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What Is Data Segmentation?

Article Details
  • Written By: Helen Akers
  • Edited By: Jessica Seminara
  • Last Modified Date: 26 November 2016
  • Copyright Protected:
    2003-2016
    Conjecture Corporation
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Data segmentation involves identifying and organizing data clusters into defined categories or groups. It is sometimes referred to as market segmentation. Marketing professionals often use the technique to target certain population segments in order to increase sales. The process identifies traits or characteristics, and uses these to group customers into market segments.

One of the fundamental aspects of the market research process is data gathering on prospective customers. Potential buyers for a company's products or services are identified through demographic and lifestyle factors. It is these factors or variables that are used to separate those buyers into distinctive target market segments.

When a data segmentation analysis is performed, target markets might be given a name such as "young professionals" or "blue blood retirees." These descriptions might refer to shared demographic characteristics, such as income level or age. This is a strategic method of identifying and targeting a broad sub-sector of the general population while remaining cost efficient.

Companies use technology to track customer purchase activity and identify variables that make certain consumers more likely to shop at a certain retail location or buy a particular brand of product. Loyalty discount cards and market research tracking companies compare consumer purchase data against combined demographic variables such as household size, age, geographic location and income to identify market segments. In the data segmentation process, the data is compiled into a database and analyzed for trends.

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Data mining is another aspect of the analytical process. It involves the use of software programs to uncover hidden trends or patterns. The technique is becoming increasingly useful as a means of isolating information to increase sales revenue and cut operational costs. For example, data mining helps uncover which products are most likely to be purchased by a certain type of customer on a particular day of the week. This type of information assists with store level incentives, store in-stock preparation, store point of purchase display decisions and targeted advertising.

An important component of data segmentation is identifying which customers are likely to be the most profitable. In a competitive marketplace, advertising to a mass audience in the hope of gaining sales revenue and market share is likely to be insufficient. A firm is in a better position to compete when the needs and desires of various customer market segments are understood. For example, some retailers position or identify themselves as having the lowest price, while others compete based on prestige or high quality.

Lifestyle characteristics of a particular target market are often revealed during data segmentation. A luxury car manufacturer, for example, might want to target upper middle class and affluent females. By identifying the group's shared hobbies, perceptions and values, the manufacturer will be in a better position to appeal to the benefits this group might be looking for. Perhaps this group is primarily motivated by a car's appearance and brand prestige; the manufacturer will want to capitalize on these motivations in an advertising campaign.

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