segments cannot be easily recognised. As with customer loyalty cards or internet contact we know how an individual makes their choice and how they can be influenced, but we do not have a complete picture of the segment itself. For this reason it is advisable to link value segmentation to the data that determine primary and secondary segments.

Value segmentation enables us to follow a number of different routes and methodologies but the starting point is to measure the choice behaviour and differentiating sensitivities of the consumer.
Latent class analysis.

Take a group of consumers and give them on fifteen occasions a choice of three different cola bottles. Define the cola bottles by brand, content, type and price. It is possible that the first consumer always starts by looking to see if their preferred brand (Coca Cola) is among the three bottles. The second consumer may always look first for an A brand (Coca Cola or Pepsi) whilst the third consumer finds out first which of the three bottles is the cheapest. The final choice is then based on the remaining characteristics.

Latent Class Analysis determines which "choice pattern" is followed and how the choice patterns are distributed across the population (figure 1). The more widely applicable Hierarchical Bayes Regression Analysis was developed as a successor to Latent Class Analysis 2.


Figure 1: Segmentation based on choice patterns

Structured Open Association Pattern (SOAP)
SOAP is the structured and quantitative form of the laddering technique. This technique enables us to determine feature sensitivity. It allows us to evaluate in a systematic manner not only what is the specific advantage of the characteristic that the individual is sensitive to, but also why that specific advantage is important. We can structure and quantify the links between characteristics, the advantages of those characteristics, and the end values that the consumer is trying to reach by purchasing the product. Thus the market can be divided into segments that want the same advantages and values (figure 2). These segments can be directly linked to product and communication policy and can form the basis for positioning 3.


Figure 2: Segmentation based on means-end chains

As consumer behaviour becomes increasingly disparate, so consumers "hop" more from one segment to the other. A 1-on-1 marketing strategy is now applied and primary and secondary market segmentation will lose importance. The use of information collection shifts from predicting and directing towards describing and explaining.

Provided one can determine which segment a consumer belongs to in a particular situation and provided one can differentiate those segments, value segmentation retains its essential ability to predict and direct.

1 Heiszwolf B., Huisman D., ValueCreator Pricing, de specifieke rol van prijs(onderzoek), "Onderzoek", February 2000.

2 Lenk, P.J., e.a., Hierarchial Bayes Conjoint Analysis: Recovery of partworth heterogeneity from reduced experimental designs, "Marketing Science" 15, pp. 173-191.

3 Parisi F., Rameckers L., Emotional information in strategic decisions, ESOMAR seminar Automotive Marketing 2000, Lausanne, 2000.