Growth
data.world is a cloud-native data catalog built on a knowledge graph. Our platform gives organizations more transparency into their data stack and helps them simplify data discovery and access. The tools within the platform are created with both technical and business-minded folks in mind, providing easy-to-use features for data discovery, governance, enrichment, and much more.
Our platform has both a private enterprise offering, as well as a community offering. The community offering includes a free tier and individual paid tiers. In 2020, we were bringing in a good amount of new community users per day. While new user acquisition is an important metric, we naturally had to follow up with the logical next question: ‘how can we best position our product offerings in a way that leads to higher paid conversion rates and lower churn among our community users?’
Company —
data [dot] world
Role —
Product designer; taking ideas and translating them into actual implementation designs
Challenges —
Dissecting and understanding user goals at each stage of the funnel
What excites me —
Testing assumptions and analyzing results
THE GOAL
Increase the number of paid community users through quick, data-driven growth strategies
A small team within the product and engineering departments wanted to A/B test quick, iterative solutions that were designed to convert free users to paying customers. The intent was to not stop everything else we were doing, but rather to have this as a supplemental project. We wanted to make data-driven decisions for our conversion funnel by AB testing interventions in current user flows.
Common personas in our community include data hobbyists, university data students, and some non-profits!
THE PROCESS
Used common platform interactions to identify the target user group
In our bimonthly thirty-minute meetings, we first needed to identify the target user group for our tests. We had specific UI-events and general actions that indicated the user would be an ideal target for user conversion, such as:
Requesting access to data sources
Hitting data storage limits for datasets
Hitting the limits for creating projects
AB tests at different points of the conversion pipeline
After we identified the actions and workflows these target users were going through, we created a series of tests. Some of those tests included improvements to the copy used to market our product offering, nudges to encourage users to try out our high-value features, and a revamp of our pricing page.
THE CONCLUSION
We need additional resources and a different marketing strategy to attract more users at the top of the funnel.
At the end of the quarter, we ran four AB tests at different levels of the funnel to optimize our conversion rate. We concluded that:
There was not a lot of low-hanging fruit for product changes.
Any significant increase in upsells will require dedicated marketing resources and a different strategy to attract the right users at the top of the funnel.
Our main product offering is an enterprise data catalog, so bifurcating our product positioning in the market is not a good idea.
Regardless of the result, this project was still considered a success because our small team was able to address an ongoing question within the organization without sacrificing any customer commitments. We have data to show that our current strategy is not working well, and indicators to guide us on a different strategy.
Since the end of this initiative, our external-facing teams have taken a new approach of working in partnerships with champions on the community side.