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Listen to the whole conversation with Sean on the Grow With It podcast:
Identifying, surfacing, and operationalizing data is a challenge for any early-stage startup. Many companies launch their product but don’t have the infrastructure to build hypotheses or measure outcomes. But data is crucial for building a successful sales engine. The big question is: what data is worth tracking, when is this data required, and how do you get it
Identifying and structuring the right data to educate a sales team, product team, and growth team is something Sean Whitney, Principal at Craft Ventures, knows all about. Before Craft Venture, Sean managed the corporate operating model and ARR reporting at Figma. In the early days, Figma was a self-serve business, and there wasn’t a sales team. “It was very start-up,” Sean recalls. “There was no piece of our go-to-market [strategy] that I didn’t touch, in some way, shape, or form.”
Sean knew when he started at Figma that he had a challenge ahead. His mission was clear; “Let’s be as accurate as we can with a prediction model, predicting each step of the funnel - starting at the traffic to the website to signups,” he shares.
“There’s a little more nuance with Figma as they monetize teams, not really individual users, [but we were] seeing how those teams convert to paid [users] over time, if they retain and how they grow, and then if they churn. Each step was a tab in an Excel model that we would just dump a bunch of data in from just writing SQL queries, and then iterate constantly to be as accurate as we could and understand where the main drivers were.”
Fortunately, Figma wasn’t short of data to play with. “We were lucky to have millions of rows of data because we had so many users in Figma, even in the early days, and it took a lot of dissecting [to uncover] how certain changes (e.g. new features, moving paywalls, etc.) might influence different parts of the funnel.”
Sean says there was a close collaboration between the growth team and the strategic finance and business operations team to uncover opportunities in the funnel and implement data-driven experiments to improve conversion.
“Data analysts were designing how the data was structured…they had SQL wisdom and were so good at understanding what was working and what wasn’t in the funnel, and then we would use that data to educate the growth team about different projects,” says Sean. “[Data-driven] experiments included the re-engineering of the free tier, which took a year to do, and led to a substantial conversion increase in the business.”
When it comes to data, centralizing your data is key for informing business strategy. Sean shares his three guiding principles.
“Put yourself in a position to be able to tell a strong data story,” says Sean. “Without knowing what’s going on in your tool, you’re screwed. Build a dashboard that shows you signups, where the drop-off is happening, where usage is happening, what features are used, etc.”
“Find a way to summarize your data and have that as a single source of truth,” says Sean. “Don’t have it in its raw form. Institute tracking, pull out what you think matters, augment it with firmographic data, and build out a central table of users and customers and companies – whatever your unit is – and make sure that it is updated constantly.
"That’s your source of truth for everything you’d ever want to know about your customers or your user, and then utilize that for experiments, for optimizations and improvements, and the entire go-to-market flow.”
“Get down to the nitty-gritty details on what your ARR is, so you can show how each customer has grown over time,” says Sean. “Otherwise, how will you know what to work on in the product?”
The use of Salesforce, access to data, and having that data at their fingertips was critical to the operation of the business. Having the right data structure enabled quick sales cycles which helped to accelerate the growth of the business. “Efficiency is the best word for it.”
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