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ARF DASH Study Use Cases
| USE CASE | DESCRIPTION | EXAMPLE |
|---|---|---|
| Assign digital devices and accounts to persons and households | Media is predominantly digital, and digital devices and accounts are routinely shared, creating significant enumeration challenges. DASH respondents provide detailed, device-by-device and account-by-account usage and sharing, allowing licensees to resolve digital IDs reliably at the person or household level. | An online retailer could use DASH data to develop an accurate projection of market share among young adults, many of whom “piggyback” on their parents’ or friends’ accounts. |
| De-duplicate HH and person-level impressions | Media measurement is no longer bound by historical standards or ratings currencies. Advertisers, media companies and ratings services need to be able to reconcile HH- and person-level audience counts. | An advertising technology company could use DASH data to roll up its person-level impressions into HH impressions, to enable direct comparisons across ratings schemes. |
| Fill gaps and control for bias in datasets | Even large, census-level datasets can suffer from non-representativeness on important profiling and usage dimensions. A comprehensive, single source cross-section of US media and ecommerce consumers, DASH can serve as an independent and reliable “true north.” | An advanced TV measurement service could use respondent-level DASH data to develop projections that correct for overrepresentation of older suburban households in its STB dataset. |
| Enrich customer databases and target-segment profiles | Cookie deprecation and privacy regulations make marketers and media companies increasingly reliant on first-party and non-PII third-party data. Respondent-level DASH data can enhance consumer profiles for better research, strategy and activation. | A telecom provider could use DASH data to improve volume forecasts and media targeting for a new SMB services package. |
| Project samples to broader universes | A single-source study that covers a broad range of media and ecommerce behavior, DASH produces many useful “hooks” for projection of samples to known universes. | An ecommerce panel could use DASH data to help scale up and refine sales projections for a new video game title. |
| Train algorithms and machine learning sequences for advanced measurement and activation | The vast streams of passive data produced by digital media promise superior programmatic measurement and execution. One key to unlocking that potential is a “calibration set” like DASH. DASH contains rich signals of behavior on every device in the household and by whom. | A developer of passive media measurement technology could use DASH data to improve the accuracy and interoperability of a core machine learning scheme. |
| Enhance identity graphs | The DASH survey covers individual and household device ownership and usage, yielding its own identity graph among 10,000 representative respondents. This graph can serve as a seed to enhance much larger graphs and to verify the distribution of profiles and segments. | An identity company could use the DASH graph to model persons’ usage by time of day across a set of household device bundles. |
| Contextualize digital media usage | Strategists and product developers need to understand how their services fit into the “big picture.” DASH respondents complete a comprehensive survey that covers most aspects of their digital media usage. The data can be analyzed at an aggregated or respondent level, depending on the needs of the licensee. | A streaming media service could take advantage of the holistic view the DASH data provides to identify and develop strategies for a high-potential, new prospect segment. |
To learn more, contact Paul Donato, ARF Chief Research Officer, or Jim Meyer at DASH@thearf.org or by clicking the link below. by clicking the link below.