DataScienceLab2017_Как знать всё о покупателях (или почти всё)?_Дарина Перемот
0 likes•455 views
The document discusses methods for understanding customer behavior through data analysis across different datasets, highlighting the importance of discovering customer similarities to enhance targeted advertising and marketing strategies. It presents findings from Ukrainian and American datasets, focusing on merchants' needs for customer insights and behavior patterns to identify target audiences and competitors. Additionally, it includes references to related tools and further resources for data science in marketing.
DataScienceLab2017_Как знать всё о покупателях (или почти всё)?_Дарина Перемот
- 1. How to know (almost) everything about customers?
- 2. Magic time Choose any number
- 3. Tricky questions time Who would want to read your minds?
- 5. What would the merchant want? Tricky questions time
- 7. Tricky questions time What to sell? What is its price? When to sell it? Whom to sell?
- 8. What is the purchase formula?
- 12. Experts Ukrainian dataset 2,5 years 210 K rows 7 K customers 22 K POS American dataset 1 year 180 K rows 12 K customers 40 K POS What they have in common?
- 13. Cash Food stores Cafe & restaurants Gas stations ...
- 14. Tricky questions time Is there any “similarity” in client's behaviour?
- 15. Key points Uses less or equal than 4 MCC codes. Ukrainian dataset: 64% American dataset: 61% Uses less or equal than 6 MCC codes. Ukrainian dataset: 73% American dataset: 76%
- 16. Tricky questions time Why do we interested in the “similarity” of customers?
- 19. Benefit Targeted advertising in real time
- 20. The musketeers Find the target audience
- 21. The musketeers
- 22. Behavior pattern Behavior pattern = customer’s tag
- 23. Behavior pattern Customer’s feature connected with distribution of expenditures via budgeting categories, locations, time.
- 26. Tricky questions time Why would merchant need all this stuff?
- 27. Tricky answer time Discover unexpected target audience Get to know the competitors Find out the locals
- 28. The musketeers
- 30. DBSCAN
- 31. The musketeers
- 33. OneKarma beta
- 34. OneKarma beta
- 35. Links YaCM 2016 (about hyperlocation adds): https://events.yandex.ru/lib/talks/3469/ Sberbank Data Science Journey (MCC2Vec for Sberbank): https://www.sdsj.ru/slides/MCC2VEC.pdf https://youtu.be/0q5p7xP4cdA?t=23702
- 36. Links DBSCAN vs others: http://scikit-learn.org/stable/auto_examples/clu ster/plot_cluster_comparison.html OneKarma beta-test UI: https://onekarma.synergyone.pro Comics: https://marketoonist.com/
- 37. Thank You Darina Peremot ML engineer in SynergyOne email: [email protected] facebook: @darina.peremot telegram: @peremot