A discussion of using machine intelligence for

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/