Observed Signal · May 4, 2026 · Product Launch · Source: Adzine · Impact: 2/5 · Sentiment: Positive
Next Step After Attention: Measuring Ad Effectiveness
ADZINE reports on a measurement approach from Munich-based AdTech firm Screen On Demand (SoD) that bundles multiple signals into a single “Wirkungs‑Score” (effectiveness score) to move beyond raw attention metrics. The score aggregates four normalized components — Attention, Brand Impact, Behavioral Impact and Cost per Effect — into a weighted value (0–100) that is campaign-specific and disclosed in reporting. Data sources include creative telemetry and player events (validated via standards such as IAS or MOAT), panel-based brand‑lift tests, movement/footfall data, search-volume APIs and website traffic. SoD distinguishes directly measured, up‑scaled and fully modelled contributions, exposes confidence classes for uncertainty, and intends the score to feed back into automated campaign steering (targeting, frequency, budget). The article highlights methodological limits: cross‑channel comparability, sample-size constraints, data quality variance across screens, and remaining causal attribution challenges.
Introduces an aggregated, cross‑channel effectiveness metric and transparency measures (confidence classes) that advance measurement practices — relevant to advertisers and measurement vendors — but is a vendor-level approach with methodological limits and not an industry standard.
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Key Takeaways & Evidence Grounding
- Munich AdTech company Screen On Demand (SoD) developed an aggregated "Wirkungs‑Score" to measure advertising effectiveness.
- The Wirkungs‑Score combines four components: Attention, Brand Impact, Behavioral Impact and Cost per Effect, each normalized to a 0–100 scale and weighted into a campaign-specific total.
- Data sources include ad telemetry and player events (validated against IAS/MOAT), panel-based test/control brand‑lift studies, movement/footfall panels, search‑volume APIs and website traffic.
- SoD distinguishes between directly measured data, upscaled estimates and fully modelled shares and reports confidence classes to express uncertainty.
- The score is designed to be fed back into campaign automation to influence media-buying decisions (targeting, frequency, budget allocation).
Ontology Mapping & Concepts
Related Market Signals & Shifts
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