We started with a hypothesis and some quick concept testing rounds to make sure we were on the right track
We leveraged a feature we shipped earlier in the year as one of the key ingredients.
This allowed us to surface key insights about the users' audience when showing the campaign benchmark
We used machine learning to analyze content across thousands of campaigns to effectively predict the customers’ business vertical and find benchmarking data to compare against.
We enhanced our feature with a recommendation system that provided guidance based on the results of the campaign. For instance, if the campaign remains unopened, our recommendation would be to re-send to those un-opened contacts. When the campaign performed above industry averages, we celebrate with a welcome message.
We didn't want empty states to be a blocker for customers to start seeing benchmarking data.
A quick feature summary and a clear call to action gives users a clear path to access.