Finding New Customers in Your Best Customers
Through a special arrangement, presented here for discussion is a series of recent articles from Lenati’s blog.
In practice, customer acquisition strategies involving segmentation often draw artful conclusions that lack real-world application. Often there is disparate implementation across organizations. When acted upon, they fail to deliver intended results. In these situations, companies can benefit by taking a different approach to customer segmentation and targeting by using "bright spot" analysis.
This approach can help:
- Drive higher ROI and customer lifetime value (CLV) by identifying segmentation characteristics and attributes that improve targeting.
- Improve positioning and messaging by uncovering unexpected brand or product attributes that resonate with customers.
The bright spot approach focuses on mining current customer data for pockets of success. These customers may not align with the decided acquisition segmentation strategy, but ultimately they represent the most loyal and valuable customers. A company’s goal should be to not only identify bright spot customers, but understand why they are so loyal. As Chip and Dan Heath, authors of Switch and Made to Stick, state, companies should ask, "What’s working and how can [we] do more of it?"
The bright spot approach to mining your customer base can be accomplished with different analytical methods. One approach, RFM segmentation, groups customers into clusters based on the Recency, Frequency, and Monetary value of purchases. Once isolated, the characteristics of the bright spot customers are assessed to determine their common demographic and behavioral attributes. These attributes are leveraged to identify actionable profiles of the acquisition targets.
A national online retailer applied this strategy when launching a new category. The retailer used RFM analysis to create six clusters and isolated the top 12 percent of customers as their bright spots. Behavioral profiling of this group identified that these customers had been shopping with this online retailer for many years, had purchased in categories similar to the new category and were members of a loyalty program. These attributes were used to identify which prospects to target for acquisition.
This bright spot approach was then tested in a controlled e-mail environment. The same promotional e-mail was sent to both the "treatment group," prospects that have similar attributes to the bright spots customers, and the "control group," prospects identified by the retailers’ legacy targeting method. The results of this test showed the treatment group purchasing 35 percent more new category products than the control group. If the bright spot theory is correct, this group of customers is also more likely to make purchases in the future.
The bright spot analysis takes acquisition strategies to the next level by helping businesses target and acquire new high value customers, based on what they already know.
What do you think of the “Bright Spots” approach to customer acquisition strategies described in the article and the overall merits of retailers targeting customers with similar profiles to their best customers?