Cardlytics: Cardlytics drives 15-20% response rates to personalised promotions by machine learning analysis of the purchase history of over 100m consumers | Case Study

Cardlytics drives 15-20% response rates to personalised promotions by machine learning analysis of the purchase history of over 100m consumers

Cardlytics aggregates credit card spending history of over 120m consumers in the US by working with financial institutions like Bank of America and 2,000 others. They offer personalised promotions to customers to get cash back on brands such as Starbucks and Whole Foods. This has driven a 15-20% response rates to personalised promotions compared to single digits for the industry.

Context

"Cardlytics works with financial institutions like Bank of America and 2,000 others to run cash back programs. It partners with brands across restaurant, retail, travel, grocery and home subscription categories to offer discounts. Starbucks, Spotify, Airbnb, Hilton and Whole Foods are amongst the places where banking customers will find deals. The business presents consumers with targeted offers based on their purchase behavior."

The Project

"Because the offers are based on purchase behavior and where an individual customer shops, they are highly targeted. Merchants that use the service can reach customers, or competitors’ customers, in a simple, targeted way that can be tracked through actual usage."

AI Usage

Machine learning and advanced analytics.

Data

"Cardlytics aggregates purchase data from about 2,000 financial institutions. In 2016, its platform analyzed more than 18 billion online and in-store transactions across more than 94 million bank accounts in the US."

Results

Response rates average 15 to 20 percent, as compared with the low single digits for most traditional campaigns.

Related Use Cases

Use Case

Optimise price and promotion offering by individual or segment

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