Bring 'Em Back
By Lisa Yorgey Lester
Reactivation models can maximize your efforts to woo lapsed customers.
We've all heard the maxim that it costs more to acquire a new customer than it does to retain one. The same logic can be applied to winning back inactive or lapsed customers. The inactive portion of your customer file is low-hanging fruit ripe for the picking.
However, not all inactive or lapsed customers are equal. There is little value in marketing to inactive customers who are not likely to respond. Based on the information you already have on your inactive customers within your database, however, a reactivation model can help you focus your efforts on those individuals who are most likely to return to the fold. This helps you reduce acquisitions costs and leverage the investment you've made in acquiring these customers in the past.
Quantify Your Attrition
Before you begin to build your model, you must first quantify and define what you consider to be attrition. Is it all customers who haven't made a purchase in the past 12 months? Is it all customers who haven't renewed a contract?
Your definition of attrition will depend on your company and the industry it serves. A bank, for example, may define attrition as a significant decrease in balances or a closed account. In retail, it may be all customers who haven't shopped in the past 12 months, but shopped during the previous 12-month period.
"Generally, you look at the frequency of current customer purchase behavior to try to assess the length of time where you can indicate someone as an inactive," says Kurt Ruf, vice president and partner, Ruf Strategic Solutions. For example, he explains, "If your current active clients have a median transaction frequency of every three months and you've got a whole base of customers at six months of inactivity—that's where you might draw the line."
While there is no hard and fast rule as to when a marketer should develop a reactivation program, it usually becomes an issue when the "net flows of people in an organization start slowing down," according to Steve Briley, chief statistical officer, Merkle Direct Marketing. That is, when customer acquisition efforts are compromised by the number of customers "falling out the bottom of the funnel," he explains.
Smart marketers will plug the bucket beforehand by monitoring defection rates. Marketers need to have an "ongoing review of the sheer volume of those lapsing or defecting from an organization," notes Briley, adding that it might be at the point that represents from 2 percent to 5 percent of your acquisitions where you really have to start paying attention to your attrition rates so you can get "in front" of the problem before it affects your net growth.
Dan Rubin, vice president, analytics, Harte-Hanks CRM, also recommends making attrition a key metric that is frequently tracked and updated, but not so often that you can't differentiate "noise from reality." He adds, "Know what's normal for that time of year and be able to understand the cycles you see."
A reactivation model is one potential tool that can be used to implement a reactivation plan. The decision to build a model is determined by the number of inactive records or if the data set simply is large enough to support a statistically valid model, according to Ruf.
Another consideration is how you house your data. "If you have a relationship database and all your reactivation modeling is being implemented there, and campaigns are being executed there, developing a model is a great idea," says Briley. However, he explains, if you need pieces of data from three or four different legacy databases to implement an effective reactivation model, you're probably going to need to take some smaller steps before tackling the challenge of building the "ideal" model. One way to do this, offers Briley, may be to focus on the "historical purchase history to estimate a 'good' model that gets you 75 percent to 80 percent of the way there."
Once built, a reactivation model will predict which of your inactive customers are most likely to respond to an offer. This is achieved by first analyzing your inactive file and finding a subset of those records that have at one point or another reactivated.
The next step is to develop a regression model that discriminates between those two segments. "Those [customers] that reactivated as a result of promotions will have a special fingerprint relative to the group that deactivated and never came back," says Briley.
The model then can be used to score and rank your pool of inactives according to each customer's propensity to reactivate.
Paint a Data Picture
When building a model, says Rubin, you want to look at all behavioral data that existed when customers were active. In addition to RFM-related data, this may include products they bought and from which categories. When combined with other information, geographic and demographic data also can be helpful.
"You start to paint this big picture that tells you what they did when they were active from a purchasing standpoint, where they live, what their households look like, etc., so you can get a feel for what types of things they are potentially buying and what's important to them," says Rubin.
You'll also want to include any other data elements that "might help you understand their level of engagement," Rubin adds. This could include any customer-generated action such as Web site registrations, response to past promotions, call center interactions or e-mail inquiries, as well as any survey data that might pinpoint the reason for defection, such as a move or product dissatisfaction.
Briley also recommends looking at the relationship from "the other side of the fence." For instance, what kind of contact history did the company have with these individuals? Were they involved in frequent promotion campaigns? Were they targeted for thank-you letters? Did they receive special offers?
You want to use this information to develop a profile of people who stayed longer versus those who only stayed a short period of time, according to Briley. "In general, the people who are more likely to come back had a rich history with the company, as opposed to the casual surfer," he says.
Adds Rubin: "The key is not just looking at one variable; you're looking at the interaction from all those data themes and types of data."
Sometimes, however, your data may not be rich enough to support your scoring and marketing efforts.
"In many cases, a client data sample will be too thin to provide statistical validity because either they haven't been tracking properly or they just don't have enough inactives that have converted to actives over a period of time," shares Ruf.
In this event, he indicates, you can score your inactive files by either analyzing current responders to a mailed file or by analyzing your best customers with RFM as the dependent variable in a custom model or household cluster study.
"Ultimately that isn't going to be as good as using a custom model of new actives from the inactive file, but it does give us a targeted, aimed shot versus a shotgun blast at reactivation opportunities," says Ruf.
How Deep to Dig
"When you're done with your model, you're usually able to focus on the top 10, 20 or 30 percent of your population with a high degree of confidence that you're going to be able to find good targets for reactivation," indicates Rubin.
How deep you go into your pool of inactives, however, will be dictated by your model results, budget and profitability. You need to understand the costs associated with reactivating each segment of your inactive file. For example, it may make better sense to mail your best offer to the top 10 percent of the file and reduce the cost of the mail piece for those lower down on the list, offers Briley. "At the end of the day, you want to know what is the expected incremental revenue relative to the incremental cost of [creating] the campaign."
In certain industries, adds Rubin, you might find you can go deep into the file, reaching even those deciles that score below average, because if you can get enough of these inactive customers to respond, the potential spend is large enough to validate the cost of marketing to them. Casinos are a prime example, says Rubin, because the potential spend of these inactive customers can be huge. A retailer, on the other hand, may choose to focus only on the top 20 percent of its inactive file.
"It's a balancing act of where you start losing money and where you're investing intelligently," Rubin explains.