You can’t advertise your way out of a pricing problem.
Early this year, we were working closely with a premium nutrition brand that had launched into Amazon from a relatively successful DTC business. They were pouring dollars into advertising on Amazon but, despite their influx of ad dollars, topline sales didn’t seem to budge. Our platform confirmed this disconnect, reporting extremely low incremental return on investment (iROI).
Their advertising just wasn’t moving the needle on Amazon and they were struggling to understand why their success in DTC wasn’t translating to Amazon. The “aha moment” occurred when the platform highlighted an extremely large price difference relative to competitors as hurting topline sales.
They had taken pricing that worked in DTC but fell apart in the extremely price-competitive marketplace of Amazon. There is no hiding when your competitors’ products are right next to yours in search results at a quarter of the price tag.
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Odds are everyone has heard the foundational marketing concept of the 4Ps (product, promotion, price, placement) as the basic levers of driving growth. Now, there are newer, more ecommerce-specific flavors such as CARS (Content, Assortment, Ratings & Reviews, Search) but they all get to the same concept — that growth is a multi-dimensional problem. However, media measurement tends to focus on just one potential lever of growth, missing the extremely interconnected nature of these growth drivers.
The power, promise and potential of retail media lies right in this nexus of the 4Ps. Retail media doesn’t just operate as advertising but as distribution. It can be used extremely effectively to amplify pricing and promotion, acting like a digital endcap. The challenge of measuring retail media lies in untangling these interconnected commerce factors.
Decades ago, when the other 3Ps were relatively static, you could, to a certain extent, ignore their interaction with advertising. When the planogram for a retailer only changed monthly, you didn’t have to be ever vigilant about new competitors gaining a share of the shelf or running promotions during your campaigns. In the highly dynamic environment of ecommerce, where position on the page, pricing and new products on the digital shelf are changing constantly every day, that simply isn’t an option.
This dynamic environment poses unique challenges for historical approaches to media measurement. Marketing Mix Models (emphasis on marketing not just media) have often included price and distribution features. Retail media and ecommerce complicate, if not break, the traditional way of incorporating these factors due to the granularity and frequency of changes.
For a brand with multiple SKUs, some may be increasing in price, and some might be decreasing. Some may have competitors on promotions, others may have your product on promo. All that variation gets washed out when it is aggregated up, like the destructive interference of two waves canceling each other out. For a modeling approach that relies on variation to infer relationships, no variation means no relationship. As some have suggested, this could be the reason why retail media often doesn’t perform as strongly in traditional marketing mix modeling (MMM).
Lift testing or experimentation, more broadly, is also complicated by this ever-changing environment. The results from any experiment should only be applied to a set of conditions like those during the experiment. In a world that is constantly changing, that means the applicability of experimental results does not last long, as the utility of those results decays rapidly as the marketplace drifts further and further from the conditions that were present at the time of the experiment.
This necessitates advertisers and their analytics partners to rethink how these non-media commerce signals get incorporated into measurement, and retool the data engineering required for these types of data to be available alongside more traditional media metrics.
To address this, we are seeing innovative brands integrate commerce factors into their media measurement in two ways.
First, commerce or product graphs are being developed with SKUs as the core unit. These graphs connect otherwise siloed data across the 4Ps (Product, Price, Place, Promotion). This enables the linking of media campaigns to the specific products they support, their ratings and reviews, their placement on the digital shelf for a given keyword, and their competitors’ share of voice (SOV) for those same keywords, along with their active promotions. This allows them to build a 360-degree view of the factors around a given SKU across both media and commerce to better layer context into their campaign reporting. For example, if a campaign’s performance suddenly drops, it makes it easier to diagnose what commerce factors might be interacting to cause this.
Second, for those going a step beyond integrating commerce and media data to modeling the relationship between these factors, we’ve seen a shift toward continuous learning models — as opposed to attribution models being trained at a single point in time and then used over a much longer duration. These highly adaptable models, which can recalibrate and learn on the fly, maintain their accuracy far better within the highly dynamic world of ecommerce compared to modeling that is only trained or learns at predetermined points.
Those brands that can better integrate these cross-channel media and commerce signals into their measurement will be able to generate a strong competitive advantage by taking advantage of the interconnected nature of these levers of growth.
David Pollet is the CEO at Incremental. A skilled go-to-market growth leader with experience scaling SaaS startups and public company divisions ranging from $5M-$100M+ in ARR, Pollet has 25 years of experience in sales, marketing and strategic leadership roles. In his role as CEO, he is responsible for accelerating the company’s growth and transforming GTM operations as Incremental establishes its leadership in the ecommerce category. Prior to joining Incremental, Pollet was most recently CRO at convergent TV platform Cross Screen Media, and has held leadership roles at companies including Drawbridge, Neustar, Bank of America, and LendingTree.
