Consumer Tech Startup Key Metrics Template
JUNE 25TH, 2020
I look at and invest in a lot of early stage consumer technology startups. As part of that process, I’ve compiled a metrics sheet to help benchmark performance versus other sucessful early stage consumer tech startups I’ve invested in. While no living document like this is ever perfect, this is currently my template to evaluate the overall health of a consumer technology business. I’m sharing it so that others may benefit and improve it. If you have suggestions, please let me know.
Google Sheet: Consumer Tech Startup Key Metrics Template
The template is broken into six sections: People, User Growth, Engagement & Retention, Cash & Monetization, and Engineering.
People is the first section because people are any startup’s most important metric. This section serves as a guage of employee satisfaction, headcount, and recruiting metrics. Indicators of challenges include a spike of non-regretted attrition or a decreasing employee satisfaction score.
User Growth starts with DAU and MAU, looking for high level growth trends. I then dig deeper and look at ratios suchs as ‘DAU:Registered’ and ‘DAU:MAU’. Next, I funnel user data through the Growth Accounting Framework. This looks at the underlying active user dynamics by segmenting new users, reactivated users and inactive users. I then look at acquisiton funnels, with an eye towards organic viral growth versus paid growth and break paid growth down to arrive at an Avg CAC.
Engagement & Retention looks at typical cohort retention checkpoints (D1, D7, D30, D365). I also look at average time per session, average sessions per month for both the overall cohort and power users within the cohort. I like to look at returning users in total and as a percentage of MAU to get additional feel for stickiness. Lastly, I try to arrive at an average user lifetime.
Cash & Monetization looks at cash burn versus projections and revenue versus projections in order to guage a team’s ability to forecast. I then dive deeper into revenue per MAU, infrastructure cost per MAU and gross profit per MAU. Once I’ve arrived at monthly gross profit per MAU, I can apply this to the average user lifetime calculated in previous section to arrive at a user LTV. I then compare the LTV to CAC and benchmark against sucessful consumer starturps on this metric.
Engineering guages performance of the product team. It guages average days between releases, and average delay (in days) per release. This section could be improved. That said, it’s a good starting point. The best teams that I’ve invested in push releases at a torrid pace, constantly iterating based both on user feedback and team intuition about what users don’t know they want.
The template is meant to provide a high-level overview of a consumer technology business and identify areas of excellence and areas worthy of deeper investigation. It’s a tool, not a comprehensive analysis of a business.
