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The CEO of OtherLevels, Brendan has over 20 years’ experience in the mobile ecosystem. He managed global accounts such as the Cable and Wireless group for Oracle Corporation, before growing Oracle’s Asia/Pacific services offering to a $150m business. He subsequently led a US mobile messaging business in Asia/Pacific prior to its acquisition in 2001. In the past decade he has been an active investor and director in mobile and on-line properties.

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Mobile Analytics: Driving Greater Engagement for Loyalty Programs

Written on
Mar 6, 2013 
Author
Brendan O’Kane  |
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Mobile Analytics: Driving Greater Engagement for Loyalty Programs

Customer loyalty was a top theme at the recent National Retail Federation Big Show, where thousands of retailers gathered to discuss trends and learn from the industry’s top names.

This theme frequently overlapped with another important topic that is linked to loyalty: a strong focus on customers and on personalizing the retail experience.

Customer-centricity was at the heart of a presentation from large Canadian grocery-store chain Metro and dunnhumby, which specializes in helping companies personalize their loyalty programs. In their presentation, the executives pointed out that the average Canadian has 14 loyalty memberships.  In the US, that average number is about 18 memberships per household.

So, how does a retailer like Metro stand out from the pack? By using data gathered through customer interactions across all channels to personalize marketing messages and strengthen engagement with its most loyal customers.

Of course, fine-tuning all sorts of marketing messages – in print, on television and on desktop-based Web – is important to any retailer’s overall strategy. But, to build top engagement with its highest-value customers, the retailer will need to put a strong emphasis on mobile.

That’s because, as techniques such as action analytics, A/B split testing and retargeting for mobile record how loyalty members interact with messages, they also generate data. And that data can tell the retailer a lot about a customer’s preferences, painting a detailed picture of individual loyalty members.

In order to get to know their mobile-toting loyalty members better, retailers can employ this three-step technique:

  1. Action analytics: This method collects deep granular data – e.g., messages sent vs. opened, organic vs. prompted, time since last open, session times and location – that links specific message copy to particular user behaviors and outcomes. This information reveals a lot about customer preferences by showing exactly how they’re interacting with messages down to individual taps and swipes. The information can be used to tailor further test message effectiveness.
  2. A/B split testing: Using data gathered through action analytics, retailers can identify particular audience segments and send them two versions of one marketing message to see which one drives more conversions. The winning message will present an ever clearer image of an audience segment’s preferences.
  3. Retargeting: After running an A/B test, the retailer can use the results to fine-tune the message even more and send it to loyalty members who did not open the first message or who did open it without following the call to action.

For example, a fictional grocery chain with a mobile app-enabled loyalty program might run an A/B split test to see what kind of push notifications drive higher conversion rates.

From previous analysis of a particular loyalty member’s interaction, the chain knows the segment it’s targeting regularly buys large amounts of eggs, sugary breakfast cereals and, say, Sunny Farm brand milk. These loyalty members fit the profile of moms with big families. So the chain deploys the following:

A. “This week only! Get a FREE pint of Sunny Farm Ice Cream for the kids when you buy 2 gallons of Sunny Farm Milk!” Message A had a 40% open rate and a 20% conversion rate. For every 100,000 messages sent, 8,000 led to a redemption.

B.” Moms! Get a pint of Sunny Farm Ice Cream FREE when you buy 2 gallons of Sunny Farm Milk now thru Sunday!” Message B had 30% open rate and a 30% conversion rate. For every 100,000 messages sent, 9,000 led to a redemption. Though it had a lower open rate, message B yields better ROI because it had a higher redemption (9% vs.8%).

Using what it learned from these results, the chain further fine-tunes the message and re-deploys to customers who ignored the message and those who opened it but didn’t convert.

After these tests, the chain knows this customer segment much better than it did before. And that knowledge leads to ever-more personalized messages that can drive engagement and ROI.

For any retailer with a loyalty program, action analytics is critical to tailoring messages that speak to what customers really want and building stronger relationships that last.





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