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.

1. AI-driven personalised email marketing

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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.

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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.

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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?

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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!

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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!

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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!

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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!

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There are three key components in our Personalisation

10. Email Examples - Mega-Intro + Christmas Content
relevance
->
previously
purchased
generic
discovery_seg
->
recommended—single
category
discovery
->
recommended—cross
category

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Let’s get into the
nitty-gritty
technical details!

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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

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What are the challenges and how to solve them
It was a tough decision between cold start v.s. warm start

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Exploration v.s. experimentation

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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...

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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

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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. 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
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