All About Shopper Measurement

After all these years, shopper marketers still have a unique and challenging job. With advances in technology and increased access to data, one would think the job would get easier, and the path to impact would be clearer. But with data coming from different directions — and at different levels of granularity — it’s hard to know what’s most useful and actionable. Some data is not easily accessible, which creates unfortunate gaps and blind spots.

For these reasons, the SM2 Commission was formed to standardize definitions and measurement approaches of shopper marketing for the industry. With the release of our updated “Industry Playbook” last fall, I believe we took a meaningful step forward.

Based on that effort and my previous work on the marketing science team at Meta, I’d like to offer a vision of the future of shopper marketing measurement that’s grounded in three key principles.

Increase the quality of data inputs.

“Garbage in, garbage out.” We’ve all heard and likely used this phrase to express the idea that poor quality inputs will always produce poor quality outputs. The future of measurement for shopper marketers — a future with higher-quality, actionable outputs — relies greatly on improving the quality of data we feed into our measurement systems, or inputs.

This starts with increasing secure access to data that enables meaningful analyses and measurement of business outcomes that truly matter. From there, granularity and consistency of those inputs is key if shopper marketers hope to conduct any cross-channel analyses. The SM2 Commission learned this in our modeling pilots, and Steve Tobias, an executive in the Media Center of Excellence practice at IRI, summarized it well. He said, “If we benchmark shopper marketing impact as an aggregate effect versus measured down at a more tactical level, the models with more disaggregate shopper marketing inputs actually produce a greater total contribution and ROI.”

Similar dynamics were seen at Meta. In a marketing mix modeling (MMM) meta-analysis completed with Accenture in 2022 (and involving 30-plus brands and five verticals in North America), Meta found that its ROI increased by 7.8% to 12.7% when using granular data inputs in MMMs. Whether measuring a key component of a CPG’s marketing mix like shopper marketing, or the impact of an individual channel like Meta, model outputs can vary significantly when more granular data is used.

Innovate new and better measurement solutions.

Working closely with trusted third-party solution providers will continue to be an important part of the measurement and analytics strategy for shopper marketers, given these third parties’ access to data and specialized skills.

Additionally, it is so important for shopper marketers to take note of and consider capitalizing on new technologies that can advance their own organizations’ capabilities. For example, Open Source Techniques (OSTs) aim to democratize access to advanced analytics tools leveraging publicly available, open-source code. These techniques represent opportunities for shopper marketers to move capabilities in-house and enable their teams to increase focus on business outcomes, adding rigor to existing measurement practices.

Improve marketing performance by prioritizing experimentation.

Experimentation is the primary means of helping all businesses grow by transforming marketing practices grounded in data and science.

One of the valuable outputs from the SM2 Commission’s modeling work is the set of findings from the modeling phase that, while not conclusive and scalable on their own (we only modeled three brands), provides the industry a strong basis for further experimentation on three important variables:

• Core product messaging versus innovation messaging;
• Presence of an offer or incentive; and
• Channel-level performance (namely digital versus traditional shopper tactics).