1/ VC efforts to build internal technology products continue to grow since I tweeted this in 2018. Most of those efforts are to influence perception of LPs or potential portfolio companies. Some are having impact. Here is my evaluation framework.
https://twitter.com/ryan_caldbeck/status/1010335177875177472
2/ First focus on the problem that is being solved by the data. Investing into tech for tech sake is dumb (that’s a real term). Let’s debundle the VC/PE process to identify what the job is, then evaluate where data/tech is likely to help:
3/
-Raising money
-Sourcing companies
-Evaluating companies
-Winning deals
-Post-close (waves hands)
-Raising money
-Sourcing companies
-Evaluating companies
-Winning deals
-Post-close (waves hands)
4/ The most common misstep I hear, even from top tier GPs & LPs, is lack of clarity around what problem the tech is solving. I typically hear excitement around a slick demo or impressive backgrounds.
5/ But there is often a lack of the product management work that is critical when building technology: *what problem are we solving*.
6/ That shouldn’t be surprising. Typically VCs hire an engineer or data scientist with an impressive background, but PMing is done by a GP (or more likely an Associate thrust into the job). In most cases the person has no PM experience and is doing it off the side of their desk.
7/ Result? The technology being built is naively expected by all stakeholders to solve all problems. Sourcing, execution, post close. With a typical team of 1-4 eng/data scientists, that’s a hopelessly complex rats nest of problems to be solved.
8/ Those (few) VCs that have demonstrated some success w/ these in-house technology efforts have narrowed the problems to be solved to a much more limited set, and are tracking the impact of the efforts. Here are some of the questions I would ask if I were evaluating the impact:
9/ Sourcing:
-what portion of the deals done in your space were proactively ‘Tracked’ by the technology [6] months ahead of time? (a pseudo measure of recall)
-what portion of the deals done in your space were proactively ‘Tracked’ by the technology [6] months ahead of time? (a pseudo measure of recall)
10/
-what portion of the deals that are ‘Flagged’ by the tech are ‘Interesting’? (trying to get at precision) T, F&I need to be defined.
en.wikipedia.org/wiki/Precision….
-what portion of the deals that are ‘Flagged’ by the tech are ‘Interesting’? (trying to get at precision) T, F&I need to be defined.
en.wikipedia.org/wiki/Precision….
11/ For those with longitudinal sourcing data to feed into the tech:
-what portion of companies acquired/IPOed or other breakout measure were Tracked/Flagged [3-4] years in advance
Don’t let them wow you with billions of data points millions of co's - what matters is impact.
-what portion of companies acquired/IPOed or other breakout measure were Tracked/Flagged [3-4] years in advance
Don’t let them wow you with billions of data points millions of co's - what matters is impact.
12/ Evaluation: (warning- this is mushier)
-What is performance of deals where tech was intimately involved in evaluation vs. those where it wasn’t?
-What is performance of deals where tech said to invest (TI) vs. humans said to invest (HI) vs. both said to invest (BI).
-What is performance of deals where tech was intimately involved in evaluation vs. those where it wasn’t?
-What is performance of deals where tech said to invest (TI) vs. humans said to invest (HI) vs. both said to invest (BI).
13/ Post-close: (the most mushiest)
-can you point to tangible impact at portfolio companies where the tech had impact? New hires, new distribution, product launches, changes in strategy.
-can you point to tangible impact at portfolio companies where the tech had impact? New hires, new distribution, product launches, changes in strategy.
14/ Look at the tech. Roll up your sleeves and play around with it. Don’t just accept the slick demo. Poke and prod. Demos can be fancy- but that doesnt mean the tech has impact on the VC/PE firm’s performance.
15/ If you’re being told the VC firm has an information advantage from in-house tech, focus on where the data comes from. If the GP is talking about LinkedIn, website traffic and credit card data….that data has become commoditized. Revert to asking about impact.
16/ Also recognize that in-house tech efforts aren’t the only answer to leveraging tech to enhance the effectiveness of the GP. The VC can outsource a lot very intelligently, particularly related to CRM (seperate from proactive identification of prospects), internal comms, etc.
17/ Also interesting to dig into is GP’s willingness to pay. In PE (more than VC) investors will spend $X00k on a 4 wk consulting project to diligence but struggle to find budget for 6-fig annual data contract. If the problem they are solving is around sourcing.. this must change
18/ They are often willing to pay more for that consulting project because they can expense it directly to a deal. Whereas the annual data contract gets paid for by the GP. That's a good indication of how much the GP believes in the impact of the data (or tech if relevant).
19/ To be clear, some GPs think in-house tech investment doesn't make sense . That’s their view. But those that are investing, and touting their efforts, should be prepared to talk about the actual impact in detail. Including what problems the tech is solving.
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