Optimizing Thimble's Checkout Experience
A conversational checkout experience designed to maximize conversion based on the users industry.
In 2021 Thimble launched three new insurance products which added additional steps to the funnel, more sophisticated underwriting, and increased pricing. These new products improved our unit economics, but at the cost of our conversion. After dropping by 22% in the first month, I was assigned the task of optimizing conversion.
I dedicate one week to data analysis and research, creating Amplitude dashboards with high-level conversion and a detailed funnel analysis, watching FullStory sessions, and calculating the tradeoff between higher conversion and higher order value. There were 2 primary points of drop-off:
After performing more detailed analysis on these screens, it was clear there were patterns based on the users industry. I created an experiment roadmap, top heavy with quick experiments aimed at offering certain gig-based industries (like DJ's and Photographers) shorter policies with less coverage, while maximizing the coverage offering to more established industries (like Contractors and Plumbers).
Our traditional General Liability policies are available by the hour, day, or month, which is our strongest differentiator from competitors. Unfortunately due to regulatory restrictions, these new coverage were only available in the form of long term policies. In an effort to increase Average Order Value, we launched these new products by asking users to select their desired coverages first, and then displayed the available durations. If any of the newly launched coverages were selected, you were ineligible for short-term coverage.
The positive: we had a lot of users opting to proceed with additional coverages (but more on that later). But after proceeding, they'd discover that short-term policies were not available and grow frustrated. The screen did not provide a lot of clarity as to why short-term wasn't an option.
It was pretty clear that by explaining to users how to get a short-term policy, which was only buying General Liability and Professional Liability insurance, we could drastically increase conversion. But this was a classic case of whack-a-mole, Thimble had a number of targets, including very aggressive and important Average Order Values targets. Updating the experience to drive every user to short-term would have a heavy impact on our attachment rates (since these new products can't be purchased short-term), impacting Average Order Value.
The experiment was to give users in select industries the option to select their duration up front, while highlighting the value of long-term policies that offer additional coverages. This would allow us to increase conversion of users looking for 1 day policies, where we didn't stand much of a chance converting them to annual policies. Data showed that even prior to launching these new coverages, there were clear segments of industries that were more inclined to buy long-term coverage, because they worked....long term! Whereas certain industries were heavily lopsided to short-term coverage, including some industries that were 99% short-term.
In rolling out these new coverages, we continued our approach to increasing Average Order Value by defaulting a lot of coverages to on. This resulted in some fairly jarring pricing being displayed, when users had just shopped around for General Liability in a few other places.
Again, Thimble had to prove these new products were a success, and that we were capable of launching and selling additional coverages to our customers. We have the option to make them fully optional, but that could have had a huge effect on our bottom line.
Using data, I segmented industries based on their likelihood to buy these individual coverages. Now that we were allowing duration-selection first, we were already dealing with users that were committed to long term coverage for long term work. We could leave certain coverages optional and rely on marketing to upsell them, given the actual importance of these coverages to full-time businesses.
Each industry was assigned a value of defaulting to on or off for each coverage based on their prior behavior. I also updated the screen to included individual pricing for each coverage to entice users to add-on coverages for a small cost.
The overall increase in conversion resulted in a net positive on Revenue, and we decided to roll this out permanently.
