From time to time I work with promising, small startups who are not ready for their first PM but are looking for product expertise to guide them towards product-market fit.

Recently, I was working with a startup that focuses on helping their enterprise clients identify employee engagement issues. Since they felt comfortable in their current core product, they were interested in expanding their product into something they believed would be complementary to their core value proposition: providing advice to corporate leaders on how to take action to fix/mitigate/reduce HR/personnel issues, which would then boost employee satisfaction.

Being a bootstrapped and small startup, expanding their product line would be risky, especially as they have a small developer team and could either work on improving their core product, or work on this new advice system that would enhance their product value, deepening their retention hooks, and further differentiate themselves from their competitors.

The team was also very excited about incorporating machine learning into their advice system, allowing them to scale the service to match their current customer base and their future base.

After internally talking through the risk of working on a new product feature and the opportunity risk of not exploring the new product feature, the team was confused on how they should proceed.

That’s when they called me…

Once I was caught up, I could see the dilemma they were in. There was a lot of value in guiding corporate leaders on next steps, and if they could prove their system did provide such value, they could use their core product to measure the change in employee satisfaction based on their advice!

So how could they potential explore this new advice system and reduce the huge risk of getting it wrong?

We first look at all the assumptions, implicit and explicit, ordered by risk, so that we could analyze and validate each one:

Critical Assumptions

1.  Leaders want to be better
2. Leaders are looking for content through a non-physical conduit (ie not looking for a coach, not looking for books, but looking for an online channel)
3. Leaders want content from us
4. Leaders find our content useful
5. Leaders are willing to pay more for recommended content and/or personalized content (why not?!)

By just creating and ordering the list by risk, it was very easy for the team to come to an agreement on what their first MVP needed to be. They needed to build something, with code or without that proved “Leaders want content from us”.

 

 

But before we could prove that assumption, we needed to look at the two previous assumptions that had a higher level of risk: “Leaders want to be better” and “Leaders are looking for content through a non-physical conduit.”

Thankfully, and quite easily, it was easy to prove these two assumptions as the startup could interview the users that fit the leader/executive persona and validate where they were actually wanted to improve their interpersonal skills and their organizations through discovery interviews.

Once the team validated the base assumptions, they could now embark on a new exciting product journey: proving that their clients wanted content from them and the content was indeed useful. Currently, the startup is working on identifying patterns based on aggregate client data, and iterating on how content can help leaders take action, before heavily investing in personalized content a la machine learning.

I’ll provide an update once the team proves Assumption #3 and #4 and where they net on how machine learning will influence their final product!