adaptive pricing with machine intelligence
TRANSCRIPT
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
E VA N P R O D R O M O U
● Founder & CTO, Fuzzy.io
● Former CTO, Breather
● Founder, StatusNet
● Founder, Wikitravel
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
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
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
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
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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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
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
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
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
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
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
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
W H AT FA C T O R S C A N A F F E C T P R I C E ?
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
C AT E G O R Y P O P U L A R I T Y
● Similar to product popularity, but for product category
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
S A L E S P E R H O U R O F W E E K
● Discrepancies between weekday/weekend, night/day
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!
U S E R R E C E N C Y
● How long ago did the user sign up?
U S E R A C T I VAT I O N
● Number of sales or dollars
M A R K E T P E N E T R AT I O N
● For geographical markets
● In number/million
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
R U L E S
● Map input factors to output discount
● Usually linear
● Occasionally inversely linear
● More complex rules possible
I N T E G R AT I N G W I T H S T O R E S O F T W A R E
● Using an SDK
● Or a plugin
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
T H A N K S
Evan Prodromou
https://fuzzy.io/