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In the past, officials at HuHot Mongolian Grill had segmented loyalty program members based on total number of visits. But, they sought a solution that would allow them to target their “least likely” visitors.
As a result, HuHot Mongolian Grill—which comprises 70 HuHot restaurants in 18 states, with the vast majority located in the Midwest and Mountain West states—opted to use Paytronix Systems scoring to motivate its most unlikely visitors to come in and purchase meals. The scoring algorithm analyzes visit and purchase behavior to predict which guests are likely to visit with and without promotional offers.
HuHot is leveraging the Paytronix predictive scoring capability to increase incremental revenue by sending loyalty and promotional offers only to customers who would not otherwise visit without the campaign offer.
The Paytronix scoring solution predicts future customer behavior by blending data to indicate the likelihood that a member will present specific behavior or attributes. Each member is assigned a score that’s as easy to interpret as a FICO credit score. What’s more, the Paytronix scoring ensures that the brand does not cannibalize sales by giving people rich offers if they would come into the restaurant anyway without the promotion.
HuHot franchisees were skeptical, at first, believing that the loyalty program would discount people who would come into their restaurants anyway. But, judging from the results, they saw that the aggressive loyalty promotions were only targeting guests that weren’t likely to visit.
Senior Director of Digital Marketing Monica Minford talked to Loyalty360 about what attracted the company to Paytronix scoring.
“We liked that the Paytronix engineers were doing real-time work in the background to classify our members and predict future behavior,” Minford explained. “Previously, we had been segmenting our users based on their past visit totals, which was not as accurate. The Paytronix scoring algorithm makes us feel more confident that we’re targeting guests based on their current and future visit frequency.”
HuHot Mongolian Grill began using the scoring a year ago.
“By using control groups for our segmented offers, we are able to show an increase in incremental sales for offers sent to those least likely to visit,” Minford said. “For example, for National S’mores Day last year, we sent an email to all our members, but only those least likely to visit received an offer attached. Those unlikely visitors who received the offer were 50 percent more likely to visit than the control group of unlikely visitors that did not receive the offer—compared to a similar unsegmented offer we had previously sent that only gave us a 25 percent bump in visits. We are able to send more aggressive offers knowing that we’re not cannibalizing visits that these unlikely visitors would have otherwise made.”
Minford is excited about using the Paytronix scoring.
“Our old method of segmentation was based on visit history, however, there is room for error with that method,” she added. “Someone who visited 10 times in the previous year would look like a frequent guest on paper, but they could actually be lapsed if all those visits occurred in the first half of the year. Paytronix scoring looks at the full picture to give a more accurate prediction. All members receive messages, but the more likely they are to visit based on Paytronix scoring, the smaller the offer needs to be to entice them to come in during the promotion period.”
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