The advertising industry’s shift from last-click attribution to multi-touch attribution (MTA) initially promised a deeper understanding of marketing effectiveness. However, limitations like signal loss and the ineffectiveness of non-clickable media like TV have emerged.
Even with standardized identifiers like cookies and MAIDs (mobile advertising IDs), MTA remains challenging. As a result, many have reverted to media mix modeling (MMM) and are exploring other creative solutions.
Despite these obstacles, some continue to advocate for multi-touch through techniques that essentially filter out traffic from other sources, which merely rebrands last-click attribution. This trend is particularly concerning in TV measurement, where many solutions already present an inflated picture of performance.
Verified attribution is not a silver bullet
Verified attribution, which isolates TV ad impact by filtering non-TV traffic, has a major flaw: It ignores the influence of unclicked-on ads.
Imagine a user sees a brand’s social media ad but doesn’t click, only to later see a TV commercial for the same brand and visit the website. Traditional models might credit the TV ad entirely, neglecting the initial social media exposure. This overlooks the power of social impressions.
Despite low click-through rates across social platforms (e.g., Meta’s average CTR range is 0.73% to 2%), most impressions (99.1%) still influence consumer behavior later in the customer journey. Discounting them inflates TV’s impact, just as cost-per-click (CPC) metrics overemphasize paid search – or as last-click attribution underestimates the impact of non-clickable media. Instead of relying on closed-loop MTA, marketers should focus on data science and MMM to understand incremental performance.
If not MTA and not Verified attribution, what to use instead?
The answer depends. Modern solutions like Tatari use a combination of baseline+lift models and IP-level matching (with or without device graphs) for linear and streaming TV, respectively.
These models are realistic and highlight TV’s incremental value. Still, they have limitations. For one thing, they primarily benefit digitally native brands. Brick-and-mortar advertisers might find greater success with traditional methods like geo-testing.
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These models (like Tatari) seek a direct causal relationship between a TV ad and an outcome (e.g., a sale). However, consumers experience touch points across channels, each contributing to the response.
For larger, established marketing operations, therefore, MMM offers a more holistic approach to TV measurement by assessing the overall impact of the marketing mix across different channels. This method helps optimize budgets and understand channel interactions, which is crucial for strategic planning. While MMM requires significant data and time investment, recent technological advancements have enhanced its efficiency and accessibility, making it a viable option for comprehensive and actionable insights.
The Bottom Line: Embrace Complexity
Attribution is messy, but our message is nevertheless loud and clear: Remain vigilant and question the feasibility of models promising complete or near-perfect attribution – looking at you, Verified attribution – and carefully select your measurement methodology based on your situation, even if it is old school. Consider using multiple models to triangulate for the real answer.
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