Imagine DoorDash, but you can buy anything from within a 10 mile radius of your location, and have it delivered in 90 minutes or less. Today you can shop DoorDash by browsing restaurants and retail stores nearest your location, then selecting from available inventory. This model is known as “store-first”, which ensures one merchant captures 100% of a customer's potential spend. In addition to store-first, through search you can browse results in an “item-first” model. Enabling customers to shop for specific items they want, but constrained to the realities of today: order minimums, delivery logistics, and merchant segmentation (grocery, retail, etc).

The result of these two models – operating together – can be complicated pricing, multiple separate deliveries, and difficult tipping decisions. By approaching shopping in a truly “Customer-First” way, DoorDash can provide the most seamless shopping experience, surfacing the best prices available, and delighting customers with positively-unexpected outcomes.

In order to shop across every category of "retail" – from groceries and restaurants to electronics and pet food – with just one cart, required what I called shopping continuation. Ensuring that picking up where you left off is intuitive without getting in the way of ordering lunch right now. The challenge was making the cart smart enough to know what your intent is, every time you open the app.

In UXR we uncovered that customers favored the ability to decide up-front whether to shop for something new, continue shopping, or to finalize an existing order. In normal every day use, you could mix any of the three states, making it possible to have many items from various stores in one cart (that's the idea). Because you probably don't want to wait for someone to pick out 100 groceries before they deliver your lunch, we landed on a solution that would automatically suggest aggregating existing carts of the same type, when relevant or complementary. Each time you opened the app, there was an opportunity to continue building your weekly grocery cart, or pivot and order today's lunch.

Today there's no other service offering Amazon-like distribution of goods from multiple merchants at different locations. Because of this, it's challenging to change people's existing mental-models, and set new expectations for timing, cost, and logistics. Imagine if you were shopping somewhere, filling a cart with items, and something came up; you had to leave the store, without finishing your order. This is generally how people shop in the DoorDash app. The caveat is that once you return to that store in the real world, weeks later, you would expect that someone had emptied your cart, and returned the items back to the shelves.

On DoorDash carts, and the items in them, exist indefinitely. Regardless of how many items or the distance from you, and despite knowing an identical item was purchased elsewhere. This idea of a persistent cart meant that everything was always inadvertently saved for later. In order to start a new grocery cart at Safeway, you would need to empty your existing one. In our solution, we flipped this equation, and developed logic for when items were no longer expected to be in a cart. Instead of having many abandoned carts, items became easy-to-bundle recommendations for shopping experiences throughout the app.

The final hurdle to transforming the shopping experience was to address any changes to the core value proposition of the DoorDash app. Getting warm food delivered quickly. Any disruption to the existing logistics, like a 20 minute detour for picking up groceries, risked breaking that promise. The common UX for this is to pass the logistics burden on to the user through checkout decisions. Choose to bundle all of your separate deliveries on a later date, or have them all delivered ASAP, but incur multiple deliveries of separate boxes. When shopping across many different types of retail, the important aspects of your delivery vary for each new scenario.

One thing remained true: you always want your food warm, and as quickly as possible. However, you don't mind your Sephora cosmetics arriving later the same day. The power to choose from: time (ETA), price savings, environmental impact, and item preferences all needed to be accounted for somehow. We landed on a solution that presented the most value across both expedience and savings. An "Easy Buy" method that leveraged AI to map every item in your cart, to inventory from any merchant in a 10 mile radius, for the lowest price, fewest amount of stops, and the fastest delivery. Alternatively you could choose the standard method of receiving things as fast as possible – regardless of how many deliveries – or at a scheduled time in the future.