Using AI for Ecommerce Analytics

1. Using AI for Ecommerce Analytics Toronto June 24th 2019

2. Alex Galea Senior Data Analyst | Ayima ● Full stack data analytics ● Author of Beginning Data Science with Python and Jupyter ● M.Sc. Physics ● Leafs fan

3. What is AI? “Artiﬁcial Intelligence” technologies essentially boil down to one thing: Making Predictions

4. What is AI? Virtual assistant Self-driving cars Recommendation engines Digital advertising Predict answers to questions Predict how to steer a car Predict what things to show users Predict what people see your ad

5. Artiﬁcial Intelligence: Hype VS Reality

6. “Artificial intelligence is not that artificial; it’s human beings that are doing the work” - Anonymous Google employee [source] Artificial Intelligence: Hype VS Reality

7. Artiﬁcial Intelligence: Hype VS Reality

8. Hype: ● “100% REAL ARTIFICIAL INTELLIGENCE” ● “JUST CONNECT, AUTOMATE AND INCREASE CONVERSIONS BY 100%!” ● “SIT BACK AND RELAX WHILE THE MAGIC WORKS” ● “AI technologies free up human brain power and intellect” Artiﬁcial Intelligence: Hype VS Reality

9. Reality: ● Even the best AI solutions are highly specialized and limited ● No guaranteed wins. Each case is different. ● Black box technologies make it easy to build bad models. ● AI models rely on domain expertise. Artiﬁcial Intelligence: Hype VS Reality

10. AI Applications in Digital Marketing Provide insights to help determine: ● Product ● Promotion ● Price ● Place Example: a promotional email ● What products to include? ● What is the price? ● How many emails to send? Who are they sent to?

11. Top Areas of Impact 1. Matching customers to products - Showing ads & promotions to the right people - Good product recommendations 2. Determining offerings - Product price points - What products / bundles to sell - Timing promotions 3. Optimizing UX and content - Personalized experiences - Webpage / blog topics & keywords

12. Today’s Topics 1. Matching customers to products - Showing ads & promotions to the right people - Good product recommendations 2. Determining offerings - Product price points - What products / bundles to sell - Timing promotions 3. Optimizing UX and content - Personalized experiences - Webpage / blog topics & keywords

13. Outline ● Google Merchandise Store ● Sales Forecasting ● What’s in your customers shopping carts? ● What are your customers searching for?

14. Exploring the Google Merch Store ● We explore these data modeling topics using Google Merch Store analytics data ● GA sample data from 2016-2017 is available on BigQuery [1] ● Analysis in the following slides can be applied to your website, but the story will be different. https://shop.googlemerchandisestore.com [1] https://support.google.com/analytics/answer/7586738?hl=en

15. ● Google Merch Store Data Mining https://github.com/Ayima/google-merch-data-mining http://ayi.ma/s6u0y ● Onsite Search API Data Extraction & Modeling https://github.com/Ayima/onsite-search-data-mining http://ayi.ma/gkbt8 Everything you see Today is Available on our GitHub

16. Sales Forecasting ● Plan for the future - Data driven motivation for big-picture marketing efforts ● Compare sales expectation to actual - Justify marketing spend - Be accountable for wins and losses ● Product forecasts - Inform when and which products to promote ● Anomaly detection - Identify and ﬁx issues fast

17. Open Source Approach to Forecasting Forecast ● Modeling daily sales data, to forecast revenue in upcoming quarters ● Facebook’s open source Python/R library Prophet ● Alternatives: - PowerBI - Tableau - Excel (ARIMA)

18. Quantify Expectations Forecast ● What do we expect, based on momentum and trends from prior years?

19. Justify Marketing Spend ● How did actual sales compare with the prediction? ● Especially helpful when during downtrends, when forecast YoY is negative

20. Use Segments to Narrow Your Focus Device Trafﬁc Source Region ● Forecast on a per-segment basis to inform marketing plan & assess outcomes - e.g. Forecast mobile + homepage to compare cell phone UX upgrade with expected performance ● Fine tune forecasts by removing segments - e.g. Remove previous campaign trafﬁc to show “clean slate”

21. Data Driven Product Strategies Men’s Zip Hoodie Google Sunglasses Forecast peak / minimum demand ● 💡 Time paid advertising campaigns on a per-product basis (can be automated)

22. Data Driven Product Strategies Men’s Zip Hoodie Google Sunglasses Understand long-term trends ● Useful for product design teams ● Attempt to correct trends with new marketing angles (“fresh coat of paint”)

23. Data Driven Product Strategies Men’s Zip Hoodie Google Sunglasses Quantify expectations

24. Sales Forecasting ● Forecasts are a highly applicable use case for AI in marketing ● By using open source, your forecasts can be: - Automated & integrated with other processes - Customizable - Free - 100% owned by your company

25. What’s in your customers shopping carts? ● Identify similar products - Use knowledge of prior purchases for targeted marketing campaigns - Identify promotional bundle opportunities - Implement / audit realtime recommendation engine - Inform UX design

26. Obvious Patterns = Quick Wins: Buying in Bulk ● People prefer to buy in multiples of 5... ● 💡 Offer bulk discount rates?

27. Obvious Patterns = Quick Wins: Paired for Success Top product pairs in transactions: Men’s and Women’s Hoodies ➔ 💡 Link to other gender pair item on PDPs?

28. Obvious Patterns = Quick Wins: Paired for Success Top product pairs in transactions: Youtube Products ➔ 💡 Offer discounted bundles of YouTube items?

29. Obvious Patterns = Quick Wins: Associative Heatmap More frequently bought together (colorbar) Popular product combinations: Stickers ➔ 💡 Make stickers available as add-on item?

30. Mix and Match Popular Google Sunglasses colour combinations: More frequently bought together (colorbar) Red + Blue ● 77% of red orders have blue ● 60% of blue orders have red

31. Association Rule Pattern Mining

32. Association Rule Pattern Mining Association rules

33. Association Rule Pattern Mining How many times is the rule observed?

34. Association Rule Pattern Mining How often does the rule hold true?

35. Association Rule Pattern Mining Is the conﬁdence trending up or down?

36. Conﬁdence trending up! Rule: 15 oz Mug + Sticker Sheet Laptop and Cell Phone Stickers

37. Conﬁdence trending up! Rule: 15 oz Mug + Sticker Sheet Laptop and Cell Phone Stickers A transaction broke the rule :(

38. Conﬁdence trending up! Rule: 15 oz Mug + Sticker Sheet Laptop and Cell Phone Stickers

39. What’s in your customers shopping carts? ● Basic transaction patterns can be inspiration for promotions (add ons, bundles, etc…) ● Algorithmic pattern mining ﬁnds more complicated patterns - Can be fully autonomous: - Given shopping cart X, what items to recommend? - Constantly update rules based on customer behaviour

40. ● Looking at onsite search data ● Understand customer point of view - Are there navigation pain points? - Should you think about stocking new products? ● Identify and track trends to inform marketing efforts - Time promotions with periodic peak interest - Look at overall trends. Are these speciﬁc to your website or global? - Build content for upwards trending topics (+ SEO keywords 📈) What are your customers searching for?

41. Onsite Search Dataset ● Google Merch Store onsite search is not publically available ● We extracted onsite search data from GA for a top European fashion retailer ● Full source code for data extraction via GA API with Python is available on our GitHub [1] [1] https://github.com/Ayima/onsite-search-data-mining

42. Understanding your Customer: Right Brain Thinking ● Word clouds offer a visually stimulating way to interpret information ● Appealing to “right brain” ● SEO keyword research + blog topic inspiration

43. Understanding your Customer: Right Brain Thinking Onsite Search - Summer

44. Understanding your Customer: Right Brain Thinking Onsite Search - Winter

45. Word clouds kind of suck though... ● No context, hard to read, very little data, etc… ● Better strategy: model and visualize topics

46. Natural Language Processing (NLP) 1. “Supervised” approach: a. Manually bucket sample of search terms into topics b. Train model c. Apply to full dataset 2. “Unsupervised” approach: a. Train model to automatically group related words from search terms into topic groups b. Apply to full dataset

47. 1. “Supervised” approach: a. Manually bucket sample of search terms into topics b. Train model c. Apply to full dataset 2. “Unsupervised” approach: a. Train model to automatically cluster related words from search terms into topic groups b. Apply to full dataset We’ll use Latent Dirichlet Allocation (LDA) Natural Language Processing (NLP)

48. Brain Power Required ● Without “domain knowledge”, the model outputs are useless... ● Iterative process: a. Review clusters by eye b. Adjust model parameters and/or training data c. Retrain model ● Once happy with results, manually add topic labels

49. Let there be Topics! ● Map labelled topic model back onto source data (onsite searches)

50. Look for trends 🔎 “Dresses” and “Exotic Styles” most popular in the summer

51. Look for trends 🔎 Jeans on the other hand...

52. Paint a Clear Picture with Forecasting

53. Paint a Clear Picture with Forecasting ● Seasonal trend projections for coming year(s)

54. Paint a Clear Picture with Forecasting ● Long term trends up or down

55. Look for Opportunities or Diagnose Issues with Google Trends ● Google trends data* is good reference for how your users “should” behave * CSV tables available online or via unofﬁcial APIs Hmm, we would expect more searches for “long sleeve tops” in the Fall …

56. ● Onsite search provides a rich dataset where AI techniques can be applied to help understand your customers and inform marketing decisions - Keyword research & content inspiration - Topic discovery - Trending topic analysis What are your customers searching for?

57. ● We looked at an assortment of general AI topics - Note: AI excels at solving speciﬁc problems ● Sales forecasting - Using open-source tools to customize, automate and own your forecasts ● What’s in your customers shopping carts? - Generating product recommendations - Identifying products & bundles to promote ● What are your customers searching for? - Timing promotions - Topic discovery & trend analysis Summary

58. Everything you saw Today is Available on our GitHub Thank you to the open-source Python development community for building the tools I use! 👏 ● Google Merch Store Data Mining https://github.com/Ayima/google-merch-data-mining http://ayi.ma/s6u0y ● Onsite Search API Data Extraction & Modeling https://github.com/Ayima/onsite-search-data-mining http://ayi.ma/gkbt8

59. Thank you for your time!

Using AI for Ecommerce Analytics

Alexander Galea

Using AI for Ecommerce Analytics