In venture, investors review, engage with, and diligence numerous startups for possible investment. Depending on the firm’s industry focus, thesis, stage, geography, and more, the overall count of companies can be quite large.
At WiL, we use data-driven techniques like clustering and vector search via thesis embeddings to prioritize new companies to engage with. In addition, we focus on emerging categories and strategic areas for our Japanese corporate LP base. These approaches help us solve the problem of how to prioritize initial outreach, but a separate challenge still exists for managing our growing network and thoughtfully re-engaging with the right companies. As the number of companies to re-engage with grows, so does the challenge of keeping in touch. For example, WiL’s US investment team members had an average of just less than 200 companies in their pipeline as of April 2024. These were startups they were monitoring and had initiated some form of communication with, be it through outreach attempts or actual connections.
Moving from legacy to optimized
With time as a limiting factor, how can WiL or any other VC firm optimize the follow-up process?
The legacy approach is this: each investor keeps stock of which key companies to reach out to in the future and then sets up reminders in their system of choice (e.g. Google Calendar, Asana). This can work decently for specific cases in which founders suggest reaching back out at a specific time but not as well for situations that are less clear. What if founders simply say to reach out in the future without specifying a time frame? Perhaps your firm has an agreed upon rule to re-engage with all companies within a certain # of months of initial contact so you reach out again by the end of that period. But what happens if you never get a response? The complexity of these situations can escalate quickly, and the manual approach of keying in reminders often doesn’t scale well.
An intermediate solution–perhaps a more useful one than the former but still not sufficient–is to leverage sales or recruiting tools used for email sequences. What these tools add is more structure and visibility to the outreach process. If an initial email isn’t replied to, then the program waits a pre-set # of weeks or months to send a followup email; if that email isn’t replied to, then it waits another length of time to send the next follow up email; and so on.
- Pros: There’s far less risk of companies slipping through the cracks, reducing investment pipeline leakage. Additionally, investors can use their judgment to decide if they need to reach out when the reminder hits, either following up or canceling the sequence event, and can tailor messages according to the amount of time that’s passed.
- Cons: This system doesn’t really address the volume component of the original problem–investors still have to decide how to prioritize companies from their assigned pool and which they’ll write to. This solution also isn’t all that desirable from a founder perspective either as it can result in receiving a lot of repetitive emails in the worst case scenario.
The question remains… What can we do to get more targeted to optimize this process?
How we broke the bottleneck
At WiL, we’ve found that a combination of filters curates the larger pool of companies into a more manageable and actionable set. See below for some of our logic:
(1) To ensure that communications don’t idle too long, we filter for companies that haven’t been contacted in at least 90 days.
(2) To capture companies that might be raising, we filter for companies that had their last financing between 5 years and 9 months ago.
(3) To identify possible inflection points, we filter for companies using a number of different traction metrics when available–employee headcount, web traffic, GitHub forks and commits, product reviews, etc. Pick your traction metric(s) of choice keeping in mind that indicators move at different rates and can have lags.
We then share these companies with our investors via a Slack application on an ongoing basis as the various filters surface different companies. In each automated Slack message, we include a hyperlink to the company’s Affinity (our CRM) profile and webpage, the last contact date, the last financing date, and the traction metric(s) that triggered the company to surface in our system.
The goals are twofold: (1) to refresh investors about the company and (2) to motivate outreach as seamlessly as possible, reducing the cognitive load of sorting through however many companies otherwise. On a monthly basis, our logic helps reduce the pipeline volume to between 4 and 9 companies per investor on average.
All of this isn’t to suggest that manually keying in reminders or using email sequencing tools is bad. In fact, we leverage both of these approaches in addition to the more data-driven solution outlined above. There’s certainly value to keying in specific dates to follow up with founders when the data is available, just as it can be useful to eliminate pipeline leakage through sequencing. However, using a variety of data points to help cut through the noise results in major efficiency gains, sets a clear standard across investors, and allows for iteration for future improvements.
Stay tuned for more insights from Max on data in VC on the WiL blog! You can also find Max on LinkedIn. To read part one of our Unleashing the Power of Data in VC blog series, click here.