Main Takeaways: Listen & not solve: I personally was always very eager to jump in and solve a problem the moment I heard about it - and I learned the hard way, that it doesn't work. This takeaway is about removing the need to solve and just learning to listen, ask questions, and understand the customer. Data as a compliment, not a replacement: It is easy for PMs to resort to data to understand problems. Data is usually the first indication of a problem, but it would rarely tell us what exactly the problem is. Share and tell customer stories: I've personally seen how this transforms the entire product team - they care about the customer and their problems, and that gives rise to really great products & features.

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Customer Empathy to Build Products
by Booking.com Product Leader

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5. Customer Empathy for Product
Managers
Saujanya Dasika
PM, Booking.com
LinkedIn

6. What is customer empathy?
Why?
Customer
Problem
Size of the
problem
Customer empathy, is simply to put yourself in your customer’s shoes

7. Why is customer empathy important?
We are not our customer
Specific problems need specific solutions
Specific situations need specific solutions
A deep understanding of your customer, helps build solutions that
work

8. Guiding Principles & Techniques

9. What to keep in mind, to build empathy?
● Listen, don’t solve
● Data complements empathy, doesn’t replace it
● Share customer journeys & stories

10. Listen, don’t solve!
“I don’t know if this shirt would fit me!”
Use different models
Ask for
measurements
Ship multiple sizes
“Are my customers
aware of what model
they look like?”
“Do my customers have
the means & willingness
to share this?”
“Do my customers have
a rough idea of their
size?”
What solution would you pick?

11. How can we listen better?
● Be conscious of biases, judgements
● Potential tools:
○ Customer interviews
○ Customer service calls
○ Store visits
○ Qualitative feedback
○ User testing
○ Hypothesis testing
● Ask more questions, and then some more!

12. Data complements empathy, doesn’t replace it
Data: Returns are high
Data: Damaged returns are the highest
Research: “What does ‘Damaged’ mean?”
Research: “What is the biggest ‘Damage’ type?”
Data: After solution, have returns reduced?

13. Using data and research together?
● Decide what questions can be answered by data, and
what would need research
● Go back to the same tools, we used to build
understanding
● Hypothesis testing, a data tool, to validate our
understanding

14. Share customer journeys & stories
What do you relate to most?
“20% of our customers said they need a kitchen when they travel”
“Since I travel with my family, it is very expensive for me to eat out. Therefore, I need
to have a fully equipped kitchen & a grocery store nearby - so that we can cook.”
OR

15. How & when do you share customer stories?
● Share customer story with the product team during
brainstormings, during sprint planning
● Share customer stories, when sharing product strategies
with stakeholders
● Share customer stories, on how your product helps them

16. What to keep in mind, to build empathy?
● Listen, don’t solve
● Data complements empathy, doesn’t replace it
● Share customer journeys & stories

17. Thanks!

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