Consumer Panels — Trial and Repeat Purchase 


Exhibit 7.11   Trial of a new body wash brand.

The success of a new product launch hinges on two factors — its appeal and its adoption.

Appeal: New products generate appeal or desire amongst prospects, to experience the product. This is a function of the product concept and the manner in which it is communicated through product, packaging, display and advertising. Appeal translates into trial; the broader the appeal, the greater the incidence of trial of a new product.

Adoption: This is the willingness to continue buying, after experiencing the new brand. It is a function of the extent to which the new product lives up to/exceeds consumer’s expectations. Adoption translates into repeat purchase; the higher the level of adoption the greater the extent to which the new product gets channelled into the consumer’s repertoire of purchases.

Trial and repeat purchase are the two metrics we need to closely monitor to assess a new FMCG product’s market potential. For the product to succeed, it needs to establish a base of regular consumers who continue to buy it. It must generate appeal so that a substantial number of consumers try it. Once they experience it, an adequate number of them should be willing to continue buying it.

Trial or cumulative penetration at a time t is the percentage of households or individuals who purchased the product from the time it was launched until time t.

The build-up of trial for the new body wash brand shown in Exhibit 7.11 is unusual in that it differs significantly from the benchmark. Usually heavy promotions are scheduled during the early launch phase, but for this brand the promos kicked in more than 6 months post-launch.

%Repeat Purchase is the percentage of buyers who bought more than once. A variation of %Repeat Purchase is the 1 +, 2 +, 3 + %Repeat Buyer, where the 1 + Repeaters are those who bought the product at least once, 2 + at least twice and so on.


Exhibit 7.12   Purchase Frequency analysis reveals the distribution of buyers and volume across months, over a time horizon such as a year.

For products where repeat purchase is the norm, the analysis of purchase frequency (Exhibit 7.12) is an important indicator of brand health.

Typically this reveals the distribution of brand buyers and brand volume, across the number of occasions (usually months or weeks), over a time horizon such as a year. Weaker brands have a high proportion of buyers who buy the product across only one or two months of the year. Stronger brands on the other hand, have greater proportion of consumers who buy more frequently.


Exhibit 7.13 Repeat decay analysis depicts the proportion (probability %) of buyers who repeat purchased at least once, twice, thrice and so on, and their repeat decay rate.

The repeat decay analysis shown in Exhibit 7.13 depicts the proportion of buyers who repeat purchased at least once, twice, thrice and so on, and their repeat decay rate.

Computed as probability × loyalty, the repeat decay represent declining sales contribution across repeat occasions, over a time horizon.

Comparisons for the repeat decay curves over different time horizons, and across brands, provide a key measure of a brand’s health. Brands that command greater loyalty decay at a slower pace, across repeat occasions.

If the repeat decay curve rises from one time horizon to another, it reveals that the brand has greater resilience to retain consumers over repeat purchase occasions.

Trial and repeat purchase is re-visited in the Product Validation chapter which introduces the TRB model for forecasting sales of new products, and provides an understanding of a number of concepts and metrics that relate to the evaluation of new products.

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Demo - %Trial (Penetration)

Demo - %Trial (Penetration)

Demo of %Trial (Penetration) analysis on the Delphi analytics platform.


Demo - Repeat Frequency

Demo - Repeat Frequency

Demo of Repeat Frequency analysis on the Delphi analytics platform.


Demo - Repeat Decay Curve

Demo - Repeat Decay Curve

Demo of Repeat Decay analysis on the Delphi analytics platform.