Optimize Loyalty Program

Optimizing your Loyalty Program through In-Market Tests

In Data & Analytics, Loyalty & CRM, Technology, Whitepapers by Kim XrossingLeave a Comment

For more and more companies, having a loyalty program is a matter of course. Just this morning, I swiped my rewards card at my local mom-and-pop coffee shop, checked my credit card points balance and received an offer from a favorite retailer. These programs have become increasingly comprehensive, offering “soft benefits” in addition to traditional points/rewards. They’re also customizing offers and messaging to individual customers like me.

APT now has a whitepaper on Strategically Building Customer Loyalty: A Data-Driven Approach to Loyalty Programs.  

You can view the whitepaper here >

But competition to attract and retain customers is fierce, and companies are routinely looking to enhance their customer engagement. Levers to improve loyalty programs include altering points vs. dollar rewards, frequency of communication, promotional discount level and upgrading membership tiers.  Yet, it is often difficult to measure if these strategies work and how to evaluate success (e.g., redemption rate, margin per customer, new customer acquisition).

In order to navigate these challenges and drive value from loyalty programs, many companies are turning to analytics to test key initiatives and measure their incremental impact. In fact, a recent survey we conducted revealed tactics such as personalized offers (38% of respondents) and loyalty programs (31% of respondents) are among the top strategic priorities for in-market testing.

Conducting in-market tests of loyalty programs can help you understand if an initiative will generate enough incremental sales or margin impact to break even once you take into account all of the associated costs (e.g., mailers) and lost margin from discounts/rewards. Commonly, programs with high redemption rates can be falsely deemed successful. As a result, organizations must strike the balance between offering attractive awards that will entice new or lapsed customers, while making sure not to subsidize purchases that customers would have made anyway. Using a test versus control approach, companies can deploy an offer to a small subset of their loyalty customers, compare those “test customers” to similar “control customers” who did not receive the offer, and measure the true margin impact attributable to that specific initiative. Further, segmenting these results enables organizations to tailor the content and frequency of the offer and target it only to the customers who are predicted to respond profitably.

One such company used our Test & Learn® software to understand the impact of a loyalty offer that rewarded customers who purchased select featured products with bonus points that could later be redeemed as store credit. While the retailer’s management believed these types of promotions were generally profitable, it was difficult for them to discern the effectiveness of each element of the program and determine which products to put on promotion, how many points to offer and which customers should receive the offer.

Applied Predictive Technologies has more intriguing Test & Learn® software case studies here>

Analysis revealed that the promotion did not drive a significant lift in sales overall and was not effective for all types of products. By using this information to avoid broad implementation of the campaign, the retailer avoided significant potential margin loss. However, for some types of customers, the program was successful. For instance, the offer drove incremental sales from customers who had not shopped in the previous 30 days. Targeting the offer only to those profitable customers generated substantial benefit. In addition, the retailer determined that it was optimal to offer 50 points, rather than 100 points, as the extra points did not drive a significantly different result.

As companies introduce or continue to refine their loyalty programs, adopting an in-market testing approach can accelerate innovation by quickly separating the winning offers from the unprofitable ones. Executives can then be confident in the results of their analysis and focus on what really matters: incremental profit.

You can now view Applied Predictive Technologies whitepaper here >

By Cornelius Kaestner

Join us at America’s Customer Festival 2015 on September 23 to hear how APT’s take on smarter data can transform your business.

Leave a Comment