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DATA: Marketing Analytics. How Not to Shoot Yourself in the Foot!

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Publié le

Ivan Ravovoy, Head of Marketing, Verv
Dmitry Severinets, Data Science Team Lead, Verv

Publié dans : Technologie
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DATA: Marketing Analytics. How Not to Shoot Yourself in the Foot!

  1. 1. Marketing Analytics: How Not to Shoot Yourself in the Foot Ivan Ravovoy Dmitry Severinets
  2. 2. Such products as: - Weight Loss Fitness - Weight Loss Running - Weight Loss Walking Main Goal: Help people to achieve personal lifestyle goals and live a healthier life with the help of high-quality and easy-to-use apps. We are developer of customized mobile apps with a strong focus on health and well-being
  3. 3. A couple of facts about Verv app downloads worldwide 75 000 000+
  4. 4. During the course of 2018 alone we achieved
 the following results:
  5. 5. Weight Loss Walking about 900,000 km
  6. 6. 22 times the distance around the Earth
  7. 7. Weight Loss Running about 18,000,000 km
  8. 8. 47 times
  9. 9. Weight Loss Fitness about 250,000 kilos
  10. 10. 20 «Falcon 9» rockets to deliver such weight to the space
  11. 11. Before we start :) Couple of questions
  12. 12. Why it’s important?
  13. 13. 1. (in)correct data transferring 2. Wrong optimization metrics (events) 2 cases:
  14. 14. (in)correct data transferring
  15. 15. App Appsflyer
  16. 16. App Appsflyer
  17. 17. Case 1: Server / App issues Example: Shutdowns / Delays / No internet / SDK failures / App crashes Solution: - QA. To reveal and describe all possible weaknesses - Try to fix or avoid them
  18. 18. Case 2: Duplications Example: Payment duplications / sending single event multiple times Solution: - Compare aggregated internal data with analytics/MMP - Additional event/receipt validation before sending - Comparative QA and fixing bugs
  19. 19. Case 3: Test/internal traffic Example: Events from QA or company employees Solution: - Whitelist all test/QA devices - Exclude test data from analytics / calculations
  20. 20. Case 4: Different time zones Example: UTC +3 (Minsk) vs UTC -7 (Los Angeles) Solution: - To chose one time zone for all systems - To align all internal and external analytics to one time zone
  21. 21. Case 5: Incorrect event tracking (or none at all) In fact: Weak analytics (No analytics) Solution: - Start doing something :) - To develop solid event structure on the basis of user behavior / funnels - Check/Update SDK’s for analytic systems - A lot of QA
  22. 22. Case 6: Logical mistakes. Defining events incorrectly Example: Refunds vs Cancels Solution: - Perform full event taxonomy. With a single documentation for everyone - Detailed event description, including all possible cases - QA and control permanently
  23. 23. Case 7: No refunds/Cancellations Example: Payments without refund information Solution: - Adding refunds and cancels for data enrichment
  24. 24. vs
  25. 25. Automated Data Validation with Apache Airflow
  26. 26. Slack notifications in case of data inconsistency
  27. 27. Detailed data validation report in Tableau
  28. 28. Detailed data validation report in Tableau
  29. 29. Wrong optimization metrics (events)
  30. 30. Key customer’s actions in events log event Install Onboarding Trial Payment event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event eventevent
  31. 31. Key customer’s actions in events log event Install Onboarding Trial event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event eventeventevent Payment event event event event event eventevent
  32. 32. Key customer’s actions in events log event Install Onboarding Trial event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event eventeventevent Payment event event Weak correlation event event event eventevent
  33. 33. Key customer’s actions in events log event Install Onboarding Trial Payment event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event eventevent
  34. 34. Key customer’s actions in events log Install Onboarding Trial Payment event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event eventevent event
  35. 35. Key customer’s actions in events log Install Onboarding Trial Payment event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event eventevent event
  36. 36. How to find these events?
  37. 37. Create features from Events log event event event event event event event event event event event event event event event event event event event event event event event eventevent
  38. 38. Create dataset to train the model event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event event . . .
  39. 39. Train the model to find key events event event event event event event event event event event event event event event event event event event
  40. 40. - Be aware of data correctness, cover all possible failure cases - Try to perform sophisticated analysis for finding the best optimization metrics To sum up:
  41. 41. Be aware of data correctness, cover all possible failure cases - Try to perform sophisticated analysis for finding the best optimization metrics To sum up:
  42. 42. Be aware of data correctness, cover all possible failure cases Try to perform sophisticated analysis for finding the best optimization metrics To sum up:
  43. 43. Thank you!
  44. 44. Q&A

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