ClearSaleing Confidential . www. clear sale ing .com

OUR DEFINITION ClearSaleing Confidential Clear’-Sale’ing (klir sāl ing) 1. an advertising analytics company 2. the only effective way to manage an online advertising portfolio and increase profit (ROI) for online advertisers v. 1. continually improving the ROI of online advertising campaigns 2. easily and transparently increasing sales and profits

Clear’-Sale’ing (klir sāl ing)

1. an advertising analytics company

2. the only effective way to manage an online advertising portfolio and increase profit (ROI) for online advertisers

v. 1. continually improving the ROI of online advertising campaigns

2. easily and transparently increasing sales and profits

FORESTER RESEARCH - FEBRUARY 2009 ClearSaleing Confidential

THE LAST CLICK FALLACY ClearSaleing Confidential

ClearSaleing Confidential ATTRIBUTION MANAGEMENT HIERARCHY

Attribution Management Engagement Mapping Attribution Conversion Mixed Media Management Marketing Mix Analysis Multi-Campaign Attribution IT’S EVERYWHERE

Attribution Management

Engagement Mapping

Attribution Conversion

Mixed Media Management

Marketing Mix Analysis

Multi-Campaign Attribution

PURCHASE PATH™ ClearSaleing Confidential Multiple ad sources will often contribute to a single conversion This view displays the relationships between the ad sources that contribute to sales You can then drill into any path by clicking on the (+) to the left of the path. This displays line item detail of the specific ads consumers clicked along the purchase path

Multiple ad sources will often contribute to a single conversion

This view displays the relationships between the ad sources that contribute to sales

You can then drill into any path by clicking on the (+) to the left of the path. This displays line item detail of the specific ads consumers clicked along the purchase path

PURCHASE PATH™ DETAIL ClearSaleing Confidential In this example we drilled into the AdWords > AdWords path to see the specific ads that were clicked on en route to purchase.

INTRODUCERS , INFLUENCERS, AND CLOSERS ClearSaleing Confidential Ads are classified into 3 categories. Introducers - the very first ad someone clicks or sees to find your site; Influencers- are ads that are clicked on after the Introducing ad but before the Closer; Closers- are ads that get clicked or seen at the very end of the Purchase Path™. This classification system makes it easy for an advertiser to measure an ads value even if it is not the last ad clicked or seen prior to purchase.

PURCHASE PATH™ ATTRIBUTION MODELS ClearSaleing Confidential Several methods for setting attribution management models are baked into the application. You can choose to attribute Profit and Revenue evenly across the team of ads that generated a conversion or you can customize the attribution model yourself.

PURCHASE PATH™ TIMING ClearSaleing Confidential To further increase the accuracy of attribution, an advertiser is able to choose the maximum number of ads they are willing to attribute credit to, the maximum days between ad clicks/impressions that are acceptable, and lastly the attribution time window which is the amount of time from first click/impression to purchase that is acceptable for an ad click/impression to occur within to get credit.

PURCHASE PATH™ EXCLUSIONS ClearSaleing Confidential Another method created to help with attribution is create a method for excluding certain types of ads from receiving credit even if they occur in a Purchase Path in the allowable window. For example, many of our clients exclude giving credit to branded ads that happen at the end of a path because those ads are used to navigate back to their site.

ClearSaleing Confidential

There are simple prove am rule ClearSaleing Confidential

There are simple prove am rule

BRAND KEYWORDS ClearSaleing Confidential

BRAND SEARCH THE CLOSER ClearSaleing Confidential Step2 = Branded Term

BANNERS INTRODUCE …SEARCH CLOSES ClearSaleing Confidential 11 of 18 keyword queries following a display impression were branded-focused Vision into the role of “Introducer” and “Influencer” are critical to understanding the value of the display advertisement.

287,000 orders in 2008 35% of all orders in a Purchase Path 100,450 orders in a Purchase Path 45% ended in Brand Terms 45,202 orders ended in Brand Terms Avg. order was $17 $768,000 re-attributed Total profit increased by 28% PURCHASE PATHS – CLIENT XYZ ClearSaleing Confidential

287,000 orders in 2008

35% of all orders in a Purchase Path

100,450 orders in a Purchase Path

45% ended in Brand Terms

45,202 orders ended in Brand Terms

Avg. order was $17

$768,000 re-attributed

Total profit increased by 28%

Disclaimer Due to time constraints we will not be able to explain every aspect of the model nor will we be able to speak to every input, scenario, and output. ClearSaleing Confidential

Due to time constraints we will not be able to explain every aspect of the model nor will we be able to speak to every input, scenario, and output.

DR. PURUSH PAPATLA President and Founder; Vetra Analytics Staff – At least a Master’s in Statistics with a background in Engineer or Mathematics or a Ph.D. in Statistics Ph.D. from Kellogg School of Management at Northwestern University Published in top-tier marketing journals Marketing Science Journal of Marketing Research Journal of Business Research Journal of Retailing Journal of Interactive Marketing ClearSaleing Confidential

President and Founder; Vetra Analytics

Staff – At least a Master’s in Statistics with a background in Engineer or Mathematics or a Ph.D. in Statistics

Ph.D. from Kellogg School of Management at Northwestern University

Published in top-tier marketing journals

Marketing Science

Journal of Marketing Research

Journal of Business Research

Journal of Retailing

Journal of Interactive Marketing

Verticals Served Financial services Insurance Travel Media CPG Auto Retail Catalog High Technology Non-profit Online

Verticals Served

Financial services

Insurance

Travel

Media

CPG

Auto

Retail

Catalog

High Technology

Non-profit

Online

CONSUMER DECISIONS

DECISION INFLUENCER What we know Our Communications Paid Search Banner Ads e-mail Onsite Promotions Comparison Shopping Affiliate ad Consumer Search Organic search Site visits to us What we don’t know yet Competitor Communications Consumer search Site visits to competitors Product trials …… . Other sources Social Media Word of mouth Opinion sites Expert opinions Traditional Mass Media

What we know

Our Communications

Paid Search

Banner Ads

e-mail

Onsite Promotions

Comparison Shopping

Affiliate ad

Consumer Search

Organic search

Site visits to us

What we don’t know yet

Competitor Communications

Consumer search

Site visits to competitors

Product trials

…… .

Other sources

Social Media

Word of mouth

Opinion sites

Expert opinions

Traditional Mass Media

MODELING CONSUMER DECISIONS Build a mathematical model to predict consumer decisions Using data on influencers that we are able to track and measure Representing data on influencers that we can’t yet track and measure - our uncertainty - through a statistical distribution Calibrate the model on observed consumer decisions Purchase - yes/no Purchase size - dollar volume, # of units ….. Repeat purchases Word of mouth ……… . Test the model’s quality by comparing predicted and actual behavior

Build a mathematical model to predict consumer decisions

Using data on influencers that we are able to track and measure

Representing data on influencers that we can’t yet track and measure - our uncertainty - through a statistical distribution

Calibrate the model on observed consumer decisions

Purchase - yes/no

Purchase size - dollar volume, # of units …..

Repeat purchases

Word of mouth

……… .

Test the model’s quality by comparing predicted and actual behavior

CONSUMER DECISION MODEL Consumer’s Decision = f( Our Communications , Consumer Search , Competitor Communications, Other Sources ) = f( [ Paid Search, Banner Ads, e-mail, Onsite Promotions, Comparison Shopping, Affiliate ads ], [ Organic search, Site visits to us ], [ uncertainty ])

MEASURING THE EFFECTS OF KNOWN FACTORS We assume that each of the known influencers has an influence potential

MATHEMATICAL MODEL FOR CONSUMERS DECISION ClearSaleing Confidential * The β ’s are the attribution weights

* The β ’s are the attribution weights

GETTING THE ATTRIBUTIONS We calibrate the model on data from the ClearSaleing platform The data includes but is not limited to: Purchase Path ™ data Record of consumer’s decisions Purchase/non-purchase Product(s) purchased Amount spent Repeat visits and purchases

We calibrate the model on data from the ClearSaleing platform

The data includes but is not limited to:

Purchase Path ™ data

Record of consumer’s decisions

Purchase/non-purchase

Product(s) purchased

Amount spent

Repeat visits and purchases

GETTING THE ATTRIBUTIONS Calibrate the model on the ClearSaleing data Find the values of β ’s which will help us predict consumer decisions as accurately as possible Model is calibrated using Maximum Likelihood or Bayesian methods The β ’s are the attribution weights!

Calibrate the model on the ClearSaleing data

Find the values of β ’s which will help us predict consumer decisions as

accurately as possible

Model is calibrated using

Maximum Likelihood or

Bayesian methods

The β ’s are the attribution weights!

MODELING THE INFLUENCE POTENTIAL Influence potential of an influencer = f (# of exposures, when each of the exposures occurred, decay rate of the effect of exposures)

EXAMPLE WITH SIMULATED DATA Simulated data example

EXAMPLE PATH DATA ClearSaleing Confidential

MEANING OF CALIBRATION ClearSaleing Confidential

INFLUENCIAL POTENTIAL (PAID SEARCH) ClearSaleing Confidential

Do models work?

EXAMPLE: WORD OF MOUTH TRACKING MODEL

OTHER INSIGHTS PATH DATA CAN PROVIDE Measures of the rate of decay of the influence of Paid search e-mail Banner ads Comparison shopping Site visits Past purchases … . Can be useful in deciding on frequency and recency of different types of ads. ClearSaleing Confidential

Measures of the rate of decay of the influence of

Paid search

e-mail

Banner ads

Comparison shopping

Site visits

Past purchases

… .

Can be useful in deciding on frequency and recency of different types of ads.

Does a consumer’s choice of an ad source influence his choice of whether or not to use another source? E.g., Does exposure to a banner ad lead to greater interest in search? Branded search? E.g., Does exposure to comparison shopping lead to greater interest in search? Branded search? OTHER INSIGHTS PATH DATA CAN PROVIDE ClearSaleing Confidential

Does a consumer’s choice of an ad source influence his choice of whether or not to use another source?

E.g., Does exposure to a banner ad lead to greater interest in search? Branded search?

E.g., Does exposure to comparison shopping lead to greater interest in search? Branded search?

KEY TAKEAWAYS Get the data Purchase Paths™ Time All advertising sources Online and offline conversions Etc Applying simple proven attribution models (e.g. Brand exclusion at end of path) can be easily applied and can improve campaign performance Advanced attribution is possible to solve with sound mathematics Accuracy in ad valuation leads to more profit earned from your ad dollars ClearSaleing Confidential

Get the data

Purchase Paths™

Time

All advertising sources

Online and offline conversions

Etc

Applying simple proven attribution models (e.g. Brand exclusion at end of path) can be easily applied and can improve campaign performance

Advanced attribution is possible to solve with sound mathematics

Accuracy in ad valuation leads to more profit earned from your ad dollars

BOTTOM LINE We are barely scratching the surface of the potential of path data with the attribution models!!! ClearSaleing Confidential

We are barely scratching the surface of the potential of path data with the attribution models!!!

CLEARSALEING AND VETRA Today: Our technology with Vetra’s team can build custom attribution models Q3/4 2009: Vetra’s attribution models will be baked into ClearSaleing’s technology ClearSaleing Confidential

Today: Our technology with Vetra’s team can build custom attribution models

Q3/4 2009: Vetra’s attribution models will be baked into ClearSaleing’s technology

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ClearSaleing Confidential . www. clear sale ing .com