As our segments and personas are defined, we can analyze the data to discover (1) why people love Narada, and (2) how you can help more people love Narada. We can then funnel these insights back into your to decide where to “deep dive” and shift the "somewhat disappointed" to become enthusiastic advocates. A. Segment to target personas
Examine the users who would be very disappointed without Narada — these are the people who most love Narada — and use their personas to narrow the market. We should see the very disappointed group fall into our persona groups which will allow us to do deep dives into those groups (and go find other users).This will eventually grow our product market fit score from our initial baseline
We can toggle the personas below to see how changing the market affects the product/market fit score. This is much more efficient than asking the engineering team to develop every feature that comes up in a feedback call. These are the personas from our Trello.
Instructions: Check the Is Target Persona boxes below to decide which personas to include in our target market. Then, look at the charts below to determine if we have product market fit within your selected basket of personas.
Survey Results
The important number to look at here is the “very disappointed” percentage. If it’s over 40%, we have product-market fit.
Product market fit threshold: % Selected personas above: % very disappointed →
All survey respondents: % very disappointed →
Segmenting to target personas may not be enough. In order to reach a 40% threshold we should also understand why these users really love Narada—and how we could help more users love Narada.
This is why we included additional key questions:
Why do people love Narada? What holds people back from Narada? B. Why do people love Narada?
Method: What benefits do very disappointed users enjoy?
To understand why people love Narada, we take the users who would be very disappointed without Narada, and analyze their responses to question #3: "What is the main benefit you receive from Narada? We can collect the data into word cloud like the one below to see what trends emerge.
"Main Benefit" responses from target persona users who would be if they couldn't use Narada anymore:
C. What holds people back from loving the product?
Method: What improvements do somewhat disappointed users want?
To understand what holds people back from loving Narada, we take the users who would be somewhat disappointed without the product, and focus on the subset for whom the main benefit of the product matches the theme we just identified in the previous step — (These are the users who are on the verge of loving the product, but something — and likely something small — is holding them back.) We then analyze their responses to question #4: "How can we improve Narada for you?".
We can use the keyword search below to explore how Narada’s benefits corresponded to areas of improvement.
Segment the "somewhat disappointed" responses by your main benefit keyword
Enter a keyword for the main benefit you would like to analyze:
Word cloud of responses to "How can we improve Narada for you?" from somewhat disappointed users who found the main benefit
This might be evidence that we are missing a feature or quality of Narada that could enhance our product market fit, but we have limited resources and cannot develop every feature or fall into the Second System trap . This leads straight to our next step: the !