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Attribution Modeling: The Pathway to Greater Marketing ROI
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Attribution Modeling: The Pathway to Greater Marketing ROI

As marketers seek greater accountability in today’s increasingly omnichannel shopper landscape, demand for outcome-based return-on-investment (ROI) measurement has become more important than ever across a variety of sectors, including media, retail, fast-moving consumer goods (FMCG), apparel, automotive, digital and more. Attribution also holds tremendous promise to deliver both granular measurement and speed that brands, retailers and publishers can lean on to understand marketing and advertising effectiveness—regardless of the platform.

But in this complex, connected landscape, how should marketers address the state of data and the challenges that the industry is faced with in order to work responsibly across data sets? Accessing data from various data sources to accurately measure ROI for each touchpoint along the customer journey is complex, but one that can be overcome by leveraging gold standard data collection and measurement methods.

When it comes down to it, being really good at building strong algorithms or attribution models is necessary, but not sufficient; the true challenge for marketers lies within the need for better data. Access to high quality granular data can help marketers achieve a more accurate understanding of consumers’ realistic purchase behaviors, whether those behaviors are influenced in-store, online, from a mobile device, across social media platforms, on TV, through an email marketing campaign—or a combination of them all.

For marketers, it’s important to remember that the shopper’s journey has multiple touch points across the path to purchase from upper funnel brand building to lower funnel stimuli at the last mile or last click to influence incremental conversion. In order to more effectively measure incrementality to drive ROI, marketers should keep in mind four key data considerations:

  1. Delivery: It’s important to ensure that the target audience for a campaign sees the marketing message, validated with data from real consumers. As marketers rely on data models, they need to realize that they cannot simply rely on machine-based data, but hone in on data tied to real consumers and their real-life behaviors. 

  2. Onboarding: As omnichannel continues to expand, the need to merge metrics for both offline and online content is more important than ever before.

  3. Devices: The average consumer has three devices, but how should the industry treat each view? Should marketers count three points of reach for each device, or one point of reach for the consumer with three times the frequency? 

  4. Ad blocking: As it becomes increasingly harder to reach consumers in today’s marketplace, data owners need to think about the value in third-party verification. To do this, marketers should use data in its original source to avoid privacy concerns and better define the description around the data itself to provide context and make attribution models run more intelligently and faster.


In an effort to overcome these current challenges, Nielsen is focused on seeking out better data and is making significant strides to acquire data and partner with other companies in the space. Nielsen is striving to tackle the de-duplication of devices to better analyze ROI, data hygiene and privacy compliance, so as to insure an attribution model that better allocates marketing dollars.

In addition to more effectively measuring incrementality through the above four considerations, marketers need to understand the combined power of audience insight and attribution modeling. Through the expertise of Nielsen Visual IQ, our strategic people-based measurement arm, marketers can use this combined power to gain the actionable intelligence they need to optimize budgets and drive higher ROI, while delivering coordinated experiences that maximize business results.