Adaptive Pricing with Machine Intelligence

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

[email protected]

https://fuzzy.io/