Consumer Panels — Buyer Groups

A buyer group is created by filtering the data, on some criteria, to form a group of households. Grouping households or individuals in this manner allows for drill down analysis of the data.

Demographic groups, segments and user-defined groups are the most common types of buyer groups.


Consumer panel data may be used to segment consumers according to their purchasing behaviour. The statistical techniques deployed to craft segments for panel data are the same as those described in the chapter Segmentation. The metrics typically used include spend per month, frequency of shopping, channels/chains visited, brands/segments/ categories shopped, etc.

Households may be classified in terms of their store choices (shopper segment) or in terms of their choice of products/brands (consumer segment).

By way of example, in the U.S., Tesco segments shoppers into the following 5 groups and 10 sub-groups, based on data collected from their Clubcard loyalty panel:

  • Convenience. This is further sub-divided into
    • Time-poor, food-rich and
    • Can't cook, won't cook
  • Price-sensitive:
    • Stretching the budget
    • Cheapest I can find
  • Finer foods:
    • Natural chefs
    • Cooking from scratch
  • Mainstream:
    • Kids’ stuff
    • Commonplace brands
  • Less affluent:
    • Traditional
    • Price sensitive

Consumer segments may also be crafted for a category, based on their buying behaviour within the category. Since households can exhibit fairly different buying characteristics from one category of goods to another, category-based consumer segments do vary across categories.

Demographic Groups

Important demographic parameters include income, age, ethnic group, household size, presence of children and life stage. The phrase “life stage” refers to the different stages of development of a family. According to a Nielsen definition, life stage groups comprise of categories labelled as young family, older family, mixed family, adult family and young couples. A young family is one where all the children are below 12 years of age; an older family is one where the children are above 12; mixed families have both “old” (>12 years) and young children; adult families (e.g. empty nest) and young couples do not have any children.

User-defined Buyer Groups

Exhibit 7.7   Buyer groups based on heaviness of buying for Lipton tea.

A common approach to crafting buyers groups is in terms of specific behaviour characteristics of the households. There is great flexibility in how these groups may be defined. For example:

  • In terms of what they buy: e.g. buy coffee, or buy Nescafe, or buy body wash but not Lux body wash, or buy milk and cheese.
  • Where they buy: e.g. shop at Carrefour, or shop at Walmart but not Tesco, or buy Pantene shampoo at Tesco.
  • How much they buy or how often they buy: e.g. buy more than 5L of Coca-Cola, or buy Nescafe at least 5 times at Carrefour, or loyalty for Heinz ketchup is greater than 50%, or heavy buyers of Pepsi.

Most of the analysis described in this chapter are essentially special types of buyer group analysis. For instance, a brand’s basket analysis is an analysis of transactions by the brand’s buyers, i.e., the buyer group is brand buyers. Similarly, the analysis of consumption, described in the previous section, represents the volume or value per buyer. In this case, the brand’s buyers form the buyer group, where as for the overlap analysis, the buyer group composes of the buyers of a set of brands.

Buyer groups based on heaviness of buying (i.e. volume per buyer) are usually segregated into 3 roughly equal groups — Heavy, Medium and Light. Exhibit 7.7 provides a fictitious example for Lipton tea, where heavy buyers who comprise 35% of Lipton’s buyer base, contribute 80% of the brand’s volume. On the other hand light buyers who also comprise 35% of brand’s buyers, contribute merely 5% of the brand’s volume.

Once the groups are configured, analysts drill into them in an effort to identify market opportunities. For instance, for the Lipton tea data, while examining the basket of purchases of heavy buyers within hot beverages as a whole, it was found that these were predominantly tea drinkers, with Lipton accounting for 64% of their total hot beverage basket. The medium buyers on the other hand were predominantly coffee drinkers, with coffee accounting for 78% of their total basket.

In contrast the 35% light buyers were quite loyal to Lipton. The reason they contributed only 5% to Lipton’s volume was because they were light consumers of hot beverage. The brand’s manager might consider different approaches to induce these consumers to consume more Lipton tea.

Before progressing tactics and strategies to exploit the perceived opportunity, the brand manager would need answers to a number of related questions. For instance, what might be the reasons why light consumers consume less hot beverage? Are they small households? Do they consume more out of home? What is the contribution of hot beverage to their share of throat? Most of these questions can be answered by drilling further to obtain relevant details such as demographic profile and beverage consumption habits (beverage basket of purchases).

A tactical plan however, is not always easy to execute with consumer panel data. How for instance do you engage with those consumers that have been identified as your target? In this example it might be via small packs, but often there are practical limitations.

With regard to tactics at a store level, big loyalty panels have an edge over consumer panels. Since they comprise a large proportion of the universe of store shoppers, they are better suited for executing tactical plans.

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