This is an article which reviews practitioner and academic literature regarding several consumer segmentation trends, written by Brownwyn Higgs and Allison C Ringer. You can get the pdf of the behavioral targeting article here Trends in Consumer Segmentation.
Consumer generated media, such as product review sites and social networking sites have allowed online consumers the chance to influence what kind of content they want to see online. This, in turn, influences how online business deals with its transactions. In addition, there are more intelligent consumers now than ever before, with the particular characteristic that they want to see content and purchase goods and services based on their individual needs. Demand has indeed become individualized, and so mass-customization is born.
Communication channels that have interactive features are now everywhere, and they continue to grow in number and sophistication. Interactivity allows consumers to talk to marketers and vice versa. These transactions are documented and used to improve how markets respond.
Overview of Market Segmentation
Market segmentation was developed during the mid 20th century because at that time, purchasing and demographic data, and even distribution and advertising channels was only made for consumer groups. Segmentation is composed of four methods, the traditional ones, post-hoc and a-priori, and the flexible ones, componential and dynamic.
A-priori method is such that an analyst selects a segmentation base and does his or her analysis, while post-hoc does the analysis first before forming bases. On the other hand, componential segmentation focuses on making predictions and deemphasizes on partitioning, while dynamic segmentation models simulated conditions in which analyses can be done on how consumers respond to the characteristics of test products.
Segmentation is important because it aims to identify segments that vary in terms of market behavior, aspirations and purchasing power. The kinds of data that are obtained to properly segment consumers include consumption, purchasing and attitudes toward products or services. As a result, most segmentation techniques are brand-driven and tactical.
Segmentation has several limitations, including its inabilty to narrow down groups into sufficiently small custers. Another criticism of this process is that it relys heavily on one off surveys. Ideally, continuous data collection hhelps prevent certain marketing dynamics difficulties in the long run. Still, segmentation is endeared by many practitioners and there is a lot of research going to make analysis more sophisticated and improve on segmentation approaches.
Certain types of segmentation, including those used for advertising, have diverged in terms of development and improvement because of their unique purposes and goals. A different set of methodologies and procedures for analysis are employed as well, and unique instruments are carried out to engage in segmentation studies. Aside from advertising segmentation, CRM and direct marketing segmentation also evolve unique segmentation strands, creating new frameworks, segmentation techniques which employ highly extensive mining of data.
Finer and Hyper-segmentation
Finer segmentation is used to group markets into narrow clusters more precisely, and has been improved by advances in information technology over the years. Hyper-segmentation, on the other hand, is used to identify an individual consumer’s segment. There are two common methods for hyper-segmentation, progressive profiling and addressable marketing.
Progressive profiling involves collecting data through interactive websites, asking consumers a question or two during transactions in a continuous process, gathering rich data about individual consumers and his or her preferences. Addressable marketing, on the other hand, uses digital communication services to collect information regadrding online behaviors like advertising exposure, content involvement, site engagement and site visitation.
Behavioural Based Targeting
Behavioural-based targeting or BBT involves aggregating the market rather than partitioning it, by determining the behavior and patterns that a user forms across the web. Two types of data are used for Behavioural-based targeting: first is sample populations of site visitors and a target users list. With BBT, marketers can identify valuable information regading user concentrations across websites.
Implications and Conclusion
These segmentation trends will improve the quality of data one can get for marketing purposes, but at the expense of increased complexity in processing among others. Certainly, as marketing improves so will the number of segmentation approaches increase.