Engineering Product and Market Fit

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(This article is derived from a presentation given by Gideon Spencer)

As creators and developers, we are constantly coming up with new ideas for what we believe will be the next big product. However, creating the hottest product on the market is about more than just having a big idea; it is about engineering the product/market fit to ensure that you have an item that users cannot live without. In this post, I will be outlining a systematic approach you can take to product/market fit, as well as examining a case study on this process. Another great source of information on the topic is the original post written by Rahul Vohra, the founder of the company in the case study, Superhuman.

What is Product/Market Fit?

According to Marc Andreesen, “you can always feel product/market fit when it is happening. The customers are buying the product just as fast as you can make it — or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You’re hiring sales and customer support staff as fast as you can. Reporters are calling because they’ve heard about your hot new thing and they want to talk to you about it. You start getting entrepreneur of the year awards from Harvard Business School. Investment bankers are staking out your house.”

While Marc Andreesen does a good job of describing the event that tells you that you’ve achieved product/market fit, his examples are lagging indicators that only appear once you’ve achieved success. To actually engineer product/market fit, you need leading indicators. 

Sean Ellis, who ran the early growth for Dropbox, LogMeIn, and Eventbrite, and coined the term “growth hacker,” came up with one of the best leading indicators for product/market growth. He argued that if at least 40% of your users would be very disappointed if they could no longer use your product, then you’ve achieved product/market fit. This is great because it provides a leading indicator we can measure throughout development; the number of users who would be very disappointed without your product.

Engineering Product/Market Fit

When we engineer product/market fit, we are creating a systematic approach that allows us to reach that 40% user metric. The process is also built on the fundamental belief that it is better to have a few people who really love your product than a lot of people that only like your product. We want to identify that small number of fanatics that love the product and then, through analyzing their feedback, actually grow both the size of the user base and the percentage of users that really love the product. 

Engineering product/market fit can be broken into four steps:

  1. Segment users who would be “very disappointed” without your product (the fanatics). This is most easily done with a survey or something equivalent. 
  2. Analyze feedback from the fanatics and convert on-the-fence users. By understanding why the fanatics love the product and how they are using it, you can then make changes to improve your product, while also converting users who are on the fence into fanatics.
  3. Generate a roadmap that focuses on improving the experience of fanatics and users who are on the fence. You want to ensure that you are being intentional about making those iterative improvements.   
  4. Repeat. User feedback loops ensure that you are continuing to make those iterative improvements and continuing to see improved metrics over time. 

Case Study: Superhuman

Superhuman is a startup that was created as a means for processing high volumes of emails. It was designed for users who receive 100-200 emails per day to help them process, organize, and reply to in a much more effective means than you can find with common email clients.

To grow their startup, Superhuman applied the four steps of the systematic engineering approach above. The first step in engineering product/market fit is to segment users to find fanatics. Superhuman sent out a survey with four simple questions. The first question read as follows: 

This question was designed to identify the users that would be “very disappointed” if they could not use the application. When Superhuman received the results of the survey in 2017, they found that only 22% would be very disappointed if they couldn’t use the app, which meant they were only about halfway towards the 40% target. 

52% of users responded that they would be “somewhat disappointed” if they could no longer use the app. This was a promising metric because it meant that 52% of users had the potential to be shifted to the “very disappointed” zone. While they couldn’t realistically shift all 52%, it did give them a good starting point. 

Going forward, the “very disappointed” and “somewhat disappointed” groups are the ones that Superhuman wanted to target. While you shouldn’t outright disregard the remaining users, they do not represent the greatest return on investment. Additionally, when you are first releasing an app, a percentage of users that download it are simply testing your product or searching for a specific need that your product will never satisfy. If you listen too intently to these voices, you have the potential to be steered away from the original goals of the app you created. 

The second question that Superhuman asked their users was: 

This question identifies the personas and the type of people that would be disappointed if they could not use the app. When you have people who absolutely love your product and you ask them who will benefit from your product, most of the time the user will describe themselves. Superhuman found that the primary people who loved their product were founders, managers, executives, and people in business development. 

Now that they understood the personas that had the potential to really love the product, it allowed them to filter out the personas that were less likely to be converted into fanatics. Superhuman then filtered out those personas that they didn’t want to focus on yet. For example, people who were in sales, engineering, and customer success were eliminated in favor of those who were more likely to become fanatics. By segmenting the userbase in this way, they were able to go from 22% of users being “very disappointed” without the app to 32% of users being “very disappointed.” This showed that they were starting to better understand their userbase and were increasing the odds of finding that successful market fit. 

Once they successfully segmented their userbase, Superhuman was then able to move to step two of the engineering process, which is analyzing the feedback from the fanatics and converting the on-the-fence users. Superhuman accomplished this step through the information gained from the next two questions of the survey: 

This question helped Superhuman identify why the fanatics loved the product. When they looked at the data, they found that the fanatics really loved the product because of the speed with which they could reply as well as the extensive keyboard shortcuts that the app utilized.

With this information from the fanatics, they could then look at their userbase again and identify which on-the-fence users could benefit from that core offering. When they looked at the data, they found that 30% of the users who were on the fence understood that the speed that Superhuman provided was the biggest benefit of the app. So then the question was, “what was stopping them from loving the product entirely?” 

That brought them to the final question of the survey:

The fourth question allowed Superhuman to remove the roadblocks that were keeping the on-the-fence users from becoming fanatics. They focused on the responses of the 30% of on-the-fence users who understood the core benefits of the app. When they looked at the responses from these users, they discovered that the two main roadblocks were that there wasn’t a mobile app and that it lacked integration into other email providers. 

Now that Superhuman understand the responses from users, they could move to step 3 of the engineering process: Generating a road map that focused on improving the experiences of fanatics and users who are on-the-fence:

  1. The first thing they wanted to do is double down on the things that the fanatics loved. Superhuman focused their efforts on making the fanatics love the product even more by showing them that they were listening to them and building the product for them. 
  2. The second thing they wanted to do was remove the roadblocks that were holding back the users that were on the fence. There is overlap between the first and second steps in this process, since improving things for one group automatically improves it for the other group. By structuring the backlog this way, Superhuman was increasing the love while removing the roadblocks, which increased the rate at which on-the-fence users began to love the product. 

Finally, Superhuman moved to the fourth step of engineering a product/market fit, which was to repeat the process. What Superhuman found was that after only three quarters, they went from 33% of their userbase being fanatics about the product to 58%.

Takeaway

It is possible to create product/market fit for your product through strategic engineering practices. When engineering your product/market fit, you always want to: 

  • Identify the personas that are fanatics and understand why they are fanatics. 
  • Identify those users that are on the fence but have the potential to become fanatics and understand what’s holding them back. 
  • Double down on what the fanatics love.
  • Improve on what’s holding back the potential fanatics.
  • Repeat

I’ve used this method with numerous new apps and startups, and have found that, when applied correctly, it can be incredibly successful. For more information on how Grio can help your product achieve product/market fit, visit https://grio.com/our-services

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