Verticalization As Applied To Advertising As An Enterprise System

San Diego, California. Sept 2012. Edward Montes

Digilant is an independent marketing technology company that provides marketers with a platform to support the entire media buy –from planning through execution, measurement and optimization– with ultimate precision and transparency.

Advertising As An Enterprise System

An Enterprise System represents a cross-functional, integrated information system, used by organizations to support business processes and provide an underlying platform for data integration

Generic Systems

• Systems are complex, tend to be comprehensive, tightly integrated software consisting of business process or logic that is hard-coded into the system

• “One Process Fits All”• Options:

1) Reconfigure the system or 2)re-engineer the process to fit the system

• One Process Fits All Approach Destroys Competitive Advantage

Vertical Strategy

• A strategy aimed at minimizing the gap between practiced processes and system embedded processes

• Specialized software for specific verticals where industry-specific processes are embedded into the system from the design stage

• Better Tailoring Products To Practices

Today’s Truisms

• Too Many Choices

• Competition Is Fierce

• Black Box Mentality

• No Material Difference between Targeting and Valuation

• Optimization is the equivalent of winning in a specific attribution model

Simple Methodology

One day attribution:

The process takes one day of clicks /conversions and 45 days of preivous impressions/clicks and marks each event with their corresponding weights according to the attribution models.

The process is then applied for each day with at least 45 previous days of data.

0 0 0 1 0 1 0 1 1 0 Target

I I I C I I I C I C ConvTime

45 days of imp / click Day of

execution

More Methodology

Day of

execution

45 days of imp / click

Time

45 extra day to check if

impression/ click is a zero

Period with full information about target

The first 45 don’t have all the possible events that could have caused the conversion / click, and the

last 45 don’t have the conversions / clicks needed.

Click and Conversion Models: Predictors

A number of target based predictors exist for each model:

Target is computed in previous days of the events selected in the sample aggregating by:

Geo

Url-domain Hierarchy (Url is used if it has enough data, domain is used otherwise)

Two different depths are used to capture short-term and long-term effects:

2 previous days (one predictor for geo and one for url-domain hierarchy)

20 previous days (one predictor for geo and one for url-domain hierarchy)

Time

Day of sample20 days aggregation

2 days

aggregation

Parameter DF Estimate StandardError

WaldChi-Square

Pr > ChiSq StandardizedEstimate

Exp(Est)

Intercept 1 1.1078 0.0506 478.76 <.0001   3.028

W_BROWSER 1 -0.4926 0.0215 525.30 <.0001 -0.1128 0.611

W_GMTGCL2 1 -0.3585 0.0502 50.95 <.0001 -0.0279 0.699

W_UMTGCL2 1 -0.1537 0.0191 64.38 <.0001 -0.0527 0.858

W_UMTGCL20 1 -0.7971 0.0162 2419.47 <.0001 -0.3329 0.451

W_USERLANGUAGE 1 -0.1040 0.0385 7.30 0.0069 -0.0111 0.901

W_VISIBILITY 1 -0.6253 0.0375 278.16 <.0001 -0.0707 0.535

Modelling Results: CPG Click Attribution

Target based predictor by URL-DOMAIN hierarchy for target [TARGET] and depth [DEPTH]

Modelling Results: CPG LCLI Attribution

Parameter DF Estimate StandardError

WaldChi-Square

Pr > ChiSq StandardizedEstimate

Exp(Est)

Intercept 1 1.8535 0.0542 1168.69 <.0001   6.382

W_BROWSER 1 -0.4581 0.0271 285.51 <.0001 -0.0801 0.632

W_CREATIVECATEGORY 1 -0.8896 0.0485 336.64 <.0001 -0.0734 0.411

W_GMTGCOLCLIWOR2 1 -0.7736 0.0221 1224.56 <.0001 -0.2827 0.461

W_GMTGCOLCLIWOR20 1 -0.1514 0.0244 38.63 <.0001 -0.0468 0.859

W_GOOGLEMAINVERTICAL 1 -0.2587 0.0506 26.19 <.0001 -0.0258 0.772

W_UMTGCOLCLIWOR2 1 -0.0458 0.0240 3.65 0.0561 -0.0103 0.955

W_UMTGCOLCLIWOR20 1 -0.7822 0.0182 1850.60 <.0001 -0.2404 0.457

W_VISIBILITY 1 -0.2508 0.0568 19.49 <.0001 -0.0202 0.778

20 Day surfing behavior still strongest predictor but 2 Day GEO importance jumps dramatically

Targeting vs. Valuation

• Understanding the most important predictors of performance allows you to target to desired outcome

• Understanding most important predictors of price, in conjunction with target, creates efficiency

We believe the future of Advertising Technology is to allow for Customization At Scale:

Your Data

Your Inventory

Your Algorithm.

For more information, please contact: info@digilant.comOr visit our website www.digilant.com