Fast Data Driving Personalization - Nick Gorski

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Fast Data Driving Personalization

Nick GorskiAugust 7, 2014

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Fast data driving personalization

• Fast data paradigm– There are common big data / distributed systems patterns– Lambda architecture– Grounded example: features of user behavior

• Deep dive into TellApart personalization secret sauce– Retargeting bidder– Predictions built on top of fast data architecture

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Fast data?

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Fast data

• Big data• All-time data• New events readily available• Served with low latency

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Fast data driving personalization

• Predictive marketing platform– Ingest diverse events over all-time– Indexed by user– Reflecting events that occurred seconds ago– Served in real time (ms latency)

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Bidding for Transactional Retargeting

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Transactional retargeting• E-marketing• Display advertising• Site retargeting• Personalization for marketing• Transactional retargeting

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It’s 2014, you know what marketing is

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Display advertising funnel

Brand

Demographic targeting

Paid search

Awareness

Interest

Intent

Consideration Site retargeting

Purchase

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It’s 2014, you know what retargeting is

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TellApart transactional retargeting

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Aligned incentives

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Secret sauce

TellApart’s bidder for transactional retargeting

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TellApart’s bidder: our secret sauceReceive real-time bidding (RTB) request

Submit RTB response

Predict expected revenue

Predict auction environment

Get features of user

Calibrate bid with real-time data

Bid

flow

100ms 40ms

30ms

30ms

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Getting features of a user’s behavior• We’d like to predict a user’s value to a merchant

– Lifetime value– Today– If we showed them an ad right now

• Proxy used throughout ad-tech: CTR– What is

– What is

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Informative features

Merchant events• recency• product views• added items to

cart• purchases

TellApart events• ad views• ad clicks

Real-time (RTB)• publisher• TOD• vertical

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Special purpose fast data

Events

MapReduce

Event logs Feature vectors

Model training

P(click)

Model

User events

Application

Features of user

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Lambda architecture for general fast data

http://lambda-architecture.net/

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Fast data for machine learningFeature speed layer

Feature batch layer

Model batch layer

Feature serving layer

Model serving layer

Feature API

Model API

Predictionsabout users

Summarize

Training

Materialization

Feature topology

Registration

ExtractEvent logs

Kafka topics

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Case study: Freshplum

• Challenge:– Port existing feature

extraction and model training to lambda architecture

• Dynamic offers– Who should receive an offer?– Gradient boosted decision

trees– Session-based features

• Same AUC, similar performance• Week of dev time

Lambda architectureFreshplum infrastructure

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Fast data for fast features

• Lambda architecture applied to feature extraction– Unified offline and online extraction– Robust and fault tolerant– Feature engineering is fast– Supports features that are otherwise expensive to deploy

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TellApart’s bidder: our secret sauceReceive RTB request

Submit RTB response





Bid

flo

w

40ms

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Modeling is hard, modeling is easy

• Building a retargeting bidding strategy is hard

• Effective valuation strategies for retargeting:– Pass the buck to your client– Bid infinity– Bidding proportional to lifetime user value– Bidding proportional to P(Iclick)

– Formulating as an MDP and learning the optimal policy

“Smart scientists don’t just solve big, hard problems; they also have a knack

for making big hard problems small.”

-DJ Patil

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Modeling is hard, modeling is easy

• CPM – ads are valuable when they are displayed• CPC – ads are valuable when they are clicked• CPA – ads are valuable when they lead to an action

• TellApart bills on a CPA basis, charging a revenue share for each click conversion

– Proxy for true value– Auditable by merchants

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The value of a TellApart ad

• The value of a TellApart ad is the click conversion revenue it drives

• Decompose into a chain of simple models

• Further decompose probabilities– Train model– Calibrate with offline data

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Does it work?

• 85-90% of clicks are made by bids in top quartile

• 96% of conversions made by bids in top quartile

• 80% of conversions made by bids in top decile

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Submit RTB response





Bid

flo

w

40ms

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RTB auction

Publisher A Publisher B Publisher C . . .

Bidder Bidder Bidder . . .

RTB exchange

Exchange ?

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Auctions and equalibria

• RTB auctions are second-price, sealed bid– Vickrey with stable Nash equilibria– Not repeated, but not one-shot either

• Impressions shown milliseconds apart• Multiple exchanges• Information leakage

– Sliding fees violate second-price assumption– Multiple slots (generalized second-price), but pay per

impression

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Value and bid price across auctions

• Bidding infinity maximizes revenue – Win every impression, get every possible click and drive

every possible conversion (naively true)

• Bidding our true value maximizes profit

• Winning our true value maximizes affordable revenue

• Win at true value on average (minimize variance in mean)

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Winning at a target clearing price

• Goal: win at true value on average, minimizing variance of mean

• How do we do that in a second-price auction?

• Model the competition

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All publishers are not created equal

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• Given a win price and features of environment, predict the bid price that will clear at that win price.

• Modeling this– The easy way– The good way

Predicting the markets

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The easy way: bid to win

• Local linear isotonic regression

0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.50

0.5

1

1.5

2

2.5

Win CPM

Bid

CPM

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The right way: bid to win

• Mixture of Gaussians– Identify clusters– Share information to

leverage trends in features across clusters

• EM may be the right way, but MapReduce EM is not the easy way

• Big impact to revenue and performance

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Submit RTB response





Bid

flo

w

40ms

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Calibrating overall performance

• Building models is great, but the real world is messy– Non-stationary adversarial environment– Biased data and imperfect models

• We bid to maximize affordable revenue– If we spend too little, we sacrifice top-line revenue for profit– If we spend too much, we can’t afford the revenue that we

drive

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PID control

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Calibrated bidding

Bids

Gain

Difference

Production(taba)

target

control signal

error

bid $

real-time spend $revenue $

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Controlling bids strategically

• Control signal says “spend more” or “spend less”

• When we spend less– Don’t bid less than true

value– Instead, threshold low-

value bids

• When we spend more– Bid more across the board

Bid

$

Bid rank

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Thresholding increases efficiency

Spend (legacy)

Spend

Revenue

Bids

Ranked bids

Perc

en

t of

Tota

l

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Control is hard

• Given limited time, we prefer improving our models• Reasons for control

– Unpredictable market dynamics– Predictable market dynamics– Inaccurate user value models

• Pushing responsibilities up to models makes bidder more effficient

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Submit RTB response





Bid

flo

w

40ms

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And that’s just bidding!

• Identity• Products shown in ads

– Strategies to select viewed products– Recommended products

• Data platform

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TellApart retargeting by the numbers• Handle 5.3B requests per day, at peak 100K QPS

• Lifts online revenue 10% or more

• As of December 2013, $100M ARR• Drove 1% of Cyber Monday e-commerce in 2013

• TellApart has won every head to head test with performance

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TellApart’s data philosophy

• Infrastructure dictates product, so build good infrastructure• EV[work] = EV[impact] * P(works)• Simple models, chained together• Find simple changes with big impact• Data wins arguments• Transparent and aligned objective functions

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Greenfields

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TellApart Identity Network

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Email

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Onsite personalization

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Performance-based personalized marketing

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Fast Data. Hard Problems. Insanely Great Team.

http://www.tellapart.com/careers/

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Thanks!

[email protected]@nicholasgorski

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Fast Data. Hard Problems. Insanely Great Team.

http://www.tellapart.com/careers/

Fast Data Driving Personalization - Nick Gorski

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