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AI-Driven Personalized Email Marketing

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Endeavour Groups Limited, is Australia’s largest drinks retailer with over 2000 venues across a wide range of brands. The EGL team has built a personalisation marketing engine using Databricks that performs segmentation of EGLs 4.5 million loyalty members. Personalisation is one of most important strategic investments in EGL and is expected to significantly increase revenue and improve customer experience.



This session will dive into how EGL has been able to develop effective brand campaigns that target the right people, at the right time with the right messages to millions of their loyalty members. EGL utilizes cutting edge reinforcement learning as a paradigm for Personalization Engine based on data. The Engine elegantly links marketing logics, AI and business processes together, and strives to intelligently determine the balance between the discovery and relevancy of email campaigns.

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AI-Driven Personalized Email Marketing

  1. 1. AI-driven personalised email marketing
  2. 2. 2 AI-driven Personalised Marketing Who is Endeavour Group Limited? Endeavour Groups Limited, is Australia’s largest drinks retailer with over 2000 venues across a wide range of brands. The EGL team has built a personalisation marketing engine using Databricks that performs segmentation of EGLs 4.5 million loyalty members. Personalisation is one of most important strategic investments in EGL and is expected to significantly increase revenue and improve customer experience. This session will dive into how EGL has been able to develop effective brand campaigns that target the right people, at the right time with the right messages to millions of their loyalty members. EGL utilizes cutting edge reinforcement learning as a paradigm for Personalization Engine based on data. The Engine elegantly links marketing logics, AI and business processes together, and strives to intelligently determine the balance between the discovery and relevancy of email campaigns.
  3. 3. 3 AI-driven Personalised Marketing Topics What problems are we solving? How to use AI to drive personalised marketing? What does our AI solution look like? What are the challenges and how we solved them? Personalised marketing: Who, What When. When to send, what email to use and with what products? We use AI to determine which is the best email template to send a customer Huge data volumes for the model to learn and make predictions We combine reinforcement learning, a recommendation engine and business logic to solve the challenging problem.
  4. 4. 4 What problem are we solving Personalised email marketing ● What is the theme of the email? ● What products should be in the email? ● Whom to send the email? ● When to send the email? What are the problems we are solving?
  5. 5. Place your image on top of grey square. OR use Layout: 1 Col/Right Image with Image Layout. 5 What problem are we solving … products that the customers are likely to buy, which they have purchased before. What products should we show to who? Past purchase history Demographics XGBoost model Ranks of the likelihood of purchase Buy it again and save!!! Easy!
  6. 6. Place your image on top of grey square. OR use Layout: 1 Col/Right Image with Image Layout. 6 What problem are we solving You have bought this ... Why not try this … ? … products that the customers are likely to buy, which they have NOT purchased before. What products should we show to who? Past purchases Products attributes XGBoost Ranks of the likelihood of purchase Marketing response Seasonality Harder!
  7. 7. Place your image on top of grey square. OR use Layout: 1 Col/Right Image with Image Layout. 7 What problem are we solving … 3 out of 2000 emails that you are more likely to click and then buy What is the theme of the email? Past purchases Products attributes Multi-armed bandit Likelihood of click + buy the product in the email Marketing response Email attributes Hardest!
  8. 8. Place your image on top of grey square. OR use Layout: 1 Col/Right Image with Image Layout. 8 What problem are we solving What time should we send the email Easy! No AI yet.. … just pick 35 random time slots in a week! The next step is to use AI to identify the time slots and frequency that customers will click and buy the product … … and will not lead to unsubscribe!
  9. 9. 9 There are three key components in our Personalisation
  10. 10. Email Examples - Mega-Intro + Christmas Content relevance -> previously purchased generic discovery_seg -> recommended—single category discovery -> recommended—cross category
  11. 11. 11
  12. 12. 12 Let’s get into the nitty-gritty technical details!
  13. 13. 13 How does our AI solution look like? Thompson algorithm We structure the email selection as an multi-armed bandit problem and use Thompson algorithm as the solution. We model it as a Multi-Armed Bandit (MAB problem Bandit 70% Current success rate Bandit 30% Current success rate Bandit 50% Current success rate Next choice? Decision making MAB Optimisation Choose the best product By finding the best emails Minimise the total regrets By avoiding sending bad emails as much as possible
  14. 14. 14 What are the challenges and how to solve them It was a tough decision between cold start v.s. warm start
  15. 15. 15 Exploration v.s. experimentation
  16. 16. 16 How does our AI solution look like? … we have to wait and see It was not easy to balance between exploration and exploitation for a multi-armed bandit problem...
  17. 17. 17 What are the challenges and how to solve them Algorithms and technologies are just parts of the puzzle, there are many business processes need to be considered or adjusted. ● There are some time sensitive emails which we want to send more of it in a short amount of time, e.g. Easter. We upweight those actions so they get higher rankings. The upweight is chosen in a way to minimize the clicks lost whilst getting our desired amount of sends. Additional email sent Upweight level
  18. 18. 18 What are the challenges and how to solve them Algorithms and technologies are just parts of the puzzle, there are many business processes need to be considered or adjusted. Are we sending too many emails that cause unsubscription? Do we send the same emails over and over again? How do we bring customers to a pleasant journey, e.g. onboarding How can we treat a cohort differently if the incremental sales are not the focus, e.g. retention How can we deliver consistent messages across all channels? How do we choose emails that a model has never seen before?
  19. 19. AI-driven Personalised Marketing What’s next in the EGL personalisation Journey Personalised landing page Personalised searching results Personalised customer review Personalised basket building Personalised customer retention Personalised journey Personalised App push notifications Personalised offer 19

Endeavour Groups Limited, is Australia’s largest drinks retailer with over 2000 venues across a wide range of brands. The EGL team has built a personalisation marketing engine using Databricks that performs segmentation of EGLs 4.5 million loyalty members. Personalisation is one of most important strategic investments in EGL and is expected to significantly increase revenue and improve customer experience. This session will dive into how EGL has been able to develop effective brand campaigns that target the right people, at the right time with the right messages to millions of their loyalty members. EGL utilizes cutting edge reinforcement learning as a paradigm for Personalization Engine based on data. The Engine elegantly links marketing logics, AI and business processes together, and strives to intelligently determine the balance between the discovery and relevancy of email campaigns.

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