Know Your Users


Notes on How to get to know your users by google former pm vikram chatterji...

Three Sections:

  1. Why should you get to know your users right
  2. When in the dev cycle is it really crucial and important for you to keep a pulse on who the users are and what their pain points are
  3. How do you do that? Where are the tools that you can use in order to get to know who your users are?

Part One: WHY

To think about why, we look at 5 quick examples:

  1. Virgin Atlantic
  2. DoorDash
  3. Uber
  4. Febreze
  5. Google Buzz

Part Two: WHEN

ANYTIME! Its a never-ending task! The diagram at the top of the page shows how there are basically 4 different stages that you iterate over: (1) discover & define (2) develop (3) launch and (4) learn.

At any point in time, you're never just in one stage at a time. Features can be scattered in all different stages. Each type of feedback you need would be very different depending on the kinds of questions that you need to ask for each phase.

Part Three: HOW

The fundamental building block for decision-making at Airbnb

  • Need to figure out is it safe to launch feature? Can't assume that its safe for business. Is it actually driving my specific goal?
  • Motivational factor: how much is this experiment contributing? How are the late nights and hours contributing to team goal?
  • AB testing: when you're not running AB tests, no counterfactuals-- what would happen if we did not launch the treatment?

Characteristics About Experimentation:

  • How many populations could you be testing something on?
  • What is the visit pattern of use?
  • How can you map logged out traffic to logged in traffic? If this is not solved, data might be a bit muddled. Experimentation might take longer to see
  • How can you track multiple devices and should you track them independently?
That was Airbnb’s real innovation— a platform of ‘trust’— where everyone could not only see everyone else’s identity but also rate them as good, bad, or indifferent hosts or guests
— Thomas Friedman

Unique characteristics about experimentation at Airbnb:

  • 2-sided marketplace
  • Users only visit when planning trips (guests) or adding a home (hosts) [visit pattern isn't consistent like for example Facebook]
  • User decisions take a long time (conversion funnel of 2-3 weeks)
  • Users often logged out when researching
  • Users browse from multiple devices
  • Offline experience that you can't control

Case Study: Creating and increasing Airbnb's platform of trust

By 2012, most hosts around the world were eligible a free professional photoshoot of their listing to move from the couch-surfing feel to something more aesthetic and professional. From 2011 to 2016, they found that providing this service was also a large business operation, where multi-million dollars were spent every year.

Fast Company article here.

Was professional photography still serving its function of building trust in 2016? Was there a changing landscape? Do photos still equal trust?

Changing landscape:

  • 2013: launched "verified ID" system
  • 2014: changed rating system to be more "honest" (double-blind ratings)
  • 2014: normal publicity- Inc's Company of the Year
  • 2015: Apple's iPhone started including a 12-megapixel camera

Did professional photography matter anymore?

AB Testing to measure impact of professional photography:

Group A (50%): Eligible for photography
Group B (50%): Received "Sorry, no photographers available at the moment" email

Experiment challenges:

  • Endogeneity: Only accessible by people with the willpower. The people who go do it are fundamentally different than the people who are just otherwise floating and might not do it. Are we comparing two of the same intent populations?
  • Infeasible guest-facing test: When a guest visits the site, they would either see all professional photography (treatment) or all cell phone photography (control). Unethical because you'd fake pictures
  • Bookings cannibalization: Are the set of professional photo listings cannibalizing the other ones? How do you know if its an extra booking vs a booking that shifted from one to another listing? Maybe overall bookings are the same but they shifted from one listing to one listing.
  • Statistical power and significance: Subset of people who request professional photography is already small. So if you further narrow it down, how long is this going to run? Am I going to get statistical significance?
  • The human element, CX agents: can try to control it but "customers are always right"
  • Metrics: proxy of trust is bookings as a host. Is the metrics that you see the true effect or is there some cannibalization factor that you have to account for?


  • Negative impact on global Airbnb bookings (trust) was almost undetectable
  • Upper bound- considering no cannibalization
  • Professional photos mattered to hosts in certain markets more than others
  • Positive impact on owner bookings and booking value

Conclusion: Offer a unique benefit to hosts at a nominal fee. Allow more hosts especially in newer markets, to uplevel their listing appeal

Rolled out Globally Available Professional Photography for Hosts.

  • Only pay once they get a booking
  • Affordable based on average earnings in every market
  • Allows to scale up service and be available for more hosts
  • Saves Airbnb money to focus on different methods of driving trust and safety for owners

Airbnb's Data University Vision:

A set of courses taught by data scientists for everyone in the company to empower every employee to make data-informed decisions by providing data education that scales to their team and their role.

TechCrunch article here.
Medium article here.

Data 101: Data-Driven Decision Making and Problem Solving

  • Identify root cause of issue
  • Set data-informed targets and goals
  • Generate hypotheses tree (or list out all possibilities)
  • Solve root cause of issue
  • Consider data as a factor in all aspects of problem solving
  • Tool: 5 Why's
    • It might take asking WHY 5 times to get to the root of the problem
  • Tool: Issue Trees
    • How do you in a data-informed fashion deep dive and avoid all-over-the-place intuitive thinking but use a data-drive approach to reach the cause?
    • Make sure each issue is mutually exclusive and exhaustive
    • Overlay with data; be concise
    • Output = concise problem statement to understand why its happening
  • Tool: Hypothesis Tree
    • Drive the solution
    • Estimate impact
    • Which one is the biggest win if you were to address it? (small wins vs big wins)