DATA: Marketing Analytics. How Not to Shoot Yourself in the Foot!
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Ivan Ravovoy, Head of Marketing, Verv Dmitry Severinets, Data Science Team Lead, Verv
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DATA: Marketing Analytics. How Not to Shoot Yourself in the Foot!
- 1. Marketing Analytics: How Not to Shoot Yourself in the Foot Ivan Ravovoy Dmitry Severinets
- 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. A couple of facts about Verv app downloads worldwide 75 000 000+
- 4. During the course of 2018 alone we achieved the following results:
- 5. Weight Loss Walking about 900,000 km
- 6. 22 times the distance around the Earth
- 7. Weight Loss Running about 18,000,000 km
- 8. 47 times
- 9. Weight Loss Fitness about 250,000 kilos
- 10. 20 «Falcon 9» rockets to deliver such weight to the space
- 11. Before we start :) Couple of questions
- 13. 1. (in)correct data transferring 2. Wrong optimization metrics (events) 2 cases:
- 15. App Appsflyer
- 16. App Appsflyer
- 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. 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. 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. 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. 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. 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. Case 7: No refunds/Cancellations Example: Payments without refund information Solution: - Adding refunds and cancels for data enrichment
- 24. vs
- 25. Automated Data Validation with Apache Airflow
- 26. Slack notifications in case of data inconsistency
- 27. Detailed data validation report in Tableau
- 28. Detailed data validation report in Tableau
- 29. Wrong optimization metrics (events)
- 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. 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. 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. 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. 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. 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. How to find these events?
- 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. 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. 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. - 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. 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. 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. Thank you!
- 44. Q&A