Workshop

The pain of early stage startups is real albeit if you are not Kunal Shah or Ashish Kashyap in Indian context when you can raise $30 million in seed funding. But fundraising is not even the biggest challenge. 

You are really dealing with one of the following problems ? Boss Fight

  • No users or very small number. You don’t even understand how to reach out and engage them as any email based marketing survey has hardly any fills.
  • Very small number seems to be returning or coming back. Is it the problem, product or the UI/UX which is killing the experience.
  • If by some measures, you seem to have hit product market fit and some repeat users, you seem them churning within a few months and it’s not clear why?

The first step of analytics or product analysis at a startup is to run a bunch of quick experiments to gauge the business’s hypothesis using quantitative and qualitative data. You cannot take any assertion or hypothesis for granted.

Using openly available public search data, trends and surveys. Some insights regarding the same can be drawn which as a next step can be validated using qualitative data.

If the product is already out, trying to understand if you are already at product-market fit is very important to know. This can be inferred using the user details and usage statistics.

The curve below from Sequoia tries to draw upon the conclusion of product-market fit for 3 sets for startups. The grey line shows no fit while green and orange have a favourable outcome.

Sequoia - Retention

This has been arrived at by using cohort analysis and by tracking their activity a few days, months or maybe even years down the line but ideally it’s one of the first two.

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