Technology Category
- Networks & Connectivity - 5G
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- E-Commerce
- Retail
Use Cases
- Supply Chain Visibility
- Traffic Monitoring
About The Customer
Future Pay is an app-based wallet offering for online shopping across Future Group stores. The app aims to provide an effortless online shopping experience to more than 20 million customers. Using the Future Pay app, customers can shop at over 1500 Future Group stores including Big Bazaar, fbb, Ezone, HyperCity, Central, Easyday Club, Heritage Fresh, Foodhall, Nilgiris, Brand Factory and more. The app also provides cashless and card-less transactions by linking the loyalty cards (payback and profit club cards) to their app, eliminating the need for customers to carry these cards.
The Challenge
Future Pay, a wallet app by Future Group, faced a challenge in tracking user interactions with promotional banners on their home screen. These banners, which redirected users to selected brand pages, were a key part of their marketing strategy. However, the Future Pay product team was unable to track clicks on these banners or measure conversions from users who landed on these brand pages. This lack of visibility made it difficult to assess the effectiveness of their marketing efforts and optimize their engagement strategy. The team wanted to understand user behavior and drop-off on these banners, hoping that this would help them offer more personalized engagement and boost traffic and conversions. The criteria for a solution were to help the team understand user behavior and drop-off on these banners.
The Solution
The Future Pay product team decided to use MoEngage, a customer engagement platform, to gain insights into user app activity and execute a comprehensive engagement strategy. The team devised a three-step strategy: understanding user app activity and engagement with discount banners, segmenting users based on these insights, and creating engagement campaign workflows to optimize user communication on various discounts and offers. They analyzed user actions such as in-app searches, clicks on push notifications, and clicks on specific banners to map user journeys. This data helped them understand user preferences and the performance of the banners. The team then segmented users based on their app activity, preference, and demographics, and ran various engagement campaigns tailored to these segments. They used MoEngage’s AI-based recommendation engine Sherpa to optimize the messaging and send time.
Operational Impact
Quantitative Benefit
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