a practitioner’s introduction to attribution...•rule-based attribution models do not fully value...

#ARFAxS

A Practitioner’s Introduction to Attribution

Mericcan Usta

Sr. Data Scientist

GroupM

• ISOLATE EFFECTS OF MEDIA

•OPTIMIZE ADSPEND MIX

A Working Definition ofAttribution

Legacy: Top-Down Models

• Relate aggregate media spend to aggregate sales• How do different media perform relative to each other? • Which marketing-mix modeling choices will help isolate the impact?

• Halo effects, adstock, short-term response curves, long-term effects, mixed effects modeling…

SOURCE: Kihn, M., C. Eubanks, and L. F. Kune, Market Guide for Attribution and Marketing Mix Modeling. Gartner Research Article ID G00317823. 30 May 2018.

A Paradigm Shift

• Promise of a universal single-source media panel

• Promise of a universal single-source panel

• Customer journey is the new currency

Now: Bottom-Up Models

• Find the critical media that keeps incremental journeys in the funnel• Is my data quality good enough?

• Did people really see my ad?• Can I isolate customer journeys?• How will data privacy rules affect the quality?

SOURCE: Kihn, M., C. Eubanks, and L. F. Kune, Market Guide for Attribution and Marketing Mix Modeling. Gartner Research Article ID G00317823. 30 May 2018.

New Media Imply New Models?

display across walled gardens

video platform genres

programmatic linear TV

IoT connections to OOH

“new media” reported in aggregate

“old media” amenable to journeys

A Taxonomy of Bottom-Up Models

Rul

e-ba

sed

Met

hods

Sta

tistic

s-ba

sed

Met

hods

Rule-based methods rely on business judgement and technical simplicity…

…statistics-based methods use data.

Findings from the JAR

• Heuristics are good, but some are too simple

• “Last click” model favors paid search, but consumers typically search multiple times before making a purchase

• Rule-based attribution models do not fully value social media or email, which often don’t directly lead to purchase but can have strong behavioral impact

• Statistics-based model provides more stable attributions

SOURCE: Nisar, T.M., M. Yeung. Attribution modeling in digital advertising. Journal of Advertising Research Dec 2018, 58 (4) 399-413

Rule-based

Statistics-based

Statistics–Based Models are AboutMarginal Value

CONVERSIONShapley value concept

Shapley Value Concept: An Example

Display

SearchSocial

• Play an “advertising game”• Players: publisher types where ads are placed• How much contribution to the advertiser’s bottom line does each “player” make

in each journey?

Shapley Value Concept: An Example

DisplaySearch

Social

Marginal contributions of each player (publisher types where ads are placed) averaged across all possible journeys

Solving the Scalability Challenge

SHAPLEY VALUE APPROXIMATIONSexponential complexity with budget alternatives logistic regression & probabilistic models

memory runtime

quality/cost balance

DIVIDE & CONQUERfeatures (minimal overlap) and journeys

Putting the Ideas to Test

• Campaign run for a nationwide accessories retailer between August 7th, 2016 to October 30th, 2016 through the retailer’s website

• 400M impressions served to 120M users, 1M associated with revenue generating customer journeys

• 137K online purchases

SOURCE: Mahboobi, S.H., M. Usta, S. R. Bagheri. Coalition Game Theory In Attribution Modeling. Journal of Advertising Research Dec 2018, 58 (4) 414-422

• ISOLATE EFFECTS OF MEDIA •DIGITAL PROMISES CUSTOMER JOURNEY

•NEWEST MODELS ATTEMPT MARGINAL CONTRIBUTION

•APPROXIMATIONS MAKE IT SCALABLE• CAVEATS

Recap

DATA SCIENCE

mericcan.usta@groupm.com: mcusta