Dive deeply into one problem you find in the Wish mobile app and design a solution. How can your solution be an impact on user acquisition, retention, engagement, sales or other attributes?

Wish is a purchasing platform that caters to users who are looking for the lowest possible price. However, issues in the Wish mobile app impact the success rate of users completing their order.

To understand the problem better, I looked at Wish's Facebook Ad Library and Facebook reviews as a starting point to my investigation of a common Wish user. I decided that based on the Job-to-Be-Done, the persona should serve as a reflection of the user when they are trying to make their purchase.

I created an example of a user persona:

I brainstormed problems with a savvy shopper in mind and with some inspiration from Beatrice. I focused on an unconstrained list of problematic design patterns.

• The user doesn’t get prompted that there are additional shipping fees in the product page and product grid, thus triggering an additional decision-making point when they finally get to their cart

• • Inability to scan for prices in the initial decision-making process because the shipping cost isn’t discoverable at the same stage

  • Purchasers who want to load up their cart get penalized for not constantly checking their shipping cost

  • • The doubling of decision-making for each product makes the user second guess their choice

• For items with regional variations, there may be a significant cost difference, thus leading to product abandonment because the product grid’s price is nowhere close to the appropriate regional variant (often 2x - 10x higher for aggressive "deal bait")

• No ability to filter or sort in the search results

• • The ability to segment products based on price doesn’t exist, thus limiting the potential for the user to find something they would like based on their purchasing limitations

• Grid ordering logic does not penalize items that are less useful for the user

• • Items with 2x+ the shipping cost of the item itself are much less likely to convert into a sale

  • Items in the appropriate regional variant that cost 2x+ the shown price are much less likely to convert into a sale

• Aggressive nagging in the notifications for cart abandonment makes it more likely for new users to uninstall the app

• Initial pricing is not reflective of the variant that is most likely to convert into a sale

I chose to dive into a problem that would solve one of Beatrice's frustrations. The most important improvement is to minimize the potential of Wish mobile users second-guessing their initial decision when they click “Add to Cart” or "Buy” on the product listing page. This second-guessing leads to a high chance of abandoning the product even after deciding to proceed to the checkout flow with the item. A lot of effort is made to re-affirm the user to continue purchasing their item by offering further discounts, cart abandonment notifications, prompts of product scarcity and more. However, it’s easier to re-convince the user if they didn’t have to go through a stage of second-guessing to begin with.
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To illustrate this issue, I’ve created a flowchart to visualize the current checkout flow:

The second decision point can be eliminated by informing the user of their shipping costs before they select “Add to Cart”. This can be done in two ways:

1. Move the delivery component to the top of the product page and put the shipping cost in-line (recommended)

2. Add a shipping modal when the user clicks “Buy” or “Add to Cart” (not recommended)

3. • This adds an extra stage for the user if there are variation combinations (colour, regional variation, bundles, etc…) and it can become unwieldy navigating through a modal that’s already overloaded in some cases

   • It doesn't make sense to add a stage for a user to select their shipping option when only one option is available

I tested the new delivery component by creating a prototype in Figma and then animating it in Principle. The figure below illustrates the focus points to a price-sensitive user like Beatrice, who Wish targets.

The revised changes let the user spend less time on decision points.

The flowchart below helps to visualize the revised checkout flow:

To test this solution, we can compare cart abandonment rates on the Wish mobile app with A/B testing of the revised design versus the current design. This will help us understand whether or not minimizing the number of times the user meets a decision point affects sales conversion. Focus testing may be used to validate the design pattern, but it's best to use A/B testing in combination to understand if sales conversion improves overall, which is our key performance indicator (KPI).