The extended 3-letter acronym to drive retail direct sales

Analytics software is useful, but some concept of what should be analysed is also of value. Retailers will often create a RFV model, combining Recency (of last purchase), Frequency (regularity of purchase) and Value (either total spend or average order value, for example) to create a score for each customer.

We have found that adding additional elements expands the score from the 3-letter acronym to a multi-letter acronym such as RFVRCA where you add on the instances of Returns, Complaints and Abandoned on-line baskets committed by the customer.

This score then becomes a comparitor between customers, a selection criterion for campaign or proposition and changes in the individual’s score over time become a valid measure of success of the relationship.

Applying demographic, lifestyle and psychographic profiling provides an even more granular segmentation and the ability to apply the concept to prospects, with the benefit of making relevant offers to convert them to customers.

Such models can be created without investment in analytical software and can be applied in rules-based workflow and business process, with the dynamics regularly reviewed and the model(s) enhanced.

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