Segmentation white paper_final_111505
- 1. An Overview of Segmentation: Why You Should Consider It And a Thumbnail of Its Dynamics Prepared by: Edward J. Hass, Ph.D. Vice President – Advanced Research Methods
- 2. The Motivation to Segment The ultimate outcome of We all understand that consumers are not all alike. This provides a segmentation is challenge for the development and marketing of profitable products and superior satisfaction services. Not every offering will be right for every customer, nor will with what you every customer be equally responsive to your efforts to bring your provide. offering to their awareness. Success in these regards requires a targeted approach to ensure bottom-line efficient use of product development and promotional resources. Segmentation is an informed means to organize customers into groups that allow such targeting. The ultimate goal of segmentation for you is the pragmatism of superior deployment and utilization of corporate performance capabilities in meeting the needs and expectations of the customer population. The ultimate outcome of segmentation for the customer is superior satisfaction with what you provide. Segmentation touches on matters that directly impact, and derive from, your business strategy. On what type of customers should you focus your efforts? How broad or narrow should your focus be? In which competitive space is it most profitable for you to operate? Getting the answers to these types of questions by means of a segmentation analysis increases the chance for you to establish, maintain, and grow a loyal customer base. What Segmentation Does Segmentation partitions a general population with the goal of tailoring actions toward specific subgroups. It is a way of organizing customers into groups with similar traits, performance characteristics or expectations. These similarities can be obvious and simple at one extreme or subtle and complex at the other. Information requirements and the “organizing tools” used to create the segments vary greatly as the nature and purpose of segmentation changes. Consider a very simple segmentation exercise, one in which segments were predetermined. Such an a priori scheme might begin with dividing a customer base by zip code, with the eventual goal of regionally-based promotions. Such data could then be crossed with sales data to see which zip codes are heavier or lighter users of the product or service. The output from that analysis would then be weighed against a particular business strategy, such as working on maintaining loyalty versus winning new customers. While this approach may serve well as an exploratory attempt at understanding how customers differ, it is a rather blunt instrument in several regards. First, the partitioning takes place at a fairly gross
- 3. level, employing only the two variables of zip code and level of product use. And then, it describes a situation without prescribing the nature of an action that might be taken. Furthermore, segmentation by its very nature assumes that all customers within a segment are more alike than unalike – and that they are more different from customers in other segments than similar to them. Hence, using this very simple segmentation scheme, all customers in a zip code would be considered alike. So any action taken toward that zip code would be assumed to have the same response from all customers in that zip code, clearly a very chancy assumption. Now consider a more complex example. Instead of starting with completely predetermined segments, we actually search for a segmenting rationale to infer to a population. And we do that with variables that will inform us of customer motivations, trying to understand the Why? behind purchase behavior. We might include a bank of attitude statements surrounding purchase decisions in our survey, or ask the importance of select product/service features, lifestyle questions and demographic questions. With this we would not only have the potential for a much more finely-tuned partitioning of customers, but also a much more actionable partitioning. An Example We have a client who is planning to enter the Internet telecommunications marketplace and needs to understand what customers view as important from their phone service. Having determined whether there are differences between customers in that regard, and the nature of those differences, our client will be able to devise a strategy to target customers most in line with their currently planned service offerings. For future development, such a segmentation can also point directions for new services. We ask respondents to rate each of the following in importance when considering the type of Internet service they would use: • Conveniently available • Inexpensive • Is portable • High speed band width • Free connection • Excellent security • Reliable service • Phone interface • International access
- 4. In addition, we ask our sample to rate how well they perceive each of several providers to perform in each of these areas. Unless each of the 1000 respondents we survey gives identical ratings, our data will contain variations that we can use to cluster or group respondents together, and such clusters are the segments. The mathematical algorithms we employ produce segments such that: a) variation within the segments is minimized b) variation across the segments is maximized This yields groupings of customers that are most similar to each other if they are part of the same segment and most different from each other if they are part of different segments. By inference, then, actions taken toward customers in the same segment should lead to similar responses, and actions taken toward customers in different segments should lead to different responses. Another way of saying this, in our telecommunications example, is that the aspects of Internet telecommunications that are important to any given customer in one segment will also be important to other customers in that same segment. Furthermore, those aspects that are important to that customer will be different from what is important to a customer in a different segment. Here is what the analysis in this example showed:
- 5. Segmentation Convenient AT&T SPRINT Free Connections Segment- Segment- A Low Cost B (52%) (24%) High Speed Security Portable choice MCI Reliable VERIZON Segment- Ego Segment- COMCAST International D C PacTel Access (10%) (14%) Phone Interface Our analysis shows four distinct segments. The majority of customers (Segment A, 52%) want mainly convenience from their service; AT&T is perceived as convenient. Another quarter of customers (Segment B, 24%) seek low cost and free connections, which seems to be a Sprint characteristic. Two smaller segments seek, respectively, a phone interface from their Internet telecommunications (Segment C, 14%) or international access (Segment D, 10%); Comcast and PacTel apparently meet the first need, and Verizon the second. In this case, there are a number of strategic options for the client to consider. There seem to be open competitive spaces for customers who might want portability, or reliability. Can such a need be created, drawing customers from existing segments, or drawing entirely new customers who might desire such qualities? None of the current suppliers tested seem to satisfy jointly the need for a phone interface with international access, nor to be both convenient and low cost. Could our client make a claim to either combination, or to develop a service that would make such joint claims? There might be opportunities there.
- 6. How The Analysis Is Accomplished Segmentations are generally performed via cluster analysis. There are numerous specific techniques used for cluster analysis. Some operate by treating all respondents initially as part of the same cluster, then splitting the sample along “natural” fracture lines based on survey responses, much like cutting a diamond (divisive techniques). Others start by assuming all respondents belong in different clusters, but then build larger and larger groupings based on comparing survey responses (agglomerative techniques). All methods share in common the computation of a measure of similarity among respondents. Some techniques define similarity on the basis of feature comparison, others employ the physical metaphor of distance. In any case, the analysis takes the data we’ve collected (behaviors, geography, attitudes, beliefs, benefits sought, etc), scores respondents in a multivariate fashion, and using a criterion we’ve selected, clusters respondents based on whether they meet the similarity criterion level for inclusion with other respondents. There are many tools at the disposal of the segmentation analyst. You may hear such diverse techniques as single linkage, complete linkage, Ward’s method, polythetic, k-means interative partitioning, CHAID, latent class cluster analysis, etc, etc. In fact the choice of technique can determine the nature of the solution, so it is legitimate, standard practice to use multiple methods and search for convergence. Such convergence lends assurance that a valid solution has been found. But even with that, the utility of a clustering solution requires an interpretation of substance within the context of “reality” based on the applied discipline of the research. How does the telecommunications industry work? Does this answer make sense in the context of my knowledge of the pharmaceutical industry? What are the constraints of financial services that impart credibility to this result? The statistical solution must always be filtered through intelligence about the industry environment in order for the result to be accepted and acted upon. Conclusion Marketers accept the construct that what consumers seek out in products and services is not uniform across all potential users – we are no longer in the age of Henry Ford, who said, “The customer can have any color car he wants, so long as it is black.”
- 7. Segmentation analysis shares this same construct – to conduct a segmentation is to seek out differences in customers to capitalize upon for ensuring maximum product uptake and brand loyalty. The sophisticated multivariate techniques we employ for segmentation are simply the tools to reduce the massive amount of data on customer wants and needs to a manageable form so that we can make educated targeting decisions. Hence, strategic customer segmentation exists in service of the goal of striving to place the right product or service with the right customer, to the benefit of all involved. Next Steps Think about the business challenge confronting you. If the questions you ask include such words as “targeting”, “customer differences”, or “where to focus”, the chances are very strong that segmentation analysis will be crucial for informing your decision making. Discuss the problem with your research consultant in light of the background you’ve gained from this white paper.