Maciej Pondel, PhD Big Data Architect Unity Group Piotr Wrzalik Managing Partner Grupa Unity * what ML/AI algorithms are best suited for data analysis for online sales * what tools to use for data visualisation * what is the potential of data driven reasoning * some examples from existing projects

2. HOW TO BOOST
YOUR ONLINE SALES USING
MACHINE LEARNING
& DATA-DRIVEN REASONING
Practical examples from e-retail
and e-wholesale projects
Maciej Pondel, PhD & Piotr Wrzalik

3. A few words about Unity Group
Over 20 years of e-commerce experience, with hundreds of
projects for top brands in Poland and other EU countries:

4. A few words about Unity Group
• Discover some of our case studies at Stand B-11.
• Most of our clients gather lots of data (transactional, behavioural)
but do nothing with it
• Struck with limited methodological approaches to advanced data
analysis​, we acquired a grant to let us apply a more scientific
perspective.

5. Our plan for today
• Why machine learning matters.
• Methodology for E-commerce.
• Machine learning methods & algorithms for marketing.
• Conclusion and suggested next steps.

6. Data-driven approach
86,4% of people will
believe any data you put
in a PowerPoint slide,
even if you just totally
made it up to prove your
point.

7. WHY
MACHINE LEARNING
MATTERS

8. Why Machine Learning Matters
Companies that
use advanced
analytics:
Make data-
driven
decisions
Make faster
decisions
Adjust
business faster
Get better
financial
results
According to Nucleus Research, for every $1
invested in analytics you can get a return of $13.
As Forrester states, only 12% of data is leveraged
for analytics, while:
• Pattern discovery could be later used
to analyse new data
• Predictive algorithms could identify hidden
relationships beyond what human perceives
• Pattern detection can become more
comprehensible and precise as data quantity
grows
Creates a
competitive
advantage
Discovered
relationships
affect business
Gives measurable
business value

9. Advanced analysis flow
Descriptive
analytics
• Understand the
challenges
Diagnostic
analytics
• Why does it
happen?
Predictive
analytics
• What is going to
happen?
Prescriptive
• What should we do
to make it happen /
prevent its
occurrence?

10. METHODOLOGY
FOR E-COMMERCE

11. Advanced analysis for marketing
Goal
definition
What
challenges do
need to be
addressed?
Target group
definition
At whom is
this campaign
aimed?
Clustering Prediction
What
products are
on offer?
Pattern
discovery
Prediction
Under what
conditions?
Discount
strategy
proposal
Key
messages
Contact
channel

12. Goal definition - communication
• Increase CTR, OR, Conversion
Rates
• Reduce marketing expenses
• Avoid customers’
disappointment due to offer
mismatches

13. Goal definition - loyalty

14. Goal definition - margins
Profit lost here
This is where you should be selling

15. MACHINE LEARNING
METHODS & ALGORITHMS
FOR E-COMMERCE

16. Prediction - how it works
Define your experiment goal.
Analyse historical data – train a model on past cases.
Test and validate this model.
Predict the future.

17. Prediction - examples
• Purchases related to sales / special offers
• New model sales
• Product returns
• Client reactions to a discount proposal
• Probability of complaints
• Seasonal trends in product purchases
• Customer Churn
• Demand estimation (of a selected product
category)
• Price elasticity

18. Prediction - a case study
Post Christmas Sale in a fashion eCommerce – Customer Demand
Prediction.
• Training a model on historical data on participation in the Jan 2016
sale.
• Validation of the model during the Jan 2017 sale.
• Results analysis and model boosting.
• Training a model using the entire database in Dec 2017.
• Campaign based on the prediction for whole customer set.
• Following the campaign conversion.

19. Prediction – a decision tree example
• How does this model work?
• A decision tree example

20. Feature importance

21. Prediction – model building
Our approach to model building consists of generating several models,
deploying various methods and using collective intelligence.
Data set
Training
set 1
Training
set 2
Training
set n
Model 1
Model 2
Model n
Combiner
Ensemble
Prediction

22. CLUSTERING - RETAIL EXAMPLES
Inspired by
the RFM method
Products divided
by category & price levels
Discount & trend-driven clients
vs. brand-oriented customers

23. Clustering – a practical example
• We divide our clients into 6 groups with different discount policies
and key messages.
• Each segment is to be treated in a distinctive way.
• We don’t have to know the borders between clusters - algorithms
should define them.

24. Clustering

25. Clustering

26. Clustering

27. Clustering

28. Clustering

29. PATTERN RECOGNITION
Association rules
Sequences detection
Collaborative filtering

30. Pattern recognition
•We analyse a selected
cluster of customers.
•We build rules of
consequent purchases.
•We predict next purchase.

31. CONCLUSION
& SUGGESTED
NEXT STEPS

32. Conclusion
• Data-driven reasoning defines thousands of hypotheses,
assessing them in terms of insights vital for your business.
• Human does not have to understand and justify those
hypotheses – they just reflect business realities.

33. • Use historical data to identify new market opportunities
• Accurately predict needs of your customers
• Feed your marketing automation with valid business hypotheses,
measure results and draw conclusions for the future
Suggested next steps
Data
Existing
marketing
tools

34. • Visit us at Stand B-11 and learn more about ,
our research and previous projects deploying ML, AI & big data.
• Book a free consultation with one of our technology experts and
discover how our team can help you out.
• Find out more on upsaily.com
Suggested next steps cont.

35. Thank you!
Maciej.Pondel@unity.pl
Piotr.Wrzalik@unity.pl
En.unity.pl/blog
unity-s-a