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DataScienceLab2017_Как знать всё о покупателях (или почти всё)?_Дарина Перемот

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DataScienceLab, 13 мая 2017
Как знать всё о покупателях (или почти всё)?
Дарина Перемот (ML Engineer at SynergyOne)
Раскроем собственный ответ на вопрос "Чего же хочет покупатель?". Поделимся результатами исследований транзакций и расскажем, есть ли у вас домашний питомец. А так же, продемонстрируем, как машинное обучение уже сейчас помогает узнавать вас ближе.
Все материалы: http://datascience.in.ua/report2017

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DataScienceLab2017_Как знать всё о покупателях (или почти всё)?_Дарина Перемот

  1. 1. How to know (almost) everything about customers?
  2. 2. Magic time Choose any number
  3. 3. Tricky questions time Who would want to read your minds?
  4. 4. What would the merchant want? Tricky questions time
  5. 5. Tricky questions time  What to sell?  What is its price?  When to sell it?  Whom to sell?
  6. 6. What is the purchase formula?
  7. 7. Customer expert wanted  Experienced  Qualified  Impartial  Honest
  8. 8. 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?
  9. 9.  Cash  Food stores  Cafe & restaurants  Gas stations  ...
  10. 10. Tricky questions time Is there any “similarity” in client's behaviour?
  11. 11. 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%
  12. 12. Tricky questions time Why do we interested in the “similarity” of customers?
  13. 13. Recommender system
  14. 14. Recommender system
  15. 15. Benefit Targeted advertising in real time
  16. 16. The musketeers Find the target audience
  17. 17. The musketeers
  18. 18. Behavior pattern Behavior pattern = customer’s tag
  19. 19. Behavior pattern Customer’s feature connected with distribution of expenditures via budgeting categories, locations, time.
  20. 20. Tricky questions time Why would merchant need all this stuff?
  21. 21. Tricky answer time  Discover unexpected target audience  Get to know the competitors  Find out the locals
  22. 22. The musketeers
  23. 23. DBSCAN
  24. 24. The musketeers
  25. 25. Significant events
  26. 26. OneKarma beta
  27. 27. OneKarma beta
  28. 28. 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
  29. 29. 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/
  30. 30. Thank You Darina Peremot ML engineer in SynergyOne email: dp@synergyone.pro facebook: @darina.peremot telegram: @peremot

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