Retail Media ROI through Behavioral Research
Rajeev Sharma
December 6, 2022

Boosting impact of omni-channel marketing with behavioral data

With the rapid evolution of omni-channel retailing for CPGs and the meteoric rise of Retail Media Networks – the borders between various marketing budgets such as media and trade are blurring. An obvious desire then is to look for performance metrics that are “unified” across different channels and treat all retail including in-store as “media”.

Unified approaches would hugely simplify marketing investments by applying traditional audience and online conversion metrics to in-store. However, given the complexity of behaviors in physical retail, such straightforward transposition can be very tricky.

For example, in-store traffic measures alone cannot be equated to simple audience metrics. The “value” of traffic varies highly in different parts of the store, depending on the context – such as the relative location to specific categories, adjacencies, trip stage, etc.

Since in-store is still the key point of influence and purchase for CPGs, it is increasingly important to augment online campaigns with in-store or location-based marketing. In that context, understanding in-store behaviors in response to retail media is important for proving/improving the ROI of omni-channel media investments.

Measuring Behavioral Impact of Retail Media

In-store behaviors are a lot more complex compared to online behaviors in terms of both media consumption and influence. Understanding the behavioral impact of different in-store media elements can benefit from various metrics along the “path to purchase” beyond just audience size or sales. Relevant metrics include multiple conversion rates along the in-store “funnel” such as exposure, engagement, and closure rates.

The behavioral impact may also extend beyond conversion rates, such as driving traffic to a targeted store area/aisle, improving shoppability (shopper experience), or improving the “brand strength” at the shelf. For example, an omni-channel marketing campaign may influence shoppers to approach the shelf with their purchase decisions already made – something that can actually be measured using AI and machine learning. See Evaluating “Brand Strength” at the Shelf.

Behavioral research can help in not only establishing the ROI of the retail media investment (both online and in-store). Given the highly flexible marketing space in-store and numerous parameters that influence engagement and conversions, behavioral testing can really help in optimizing ROI.

Here we look at just a few elements that can be optimized from behavioral data with selected examples from VideoMining’s in-store media research spanning a decade.

Leveraging Location

For any in-store marketing initiative, one of the most important attributes is location. For example, our Display Deep Dive dataset has shown that location can impact conversion rates by as much as 600% for categories such as candy. However, selecting a location is not just a matter of picking an area with the highest traffic. There is a myriad of other factors that impact performance– relative location to a category, direction of traffic flow, adjacency, propensity of shopper engagement (which varies in different parts of the store), etc. In the end, optimizing the location of a media element, including a display, demands a data-driven approach. A winning “playbook” varies by category and brand.

Case Study: A digital kiosk was being tested by a mass retailer to help with the beauty products aisle. Behavioral data showed poor usage and impact. But along with the evaluation, our diagnostics showed that even though the kiosk was in a high traffic area, it was being encountered “after” shoppers visited the relevant categories in the aisle. Changing the position to a less busy area at the opposite side of the aisle dramatically improved the performance with an overall lift of 10% in sales for the represented products. The incremental ROI helped justify the investment in the digital initiative.

Leveraging Shopper Demographics

Access to retailer loyalty data allows for hyper-targeting. However, for much of in-store marketing, it may be impractical to have 1x1 targeting. In most cases, in-store marketing would benefit from primary measurement of shopper demographics to target a specific shopper segment. For example, women respond differently from men to a given display and content. Likewise, various age groups can be targeted in-store using a different strategy – data-driven response measurement.

Case Study: VideoMining helped improve the “reach” of a new digital signage network by 81% using demographics targeting and time-of-day programming for maximizing engagement. The behavioral data helped in both proving and improving the impact of the retail digital signage network, helping the client grow its advertising business.

Leveraging Trip Analytics

Any in-store media plan would benefit from understanding the shopper trip dynamics, including sequence analysis. The simplest example of this is understanding and leveraging where they visited “before” reaching that location, providing opportunities for cross-promotions and incremental sales. More complex examples arise from segmenting specific trip missions and creating targeted “intercept” areas for special displays as the case study illustrates below.

Case Study: A bean manufacturer wanted to create in-store marketing programs for expanding the usage occasions for their category. VideoMining’s trip analytics helped understand the most commonly used paths for “Barbeque” and “Mexican Night dinner” occasions. We then identified a set of ideal locations for creating special displays for engaging shoppers on those trip missions. The additional behavioral data from display performance and shopper demographics helped the client develop and “sell in” successful targeted marketing campaigns with key retailers.

Leveraging Behavior Labs to Prove/Improve Omni-Channel Plans

Given the complexities of in-store behaviors and lack of established knowledge base about omni-channel strategies, it is important to “test” new concepts in real stores before a wider rollout. Using real stores for behavioral testing greatly improves predictability of ROI. The behavioral research also allows for the tweaking of concepts to maximize the ROI. A recent VM Nugget discusses the concept of Behavior Labs for Continuous Innovation at Retail. 

Given the fast evolution of Retail Media Networks and enormous potential of omni-channel marketing, it is important to incorporate behavioral research and testing early in the planning cycle to maximize the ROI.

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