Marketing Segmentation Theory”
Defining the Segmentation:
Segmentation can be defined as “the term given to the grouping of customers with similar needs by a number of different variables”.
In simple words it can also be define as “the act of dividing or partitioning; separation by the creation of a boundary that divides or keeps apart”.
What Does Market Segmentation Mean?
“A marketing term refers to the aggregating of prospective buyers into groups (segments) that have common needs and will respond similarly to a marketing action”.
Market segmentation can also be define as “the process of dividing a market up into different groups of customers, in order to create different products to meet their specific needs”.
The most obvious type of segmentation is between customers who buy distinctly different products. For example, in manufacturing sandwiches, you would clearly be able to make a distinction between creating sandwiches for vegetarians and those for meat eaters.
Market segmentation enables companies to target different categories of consumers who perceive the full value of certain products and services differently from one another. Generally three criteria can be used to identify different market segments:
1) Homogeneity (common needs within segment)
2) Distinction (unique from other groups)
3) Reaction (similar response to market)
What is Market Segmentation Theory?
“A modern theory pertaining to interest rates stipulating that there is no necessary relationship between long and short-term interest rates”. Furthermore, short and long-term markets fall into two different categories. Therefore, the yield curve is shaped according to the supply and demand of securities within each maturity length.
The Market segmentation theory states that most investors have set preferences regarding the length of maturities that they will invest in. Market segmentation theory maintains that the buyers and sellers in each of the different maturity lengths cannot be easily substituted for each other. Traditional Approach to Market Segmentation
Historically, the traditional approach to segmentation attempts to segment the market using one (or more) of four schemes. The first scheme attempts to segment the market based on how customers and prospects behave (behavioural segmentation). The second scheme attempts to segment the market based on who they are (demographic or life-phase segmentation). The third scheme attempts to segment the market based on how much worth they potentially hold for the organization (profitability or current value segmentation). The fourth scheme attempts to segment the market based on what customers think about the features of the product or service in question (attitudinal segmentation). Each of these four approaches to segmentation contributes an important piece in understanding the market; yet, each of these approaches displays significant limitations. Critique to the Traditional Approach:
Limitations to the traditional approach to marketing segmentation are as follow:
Behavioural Segmentation is the effort to cross-tabulate or associate behaviours with known demographics. It is the oldest method of segmentation. In essence, it classifies a customer into a "bucket" based upon whether customers are similar to other customers who have performed that behaviour in the past. Historically, behavioural segmentation has been generated by cross-tabulation analysis, or more recently by tree-based classification tools such as CHAID or CART. The problem with these methods is currency. That is, in many instances, they have been superseded by data mining (modeling-based) approaches to segmentation. Modeling approaches predict specifically for each customer the likelihood of performing that behaviour. Thus, the newer data mining methods produce a probability score for the customer or prospect. This allows the segmentation analyst to prioritize within a "bucket" who is most likely to perform the behaviour (e.g., purchase a given product.). Data mining approaches, therefore, supersede traditional segmentation approaches to customer targeting because they not only place a customer in a "bucket," but identify which customers in the bucket we should contact first. If your business objective is to best identify customers who will perform a given behaviour, then it may be appropriate for you to perform data mining to predict the behaviour rather than to try to use segment "buckets" to predict the behaviour.
Demographic Segmentation or Life-Cycle Segmentation attempts to determine customer (or prospect) targets based on different combinations of demographics. This is historically important and intuitive approach to segmentation because customers do buy different products at different stages of their life. For instance, first mortgages are highly associated with prospects in their mid- to upper-20s, and home equity loans are highly associated with customers in their 40s with children entering college. The major difficulty with life-cycle segmentation approaches is product-specificity. Since products differ by life phase, a life-cycle segmentation must be developed for each product. If we develop a demographic segmentation for each product, then, in effect, while performing the product-specific behavioural segmentation described above, and all the limitations related to behavioural segmentation apply.
Attitudinal Segmentation segments the market based on how well customers perceive the product or service to be performed. The rationale behind this type of segmentation is very sound, it recognizes that attitudes drive behaviour. The difficulty lies in the fact that most approaches to attitudinal segmentation utilize only performance data. While performance data does tells us how respondents think we are doing, it tells us precious little about what respondents actually will do. Another type of data, -importance or preference data- is much more suited for projecting what a respondent will do. Moreover, two other limitations of attitudinal segmentation should be mentioned. Most attitudinal segmentations are developed using cluster analysis. Cluster analysis is a very powerful approach, but it is "brittle." No single cluster method always works best, but often only one particular type of cluster analysis is run on the data. Secondly, even if the cluster is well separated from the other clusters, attitudinal segments are generally more difficult to classify by simple cross-tabulation, as compared to behavioural clusters.
Conclusion:
The underlying principle of market segmentation is that the product and services needs of individual customers differ. Market segmentation involves the grouping of customers together with the aim of better satisfying their needs whilst maintaining economies of scale. It consists of three stages and if properly executed should deliver more satisfied customers, few direct confrontations with competitors, and better designed marketing programmes.