This document discusses using machine learning and fuzzy logic for adaptive pricing in e-commerce. It describes fuzzy logic as an option for adaptive pricing that uses intuitive business rules rather than large datasets. Fuzzy logic allows pricing to vary smoothly based on multiple factors like product popularity, user recency, and market penetration. The document provides examples of how these input factors could map to an output discount percentage using fuzzy rules and explains how fuzzy logic could integrate with store software for adaptive pricing.

1. A D A P T I V E P R I C I N G W I T H
M A C H I N E I N T E L L I G E N C E
MTL+ECOMMERCE

2. E VA N P R O D R O M O U
● Founder & CTO, Fuzzy.io
● Former CTO, Breather
● Founder, StatusNet
● Founder, Wikitravel

3. W H O I S T H I S TA L K F O R ?
● Involved in e-commerce
− “products or services on the Web or mobile”
● Technical understanding
● Decision-making power

4. W H AT I S “ A D A P T I V E P R I C I N G ” ?
• Changing the price of a product
• Based on the situation
• User attributes
• Product attributes
• Business attributes

5. W H Y A D A P T I V E P R I C I N G ?
● Profit maximization
● Competition
● Many large retailers use it
● Guide user behaviour
● Activation
● Retention
● Referral

6. R I S K S
● Too high = don’t convert
● Too low = cut into margin
− May be worthwhile to activate a customer
● Perception of fairness

7. I M P L E M E N TAT I O N O P T I O N S
● Procedural code
● Markets
● Machine learning
● Fuzzy logic

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9. P R O B L E M S W I T H P R O C E D U R A L C O D E
● Gets very complicated with multiple
inputs
● Brittle
● Hard to debug
● Hard to maintain
● Thresholds

11. M A R K E T- B A S E D S O L U T I O N S
● Require a market
● Require something close to real-time
bidding
● Require fungible product or service
− One seller is equivalent to another

13. M A C H I N E L E A R N I N G
● Requires corpus of training data
− May not be collected
− May be difficult to experiment
● Requires training process
● Unintuitive results
● Harder to audit
● Staff are expensive

14. F U Z Z Y L O G I C
● Fuzzy sets
− Intuitive categories like “old”, “new”, “good”, “warm”
● Degrees of membership
− 0 to 100%
● Real-world wisdom
− IF userAge IS new THEN discount IS high

15. F U Z Z Y L O G I C F O R A D A P T I V E P R I C I N G
• Pros
● Uses explicit business rules
● Doesn’t require large training corpus
● Smoothly-varying output — no discontinuities with thresholds
● Handles contradictions well
● Adding and removing inputs well
● Missing data works well
● Easier to audit

16. F U Z Z Y L O G I C F O R A D A P T I V E P R I C I N G
• Cons
• Requires numerical inputs

17. D I S C O U N T
● Not a fixed price
● Can use the same agent for multiple
products
● 0% = full price, 100% = free
● Bounded to prevent outrageous prices

18. W H AT FA C T O R S C A N
A F F E C T P R I C E ?

19. P R O D U C T P O P U L A R I T Y
● Sales/week
● Smoothes over variations by day-of-week
● Ideally, pre-calculated for previous week

20. C AT E G O RY P O P U L A R I T Y
● Similar to product popularity, but for
product category

21. S I T E P E R F O R M A N C E
● Site-wide sales for the week
● Can be in dollars, or # of sales
● Very site-specific

22. S A L E S P E R H O U R O F W E E K
● Discrepancies between weekday/
weekend, night/day

23. S A L E S P E R W E E K O F Y E A R
● Especially for seasonal products
● Best for established stores
● At least one year of sales!

24. U S E R R E C E N C Y
● How long ago did the user sign up?

25. U S E R A C T I VAT I O N
● Number of sales or dollars

26. M A R K E T P E N E T R AT I O N
● For geographical markets
● In number/million

27. O T H E R FA C T O R S
● Influencer
− Number of followers on Twitter
− Number of friends on Facebook
● Social network penetration
− Percentage of followers on Twitter who have
joined
− Percentage of friends on Facebook who have
joined

28. R U L E S
● Map input factors to output discount
● Usually linear
● Occasionally inversely linear
● More complex rules possible

29. I N T E G R AT I N G W I T H S T O R E
S O F T WA R E
● Using an SDK
● Or a plugin

30. F U Z Z Y L E A R N I N G
● In production
● Feedback loop based on profit margin
on the sale
● 0% = no conversion
● Varies fuzzy set boundaries
● Varies weights of fuzzy rules

31. T H A N K S
Evan Prodromou
evan@fuzzy.io
https://fuzzy.io/