Smart marketers know that a 5 percent reduction in customer defection can increase profits up to 95 percent.
But how do you identify and target lapsed customers that are candidates for re-engagement? Many companies rely on guesswork rather than hard statistics to define customer inactivity, an approach that can lead to misidentifying lapsed customers and render reactivation campaigns less effective.
Let’s outline two simple, data-driven processes you can use to accurately assess who your lapsed customers are and build campaigns to re-engage them:
Approach #1: Use purchase frequency to identify inactive customers.
With this approach, you identify your average-customer purchase frequency and combine this with a look at each individual customer.
For example, imagine your store is a bicycle retailer, selling everything from bicycles to helmets. For the average customer, purchase frequency is every 94 days.
You have a loyal customer named John. He has made six purchases during the past 12 months.
Using this data, you can now identify that new customers should not be considered “lapsed” until 94 days have passed.
Since John has made multiple purchases, you may also want to give John extra time beyond the 94 days before considering him as a lapsed customer. However, it’s easy to see that any customers that exceeds 94 days should be put on “alert” as a lapsed customer.
Approach #2: Use RFM analysis to identify your lapsed customers.
A slightly more sophisticated option for data-driven marketers is to incorporate timing, frequency and purchase amount into your evaluation. RFM analysis uses the concepts of recency, frequency and monetary rankings to segment customers based on their past-purchase behaviors. For instance, you can use RFM analysis to organize customers into quartiles (25 percent increments) or deciles (10 percent) based on each purchase behavior.
Because this approach incorporates additional purchase behaviors such as the total number of purchases and the total lifetime value of the customer, it creates a more sophisticated view of your lapsed customers.
In this example, we’ll use RFM analysis to identify lapsed customers as:
- Customers who previously purchased frequently
- Customers that have a high ranking for total spend with your brand
- Customers that haven’t made a recent purchase
Let’s take a look at how we would use quartiles for recency categorization in this instance:
- Top 25% — customers that have purchased most recently and rank in the top 25 percent of customers according to their most recent purchase. For example, these might be customers that have purchased within 30 days.
- Next 25% — customers that have purchased within the next frequency period. This could be 31 to 90 days ago.
- Next 25% — this ranking could contain customers that purchased 91 to 365 days ago.
- Bottom 25% — these are your customers that have not purchased recently and rank in the bottom 25 percent of customers according to their most recent purchase. These might be customers that purchased more than a year ago.
Next, repeat this exercise for frequency (number of purchases) and monetary (total amount spent across all purchases).
Then, we combine these classifications. The customers that fall into the following categories will typically be classified as lapsed customers:
- Recency: bottom 25%
- Frequency: top 25%
- Monetary: top 25%
By combining these three elements, it’s easy to extend your analysis beyond just recency and zero in on those inactive contacts who were at one point loyal, repeat customers.
Whichever method you choose, identifying your lapsed customers is just the first step. Now it’s time to reach out to these customers with personalized campaigns and win them back. Download Silverpop’s “15 Post-Purchase Emails That Build Loyalty and Drive Revenue” guide for examples of the most effective campaigns to re-engage your customers.
1) Ebook: “Print Money Today: 7 Emails Marketers Should Automate to Drive Massive ROI”
2) Video: “Minimizing Inactive Subscribers”
3) Blog: “5 Creative Ways to Use Scoring”