1. Using Data in a Digital Society Mike Rich and Scott Finer October 2010

2. 2 © comScore, Inc. Proprietary. Let’s look at just one web page  Returning user?  Browser  Time on site  Pages viewed  Geo-location  Ads  Searches  Clicks  Purchases  Returning user?  Browser  Time on site  Pages viewed  Geo-location  Ads  Searches  Clicks  Purchases

3. 3 © comScore, Inc. Proprietary. Terabytes of data! pattern_i ddomain_nameurl_hosturl_dirurl_pagerecordeduseragentmimetype 1//// 10/20/2010 06:19:16:913 PMapplication/internal 1//// 10/20/2010 06:19:19:803 PMapplication/internal 5651592 images- amazon.com g-ecx.images- amazon.com images/G/01/electronics/d etail-pageasus-2._V189604747_.gif 10/20/2010 06:19:26:350 PMimage/gif 5651592 images- amazon.com g-ecx.images- amazon.com images/G/01/electronics/d etail-pagedell._V189604740_.gif 10/20/2010 06:19:26:570 PMimage/gif 5651592 images- amazon.com g-ecx.images- amazon.com images/G/01/electronics/d etail-pageasus._V189604750_.gif 10/20/2010 06:19:26:787 PMimage/gif 6048290amazon.comwww.amazon.com// 10/20/2010 06:19:27:027 PM 6048290amazon.comwww.amazon.com// 10/20/2010 06:19:27:120 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)text/html; charset=iso-8859-1 6048290amazon.comwww.amazon.com// 10/20/2010 06:19:30:320 PM 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/authportal/co mmon/cssap_global._V199044452_.css 10/20/2010 06:19:30:570 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0) 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/x- locale/common transparent- pixel._V192234675_.gif 10/20/2010 06:19:30:820 PMimage/gif 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.comimages/G/01/gno/popoversprites-h._V192570380_.gif 10/20/2010 06:19:31:043 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0) 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.comimages/G/01/gno/popoversprites-h._V192570380_.gif 10/20/2010 06:19:31:270 PMimage/gif 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/gno/images/o rangeBlue navPackedSprites-US- 16._V212310439_.png 10/20/2010 06:19:31:490 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0) 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/x- locale/common/loginfwcim._V198987923_.js 10/20/2010 06:19:31:703 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0) 4272441doubleclick.netad.doubleclick.netadjamzn.us.gw.atf 10/20/2010 06:19:31:797 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)application/x-javascript 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/x- locale/common/buttons sign-in- secure._V192194766_.gif 10/20/2010 06:19:31:923 PMimage/gif 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/gno/images/o rangeBlue navPackedSprites-US- 16._V212310439_.png 10/20/2010 06:19:32:133 PMimage/png 4272441doubleclick.netad.doubleclick.netadjamzn.us.gw.btf 10/20/2010 06:19:32:190 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)application/x-javascript 6385067turn.comr.turn.comrbd 10/20/2010 06:19:32:197 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)image/gif 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.comimages/G/01/gno/popoversprites-v._V192570383_.gif 10/20/2010 06:19:32:360 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0) 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.comimages/G/01/gno/popoversprites-v._V192570383_.gif 10/20/2010 06:19:32:570 PMimage/gif 4272441doubleclick.netad.doubleclick.netadi/N5762.adzinia.comB4795243.8 10/20/2010 06:19:32:800 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)text/html 4272441doubleclick.netad.doubleclick.netadi/N5762.adzinia.comB4795243.9 10/20/2010 06:19:32:807 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0)text/html 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/gno/images/g eneral navAmazonLogoFooter._V192 570482_.gif 10/20/2010 06:19:32:817 PM Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Trident/5.0) 4418751 ssl-images- amazon.com images-na.ssl-images- amazon.com images/G/01/gno/images/g eneral navAmazonLogoFooter._V192 570482_.gif 10/20/2010 06:19:33:023 PMimage/gif 36734042mdn.nets0.2mdn.net2344880amazon_NoFee_300x250.swf 10/20/2010 06:19:33:287 PMapplication/x-shockwave-flash 36734042mdn.nets0.2mdn.net2344880amazon_NoFee_300x250.swf 10/20/2010 06:19:33:510 PMapplication/x-shockwave-flash 4272441doubleclick.netad.doubleclick.netadi/N5762.adzinia.comB4795243.9 10/20/2010 06:19:33:723 PM 4272441doubleclick.netad.doubleclick.netadi/N5762.adzinia.comB4795243.8 10/20/2010 06:19:33:937 PM 6048290amazon.comwww.amazon.com// 10/20/2010 06:19:52:927 PMapplication/internal

4. 4 © comScore, Inc. Proprietary. Today’s agenda  What comScore does  How data are used  How we collect data and create our offerings

5. 5 © comScore, Inc. Proprietary. comScore Digital Business Analytics Audience Measurement Site Analytics Vertical Market Solutions Social Analytics Copy Testing Campaign Verification Ad Effectiveness Cross Media Mobile Audience Measurement Network Analytics & Optimization Customer Experience & Retention Management User Analytics Advertising Analytics Mobile Analytics Unified Digital Measurement™ V0910

6. 6 © comScore, Inc. Proprietary. Many uses

7. 7 © comScore, Inc. Proprietary. Selected Clients MediaAgenciesTelecom/MobileFinancialRetailTravelCPGPharmaTechnology V0910

8. 8 © comScore, Inc. Proprietary. Three primary methodologies  Behavioral data – Passively observing actions – Panel of 2mm worldwide users who consent to participate  Survey data – Actively collecting opinions – Panel of millions of e-mail addresses “opted in”  Survey + Behavioral data – Combining both methods for sophisticated insights (using Multivariate methods) 8

9. 9 © comScore, Inc. Proprietary. Our Scenario Hangover 2 debuts Memorial Day 2011!  Mission: Raise awareness among key audience  Find 18 – 24 year olds online  Determine what we should do in mobile  Reach Xbox gamers when they are not playing Xbox  Develop a list of recommendations of where to run campaign

10. 10 © comScore, Inc. Proprietary. Key Measures Internet Behavioral Data

11. 11 © comScore, Inc. Proprietary. Key Measures for 18-24, rank-ordered by Composition Index  Highest reach and engagement sites (over-indexed) for 18-24 year olds in the US “Key Measures” is the name of one of our company’s most heavily used online tools, …. It is one of 20-25 types of reports that we collectively refer to as the “interface”

12. 12 © comScore, Inc. Proprietary. Gather the data Recruitment Find people willing to be counted Collection Monitor and transmit Identification Separate data by person Gather the data Recruitment Find people willing to be counted Collection Monitor and transmit Identification Separate data by person Two steps, six ingredients, one caveat Correct for bias Enumeration Determine the size of the universe Calibration Data for target estimates Projection Weight to the universe Correct for bias Enumeration Determine the size of the universe Calibration Data for target estimates Projection Weight to the universe

13. 13 © comScore, Inc. Proprietary. Proprietary Data Collection Technology Private and Confidential Personally Identifiable Information stripped using procedures audited by outside parties comScore Internet activity, system information Software upgrades, survey invitations “cProxy” Passively track actual consumer behavior Actively survey consumers anytime, anywhere Panelist

14. 14 © comScore, Inc. Proprietary. Protecting privacy is a core value 1. TRUSTe compliant disclosure: – Describes what the software does – Describes how the data is used – Links to privacy policy and user agreement 2. Start/Programs menu entry 3. Displayed under Add/Remove Programs – Removed immediately on selection – No files left behind 4. Welcome Pop or Welcome e-mail 5. Icon displayed in System Tray 6. WebTrust Privacy seal – Independent audit of our privacy policies, practices and procedures – Assures adherence to high standards in the protection of personally identifiable information

15. 15 © comScore, Inc. Proprietary. Enumeration Survey:  Telephone study using RDD, plus “cell phone only” supplemental data  Target of 1100 completed interviews per month  Information collected includes demographics, number of computers in the home, number connected to Internet  12 months of enumeration data (in red) are used to create the curve against which the Universe Projection is then fit (in black) How large is the universe we will project to from our panel (the sample) ?

16. 16 © comScore, Inc. Proprietary. Calibration Panel: Removes Inherent Biases  Online recruitment carries with it inherent biases, – For example, the recruitment technique is itself self-selecting…. – In the US we have a standalone Calibration panel, offline random recruited, to provide metrics on, e.g., quartiles of time spent online, which become weighting variables  Outside the US, we are considering strategies to introduce improved calibration procedures – Small random persons panel or cookie panel Calibration Panel Recruited offline, Used as a ‘yardstick’ Overall Panel Adjusted to reflect metrics of the Calibration Panel

17. 17 © comScore, Inc. Proprietary. Assigning weights to “Project” from Sample to Universe; in our case, from Panel to Population  To qualify for sample: – Must have complete demography. – Must be active. Persons without activity are excluded – At least 90% of computer activity must be successfully assigned to a member of the household  Stratification variables: – Age – Gender – Duration Categories – And several others,….  Final Step, Assign “weights” to each machine – Weights change each month

18. 18 © comScore, Inc. Proprietary. MobiLens Mobile trends via survey

19. 19 © comScore, Inc. Proprietary. DEMO

20. 20 © comScore, Inc. Proprietary. Measuring Attitudes and Opinions via Survey  Invite users – E-mail lists – Web site intercepts  Gather responses – Thousands of users surveyed each month for MobiLens – Survey flows customized based on phone type  Process the data – Weight and project – Load into interface

21. 21 © comScore, Inc. Proprietary. Segment Metrix Social trends using survey and behavioral

22. 22 © comScore, Inc. Proprietary. Combine attitudinal and passively observed traffic behavior  Seek permission from survey respondent (observe privacy as disclosed to recruited panelists) for both observations and survey Feelings, Perceptions, Attitudes, Preferences, Example; Generally, I prefer to shop online for most of my household needs Visitation to sites by content category; Heaviness of online use; Frequency of searching; Frequency and volume of purchasing Etc…..

23. 23 © comScore, Inc. Proprietary. Analyze combined data set  Exploratory analysis – k Means Cluster (reduce a large data set to meaningful subgroups of individuals or objects) – Factor Analysis (reduce data set to best variables) – Analysis of Variance (many types)  Segmentation ( optimize differentiation between segments ) – Discriminant Analysis (correctly classify observations or people into homogeneous groups) – Baysian or probabilistic methods ( http://en.wikipedia.org/wiki/Bayesian_model_comparison ) http://en.wikipedia.org/wiki/Bayesian_model_comparison A key objective is to construct “Predictive models” ( A model made up of a number of predictors variables that influence future behavior, such as an online product sale)

24. 24 © comScore, Inc. Proprietary. Research outcomes and Actionable outcomes  Compare relationships between selected attitudes and behaviors – Both Narrowly and broadly (DR vs Brand)  Perform segmentation – Score panelists with segment membership – Profile segments In terms of Demographics, search behavior, traffic patterns, online purchasing  Actionable outcomes of segmentation – Report traffic or search by marketing segments – Target marketing segments Because these techniques, lead to management actions that improve revenue or lower costs; they help managers optimize efficiency. How? For example, increase “lift” for advertising campaigns, and thereby, Increase the “return on investment” ROI in marketing. Why go to all this trouble?

25. 25 © comScore, Inc. Proprietary. Segment Metrix, 18-24, Gaming Aficionados Segment Metrix is the name of an online tool that reports traffic by Segment,. Often, the segments are formulated by a combination of survey and behavioral data Here sites are rank-ordered by their composition Index strengths

26. 26 © comScore, Inc. Proprietary. Conclusions

27. 27 © comScore, Inc. Proprietary. Our Media Plan Recommendations  Web Sites – Twitter, MySpace, Vevo and Hulu are just a few of the key web sites that hit our target audience well  Mobile – About a third of 18 – 24 year olds have a smartphone, so use this medium but supplement with other ad buys – Within mobile, look for the usual suspects (social, search, entertainment) but also consider news and reference sites  Gaming Aficionados – Look to video heavy sites like Craveonline and Vevo. Also look for ways to target gaming blogs on Technorati and Six Apart

28. 28 © comScore, Inc. Proprietary. In Summary  Data is everywhere in the digital world  Respecting privacy can open the door to new knowledge  Behavioral data gives us massive information  Survey data helps fill in the blanks for what we can’t observe  Combining behavioral and survey allows for highly sophisticated insights

29. comscore.com comscorecareers.com Thanks!

30. 30 © comScore, Inc. Proprietary. Appendix

31. 31 © comScore, Inc. Proprietary. Building MobiLens

32. 32 © comScore, Inc. Proprietary. MobiLens

33. 33 © comScore, Inc. Proprietary. Example of segment output by selected sites traffic  For four 18-24 high indexing Amusement sites,….the High Ambition group has the smallest indexes.