Whether it’s a click, swipe or scan, your digital customers are always talking to you, and actions speak louder than words. This presentation explains how user behavior can be can be analyzed to improve a business’s product or service. We’ll take a closer look at Tinder, which employed Interana to discover new insights among its massive quantity of event data.
Ann Johnson, CEO and Co-Founder of inter|ana
Tweet @stimmlet and @interanacorp
Talking to Your Digital Customers
Your customers are talking to you in the most dependable
way – actions always speak louder than words.
What kinds of things do actions tell you?
• How well is your product working?
• What features do they like?
• What features should you add?
• What marketing campaigns do they respond to?
• Is something broken in your app?
How data reveals actions
What did they
Why did they do
What will they
How to change
what they do?
Case Study: Tinder
• Today, Tinder operates in 196 countries, whose users
generate an average of 1.8 billion swipes and create 26
million matches per day.
• Started with Hadoop
• Slow to answer new questions
• Only available to trained data experts
• Changed to Self-serve solution
• Fast at scale
• Easy data model
• Graphical Interface
• Behavior support
Data is not a magical temple – it’s for everyone
• What parts of the funnel have good and bad conversion?
• How are conversion rates changing over time?
• How is churn changing over time?
• What users are likely to churn?
Matching Tinder to the User
• Data shows different users use Tinder differently
• Tinder iterates on small tweaks to its algorithms to find
the best matches
• Brazil’s youth may use the app to meet new friends
• 2014 World Cup boosted use in Brazil by 50%
• S. and U.K. users 25-34 use the app to meet new people for
travel, dating, and marriage
How Should I Improve My Product?
• Should a feature be promoted?
• Should a feature be removed?
Do users like a feature? Only if they use it!
A Happy Community of Tinder Users
• A small percentage of users were swiping right on every profile
• Analyzed data to understand this behavior and limit it
• Quality of matches has increased dramatically
• In September of 2015, Tinder launched Super Like
• Using data to understand its effects:
• Product adoption
• Product usability
• Quality of matches
• Effectiveness of marketing campaigns
Is my product working?
• Counts of errors
• Much more subtle things – If people a love a feature on
iOS but never touch it on Android, maybe your Android
implementation has a bug.
• If a platform has shorter sessions, there might be a
• Tinder received reports from a small group of users that
Tinder worked on WiFi but not 4G
• Tinder diagnosed the root cause from user data: affected
users were in the same region and shared the same
• Tinder worked with the carrier to fix their routing issue
and restored 4G service to its users.
Not all problems can be caught in testing, so real-world
data is essential
Tinder Marketing uses Data
• Effects of press coverage
• Response to campaigns across demographics and
• Real-time feedback to double-down on things that work
and stop things that don’t
Kyle Miller, Marketing Manager at Tinder, says,
“I would never consider myself a data person,
but now I feel like I have the ability to
accomplish all of my data-driven tasks.”
It’s everyone’s job to listen to the customer
It’s everyone’s job to look at the data
50% of Tinder employees have daily access to
The right data tools
can be a huge help
Problem: Many data tools are built to answer only one
• Learning from data is an ongoing process. Not a one-off.
• Slice and dice across arbitrary dimensions. And beyond.
• Don’t decide what is important beforehand, in ETL, in
indexes, in schemas. Decide at read time.
Problem: Many data tools require extensive training to use.
• Visual, simple, and interactive self-service solutions enable
• Make it easy to know what data is available.
• Remove friction for the business user. Don’t rely on data
specialist to answer simple questions.
• Sharing example queries help spark curiosity.
• Problem: Many data tools require data to be downsized
• RAW data analysis. ALL your data is available.
• Tools shouldn’t break as data volumes increase
• Questions should be answered at interactive speeds.
Problem: Dashboards and reports can be misleading
• “See the math” – Where did this result come from?
• Data ingest processes, ETL, can hide calculations from
the end user
• Enable analysis down to row level detail. No aggregation
or summarization boundaries.
Summary - FAST
• Flexible: You shouldn’t have to know the question
beforehand. You should be empowered to ask “the next
• Accessible: Your data tools should need minimal
• Scalable: You need row level access to all your data to
paint the full picture.
• Transparent: Data consumers need to understand
where the numbers came from.
Interana’s solution is built around these principles.