Tag: RMNs

Why is Retail Media ROAS so Hard to Measure?

Posted on by Caroline Lewis

There are a lot of signs that trade and retail media networks (RMNs) are converging. This is driven on the one hand because retail media and trade are increasingly financed from the same source: sales co-op funds. On the other hand, they are both converging on the lower funnel – the first moment of truth. Why would tactics so close to the point of purchase be so hard to measure?

To make sense of this and to understand why retail media ROAS is so hard to measure, we have to look at the simple formula: ROAS = Contribution/Media Cost. When evaluating the profitability of retail media, there are problems with both the numerator and the denominator. The cost (denominator) is often higher for the same service (programmatic or social)  if you buy it through the retailer versus buying directly. As a client-side friend put it:

“CPMs for RMNs are (basically) made up. They’ll charge you $18.00 CPMs for a Facebook buy that is completely disconnected to the actual CPMs in the auction. They just pocket all the overage and margin.”

On the other side of the table, an industry consultant reported a Walmart executive saying:

“I’m glad that Walmart connect generates such high margin income for us to offset losses in other parts of the business.”

What is happening in practice is that the RMN costs are being bundled together with other fees as a cost of doing business with the retailer. So, in truth, there are other goodies hidden in the RMN cost such as slotting fees, preferred display locations, etc.

The other problematic part of the ROAS equation is in the numerator – the contribution or lift from media. With most performance media, the drivers of contribution are audience reach and frequency, sometimes at DMA level. With RMNs the media is driving offline sales within the retailer’s marketing area. It is also focused on specific UPCs. This specificity is a two-edged sword: it makes the impact easier to derive because it is so specific, but it also reduces its scope. In fact, only the biggest retailers (e.g., Amazon, Walmart, Kroger, Target, Best Buy, Instacart) have the scale to drive enough traffic to read.

The current practice for deriving the effect of retail media is to treat it just like any other performance media variable in a marketing mix model without paying attention to details of tactic, geography, or the mix of online/offline sales channels.

With all that in mind, here are of the top considerations to improve the quality of the estimate of lift (the numerator of ROAS):

  1. If the retailer is one of the top RMNs (excluding Amazon), then it makes sense to derive the retail media impact on sales within the trade RMAs. This requires access to chain or RMA-level data.
  2. If the RMN advertising is for specific UPCs or sub-brands, it makes sense to estimate the sales impact at least at the sub-brand level. Because RMN data moves through media systems, it is not easy to obtain UPC-specific data. Text processing is usually required to infer the sub-brand from various identifying strings.
  3. Since all these sales impacts are proportional to size of retailer marketing areas and of the sub-brand, a multiplicative marketing mix model will provide much better results than an additive model, since its effects are automatically scaled. This is similar to the way that multiplicative models are more accurate for trade promotions.
  4. The impact of adstock/saturation must differ by tactic. Onsite search is lower funnel and short-acting whereas offsite media can be much longer-acting.
  5. There are many types of impacts on sales to consider, including both direct and halo. This table shows the relationship between the type of media (onsite vs offsite) and the sales impact as we move from the most direct (onsite search driving online sales) to the most attenuated (offsite media driving sales in the same high-intent lookalike target anywhere).
Media Type Direct Halo Secondary Halo
Onsite Online (DTC) Offline sales of same retailer Offline and online sales of other retailers (informational search)
Offsite/in-App Offline and online sales of all retailers

Using advanced modeling systems such as Robust Hierarchical Bayes that take geography and UPC into account, we can accurately track the effectiveness of RMNs (the lift per impression). DTC (onsite search/display driving online sales) has the most direct attribution path. Offsite targeting is usually less effective although there are exceptions. The ROAS depends on transparency in media costs – which at the present time is caught up in the vendor-buyer negotiation and escapes rationalization.

If you would like to discuss how our approach to Robust Hierarchical Bayes can help you better measure your Retail Media investment (and your marketing mix performance as a whole), schedule a call with the i4i Team.

Breaking Down Silos to Unlock the Full Potential of Retail Media Networks

Posted on by Kevin Youts

Introduction: In today’s rapidly evolving marketing landscape, Retail Media Networks (RMNs) have emerged as a critical component of marketers’ media strategies. According to a recent article in Insider Intelligence, RMNs have grown exponentially across an exploding number of retailers, becoming a $30Billion channel in just 5 years.

With the deprecation of first-party data and the need for real-time insights into shopper behavior, RMNs offer a tantalizing antidote to both trade marketers and media buyers. However, to harness the full potential of these networks, brands must avoid the pitfalls of organizational silos, and provide the resulting teams with the optimal tools to make win/win decisions retail media decision that benefit the company overall.

Collaboration between brand and trade marketers and category managers, alongside media planners and buyers, can be crucial for maximizing return on investment (ROI) in RMNs and achieving both trade marketing and media effectiveness. Too often the two groups are functioning with separate budgetary parameters or not working off the same models to drive the most effective media buying decisions.

Let’s look at what each group brings to the challenge:

Brand and Trade Marketers:

Brand marketers, including shopper marketers, category managers and omnichannel leads play a crucial role managing sales, and coordinating merchandising and promotions with distribution partners at retailers. Their skill sets revolve around understanding consumer behavior, market trends, and effectively positioning products within both the physical and online retail environments. They typically possess the following key competencies:

• Retail Strategy: Trade marketers and brand category managers have a deep understanding of the retail landscapes in which their products are sold. They develop strategies to maximize product visibility, optimize shelf space allocation, and ensure effective pricing and promotion tactics are negotiated and well executed across an array of retail partners, increasingly in both physical and online environments.
• Merchandising Expertise: These professionals excel in creating compelling product displays and planograms that capture shoppers’ attention and drive sales. They have an eye for visual aesthetics and possess strong knowledge of consumer purchasing behavior.
• Channel Collaboration: Trade marketers and omnichannel leads and brand category managers establish strong partnerships with retailers, negotiating slotting fees, and ensuring optimal product placement. They possess exceptional communication and negotiation skills to drive mutual success.

Digital Media Planners and Buyers:

Media planners and buyers specializing in digital opportunities, play a pivotal role in targeting specific audiences and executing programmatic buying across relevant channels. Their expertise lies in leveraging digital platforms and various data analytics tactics to deliver targeted messages to the right audiences that can generate measurable outcomes. The following core competencies define their skill sets:

• Data-Driven Insights: Digital media planners and buyers are adept at analyzing consumer data and identifying targets and how best to reach them. They leverage sophisticated tools and technologies to extract actionable insights, enabling precise audience targeting and effective campaign optimization.
• Multi-Channel Mastery: These professionals have a deep understanding of various digital channels, as well as offline media, that in combination make up the optimal media mix. Their core competency lies in balancing expenditures and often dynamic investments across channels including social media, search engine advertising, display advertising, and video advertising. They are tasked with crafting integrated media strategies that reach consumers at multiple touchpoints.
• Programmatic Proficiency: Digital media planners and buyers excel in utilizing programmatic buying to automate media purchases and optimize ad placements in real-time. They possess a strong grasp of ad exchanges, demand-side platforms (DSPs), and supply-side platforms (SSPs). They lean on direction that comes from robust data analysis to retrospectively calculate ROAS, and proactively optimize their media plans to achieve optimal growth in the future.

The Pitfalls of Organizational Silos

Failure to bridge the gap between these two groups can lead to several detrimental outcomes. Budget disputes often arise when teams compete over limited resources, hindering the overall marketing effectiveness. Moreover, the absence of collaboration can result in one team allocating budgets without considering the expertise and domain knowledge of the other, leading to suboptimal decision-making and wasted opportunities.

Harnessing the Power of MMM for Optimal ROI

Retail Media Networks, especially if brands are investing in several, produce a maddeningly complex set of data to analyze (time, geo-spatial data based on physical location of stores, consumer behavior on and offline, and the staggering array of products many sales and media teams must account for).

There is renewed enthusiasm across the media analytics industry in the power of robust Market Mix Models (MMM) to make sense of increasingly complicated media planning required to optimize RMN spend as part of overall advertising strategies.

The best MMMs enable marketers to analyze behavioral, historical sales, and other causal data at the individual channel partner level across all media. With this granular understanding, organizations can identify the most effective tactics for driving sales within retail settings, analyzing overall media spend as well as the relative contribution of each media partner, including RMNs, in relation to each other.

Conclusion:

As Retail Media Networks assume greater significance in marketers’ media partner choices, organizations must dismantle the barriers of organizational silos to achieve optimal ROI.

By embracing collaboration between brand marketing and media management and providing them with effective tools like MMM, advertisers can leverage the strengths of both groups to make better-informed decisions that drive sales growth effectively.

By embracing these strategies, organizations can seize the opportunities presented by RMNs and unlock their true potential in today’s dynamic retail landscape.