A recent study comparing the effectiveness of using historical funding data vs Fintent’s CapRaise Scores to predict the next raise, finds that using funding data + CapRaise data is 3x more effective than using funding data alone.
For the study, we’ve analyzed 1,487 recent Seed, Series A/B, Growth Equity and Debt Financing raises using Crunchbase data. We limited the raises to the July 2020 to April 2022 timeframe for US only companies in the SaaS industry.
Results of a simplistic funding data model
To create a simplistic model that relies on funding data to predict the next raise, we first analyzed Seed to Series A and Series A to Series B upgrades from June 2020 to May 2022 and found that the average timeframe between the raises was 10 months.
Using this as a jumping-off point and adding 3 months of padding on both ends, we reviewed how many raises we would have predicted if we added 7 to 13 months to the date of the previous Seed or Series A raise.
The results came out to be 25% of Series A raises predicted from Seed + 7 – 13 months and 32% of Series B raises predicted for Series A + 7 – 13 months. For Growth Equity and Debt Financing, the results were 0% for both as they didn’t raise on a set schedule and there was mostly no previous funding raised.
In total, the model correctly predicted 15% of all raises analyzed.
Results with a CapRaise score model
The CapRaise Score (CRS) is a signal produced by Fintent that measures the propensity of a company to raise funding in the next 3 – 9 months. It is produced monthly and is based on the research activity of companies on the B2B web. It is used by VCs & Growth Equity investors to ensure they don’t miss out on lucrative deals.
To analyze the effectiveness of the score, we’ve examined the CapRaise scores of companies that raised funding 3 – 12 months prior to the raise. To ensure the predictions were actionable, we’ve only counted cases where there were CapRaise scores issued over multiple months (3+) and included only the observations that occurred more than 3 months prior to the raise.
In total, this model correctly predicted 28% of all raises.
Using funding data + CRS
Interestingly, there was very little overlap between the raises predicted by the CapRaise model and raises predicted by the simplistic funding model. That leads us to believe that companies that raised on a set scheduled anticipated the next raise in the near future and didn’t conduct significant research activity related to the raise.
Based on the results, we recommend combining CapRaise data with the funding data to achieve significant lift on the number of raises predicted and miss less lucrative deals.