In Part 1, we discussed interesting thinking on how on to conceptualize product-market fit, a critical toolkit item, and more importantly, how to know that your product is indeed fitting into a desired market.
In Part 1, we discussed interesting thinking on how on to conceptualize product-market fit, a critical toolkit item, and more importantly, how to know that your product is indeed fitting into a desired market. Now we move on to outlining thoughts on product-market fit as a discovery engine and its overall fit as a critical starting point.
The survey I discussed last time serves as the backbone for what Mr. Vohra and Superhuman describe as a Product-Market fit engine. As presented, the steps are survey, segment, analyze, implement, track. The engine is repeatable, scalable...let’s just call it out - it's Lean!
In case you need a refresher, Vohra’s company devised a survey that went out to their audience after they had experienced Superhuman for about three weeks.
This survey would ultimately serve as the backbone of a product-market fit engine. It is elegant in its simplicity. Here are the questions they asked survey participants:
1. How would you feel if you could no longer use [product name]?
a. Very disappointed
b. Somewhat disappointed
c. Not disappointed
2. What type of people do you think would most benefit from [product]?
3. What is the main benefit you receive from [product]?
4. How can we improve [product] for you?
Like any basic framework, Mr. Vohra revealed some nuance that needs to be included, so I’ve added those tidbits in below to give you a more complete picture of the Product-Market Fit engine, when fully revved. In order, it goes like this:
Survey – establish your baseline with a pool of users that really tried out the core offering. Send them the questions in the previous section but give them at least three weeks to use the product first.
Segment – figure out who really loves the product. These folks are the real promoters of your product. They can be identified as the customers who answer question #1 as “very disappointed,” and then go on to describe themselves when answering question #2. Having this information gives you a much more meaningful persona because they reveal their motivations, attitudes, and perceptions to you without you having to generalize about it from demographic stereotypes we’ve seen too often when persona design is mishandled (e.g. Soccer Mom. Ugh.)
Insight: users who love your product will always describe themselves - Rahul Vohra
Analyze – dig into the descriptive words of responses from those who are very disappointed in question #1 and go on to answer question #3 in detail. Generate a descriptive tapestry in a word cloud from their testimonies to visualize the priorities. It is as this point in the process, that something critical needs to happen, if the engine is going to produce the desired outcome.
Confirm – that you have identified the key benefits of your product, as described by the “very disappointed” segment. They should have been able to tell you why they love your product so much. The subsequent word cloud should have visually prioritized which benefits are the drivers for your product (hint: they’re the biggest words in the word cloud!)
Re-segment – your somewhat disappointed segment into two new groups: those that identified the main benefit similar to your very disappointed segment and those that didn’t. You are trying to find the group of users that can still be converted to get your product over the magic threshold of 40%. With this new segment, analyze question #4 to see what is holding them back in terms of features or benefits.
Get real (optional) - What should be pointed out here is that if the math tells you that you still can’t cross the threshold, even if you did convert all the re-segmented “somewhat disappointed” (i.e. resonated with the main benefit population) then you likely don’t have a product that will succeed in the long run, and it’s better to know that sooner than later.
Implement – Mr. Vohra’s team built a road map off the segmented learnings of its survey, devoting half the time on the things users loved (maintain your competitive edge), and the other half on the needs of the re-segmented “somewhat disappointed” but resonate with the main benefit audience (move the needle towards the 40% threshold).
Track – tracking quarterly, Superhuman raised its Product-Market Fit score from a baseline 33% to 58% in about nine months. Mr. Vohra was careful to stress that your mileage will vary, obviously, but the benefits seem pretty obvious beyond the key metric. Utilizing such a process prioritizes your roadmap for you. The engine requires you to maintain the dialogue you are having with your product’s users, which is a product manager MUST. The engine uses a north star - a single, defining metric that continually brings the team back together and keeps them centered and focused.
So, to bring things full circle for our restless innovator, I hope that the ideas Mr. Olsen and Mr. Vohra expressed assist you in getting your product framed up for repeatable success. Like any good starting point, though, there is much to do once the product fits.
Establishing fit identifies the easy part about features - the mission critical ones. What lies ahead for the innovator is the tricky part of scaling the product, which commonly results in making difficult decisions about which features will continue to generate demand and lift usage/revenue.
Then be ready to and defend yourself from the reality that once you are successful with your product, the competition will be on your heels.
Similar inspirit to the product-market fit engine in this post series, I’d like to walk through Kano analysis with you next time. I find it quite handy for traversing the feature expansion portion of the product lifecycle.
In the meantime, I’m always up for some Thoughtful conversations with other innovators trying their hand at bringing the next great product to market and getting a good night’s rest. Feel free to reach out, if it “fits” your day!
Bill is the Director of Product at Skiplist. He has twenty years of experience in UX and Product Management, building a portfolio of successful software experiences that are value-centered for the client, their business, and their customers. His experience with user research, information architecture, design thinking, and project management gives him a unique point of view for solving problems and fitting thoughtful software in the marketplace. He has a B.A. in Economics from Indiana University and has guest lectured at Kent State's IAKM program.