Picture this: A 22-year-old consumer, who we’ll call Joe, loves playing the worldwide smash-hit game Deer Crossing on his iPhone (News - Alert). While mobile-toting consumers spend, on average, about two hours a day using the apps on their devices, Joe spends close to three and a half hours – a lot of which is spent playing this game.
Thousands of game publishers are vying for Joe’s attention, trying to pull him away from Deer Crossing. But Joe is loyal to that app. That’s because he’s a member of an online mobile loyalty program, which rewards him for his very frequent play with a virtual currency. Using his newly earned currency, Joe can buy real-world rewards such as a free music downloads or discounts on gift cards for his favorite clothing store.
How does the loyalty program know to make these particular offers to Joe? First, it uses mobile analytics techniques to measure exactly how Joe interacts with the app and what offers he’s most likely to act on based on past redemptions. Then, it uses that information to make future offers that add value to Joe’s life and keep him coming back.
In today’s cluttered loyalty landscape, that type of messaging analysis is more important than ever.
Helping Loyalty Programs Stand Out
The average American household has 18 loyalty program memberships but is active in only eight of them. Adding to the loyalty clutter, a recent report by the Edgell Knowledge Network revealed that 81 percent of loyalty program members don’t know what their rewards are or how they are supposed to redeem them.
If that many loyalty program members are that disconnected from their rewards, it’s very likely those customers aren’t feeling engaged, or don’t find the offered rewards relevant. So, in this kind of loyalty environment, it’s necessary to focus on engagement and adding value.
How can companies with loyalty programs accessible through mobile apps differentiate themselves? By using methods such as action analytics, A/B split testing and retargeting for mobile to boost user engagement and conversions by pinpointing exactly which messages are driving redemption rates.
Action analytics collects deep granular data – e.g., number of messages opened, time since last open, and opens resulting in goals such as registrations, purchases or social shares – that links specific message copy to particular user behaviors and outcomes. All of this information can be put together, like pieces of a jigsaw puzzle, to reveal detailed pictures of individual customers’ wants and needs and used to tailor further test message effectiveness.
A/B Split Testing and Retargeting
Using action analytics data, loyalty programs can identify particular audience segments likely to be interested in an offer, sending sample groups two versions of a marketing message to see which drives more positive responses. The winning message of this A/B split test is then pushed out to the wider segments.
However, if marketers want to make sure they haven’t missed any opportunity to elicit a redemption, they won’t stop here. They retarget, using the previously gathered data to make follow-up messages – be they push notifications, SMS or mobile e-mail – to customers who didn’t redeem the offer even more enticing.
For example, a fictional upmarket apparel chain with a loyalty program app might run a split test such as this: From previous interactions, the company knows the segment it’s targeting frequently buys items by designer Roberto Rococo. So the chain sends either of these push notifications to a significant sample of that segment:
A. This week only! 25 percent off on ALL ROBERTO ROCOCO shoes with any handbag purchase over $350!
(Message A had a 40 percent open rate and a 20 percent conversion rate. For every 100,000 messages sent, 8,000 led to a redemption.)
B. ROBERTO ROCOCO fans! 25 percent off ALL shoes with any handbag purchase over $350 this week only!
(Message B had 30 percent open rate and a 30 percent 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 rate – 9 percent vs.8 percent.)
At each stage of this analytics process, more of the aforementioned jigsaw-puzzle pieces click into place. After the split test, the company uses this better-detailed customer picture to craft more enticing follow-up messages, retargeting customers who didn’t open the first message and those who opened it without converting. In this case, 17 percent of retargeted Rococo fans redeem the offer.
The retailer continues to retarget, with each round of pushes yielding further redemptions. That’s because it knows that in a rapidly evolving and crowded loyalty environment, it’s more challenging than ever to stand out.
As maximum relevance continues to be a key driver for loyalty, programs that utilize action analytics techniques to tailor messaging for individual mobile customers stand to benefit from powerful relevance that can keep customers coming back time and time again.
Brendan O’Kane is CEO at OtherLevels (www.otherlevels.com), a mobile messaging analytics and retargeting firm.
Edited by Stefania Viscusi