In most markets, buyers differ among themselves in ways that are relevant to a company seeking to market its products. They differ with respect to which benefits (functional and emotional) they desire from the product, occasion in which the product is purchased or used, purchase channel used, exposure to marketing communication channels and product information channels, combination of products used, perceptions of the brands in the market, etc. A company that disregards these differences and instead pursues all buyers in the market, using the same approach, may find itself in a situation analogous to that of the proverbial person who in trying to satisfy everyone, satisfied no one.
Buyers also differ among themselves in potential value to a company. They differ with respect to importance of quality relative to price, price sensitivity and purchase volume. In many markets, especially in B2B, a small portion of the buyers account for a large proportion of total spending in the market; distributions as extreme as 20/80% are not unusual.
For these reasons, the decision of which buyers to pursue is among the most important that a company will make. A segmentation study is critical in making this decision.
The core of the analysis in a segmentation study consists of the following steps:
- Division of the market into distinct groups (segments) of buyers that differ in ways relevant to you.
- Description of each of the segments and how they differ from each other.
- Assessment of each of the segments. The segments are assessed on the following criteria:
- Deciding which segment(s) the company should pursue.
- Construction of a mathematical equation that can be used to classify into segments buyers who were not used in the segmentation study. These new buyers may be a database of potential clients, your internal customer database or some other database.
- The competitive situation within the segment (e.g., unmet needs, the proportion of buyers who are loyal to you, the proportion of buyers loyal to one of your competitors, the proportion of buyers having a negative perception of you, etc.)
- The financial characteristics of the segment (e.g., number of buyers, spending level of the average buyer within the product category)
- Can the segment be differentially targeted in marketing communications
Some of the more commonly-used bases of segmenting consumer markets are listed below.
- Functional benefits desired
- Attitudes specific to the product category (e.g., “I know my hair in clean when it smells fresh.”)
- Personality and psychographics/lifestyle
- Behavioral (e.g., usage volume, mix of products, occasions, for what is the product used, brand(s) used most often, delivery channel)
- Demographics (e.g., life-stage, age, gender, geography)
Some of the more commonly-used bases of segmenting business markets are listed below.
- Functional benefits desired
- Degree to which technical service support is needed
- Company size
- Product mix
- For what is the product used
In recent years, major advances have been made in the statistical techniques used to make the actual division of buyers into groups. Historically, the only statistical techniques available were some type of “cluster analysis”. Cluster analysis simply divides the sample into groups of respondents that are similar to each other in their responses to the questions asked in the interview. In contrast, a new analytical technique called latent class regression has been developed that focuses on an outcome variable. That is, the mathematical objective of this technique is to identify respondents who differ from each other with respect to the drivers of an outcome variable such as purchase interest or overall satisfaction. This mirrors the marketing purpose of segmentation: divide a market into distinct groups of buyers who differ with respect to what influences purchase behavior.
Latent class regression has another advantage over cluster analysis. The advantage is technical in nature. The variables used in a segmentation study differ among themselves in how they are scaled. Attitudes are typically measured using rating scales, while other variables such as gender and magazine readership are categorical in nature. This is an issue because this difference influences the outcome of cluster analysis. Generally speaking, the results of cluster analysis are influenced more by rating scales than by categorical variables. This is undesirable; the fact that something such as gender is categorical in nature does not mean it is less important in a marketing sense. Latent class regression is not influenced by scaling differences as much as cluster analysis.
About Customer Lifecycle, LLC
Customer Lifecycle is a research based consultancy committed to helping companies avoid costly mistakes by focusing on thorough front-end planning, appropriate support for research execution, and especially in-depth deployment consulting and implementation at the back end. Outcomes are rigorous and balanced customer-focused performance metrics, improved financial results, and a superior total customer experience.
Each stage in the customer lifecycle — acquisition, service, growth, retention — has its own unique challenges and solutions to address specific business issues. Customer Lifecycle helps both B2B and B2C companies plan and conduct research to accurately identify and measure customer requirements for satisfaction, loyalty and retention at every stage of the relationship and to deploy and integrate customer requirements for performance into the processes and internal performance metrics of the organization.
If you would like further information, please visit www.customerlifecycle.us or contact one of our principals.
Download PDF: Segmentation Overview