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Using AI for Ecommerce Analytics

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I show some methods for extracting value from your marketing analytics data using modelling techniques. Topics include:
● Sales Forecasting
● What’s in your customers shopping carts?
● What are your customers searching for?

Python source code for all the charts is available on Ayima's GitHub:
● https://github.com/Ayima/google-merch-data-mining
● https://github.com/Ayima/onsite-search-data-mining

Published in: Data & Analytics
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Using AI for Ecommerce Analytics

  1. 1. Using AI for Ecommerce Analytics Toronto June 24th 2019
  2. 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. 3. What is AI? “Artificial Intelligence” technologies essentially boil down to one thing: Making Predictions
  4. 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. 5. Artificial Intelligence: Hype VS Reality
  6. 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. 7. Artificial Intelligence: Hype VS Reality
  8. 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. 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. 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. 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. 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. 13. Outline ● Google Merchandise Store ● Sales Forecasting ● What’s in your customers shopping carts? ● What are your customers searching for?
  14. 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. 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. 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. 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. 18. Quantify Expectations Forecast ● What do we expect, based on momentum and trends from prior years?
  19. 19. Justify Marketing Spend ● How did actual sales compare with the prediction? ● Especially helpful when during downtrends, when forecast YoY is negative
  20. 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. 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. 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. 23. Data Driven Product Strategies Men’s Zip Hoodie Google Sunglasses Quantify expectations
  24. 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. 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. 26. Obvious Patterns = Quick Wins: Buying in Bulk ● People prefer to buy in multiples of 5... ● 💡 Offer bulk discount rates?
  27. 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. 28. Obvious Patterns = Quick Wins: Paired for Success Top product pairs in transactions: Youtube Products ➔ 💡 Offer discounted bundles of YouTube items?
  29. 29. Obvious Patterns = Quick Wins: Associative Heatmap More frequently bought together (colorbar) Popular product combinations: Stickers ➔ 💡 Make stickers available as add-on item?
  30. 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. 31. Association Rule Pattern Mining
  32. 32. Association Rule Pattern Mining Association rules
  33. 33. Association Rule Pattern Mining How many times is the rule observed?
  34. 34. Association Rule Pattern Mining How often does the rule hold true?
  35. 35. Association Rule Pattern Mining Is the confidence trending up or down?
  36. 36. Confidence trending up! Rule: 15 oz Mug + Sticker Sheet Laptop and Cell Phone Stickers
  37. 37. Confidence trending up! Rule: 15 oz Mug + Sticker Sheet Laptop and Cell Phone Stickers A transaction broke the rule :(
  38. 38. Confidence trending up! Rule: 15 oz Mug + Sticker Sheet Laptop and Cell Phone Stickers
  39. 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. 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. 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. 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. 43. Understanding your Customer: Right Brain Thinking Onsite Search - Summer
  44. 44. Understanding your Customer: Right Brain Thinking Onsite Search - Winter
  45. 45. Word clouds kind of suck though... ● No context, hard to read, very little data, etc… ● Better strategy: model and visualize topics
  46. 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. 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. 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. 49. Let there be Topics! ● Map labelled topic model back onto source data (onsite searches)
  50. 50. Look for trends 🔎 “Dresses” and “Exotic Styles” most popular in the summer
  51. 51. Look for trends 🔎 Jeans on the other hand...
  52. 52. Paint a Clear Picture with Forecasting
  53. 53. Paint a Clear Picture with Forecasting ● Seasonal trend projections for coming year(s)
  54. 54. Paint a Clear Picture with Forecasting ● Long term trends up or down
  55. 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. 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. 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. 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. 59. Thank you for your time!

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