C-store Retailers Can Personalize the Customer Experience With AI

Marketing driven by artificial intelligence operates through four stages.

CHICAGO — To some, artificial intelligence (AI) may sound futuristic, but AI is here today and the technology can help convenience store retailers with their marketing campaigns.

"Using AI saves you time, effort and money," said Ryan DiLello, content specialist for Paytronix Systems Inc., during a recent Convenience Store News webinar. "It ensures you do not have one-size-fits-all campaigns. It allows you to learn more about your customers and meet their needs."

Specifically, AI can help c-store retailers learn more about their customers' behavior and value; segment customers in more exacting ways; make more compelling, personalized offers; maximize channel efficiency; and, find ideal customers in the marketplace using AI-constructed profiles.

AI-driven marketing operates through four stages, stated DiLello. They are:

  1. Segmentation
  2. Predictive Analytics
  3. Campaigning
  4. Analysis

One thing AI can do is provide data regarding how likely customers are to visit a c-store, as well as open emails with provided targeted offers. The data can, for example, show which days a customer is visiting a store. If a customer visits exclusively on weekdays, AI can generate targeted offers to try to encourage consumers to visit on the weekend.

AI can also help launch "Missed Visit Campaigns," which recognizes individual lapses in guest behavior and identifies guests "out of their rhythm." According to DiLello, results from the first seven days of a Missed Visit Campaign revealed guest visits increased by 42 percent and in-store spending rose by 19 percent.

Geofencing, when AI notices when a customer is near a store and provides targeted offers and promotions, is another way to draw in-store traffic.

"We have seen great results using geofencing," he said.

C-store retailers have also used K-Means Clustering. Through this method, AI looks at popular pairing items and makes recommendations based on trends in the data. Further, K-Means Clustering helps develop unique and personalized guest experiences intended to keep customers returning to the store.

"AI recommends coupon offers," DiLello said. "It is a low-risk way to win back customers."


AI can help c-store retailers beyond just marketing, making their implementation well worth the investment, DiLello stressed. The technology takes it one step further by helping to determine a key metric: customer lifetime value (CLV). AI calculates how long a person has been in a loyalty program, what the average visit cadence looks like, when the most recent visit was, how much customers spend per visit and how long the consumer is likely to stay active. These data sets are important to identify top customers, segment more effectively, optimize acquisition and realize lift — or a customer's lifetime journey — DiLello pointed out.

The objectives of CLVs are to identify and reward most valuable customers, and find lower-value customers and boost their CLV.

"People often ask what a good CLV to customer acquisition cost (CAC) ratio is. We often say a three-to-one ratio is good," DiLello explained.

Retailers can lower CAC by retaining customers longer, reducing media and advertising expenses, and reducing third-party marketplace fees, he added.


In the future, DiLello expects AI to provide c-store retailers with practical uses in operations. For example, robotic servers, kiosks with facial recognition, food waste reduction management, inventory management and smart routing for delivery are ways AI can benefit c-store operators down the road.

"Inventory management is big for c-stores," he said. "AI will allow retailers to cruise through an unsteady supply chain to order items months in advance."

An on-demand replay of this webinar, "Get Smart: How You Can Personalize the Customer Experience With AI," is available here. 

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