Presentation is loading. Please wait.

Presentation is loading. Please wait.

Internet R/F and Cross-Site Duplication Project April 9, 2003 ARF 49 th Annual Convention Roger Baron; FCB/Chicago Leslie Wood; LWR.

Similar presentations

Presentation on theme: "Internet R/F and Cross-Site Duplication Project April 9, 2003 ARF 49 th Annual Convention Roger Baron; FCB/Chicago Leslie Wood; LWR."— Presentation transcript:

1 Internet R/F and Cross-Site Duplication Project April 9, 2003 ARF 49 th Annual Convention Roger Baron; FCB/Chicago Leslie Wood; LWR

2 Manish BhatiaNielsen Net/Ratings Jason BiglerDoubleClick Lynn BolgercomScore Jim DanielsChurch & Dwight Doug HonnoldcomScore Stephen KimcomScore Allister LamDoubleClick Stacy MaloneUniversal McCann Interactive Brian MonahanUniversal McCann Interactive Kelly NiehoffMullen Scott PennistonFCB/Southern California Susan RussoNielsen Net/Ratings Marc RyanNielsen Net/Ratings Jennifer ShunkR.J. Palmer Denise SiednerDoubleClick Dave SmithMediasmith Young-Bean SongAtlasDMT Jim VailR.J. Palmer Randy WoottonAtlasDMT Thank Yous

3 Background Internet R/Fs were presented at a March 2002 R/F committee meeting Dramatically different results from each supplier Leslie Wood and Roger Baron proposed comparing campaign reach/frequency and cross-site duplication estimates provided by each supplier. David Smith and Gabe Samuels created coalition of data providers Coalition investigated and developed a methodology Ten advertisers have agreed to participate Preliminary data from one-week of tracking will be presented

4 The Team Researchers Gabe Samuels, The ARF – Project Coordinator David Smith, Mediasmith – Project leader Leslie Wood, Leslie Wood Research – Research Designer and Researcher Roger Baron, SVP, Director of Media Research, FCB/Chicago – Researcher

5 Team – continued Data Suppliers Server Centric Measures Denise Siedner, Allister Lam; DoubleClick Young-Bean Song; Atlas DMT User Centric Measures Marc Ryan, Susan Russo; Nielsen//NetRatings Doug Honnold, Lynn Bolger; comScore

6 Key Objectives Compare R/F and duplication measures from different data suppliers Server centric User centric Create standards for reporting duplication Better understand the possibilities and obstacles to developing a new methodology combining user and server centric measures into a single R/F measure. Make recommendations for next steps to modeling R/F

7 Some definitions Server Centric Measures (SCM) Count of computers (cookies) that were served an advertising message. Includes every served impression - the basis for media billing Need to differentiate US versus international exposure No demographics Participating suppliers DoubleClick Atlas/DMT

8 User Centric Measures Website exposure from a panel of Internet users who allow researchers to electronically monitor their browsing behavior. Statistically projectable to U.S. Internet users Individual user login allows demographic detail Syndicated service reports websites, not ads Must be provided ad URLs for this study Participating suppliers Nielsen//NetRatings ComScore Definitions (cont.)

9 Reach: Traditional media definition The number of different persons or homes exposed to a specific media vehicle or schedule at least once. Usually measured over a specified period of time (e.g. four weeks). Also known as cume, cumulative, unduplicated or net audience. - Advertising Media Planning, 6th ed - Sissors & Baron Reach: Internet media definition The number of different cookies or United States persons 2+ at home or at work, who have been exposed one or more times to a complete Web advertising message over the campaign period. "Reach" is synonymous with unique audience. - ARF Reach/Frequency Committee Definitions (cont.)

10 Not Reached % Reach Site 1 Two-site Duplication The number of persons or cookies exposed to both sites as a percent of those who are exposed to either site.

11 Random duplication: Assumes people who browse site 1 are as likely to browse site 2 as anyone in the population. Not Reached % Reach Site 1 % Reach Site 2 Duplication

12 Actual duplication is always more Think: people who browse IDG.NET also browse ZiffDavis Not Reached % Reach Site 1 % Reach Site 2 Actual Duplication Random always overstates Reach Duplication

13 Calculating percent duplication Definitions (cont.) Site ASite BA&B Calculated as: A&B / (A+B-A&B)

14 Study Design Report the same measures for the same advertising campaigns from four data suppliers Server centric suppliers give ad campaign URLs to user centric researchers. User centric researchers develop custom methodology to report when these ads appear on their samples browsers. Required because their syndicated reports just track websites, not the embedded ads. Custom methodology is still being refined Findings reflect both of the server centric suppliers.

15 Study Design continued Advertisers across industries Large ad campaigns Variety of ad strategies Frequency caps Targeted ROS Matched variables from both server and user centric suppliers

16 Reported Measures Total campaign impressions Total campaign reach Advertiser: How many people saw my ad? For each website in the campaign Impressions Total Reach Exclusive reach Duplication matrix of all sites taken two at a time.

17 Progress Determine technical logistics What address could everyone read What address made sense across suppliers Full agreement from suppliers to participate Testing of IABs Ad URLs completed Recruiting advertisers to allow server centric suppliers to share data NDAs from team for advertisers Several advertisers have signed agreements Data collection just beginning

18 Issues along the way Advertiser resistance and need for confidentiality DoubleClick limited to DART For Advertisers Time consuming (gratis) project Large number of creative units On-going need to notify UCMs of creative changes Complexity of Rich media creative Handling of geographically focused and tracking ads Inconsistent website name granularity Custom definitions and report formats

19 Data / Findings





24 Duplication between two websites is small (typically less than 0.25%) but slightly greater than random

25 Data / Findings

26 Learnings At this early date, there is similarity among the methods Net reach as a percent of sum of site reaches Average site percent exclusive reach Actual campaign reach vs. random Duplication between websites Duplication between websites is greater than random, but only slightly As in traditional media, random overstates campaign reach Determining User based ad campaign reach and frequency is labor intensive and methodologically immature.

27 Next steps Continue tracking pilot advertiser campaigns Add additional advertisers Report detailed findings at June ARF Internet R/F Committee meeting

Download ppt "Internet R/F and Cross-Site Duplication Project April 9, 2003 ARF 49 th Annual Convention Roger Baron; FCB/Chicago Leslie Wood; LWR."

Similar presentations

Ads by Google