How AngelList improved forecast accuracy and uncovered new points of growth
- New points of growth identified
- IndustryFintech
- LocationSan Francisco, CA
- Featuring
Derek Ou-PonticelliHead of Strategic Finance
AngelList helps investors manage funds and startups raise capital—streamlining the venture capital process. With $124B+ in assets under management, their finance team tracks enormous volumes of data to forecast trends and refine strategy.
Results
- A single source of truth for financial insights
- Enabled predictive cohort-based modeling
- New points of growth identified
Head of Strategic Finance
It’s about more than just saving time. With Runway, spotting trends is easier—now we can more efficiently tell stakeholders what number a metric’s going to be and why.
A single source of financial insights
AngelList's finance team tracked a massive volume of data, but it lived in fragmented systems. Different team members had created separate database queries at different times to calculate the same metrics, making it hard to know which data source to use.
Runway centralized everything. With a single source of truth, the finance team now tracks metrics in one place—in real-time—without juggling multiple queries or jumping between tools.
Impact
- No conflicting data sources
- Eliminated hours of monthly data validation
- Clearer, more confident reporting
Even the process of putting together the business review—it used to take hours, with all of the data and the charts, and now it takes a fraction of the time. And, in addition to making sure the numbers are updated and accurate ahead of meetings, I’ve found more time to interrogate the assumptions, and imagine different upside and downside cases.
Faster, more accurate forecasting with predictive cohort modeling
Derek’s team tracked trends manually—running data queries, manually validating and updating data in spreadsheets, and then double-checking all formulas and results to get next month’s forecast. Every month.
Runway automated all of that. Plus, Derek finally found the time to build predictive analytics based on advanced cohort models—enabling them to forecast how capital flows through their platform over time, and how key metrics are affected by the moving parts of the business (like the number of startups signing up or the amount of capital closed by fund managers).
Impact
- More accurate predictions
- Clearer cause-and-effect analysis
- Data-driven strategic planning
Once we realized we could do things more efficiently in Runway than on spreadsheets, we invested time to build cohort modeling, which was a pain to maintain in the past. Now we save time, and get deeper insights. And with the new cohort models, we can make more accurate predictions about when funds would hold their first close.
Unlocking new points of growth
While nothing was fundamentally broken, AngelList’s processes were too manual and slow—limiting the speed of insight.
Runway helped AngelList streamline and automate its processes—helping the team identify growth opportunities faster..
Impact
- New process optimizations unlocked
- Real-time variance tracking
- Continuous improvements to forecasting
Things were generally going fine. But there are new points of growth added because of Runway that might have taken us longer to identify otherwise.
More than just saving time—Runway helps you see what’s next
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