Step one: Scope

The first step is to do the exact opposite of what most product launches do, scope your target audience way down to who you have initially built for. Specifically spell out a hypothesis of who that is. Most product launches start with “What are the places we can get attention?” The better way is to start with, who specifically are we trying to reach with this v1? Then, you can think about where those people “live.”

Step Two: Figure Out Access

Once we have our initial definition, we can then come up with a bunch of ideas on how to access that audience. This will differ based on what the initial hypothesis is, if it is a product vs feature launch, and if we are working with an existing user/customer base or launching something brand new. Email, Paid, Press, Medium, Product Hunt, Hacker News, Referrals, etc. This step doesn't matter as much as step one and step three.

Step Three: Filter

You'll never be able to target your audience hypothesis perfectly with any marketing mechanism. So we need to think about how take initial interest, and filter it to our audience hypothesis. There are a number of ways to do this, but the main ones would be:



Existing Usage Data - If you are working with an existing audience, you should have a good amount of data on them. Not just who they are, but what they have and have not done in your product. This is all valuable data to filter for your hypothesis. User Submitted - Data submitted by the user. Key is to ask the right questions or collect the right data that will let you filter effectively. (See example in the section below). Passive Data - There are a lot of tools like Clearbit which help us append data to understand more about who they are.

Step Four: Search for your success signal

As you let people through the filter and use the product , you look for success signals that validate your product or feature hypothesis. I think about them in three levels, each level requiring more volume, data, and time to get:



Qualitative - NPS, Very Disappointed Survey, etc. Feature Market Fit or Product Market Fit - Healthy retention curves on a product or feature level. Feature Product Fit - Casey Winters has written about Feature Product Fit. From Casey - “Feature/Product Fit requires the feature to improve retention, engagement and/or monetization for the core product. If it doesn't this means it is cannibalizing another part of the product.”

In a lot of cases, we don't find the success signals on our first try. Thats fine. Assuming we have done our filtering, it will be easier to find out why our hypothesis is wrong which will help inform us of where we should navigate to next.

Step Five: Leverage

Once you find some initial success signals around your hypothesis, it is time to leverage it into the next layer of audience you want to target. Think about it as a layer of concentric circles, starting at the center and expanding from there. At some point, you will have built up enough success signals, successful users with strong word of mouth, and other elements that you can remove all filters and swing the doors open.

Example: Superhuman

Back to where I started this post, Superhuman. Just to be clear, I have no affiliation with Superhuman or know anyone on the team well.



Awhile back (I can't remember exactly when) I tried signing up for Superhuman. Here are the hurdles I had to jump through:

Join The Waitlist - I joined the waitlist by entering my email. Initial Survey - I then completed a survey giving info like my company, size of company, role, etc. Long Survey - I then at some point received another survey that was about 15 to 20 questions long asking me about what email client I used, how often I email, what add ons are vital for me, etc. Follow Up Email Convo - I then received an email from someone on their team, asking me Manual Onboarding - I then had to set up a time to go through manual onboarding for the product.

Most of you are probably thinking, “Holy Sh*t, this is the worst way to launch.” Before you draw that conclusion, you might want to look at the amount of word of mouth (like this, this, and this) and press (like this, this, and this) they have received because it is more than 95%+ of the product or feature launches I have seen. Not to mention the substantial amount of capital they have have raised over multiple rounds.



Superhuman is following the exact above process. They clearly have a specific hypothesis of who they are targeting with the initial versions of the product. They then have used a bunch of tactics to create a waitlist. They then have deployed a number of filters to make sure they are getting close to their audience hypothesis for their initial users. They are then looking for qualitative success signals via manual on-boarding (and I'm assuming quantitative ones internally as well). They are then leveraging that into the next layer of audience they build for and unlock and repeat the process again.



Note: Somewhere between creating this presentation, and publishing the blog version of this, the founder of Superhuman wrote a post on this process titled How Superhuman Built An Engine To Find Product/Market Fit. It is worth the read, and while targeted for startups launching initial product, the principles apply to any product or feature launch.



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