Summary
In the fourth installment of our retail media standardization series, we explore the complexities of retail media and how rigid standardization may hinder rather than help. It highlights the need for flexibility in measurement approaches tailored to individual business needs. The focus shifts from uniformity to empowering advertisers with tools for customized data management for their unique market position.
Read all of the posts in this retail media standardization series.
Advertisers and advertising organizations have been calling for retail media networks to standardize their measurement paradigms, pointing to a lack of standardization as their biggest roadblock to success on the channel. In Skai’s 2023 State of Retail Media report, the top three retail media challenges cited by respondents – driving positive ROI, measuring meaningful success, and proving incrementality – all seem related to a lack of standardization.
But is standardization really the answer?
Retail media is an emergent system, just as the Internet once was. In emergent systems, eliminating friction is the best path to growth. We saw this unfold when Microsoft decided to align Bing’s search advertising interface, metrics, and language with Google’s. It became much easier for advertisers to compare performance and build campaigns, which resulted in more efficient investment – as well as increased overall investment. We’ve already seen some similar signs of alignment in retail media, with Walmart Connect adopting Amazon’s second price auction model. But standardization won’t solve the most fundamental challenge faced by advertisers: understanding retail media data in relation to their own businesses.
How retail media standardization could make advertisers’ lives harder
If retailers standardize specific metrics – like 14-day click-based attribution, let’s say – but your customer buying cycle is significantly longer or shorter than that, the standard metric won’t be useful to your business. Or let’s say you manage a grocery brand with ads on both Amazon and Doordash. Buyer behavior is different on the two platforms, so the measurement paradigms should be, too. Standardizing metrics on both platforms won’t help you better understand the best strategies for each.
The truth is that retail media standardization may result in data that’s too prescriptive and rigid for advertisers. So what should advertisers demand instead?
What advertisers really need: control over how data is interpreted and used
Sophisticated brands, agencies, and other big spenders want the flexibility to design their own measurement paradigms so they can use data in the most valuable way for their brand.
Consider the new-to-brand measurement, which means something different to everyone. Amazon Ads currently uses a 12-month look-back window for their new-to-brand metrics. That window may be too short for an electronics brand with long buying cycles. And it may be too long for a fashion brand that releases six new collections per year and would prefer to track new customers with each season. Even if a one-year look-back period makes sense for your brand, walled gardens limit the utility of the new-to-brand metric. A customer that just bought their first widget from you on Amazon could actually be your most loyal, long-time supporter – they were just buying your widgets from Walmart for the past several years.
New-to-brand and other incrementality measurements all share these fundamental problems. Strict metric definitions and limited data flexibility make it harder for advertisers to access meaningful performance data. And managing this data solely within the walled gardens makes it challenging to compare performance across networks.
Advertisers need more control over their program data. That’s what Skai offers. Skai normalizes data from retailers to give advertisers tools like custom views, columns, and metrics. This allows advertisers to manage across all those points of disparity and ultimately rationalize the data for their own brands and goals. That data can then be used in planning and forecasting to ensure optimal program growth. AI and automation tools take this to the next level, freeing up advertisers’ time to focus on more strategic, creative, and important things. But AI requires large data sets to function well – the more, the better. And that’s exactly what we’re calling on retailers to provide – more data. Read more about what we need from retailers to support advertisers with data connectivity and control.
