Retail Media Measurement Must Extend Beyond Search and Across Channels

Retail media was one of the biggest industry conversation starters of 2023. Every retailer from Dollar General to Cars.com has launched a retail media network to capitalize on their existing first-party data.

As both a new channel and data layer empowering existing channels, it has the potential to transform the advertising industry. But one question stands out: How can marketers measure it all?

Industry surveys have shown that marketers are not confident in the measurement of retail media, and the primary reason is due to a lack of standardization. Particularly as retail media moves beyond search into off-site media, CTV and in-store, measurement standardization is critical to ensure that advertising outcomes are properly captured.

Today, the vast majority of retail media is measured through last-touch attribution using lookback windows—the length of time after an impression or click that an ad can take credit for a sale. Lookback windows vary by platform and ad type. A lot of focus has been on standardizing these lookback windows so they are comparable across platforms. While this is a step in the right direction, it oversimplifies the problem of capturing advertising’s impact.

Lookback periods should vary by platform and channel. For example, a 14-day lookback window may overestimate the impact of a search ad but underestimate the impact of a CTV ad given the difference in how these ad forms impact consumer perceptions and behavior. Ultimately, lookback windows are based on assumptions, not answers. 

The adstock effect

Rather than taking a one-size-fits-all approach, marketers should consider a core component of measurement: how and when the impact of an ad occurs. The concept of adstock offers a dynamic approach to solving this problem that takes cross-channel differences into account and has long been incorporated into measurement in other forms of advertising outside of retail media. 

Adstock is used to estimate the effect of advertising on consumer behavior and determine the saturation point or point of diminishing returns. This captures the cumulative effect of multiple exposures and the decaying effect as the impression of those ads fades with time.

There are numerous approaches to estimating these effects, such as analyzing panel data, regression analysis or running experiments. This provides a far more nuanced measurement of the impact that accounts for the differences across channels and ad types, and in purchase or consideration cycles across categories of products as well.

This is particularly important for categories with longer consideration periods like electronics, or in channels which are generally used for brand-building rather than direct response advertising. Adstock can better capture the impact of retail media as it moves into new media beyond search. 

Measuring adstock

The basic formula is: Adstock = Previous Adstock + (Advertising Exposure × Decay Rate).

• Previous Adstock is the adstock from the previous period—most often measured in days, but sometimes weeks or months. If you are starting with the first period, this number would be zero.
• Advertising Exposure is the level of advertising exposure in the current period. This could be measured as impressions or another relevant advertising delivery metric. 
• Decay Rate is the rate at which the impact of advertising diminishes over time. This is typically expressed as a decimal between 0 and 1. A number closer to 0 indicates a higher decay rate; a number closer to 1 indicates a slower decay with a greater buildup effect over time.

It’s important to note that determining the decay rate is crucial and may depend on various factors.

• Brand or category difference: Is this a category with a long consideration or research cycle? 
• Product differences: Because retail media is often tied to supporting a specific SKU, differences in consumer behavior at the SKU level need to be accounted for. Is this a product people buy once a week? Once a month? Once in their life?
• Ad memorability: How engaging is the creative or ad unit? A more engaging format such as video may generate a greater likelihood of someone recalling it later.

A combination of regression analysis on historical campaign and sales data, plus experiments like randomized controlled tests or geo-matched market tests, is typically used to estimate this decay rate. In more advanced applications, there are considerations around potential data transformations, like applying a Weibull distribution instead of a geometric one to better account for the shape of the curve and scale of spend for some media.

Tactically applying adstock to retail media 

What can be learned by applying this to retail media? 

While most would consider search (retail and non-retail) to have a fairly immediate impact, adstock can better capture the impact of larger ad units that may also have a brand-building impact—for example, Sponsored Brands on Amazon Ads due to their visual size and ability to incorporate video. Analysis has shown that these types of ad formats tend to be undercredited by last-touch attribution.

This disconnect of performance is even more important as retail media continues to move into ad units for stronger brand-building channels like CTV. If not taken into consideration, this can lead to an overallocation of investment at the bottom of the funnel.

Adstock is also a useful metric when applied to products with different consumption or consideration patterns. Today, major shopping platforms are not just purchase destinations but a starting point for product research. This usage is particularly applicable to categories with longer research periods. It can also be used effectively when planning investments around key events like holidays, promotions or product launches.

Because the combination of these efforts needs to peak within a specific timeframe, an understanding of adstock is key to optimizing media spend and ensuring that these positive effects align with the target date.

Finally, measuring adstock is a valuable tactic to improve sales forecasting, particularly in considering significant increases or decreases in spend. Utilizing the adstock effect can help to determine when these budgetary changes will impact the sales department.

Forging the future of measurement

Measurement must look beyond predefined lookback windows to a more nuanced notion of impact over time. It must have awareness of retail media and broader media, accounting for the differences in marketplace dynamics and broader media investments, and it needs to account for the notion of incrementality and draw the connection between the advertisement causing, instead of merely influencing, a sale.

Better measurement will help unlock the full potential of retail media, enabling marketers to deploy retail media effectively across the full funnel to drive growth for their business.