There is a goldmine of growth available to certain lucky businesses: those with many one-time customers. To these companies, with a large portion of new customers never returning, the flow of passers-by presents a unique opportunity.
How can this potential be tapped? What can be done to entice one-time customers to stick around? The standard approach is to pursue improvements in business operations, such as better products, greater relevancy in marketing or more personalized service. Usually, a significant improvement in growth requires a significant investment in one or more of these areas. But, given the steady flow of one-time customers, is there a way to get more for less?
Yes, there is a clever way to capture many of these prospects for a relatively small increase in expenses. If you predict which first-time customers won't return, you can target them with an aggressive conversion campaign, enticing those that would otherwise leave with a heavy discount or free trial. In this way, you turn the first date into a long-term relationship.
The challenge is in the predictive targeting. Without knowing ahead of time which new customers will turn out to be hit-and-run customers, a costly retention campaign sacrifices too much revenue from the customers who were going to return anyway. In other words, if you offer every new customer a rebate on her or his second purchase, those destined to come back will simply pay that much less when they do.
Predictive modeling does the trick. A predictive model tells you which new customers are likely to return and which are probably one-timers. The model is created with data mining methods that "learn" from the collective experience of your company contained in your sales records. The model then applies what has been learned to produce a predictive score for each new customer in real time.
In this way, new customers you would otherwise never see again are targeted and enticed to stay. Because you don't waste the retention offer on new customers likely to return, the numbers work out very well. The growth rate and medium-term profits potentially skyrocket, and immediate-term profits are not put at risk.
Targeted Rebates Increase Growth without Sacrificing Revenue
Figure 1 illustrates this process. Consider an online retail store that attracts many one-off customers with Internet advertising. New customers who have just made their first purchase enter from the left. These customers are then routed in one of two directions, according to a predictive model. Customers likely to return without any additional incentive are left alone, while customers unlikely to return are targeted with an aggressive conversion campaign, such as an email coupon offering 30 to 50 percent off their next purchase.1 If 80 percent of a company's new customers are presently one-timers, capturing just three percent of this massive flow results in a 12 percent increase in the overall customer growth rate.
Figure 1: Would-Be One-Time Customers Converted by Model-Based Targeting
The retention offer must be delivered immediately after a new customer period. If you wait to see whether the customer defects, it will already be too late. The exact timing of the retention campaign depends on the business model, as detailed in Figure 2. For some businesses, this moment of opportunity comes at the transition between clearly defined stages of the customer lifecycle. For example, retail customers are at a "stay or go" crossroads immediately after the first purchase, so offer them the coupon right away. In contrast, subscription-based services often start with a brief trial period. Will the customer cancel or maintain the subscription after the trial ends? Other types of businesses, such as free services, may require creativity in delineating the precise moment when a new at-risk customer should be targeted for retention.
Figure 2: Businesses with a High Rate of One-Time Customers
Retention Campaigns to Increase Profits
Predictive analytics is the "magic ingredient" needed to make the numbers game work. You can't afford to deploy an aggressive rebate campaign without knowing which customers will return anyway. But, without literally relying on magic, how is prediction possible?
Predictive modeling leverages your sales and customer records to discover patterns that distinguish one-time customers from repeat customers. Predictive analytics' core methodology automatically determines which attributes of a new customer, in combination with everything you observe about the first purchase or transaction, make the difference. This often comes in the form of business rules, such as the following fictional example:
If the new customer comes to the Web site off organic search results,
and buys more than $150 on the first transaction,
and is male,
and has an email address that ends with .net,
then this customer is three times as likely to be a returning customer.
I explore predictive modeling in greater depth in the article, "Predictive Analytics with Data Mining: How it Works." http://www.dmreview.com/article_sub.cfm?articleId=1019956. Go and check it out for a bite-sized dose of the inner mechanics of predictive modeling.
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