Paytronix Finds Restaurant Brand Loyalty Established With Fourth Visit

| February 17, 2017

Paytronix Systems, Inc., a provider of reward program solutions to restaurants and retailers, announced research from its Data Insights team, which finds that restaurant brand loyalty is established on the fourth visit. Paytronix Data Insights reports that it is 90% likely that those who visit a restaurant for the fourth time will continue to visit on a regular basis, demonstrating brand loyalty.
 
Read the Paytronix Research Brief:
Discover the Proverbial Gold Mine After the Fourth Visit
 
As consumers' options continue to increase, the cost of customer acquisition for restaurant brands increases and customer loyalty decreases.  For this fourth research brief on Extracting Customer Insights from Big Data, the Paytronix Data Insights team zeroes in on the science of establishing brand loyalty. The report urges restaurants to use the data collected in their CRM systems to develop a customer nurturing campaign designed to propel the guest to their fourth visits. 
 
The Paytronix Research Brief studied guest behavior over time, across separate study groups to analyze loyalty programs for quick-service chains, versus full-service. The report details how (and why) the more guests visit, the more likely they are to visit again, with a graph showing the dramatic increase in how likely guests from each group are to return after visits two, three, and four.
 
Paytronix Data Insights
Paytronix embraces Big Data, bringing together POS, loyalty, social media, and other disparate data sources to discover new opportunities for compelling visits and spending, efficiently identifying and automating 1-1 guest engagement.  This report from the Paytronix Data Insights analyst research team is provided to help customers uncover actionable insights from disparate data sources, for more effective use of their marketing budget — which ultimately results in happier, more loyal guests.
 
Catch up reading Prior Paytronix Research Briefs, Extracting Customer Insights from Big Data:

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