Measuring The Immeasurable: How to Properly Measure Your Online Marketing Mix

ClearSaleing Confidential 1

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www.clearsaleing.com


OUR DEFINITION

Clear’-Sale’ing (klir sāl ing) n. 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



THE LAST CLICK FALLACY


ATTRIBUTION MANAGEMENT HIERARCHY

» Attribution Management

» Engagement Mapping

» Attribution Conversion

» Mixed Media Management

» Marketing Mix Analysis

» Multi-Campaign Attribution

IT’S EVERYWHERE

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


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


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.


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™ ATTRIBUTION MODELS


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™ TIMING


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.

PURCHASE PATH™ EXCLUSIONS

» There are simple prove am rule


BRAND KEYWORDS


BRAND SEARCH THE CLOSER


Step2 = Branded Term

BANNERS INTRODUCE…SEARCH CLOSES


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

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.


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


Verticals Served

1. Financial services2. Insurance3. Travel4. Media5. CPG6. Auto7. Retail8. Catalog9. High Technology10. Non-profit11. 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

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

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

* 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

┼ 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!

GETTING THE ATTRIBUTIONS

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


MEANING OF CALIBRATION


INFLUENCIAL POTENTIAL (PAID SEARCH)


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.


» 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

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


BOTTOM LINE

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



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Measuring The Immeasurable: How to Properly Measure Your Online Marketing Mix

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