I recently came across a fantastic presentation on startup metrics by Andreas Klinger. It is embedded below but given its length I wanted to highlight the key takeaways:
- The biggest risk for a startup is not failing to create a good product with a market; it is having a competitor come up with something a little better. Great example is Lyft, which I am sure is a little envious of Uber.
- There are four stages for a startup to succeed. The first is discovery, generating the product idea. The second is validation, making sure the market wants the product. The third is efficiency, being able to supply the product cost effectively in quantity. Then there is scale, delivering the product to millions.
- To look at it from the user perspective, there are two key elements: finding the product the market needs and then optimizing (the former encompassing discovery and validation, the latter representing efficiency and scale). To find a product the user needs, you need to understand these needs and create something that will be sticky (i.e., that they will return to) and viral (they will talk about). To optimize, you then need to build out the right revenue model and level, and then scale.
- According to Klinger, 83 percent of startups are in the discovery phase (empathy, stickiness and virality) while most analytics are around revenue and scale.
- A/B tests, funnels, referral optimization, etc., are about optimization, not innovation and cannot replace creating a great product that people want.
- There is a way to get product insights from data to create that innovative product and you can do it with a much smaller number of users. They key is looking at whether people stay on your site or in your app, in other words, whether they are hooked.
- Focusing on improving metrics creates a false positive, you can always improve ad conversions or funnels but what looks good for investors does not necessarily improve the product. You may be converting or funneling the wrong users.