Analyse feedback

Judiciously use feedback to convert ambivalent users into people who love your product.
Now that your segments and personas are defined, you can analyse your data to discover (1) why people love your product, and (2) how you can help more people love your product. You can then funnel these insights back into your roadmap. Implementing the roadmap will help the "somewhat disappointed" become enthusiastic advocates.
Selecting target personas
Examine the users who would be very disappointed without your product — these are the people who most love your product — and use their personas to narrow the market. In our case, our very disappointed group was 22% and contained primarily founders, managers, executives, and business development. We focused entirely on these personas, and temporarily ignored all others. Just by segmenting down to these personas, our product/market fit scored jumped by 10% from 22% to 33%!
You can toggle the personas below to see how changing the market affects the product/market fit score. This is much more efficient than changing your product! Once you are ready, you can replace the personas with your own.
Search
Persona
Is Target Persona?
Number of Responses
% of Responses
% Very Disappointed
% Somewhat Disappointed
% Not Disappointed
Open
1
Student
1
2.33%
100%
0%
0%
↗️
2
Unemployed/Retired
5
11.63%
40%
40%
20%
↗️
3
Professionals
10
23.26%
70%
30%
0%
↗️
4
Labour worker
12
27.91%
58%
25%
17%
↗️
5
Rideshare driver
15
34.88%
47%
27%
27%
↗️
There are no rows in this table
43
Sum
Target Persona Very Disappointed Score:
56
% 👉
You've achieved product/market fit 😃
Search
Answer
Overall score
1
Very Disappointed
56%
2
Somewhat Disappointed
28%
3
Not disappointed
0%
There are no rows in this table
3
Count
84%
Sum
Overall Very Disappointed Score:
56
% 👉
You've achieved product/market fit 😃
Search
Answer
Overall score
1
Very Disappointed
56%
2
Somewhat Disappointed
28%
3
Not disappointed
0%
No results from filter
3
Count
84%
Sum

Segmenting to target personas is usually not enough. In our case, we were still below the 40% threshold, and so we needed to understand why these users really loved DiDi—and how we could help more users love DiDi.
I found it helpful to focus on these key questions:
Why do people love the product?
What holds people back from loving the product?
Why do people love the product?
Method: What benefits do very disappointed users enjoy?
To understand why people love the product, we take the users who would be very disappointed without the product, and analyse their responses to question #3: "What is the main benefit you receive from DiDi?". I like to collect these results into a word cloud like the one below. When you do, some themes will emerge. For us, it became obvious: users love DiDi for its speed, focus, and keyboard shortcuts.
"Main Benefit" responses from target persona users who would be
Not Disappointed
+3
if they couldn't use DiDi anymore:

"Main Benefit" responses from target persona users who would be
Somewhat Disappointed
if they couldn't use DiDi anymore:
What type of people do
Not Disappointed
+3
think would most benefit from DiDi:
What holds people back from loving the product?
Method: What improvements do somewhat disappointed users want?
To understand what holds people back from loving the product, 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 — in our case, speed. (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 analyse their responses to question #4: "What frustrates you about DiDi and how can we improve?".
Word cloud of responses to "What frustrates you about DiDi and how can we improve?" from
Not Disappointed
drivers:


This leads straight to our next step: the !

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