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Talking to Your Digital Customers

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.

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Talking to Your Digital Customers

  1. 1. Ann Johnson, CEO and Co-Founder of inter|ana Tweet @stimmlet and @interanacorp Talking to Your Digital Customers
  2. 2. Your customers are talking to you in the most dependable way – actions always speak louder than words.
  3. 3. 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?
  4. 4. How data reveals actions What did they do? Why did they do it? What will they do next? How to change what they do?
  5. 5. 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.
  6. 6. Tinder’s Infrastructure • 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
  7. 7. Retention Analysis • 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?
  8. 8. Matching Tinder to the User • Data shows different users use Tinder differently • Genders • Orientation • Age • Location • 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
  9. 9. 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!
  10. 10. 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
  11. 11. Is my product working? • Counts of errors • Latency • 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 problem lurking
  12. 12. Tinder Example • 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 carrier. • 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
  13. 13. Tinder Marketing uses Data • Effects of press coverage • Response to campaigns across demographics and locations • 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.”
  14. 14. 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 data
  15. 15. Flexible Accessible Scalable Transparent The right data tools can be a huge help
  16. 16. Flexible Problem: Many data tools are built to answer only one question. • 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.
  17. 17. Accessible Problem: Many data tools require extensive training to use. • Visual, simple, and interactive self-service solutions enable broad adoption. • 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.
  18. 18. Scalable • 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.
  19. 19. Transparent 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.
  20. 20. Summary - FAST • Flexible: You shouldn’t have to know the question beforehand. You should be empowered to ask “the next question.” • Accessible: Your data tools should need minimal training • 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.
  21. 21. Interana’s solution is built around these principles.

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