2. 1. What is Super in Superbet?
2. Why would a segmentation topic be interesting?
3. What are the steps to take?
4. How to put first model out?
5. Can it be any better?
Agenda
4. Superbet? Betting/Gaming company, here on Codiax?
• 150+ people in Tech development
• Product oriented company
• 4.7 bil euro turnover in 2020
• 250% growth YoY
• 300+k active Online users
• Offices in 6 locations across Europe
(Poland, Romania, Croatia,
London/Leeds, Slovakia)
... rapid growth is yet to start!
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Superbet is on a mission to excite the world
6. Who are we actually up against?
Best customers
• Small in number
• Have preference for better product,
which we are trying to build
• Having the best ARPU
Worst customers
• Can do a lot of damage to the
business
• Trying to exploit holes in our
proposition
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2
3
7. Who are we actually up against?
One VIP customer brings a lot of
value to the company!
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A lot of customers are posing risk
to our business without brining
value in!
We want to focus on extremes!
2
3
8. What is the proces we are trying to improve?
Arbers and bonus abusers
• Manual segmentation from trading
team
• Depending on betting behaviour,
they are segmented within first
couple of days of activity
• Betting on low profile matches
• Trying to „bet against“ Superbet
Most valuable players – VIP
• CRM segmentation based on value
done once a month (cca 30 days to
segmentation of VIP, possibly more)
• VIP player gets private account
manager so we cannot segment
everyone
• High bets, similar to arbers (most
often, different events)
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10. • Analyse data and see if we can find patterns in it
• Create and test hypothesis based on patterns
• Create a predictive model that solves the problem
• Optimise and tune the model
• Push it to production
So, what do we need to do?
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11. What did we forget?
• Collect data you think you will need
• Store data on daily basis, or in real time
• Clean the data and prepare it for analysis
• Analyse data and see if we can find patterns in it
• Create and test hypothesis based on patterns
• Create a predictive model that solves the problem
• Optimise and tune the model
• Push it to production
• Serve recommendations to business units
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16. Collect and store data
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1 Python app = 1 Kafka topic
Vertica DB = 1 table per topic
S3 buckets = 1 bucket for full
history of data
50mil records per day – only
last message is relevant
= Data is ready to be used and
analysed!
17. Clean the data and organize it
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• ETL tool? ELT tool? We
need T tool, can we do
that? - enter Airflow
framework with Github
• Apache Airflow is used
for orchestration Full
historization of
transformation queries
on Github
= Vertica gets specific data
marts with star schema
instead of 100 messages for 1
ticket
19. Hey, in first couple of days all customers
look similar with a bunch of anomalies...
Can we have a different perspective on the
customer?
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20. Get more data in
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• “Real behaviour data in
online is very hard to have!”
– unless you can use
Firebase/Google analytics
• Not all Supercompanies run
Kafka, some are still working
with “normal” databases –
that is fine!
= More behavioural data can
help segment the customers
better and give a bunch of
new insights
22. Create the first model
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• Use your laptop, don’t
overcomplicate
• When things get tough,
give it more power – enter
EC2 (or ECS*)
• For obfuscators – there is
also AWS Sagemaker,
Disneyland for people who
love coding and
infrastructure
23. Push model to production
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• Create a docker image
that can run your model
• Use existing environments
to execute workload
• Use existing scheduler to
optimize process
• Integrate findings within
Tableau or push directly
into platform (you are
guessing – Python
application!)
24. Spotting suspicious customers with 90%+
accuracy* after first bet placed, and
potential VIP players with 75% accuracy*
after 2 weeks of activity!
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26. What do we need to improve
From batch to real
time –
orchestration!
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Add more data
analysis in, enrich
knowledge about
customers! Scalability to cover
business growth
27. How to do this in real time?
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28. Something else to add?
• Think all of this is stupid? Reach out, would like to hear your thoughts!
• Think all of this is brilliant? Reach out, glad to explain more where
needed!
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Want to excite the world? Reach out and join us! Plenty of open
possitions are waiting!
29. Something else to add?
• Think all of this is stupid? Reach out, would like to hear your thoughts!
• Think all of this is brilliant? Reach out, glad to explain more where
needed!
• Looking for analytical database? Vertica is awesome, but also look for
Snowflake
• Kafka Connect exists – you dont need to code all those consumer apps
• Airflow alternative? Prefect!
• Dont have data in Kafka topics? Google: „How to push CDC logs into Kafka“
• Want to avoid Glue? Be careful with Bigquery ☺
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Want to excite the world? Reach out and join us! Plenty of open
possitions are waiting!
