How the Right Measurement Can Help Marketers Get the Biggest Bang for Their Buck

Let’s be honest: Marketers must measure the effectiveness of their efforts mostly because they need to justify their budget. 

When the CFO asks, “What did you do with all the money we gave you?” A CMO’s answer really needs to to connect their department's efforts to revenue. Marketers want to achieve better results over time, comprehensive marketing measurement is the only way to determine what worked, and provide insight as to how future investments might perform even better. 

It sounds simple, but it isn’t. 

The predominant measurement solutions, multi-touch attribution (MTA) and media mix modeling (MMM), have distinct limitations.  The problem is that both MTA and MMM are better at tracking results from digital marketing versus channels that are more difficult to track like out-of-home (OOH) advertising, radio and word of mouth. 

MMM offers channel-level insights but is static, backward-looking, and biased towards measurable channels, requiring two years of data to be usable. MTA, while providing person-level detail, often lacks completeness and is overly reliant on digital channels and identity data, delivering only a partial view of the market.

What’s worse: The actual problem is even bigger. Some Marketers believe that “you can only manage what you can measure.” This leads to them to ignore what they can’t measure, or worse, relegating it to guesswork and under or over-investment. Neither are ideal scenarios. 

A new approach using AI for marketing measurement offers a potential path forward. AIOS operationalizes AI to give marketers a comprehensive view of their marketing and visibility into areas of marketing activity that were previously unseen. 

MTA: What it Can and Can’t Do

Multi-touch Attribution is a measurement system that evaluates touchpoints along the customer journey, helping marketers understand the role each touchpoint plays in driving conversions. For example, a customer might buy a product online after seeing ads across multiple channels, such as social media, TV, and online display ads. MTA can analyze how each of these touchpoints contributed to the conversion, rather than just attributing the sale to the first, middle, or last interaction.

MTA’s limitation stems from its dependence on identity-based data. If the underlying data is incomplete due to signal loss or poor match rates across identifiers, these modeled approaches can lead to inaccurate conclusions, potentially skewing attribution and misleading marketing decisions. Moreover, MTA typically does not account for incrementality, meaning it doesn't measure whether the marketing activities truly drove additional conversions. This limitation underscores the importance of having comprehensive and high-quality data to inform budget allocation and ensure that marketing efforts are not only attributed correctly, but are also genuinely effective in driving incremental growth.

MMM: Insights from the Past, Challenges for the Present

Media Mix Modeling analyzes historical marketing campaign data to quantify how marketing activities affect sales. For example, if a company invested in advertising its product in California, MMM could be used to assess the impact of those ads on sales within the state. The methodology relies on linear regression equations that assume a direct relationship between marketing activities and sales outcomes. However, this approach can struggle to account for nuanced, real-time market changes, often overlooking factors such as shifts in consumer behavior. 

MMMs operate at a channel level rather than a touchpoint level, averaging out individual differences and assuming that everyone responds to marketing in the same way. Additionally, MMMs are retrospective, requiring two years of data to be effective, and their success heavily depends on the expertise of data scientists. This reliance on specialized talent, combined with extensive data collection and analysis, makes MMMs an expensive solution. Due to the high cost and lengthy process, many companies conduct MMM analyses only once a year, resulting in business decisions based on outdated data—an approach that could lead to misguided strategies, especially in rapidly changing markets. Imagine basing your decisions on a Covid year?

AIOS - An All-in-One-System for Marketing

At its core, AIOS is a real-time predictive intelligence system that analyzes customer interactions across all brand touch points. It evaluates marketing performance, providing actionable recommendations for optimizing media and creative investments to both measure and enhance marketing impact over time.

AIOS builds a comprehensive view of the market by modeling consumers as a universe of virtual users using consumer credit, census, and other survey data. This approach, which includes location, socioeconomic, and psychographic details, enables AIOS to account for the unique factors influencing consumer behavior—without handling any personally identifiable information. AIOS thus captures all consumers and journeys in a market, not just those you are able to “match.” 

The platform uses nested data augmentation algorithms to recover ad exposure levels while correcting for issues in buyer journeys caused by offline media, privacy opt-outs, or data duplication. This enriched dataset allows AIOS to recover the touchpoints in each journey and perform incremental attribution, simulating the impact of each marketing touchpoint. By incorporating incrementality into all its insights and recommendations, AIOS ensures the highest possible ROI for marketers.

MTA vs. MMM vs. AIOS

MMMs and MTA have limitations that affect their effectiveness in modern marketing. MMM offers channel-level insights but is static, backward-looking, and biased towards measurable channels, requiring two years of data to be usable. MTA, while providing person-level detail, often lacks completeness and is overly reliant on digital channels and identity data, delivering only a partial view of the market. In contrast, AIOS uses a privacy-resilient population backbone and data augmentation algorithms to recover journeys, understand individuals’ unique media and messaging preferences, and deliver insights about who is converting.  AIOS is an always-on system that provides real-time insights and full-funnel decision support across all channels at the tactic level, even for in-flight activities, with as little as 2 months of data.

The Bottom Line: Understanding the landscape leads to success 

To optimize their bottom line, Marketers must adopt new, more effective ways to measure the impact of their marketing campaigns and understand their customers’ paths to conversion. MTAs and MMMs offer rear-view measurement of a campaign’s success with limited predictive power.  

They do not  provide a sufficiently broad or deep view of the market, potentially misleading marketers in their decision-making. AIOS, with its innovative use of AI to recover journeys, gives marketers visibility into the full consumer landscape and activity across all channels, down to the tactic level. With real-time insights and decision support across planning, measurement and optimization, AIOS helps Marketers get more out of their campaign spend.