Big Data

Best Practices is Cross-Device and Cross-Channel Identity Measurement

A study commissioned by The Coalition for Innovative Media Measurement (CIMM) has found that marketers looking to match consumer identities across devices and multiple platforms are currently faced with an array of unclear and obfuscated approaches to choose from – making an understanding of the right questions to ask vendors a critical component for success. In addition to media platforms and connected devices, the report also looks at methods for linking offline and online behavior for the same household or individual through home mailing address and IP addresses, email addresses, mobile phone numbers, landlines, device IDs, cookies, etc. The whitepaper was based on interviews with 20 key advertisers and agencies, media companies and research vendors, from July to September 2016.

CIMM Lexicon 3.0

The world of media measurement is quickly changing and keeping track of the growing vernacular is a full-time job. We had media consultant Charlene Weisler update our Lexicon for a third time to incorporate the fast moving digitization of content and advertising, the Internet of Things, the development of Programmatic Buying and Selling and Virtual Reality. Learn about everything from what a Daisy Chain is compared to a Mullet Strategy or the difference between a Purple Cow and Skunkworks. Enjoy the education!

Return Path Data Lexicon

Data Enrichment Quality

In 2015, CIMM hired media consultant Gerard Broussard to interview data providers and end users to develop guidelines for assessing the data quality of various data enrichment providers that are increasingly delivering purchasing, demographic and lifestyle data for matching to digital and TV usage behavior for segmentation, media targeting, and ROI analyses.  The report recommends greater disclosure and transparency to improve data quality and standardization of nomenclature and metric definitions to support the expected growth of programmatic media transactions.

Roadmap for Set Top Box Data

In 2010, CIMM and Charlene Weisler interviewed over 85 executives in 58 companies to determine the challenges and roadmap for using return path data for a variety of uses in media planning and audience measurement and to support advanced advertising. The paper identified the following uses for the data, but cautioned that technical standards need to be adopted by the MVPD’s in order to insure the quality of the data.  Additionally, more data need to be made available, to provide greater representativeness of all U.S. households:

      1. Local measurement especially in the smaller diary markets.
      2. Measurement for long tail networks currently not rated by Nielsen.
      3. Use of database matching to segment consumers by geography, demographics or purchase behavior.
      4. Addressable advertising and monitoring of specific campaigns.
      5. Within-network strategizing for promo placement and audience flow optimization.
      6. Use by cable operators for signal quality, marketing, measuring customer satisfaction and in carriage negotiations.

Additionally, a re-contact of key companies was conducted in 2012.  It showed not much change since 2010, except that the MVPD’s and the MRC have finalized a data standard.  Additionally, data are starting to be used for local market measurement, and for matching to other demographic and sales data to enable targeting by purchaser segments.

Roadmap for Set-Top Box Data

Best Practices in Matching Datasets to Set-Top Box Data

In 2011, CIMM hired database consultant Myles Megdal to interview database companies and agencies and develop best practices in matching various datasets to return path data.  His study examined the categories and types of external databases that are currently being matched to STB data to support advanced and addressable advertising applications. The advantages and limitations of these databases is reviewed, as well as the processes and procedures that are employed to assure their accuracy and representativeness – especially for media planning

Best Practices in Matching Databases to STB Data