INDUSTRIAL DISTRIBUTION: PREDICTING EXTENDED CUSTOMER VALUE
Industrial Distribution’s Bill Wade, author of the column “Distribution Matters,” addresses a distributor’s concern to define a customer’s long term value.
Industrial Distribution Excerpt:
“Prediction is very difficult, especially about the future.”While Nobel physicist Niels Bohr probably had quantum phenomena in mind when he made that statement, guessing customer behavior can be at least as elusive. Uncertainty is inherent in differentially marketing to the aftermarket, but basing decisions on past performance is still the norm.
Industrial marketers with severely constricted resources have become interested in understanding the behavior that determines customers’ long-term value… the expected benefits after taking into account the costs of maintaining a relationship over an extended period. Customer relationships, while ‘off the balance sheet’, merit increased analysis. Many companies divert significant attention and resources to marketing techniques that seek to cultivate loyalty among the perceived best customers, without the tools to define that target group.
80/20 Thinking
Both suppliers and distributors continue to focus on rewarding the best customers, because they think they’ll continue to be the best customers.
As a frequent flier, airlines may give you priority for upgrades, credit card companies may waive fees and hotels may put you on the concierge floor. These are discretionary marketing investments intended to cultivate continued, profitable business among their ‘best customers’.
At the heart of this practice lies the belief that a good customer yesterday will be a good customer tomorrow. But is it possible to predict which customers will actually be best over the long haul, and to do so reliably enough to justify giving them preferred status and its attendant perks?
Sophisticated marketers attempt to pinpoint the best customers with the help of tools such as RFM analysis. This technique measures how recently customers made purchases (R, recency), how often they made purchases (F, frequency), and how much they spent (M, monetary value).
If the axiom stating that “80 percent of your business comes from 20 percent of your customers” is true, then RFM analysis can help identify who the best 20 percent of customers have been. But does this thinking allow companies to predict whether the top 20 percent from the past will bring long-term advantage in the future?