Data-driven VC #23: How to measure productivity and identify potential for improvement
Where venture capital and data intersect. Every week.
👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.
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I ended last week’s post with the following conclusion:
“I’m convinced that the application of data-driven approaches allows most VC firms to become a better, more successful version of themselves. Given the significant impact for new and just evolving VCs, even an 80/20 out-of-the-box solution (like an off-the-shelff e-bike) makes a huge difference. Don’t overcomplicate it, just start with something and avoid getting stuck in the “buy-versus-build bubble”
This last statement has sparked a number of great comments and DMs, all in the same vein of: “Yes, this is us and although we urgently want to become more data-driven, we’re stuck. We tried to work with freelancers/devs building our own solution, struggle to find the right off-the-shelf tools etc.” Well, the good news is that you’re not alone. Your current state probably applies to the majority of VCs out there.
Where to start your journey to become a data-driven VC?
Although I won’t be able to provide you with a silver bullet answering all of your questions, I dedicate this post to sharing a framework that may help you identify the strengths and weaknesses of your current setup, reveal the potential for improvement and help you to prioritize next steps to becoming more data-driven.
To start off, we first need to understand the status quo both qualitatively but also quantitatively. Simple but powerful: “If you can’t measure it, you can’t improve it”. Let’s look at the individual components and bring them together thereafter.
Qualitative status quo
There are two major components on the qualitative side: The tool stack and the investment process. We need to visualize both before getting into the quantitative part.
Tool stack: Following the lines of my previous “VC Digitization Journey” post, tool stacks look different depending on your individual stage of the digitization journey. Generally, the more advanced the stack, the more fragmented it becomes. As a result, you’ll lose plenty of time switching contexts without even noticing it. Ask your team about the tools/websites in usage, you’ll be surprised by the diversity and oftentimes redundancy, e.g. some using Google Meet while others use Zoom or Teams - within the same team🤯
Summarize all solutions (and associated costs) that make up your tool stack.
Investment process: Same same but different. On a high level, every VC investment process (excluding impact funds) looks more or less the same though sometimes details differ. The figure below shows the different stages of an exemplary investment process. The different stages are typically the “status” fields in a VC CRM system to track every investment opportunity through the process.
Visualize all stages (=status labels) of your investment process.
Quantitative status quo
Relating to my overall vision of leveraging data to become more efficient, effective and inclusive, let’s look into all three dimensions and try to understand how we can measure them and what the potential impact of data-driven approaches may be.
Efficiency: The ratio between input and output. The higher the ratio, the more efficient. In our case, input is the time of the investment team and output is the number of deals processed (yes VC is an outlier business and quality is more important than quantity, I know, read on to “effectiveness”). For more granularity, we split the input into two sub-components “how much time is spent” and “how time is spent”.
The “how much time is spent” shall be constant as we assume that the investment team would not work more or less and not hiring/firing people. Moreover, we can split the overall time spent by stages across the investment process to generate more insights. If we apply the same split on the output side, i.e. number of deals processed in the respective stages, we can measure efficiency quite well across individuals and the team. For example, 8 hours per week spent on “outbound to founder” and “initial review” stages translating into 30 new deals added to the CRM system would mean 16 minutes per new opportunity.
The only way to improve this efficiency ratio (=generate more output with the same input) would be to change “how time is spent”. No surprise.