Planning with Attention Metrics
Adelaide’s attention data can be used across all facets of the campaign lifecycle to improve advertising outcomes and efficiency. Advertisers use AU for media planning in two ways:
Flight
Control
Adelaide’s Flight Control solution uses normative data to predict the optimal AU required to achieve a specific KPI. Media planners can leverage these insights to build high-impact campaigns with predictable performance.
Planning
Tools
AU-enriched planning tools enable advertisers to easily focus their budgets on high-quality media, integrate attention data earlier in the campaign lifecycle, and plan against AU benchmarks and norms.
Flight Control: Plan Optimal Media Quality
Adelaide's Flight Control is an advanced pre-planning tool that helps marketers to find the "just right" combination of media quality and frequency to drive efficient and consistent outcomes.
AU floors are calculated using KPI, advertiser category, and media channel. Based on suggested AU floors, advertisers can set explicit media quality objectives with publishers and platforms. In addition to planning, advertisers can use AU thresholds to inform custom bidding, prebid filters, private marketplace curation, and attention guarantees with sell-side partners.
Learn more about the methodologies behind Flight Control in our free guide, Media Quality and Precision Frequency Using Attention Metrics.
Planning Tools: Unlock Precise Media Quality Measurement
With over 100,000 domains and billions of impressions measured, Adelaide has robust and normative benchmarks across more than a dozen channels, including programmatic, social, and CTV.
Brands and media agencies can integrate AU benchmarks into proprietary planning tools to identify high-quality media, reallocate campaign budgets, and plan against benchmarks and norms. Adelaide AU data is integrated with several traditional audience and media planning tools, such as Havas Media Network’s Meaningful Media Marketplace.
Advertisers are even incorporating AU into media mix modeling (MMM). MMM informed by attention metrics helps scenario planning tools identify optimal AU levels, deliver more precise recommendations, and ensure those recommendations result in consistent outcomes.
| Week | Display Imp | Social Imp | OLV Imp | TV Grp | Sales |
|---|---|---|---|---|---|
| 1 | 62.06M | 16.33M | 19.21M | 1150 | 20,704 |
| 2 | 73.75M | 13.50M | 24.10M | 1060 | 19,364 |
| 3 | 115.28M | 4.19M | 27.94M | 940 | 25,881 |
| 4 | 78.39M | 5.77M | 18.85M | 160 | 25,903 |
| 5 | 78.04M | 8.84M | 22.72M | 510 | 42,168 |
| Correlation to Outcomes: 0.41 | |||||
| WK | Display Imp | Attn Rtng | Social Imp | Attn Rtng | OLV Imp | Attn Rtng | TV GRP | Attn Rtng | Sales |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 62.06M | 32.1 | 16.33M | 40.2 | 19.21M | 51.2 | 1150 | 52.4 | 27,126 |
| 2 | 73.75M | 34.2 | 13.50M | 41.9 | 24.10M | 53.9 | 1060 | 55.9 | 25,614 |
| 3 | 115.28M | 28.1 | 4.19M | 43.7 | 27.94M | 49.8 | 940 | 51.2 | 35,012 |
| 4 | 78.39M | 35.9 | 5.77M | 41.7 | 18.85M | 49.1 | 160 | 62.6 | 28,086 |
| 5 | 78.04M | 39.1 | 8.84M | 46.2 | 22.72M | 55.3 | 510 | 64.5 | 56,618 |
| Correlation to Outcomes: 0.86 | |||||||||
To learn more about integrating attention metrics into media mix modeling, read Adelaide’s guide, Modernize Your MMM with Attention Metrics, created in partnership with the Association of National Advertisers.
