Express 24

The bottom navigation bar features Home, Search, Cart, Orders, and Profile — selected based on insights from previous user behavior. Including the Cart tab as a permanent section was a strategic decision aimed at increasing conversions. Many users tend to add items to their cart but leave without completing the order. By keeping the Cart easily accessible, we gently remind users of their pending items and encourage them to finish their purchase. This small change helps improve the overall user flow and boosts revenue.

New users previously faced difficulties selecting and saving a default payment option during their first order. We also streamlined the process of adding new payment cards by consolidating it within the same menu. These enhancements led to a 24% increase in conversion rate among new users.

User interviews revealed that many customers prefer placing joint orders with colleagues or friends to split delivery costs and shorten the Estimated Time of Arrival (ETA). However, coordinating such orders within the app posed a major challenge. To address this, we initiated an internal hackathon and developed an MVP of the "Party Cart" feature. This functionality allows users to share a link and invite others to add items to a shared cart, automatically dividing the delivery cost. The feature enhanced convenience and supported group purchasing behavior commonly seen during lunch hours or office orders.

After numerous complaints about incorrect delivery locations, we conducted in-depth user research to explore how users interacted with the address input feature. The key insight: users lacked a convenient way to manage multiple saved addresses. In response, we introduced the "My Addresses" feature, enabling users to add, edit, and switch between saved delivery locations easily.

Additionally, we implemented proximity-based notifications when users were more than 1600 feet away from the selected delivery point. These alerts appear on both the home and checkout screens. Usability testing showed strong comprehension and ease of use. Following the launch, we received highly positive feedback and recorded a 15% reduction in support calls related to address issues.

In close collaboration with a data engineer and a backend developer, we implemented a personalized recommendation system that analyzed a user's last 10 orders to suggest relevant restaurants on the home screen. This tailored approach enhanced user engagement and led to a 12% increase in the Customer Repeat Rate (CRR) among returning clients.

To increase the Average Order Value (AOV), we proposed a hypothesis based on user behavior analysis: customers often forgot to add desired items until the last moment, leading them to navigate back to the vendor page. In response, we introduced a dynamic upsell module on the cart screen showcasing the restaurant’s bestsellers. An A/B test confirmed the hypothesis — 32% of test users interacted with the suggestions, resulting in an overall 8% increase in AOV across all users.