7 Metrics to Help You Make Smarter Decisions During the Product-Market Fit Phase

Ensure your startup has the best chance of success by keeping these key points in mind

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Recently I was talking with a sales automation startup that failed. We were chatting about what went wrong and what the company might have done differently. One issue that stood out was that they didn’t define and track the relevant metrics to capture what is going on under the hood.

One of the co-founders said, “I think one of our biggest mistakes was not tracking the right key performance indicators (KPIs) and not checking them regularly enough. They would have greatly helped us make smart product decisions”.

Revenue is a very important metric when it comes to starting a business. But it’s not the only metric you should be concerned with if you are still in the hunt for product-market fit. I’ve also seen a lot of entrepreneurs get so obsessed with customer acquisition that they forget about customer retention. During fundraising, some startups are very creative with [especially] growth metrics, when it comes to justifying their business model to investors. But I don’t think those are the kinds of metrics you should be paying attention to while in the hunt for product-market fit. I decided to pull from my experience and share a list of “user-centered” KPIs you should be tracking to make smart product decisions while searching for product-market fit. And remember based on your product you might pay attention more to some KPI-s VS others.

Retention Metrics

In the product world, the term “retention” refers to users who come back to use your product after downloading or trying it the first time. Depending on your product there are a few key time periods that you should be looking at, when it comes to retention. In a nutshell, retention refers to the percentage of users who stick around.

• Day X Retention. This metric shows how many users are sticking around with your product X days after the first interaction with it. Depending on your product you might watch after Day 1 retention, Day 7, Day 30 etc. While it’s normal to see significant dropoff between Day 1 and Day X retention, depending on your product you should conduct root-cause analysis to understand the WHY. For example, if for a ticket booking app your Day 30 retention of 20% might be ok, for a social network mobile app it is not.

Retention metrics give you actionable insights into the good news and the bad news when it comes to user satisfaction and loyalty.

Engagement Metrics

Engagement metrics tell you how many people are engaging with your product regularly, so you can gauge product stickiness. When your product is sticky it is a sign of “We nailed the product-market fit”. Different products have different metrics to measure engagement. The most common ones are:

• Active users in a given period. This metric shows the number of users who interact with your product, website, or application in a week or a month. Depending on your product there are almost unlimited definitions for what “active” means. After defining the “active user” many startups [especially b2c] typically track the number of daily active users (DAU) and monthly active users (MAU).
• Stickiness ratio or DAU to MAU ratio. It measures the stickiness of your product or new feature — how often your users engage with it. For example, if on an average day you have 200 unique active users, and in a month you have 1,000 unique active users. Your DAU-to-MAU ratio would be 1:5, meaning that one out of five monthly active users come back to your product on a daily basis. Looking at the ratio rather than concentrating solely on DAU or MAU numbers, you can get a better idea of how often people are engaging with your product. It shows how many of your regular users are coming back every day versus once a month or less often. Usually, high engagement means you are in the right direction towards finding the product-market fit.

Adoption Metrics

In the context of a product or a feature, adoption is the act of beginning to use something new. The post-signup period is the most crucial in a product lifecycle: for instance in case of mobile apps after the first day adoption rates drop significantly. Therefore, onboarding new users starting with the first time they open the web or mobile app is absolutely critical. Customer success managers [especialy in b2b startups] are often laser-focused on adoption metrics, because their main goal is to help customers get the maximum value from the product. Again based on the product you might come up with different adoption metrics, but here are the most common types you can pay attention to:

• External adoption: This is very different depending on your product, but in general it shows how long it takes for new users [after the first interaction] to accomplish a goal with your product — do the first post (e.g. Medium), or make the first transaction (e.g. Coinbase), or order the first ride (UBER).
• Internal adoption: This one shows the average number of days existing users use the new released feature. Or the percentage of existing users who used the new feature for several times within a time period (the frequency and the duration of the period depend on the product). For example, the percentage of existing Instagram users who used a new story feature at least 2 times within 7 days of its release.

Adoption metrics can tell you a lot about achieving the product-market fit, but usually, startups ignore defining those metrics tailored to their product/feature.

User Task Success Metrics

Task success metrics provide an indication about the usability of the product. They signal whether or not users can accomplish their goals with a product, service, or feature. I like success metrics because they are easy to collect [if you establish the right instrumentation for data collection] and they can tell you if your users perform a task as specified. After all, if users can’t accomplish their target task, all else is irrelevant — you can’t expect the product to achieve a product-market fit. User task success is the bottom line of usability and the value of the product. [Just think for a moment if UBER had millions of downloads but nobody ordered a ride.] Depending on your product these metrics might vary dramatically. Here are some common task success metrics that can give you priceless information about the users’ experience:

• Time-on-task. The average duration of time it takes users to complete a given task from the moment they start. Seems straightforward yet time measurements are complicated and you need a proper data instrumentation to collect the RIGHT data to measure this metric.
• Task success rate: The level to which users are able to successfully complete tasks using the product. Failure to complete a task scores 0%, success equals 100%, and there are all kind of states in between to lable partial success. For example the task success rate for publishing an article in Medium might be the percentage of users who published an article out of all those who started writing. For Robinhood it might be the percentage of users who completed stock purchase transaction out of all those who opened the app.

Task success metrics [imho] are probably the most important ones after your first release. They can tell you a lot on how close is your MVP to capturing the product-market fit

Final Words

It’s amazing how all of these metrics are connected. In combination, they provide a holistic view of whether you nailed the product-market fit or not. But it can be difficult to measure and monitor all of these metrics as an early-stage startup. You just don’t have the time.

I recommend you start small and focus only on the ones that capture and reflect user value in your early stages. Also, it is important to get agreement within the team and all other stakeholders that a certain metric is important. Disagreement results in ignoring metrics and not taking action. Make sure to create a shared understanding and acceptance of WHAT are you measuring and WHY. And remember metrics can tell you the WHAT, but cannot tell you the WHY.

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Interested in everything in the intersection of products, behavioral science, data analytics. Exploring how tech giants are engineering consumer behavior and sharing what I have learnt. Feel free to reach me via twitter to chat @artashesvar