I've had the pleasure of working with over 30 companies during my career as a business consultant. In most of these businesses, especially the early stage companies, there is a lot of confusion around which product metrics matter the most.
In this post I'm going to share with you how you should think about product usage, adoption, and other key areas of focus for the head of product in an early stage startup.
Ask the right product-related questions
Every startup is different but at each stage in the life of the company there are a set of questions which you should ask yourself.
In the early days the main goal of the entire company is reaching product-market fit. This means that the questions we ask ourselves should be related to product market fit.
How do I know if my product has product-market fit?
This is a classic and difficult question. There is no specific answer but the closest rule of thumb answer I can give you is that you are seeing a substantial percentage of users using your product on a recurring bases.
Think about it this way. How does a restaurant know it has product-market fit? It's clear when there is some percentage of clientele which repeatedly visits the establishment and gives over their hard earned cash in exchange for the products the restaurant provides to the market.
The tough part is determining the ideal usage pattern for your product. The reason so many startups get this wrong is because they've built a product for someone else and don't put themselves in their user's shoes.
Another common mistake is assuming that usage should be higher than what is needed to reach product market fit. A user might use the product once a month and be more than happy with it to subscribe to the premium plan and tell their friends about it.
The best way to determine the nuances around repeat usage and the value users are getting is to talk with your power users.
Who are my power users and what makes them special?
Defining who exactly is a power user is another example of a question without a specific answer. You need to have discussions internally and come up with some logic for defining your power users.
Let's look at an example.
Let's say you have a calendar app where users can schedule meetings, plan events etc.
You look in your analytics and see the following:
- 50% of users create zero events
- 35% of users create 1 - 3 events on average per week
- 15% of users create more than 3 events on average per week
Since 85% of users create 0 - 3 events on average per week, the remaining 15% can be considered "heavy users" or "power users".
Another way to think of this challenge is asking yourself, "which cohort of users are most likely to become paying users?", or "which cohort of users are most likely to continue to use the product in the long-run". You'll find that the answer to these questions is the same as the answer to the question, "who are our power users?".
Which features are my users using and why?
Most startups suffer from "feature bloat" and end up frustrated when they inevitably find out that the features they put their blood, sweat and tears into developing aren't being used.
Instead of asking yourself why certain features aren't being used, a smarter question to ask is "which features are my users using and why?".
Instagram is a great example of why this question is important for product owners. The founders of Instagram noticed that many users were using their original app, Burbn, for taking photos. Even though taking photos was part of the functionality of the app, it wasn't the core purpose of the app. This realization helped the team pivot to what we know today as Instagram.
The why part of the question is really important. If you can find out why exactly certain features are being used, you'll be able to identify the true value your service is providing users. The best way to answer this part of the question is to get on calls with power users and simply ask them.
You may find out that your power users see your service very differently to how you see it. This is important for improving your branding, messaging and product positioning.
Invest in data collection and infrastructure from day 1
The next major pitfall that can hold you back from being able to answer important product-related questions is the quality of your data infrastructure.
I could write another 2,000 words on this topic along but instead I'll point you to some articles I've written on this topic.
- The ultimate analytics stack for startups [2020 edition]
- BI Competency Status
- The Business Operations Playbook: How To Implement Ops In Your Startup
The quality, volume and accessibility of data will make or break your ability to answer critical product related questions.
Since the entire focus of the company is on reaching product market fit, an emphasis on business intelligence is a must.
The best metrics for helping a product owner make the best decisions
Now that we've asked the right questions and have data flowing into our databases and reporting tools, it's time to start measuring our product.
Onboarding and feature adoption
The first area where we want to focus our attention is on the onboarding process and core feature adoption.
The reason we want to start here is because if users aren't finishing the onboarding process then there's no point moving forward with measuring the product.
Users that get stuck right in the beginning of the user life cycle will obviously fail to receive value from our product and churn.
The best way to measure onboarding is through a funnel analysis. Use your common sense to determine if you have any major issues with your onboarding flow. Ideally a very high percentage (>80%) of users should be finishing the onboarding and arriving within your app ready to receive value.
Adoption of core features
Once you've measured feature adoption and happy with the results, you can move onto measuring adoption of your core features.
Each product has different core features. The way to think about this is to ask yourself the following question; "what percentage of users are doing what we need them to do, in order to receive the value we provide".
This might be publishing their first blog post (Wordpress), creating their first event (Google Calendar), or creating their first zap (Zapier).
Another way to ask this question is; "what one thing we expect users to do, such that if they don't do it, we expect them to lose them as users".
Once again, you can determine this through a funnel analysis, and then I'd recommend repeating it as a cohort analysis.
My post, the feature adoption funnel, is the best article I've published on this topic and I highly recommend it.
User retention
After measuring feature adoption for your core features it's time to move onto user retention.
If adoption of your core features is low (<75% of new users) then you'll want to stop here and focus on getting that number up.
Retention is the continued usage of the product so if adoption is low then there's no point focusing on improving repeat usage.
Let's assume adoption of your core features is high and we can move on.
Retention is a big topic and I've written a few articles on the topic. My best are listed below.
Week over week, or month over month retention for the main user behavior we care about is usually a good place to start.
This should be done using a classic retention cohort table, similar to what is shown below.
Ideally your retention curve flattens out, and you have a percentage of users which stick around indefinitely. Of course, for every product it's different and you'll want to combine your findings with common sense to determine if your product is in a good, or bad state.
To summarize this section of the post, you'll want to determine the following metrics.
- Finished onboarding % - The percentage of users who finishing onboarding and can move to the next step in the process, move towards receiving value from the app.
- Core feature adoption % - The percentage of users who do what is necessary to receive the core value of the service.
- User retention % - The point where the retention curve flattens out. If your retention curve doesn't flatten out then it's important to know at the very least, how many users are retained after the first few periods (typically weeks, but could be months) after initially receiving value.
I consider the three product metrics listed above as the most critical for product owners in early stage companies.
In Summary
It can be very overwhelming for an early stage startup when it comes to understanding how the market is reacting with their product.
In this post I've covered a few key questions which a company should ask itself in order to cut through the noise and truly understand how the market sees their product.
I included a number of useful resources on establishing a company with a data-first mindset. Failure to take business intelligence seriously in the early days of the company will leave you blind and lacking the ability to make key decisions. Don't make that mistake.
I wrapped up the post by covering the three most important product metrics for early stage startups. If you can measure and continuously track these three, you'll be well positioned to make critical product strategy decisions.
