The client wanted to boost their app revenue by increasing the basket size and average order through in-app suggestions.
The client was testing different series of business rules, trying to optimize for the best possible combinations of suggestions. For example, they’ve tried to include the highest selected suggestion in the recommendations for any user basket lacking that item.
Brillio started to audit the results for the previous quarter, test the outcome, and review the general business rules. However, since the testing wasn’t set up correctly, they wouldn’t run the tests in conjunction with each other, they would do one after the other and would measure success based on the take rate. There were a lot of gaps in that system, and the client needed to optimize the suggestive selling implementation.
The quarterly analysis was conducted on the suggested items. However, there were hundreds of different combinations in place. The most popular item was displayed in more than 60 different types of suggestions. Our team started measuring the most heavily impactful combinations, which item is the highest performing suggestion, and in what combination.
Then, we started looking at the business rules – some of which overlapped. For example, a user would have a desert in the basked and would still get sweets for recommendations. That suggestion would never convert. Adobe was instrumental throughout the implementation. It allows analysts to have a complete view of the transactions – when it is completed, the amount of money spent, and what items were in the basket. It provides data to see if a particular item that’s in the basket at checkout was suggested by the app. With Adobe, analysts can also track when suggestive sell fires, and audit the impact on the checkout basket.
Brillio’s solution was originally deployed for the US market and then rolled out to other markets. The new implementation tracks several KPIs, such as the take rate, the most popular items, The impact on users that get a suggestion vs. users that are not targeted, etc.
By leveraging Adobe Analytics, we also optimized the app and provided an improved customer experience.
Increased the average order and the basket size
Mitigate potential revenue loss through missed suggestions
Significantly streamline the suggestions & list of combinations to optimize for maximum conversion