When supermarkets usurped the village shop, some felt the personal touch was lost in retail forever. But today’s technology means even the biggest business can talk to every customer as an individual. Personalization is not about mail-merging. It’s about understanding the customer well enough to be able to adapt the message to what he or she wants, and to use that knowledge as a platform for increasing customer loyalty, satisfaction and spend.
At the most basic level, you can group and target customers by their address or demographics, such as age. While that might enable you to reach most of those you need to, there will be many others included who have no interest in your proposition. By enhancing your data with lifestyle information and classifications from the Mosaic and Acorn databases, you can focus on your prospects’ interests and take a step towards understanding their needs and desires.
However, the greatest success comes when you’re able to target customers according to what they do, instead of what they are. Companies are often forced to resort to vague segmentation based on age or geography because they don’t know how to analyze the data they hold about the interactions customers have with them. One framework for defining customer behavior is recency, frequency and value (RFV) analysis – hardly a new concept but one that is more important than ever.
Recency is a measure of when the customer last engaged with the business, typically when they last ordered. In the case of e-commerce businesses, it could be when the customer last logged in or visited the site. The metric is a proxy for that customer’s awareness of your brand and a yardstick for the goodwill the customer will feel towards you, which will be high after a positive experience but will decay as the memory fades.
The frequency of orders can hint at how high the customer’s demand is for a particular product, and how strongly he or she advocates your version of it, although it’s impossible to tell how many orders of similar products go to the competition.
Frequent shoppers are not necessarily the big spenders or the most profitable to serve, so an additional value parameter is used. In catalog businesses, it’s common for customers to order goods on approval and return them if they don’t like them. For that reason, it’s more useful to count the value of non-returned orders, or even to focus on the margin the customer has contributed, than it is to look at raw orders received.
Customers can be clustered according to their RFV values. A theater might categorize its customers using an RFV-based segmentation such as single-visit customers, occasional attendance, regular attendance and avid attendees. The message will differ according to the category. Single-visit customers should be encouraged to return while avid attendees who have skipped a season could be offered incentives to book multiple shows.
Curt Bloom is managing director, international, of SmartFocus. Reach him at [email protected].