Retail media networks and the rise of data – Part 1

Digital ad spend is on a sharp upward trajectory – projected to account for 57.1% of overall ad spend in 2023 and 58.2% in 2024. This equates to around $422.8 billion across digital channels.

But interestingly, within digital, the strongest growth within digital will come from retail media, which is set to “grow in the buoyant channel by 22%”.

With such positive predictors of the retail media network industry, there are huge opportunities for advertisers to take advantage of this growth.

What is a retail media network?

A retail media network (RMN) is an advertising infrastructure that either an established retailer – or group of retailers – sets up to allow advertisers to buy advertising space across their owned (onsite) properties and paid (offsite) media, using first-party data to connect with consumers throughout the buyer journey.

Today, the largest RMNs include Amazon Advertising (which averages 213+ million unique visitors a month), Walmart Connect (100+ million unique monthly visitors), and Target’s Roundel (120+ million monthly unique visitors).

However, despite these big names having gained a foothold in the retail market, niche and category specialist RMNs are also on the rise, seeing a compound annual growth rate (CAGR) of 49% and 32% respectively.

Developments in the RMN industry

Retail media itself is expected to grow 25% annually over the next five years, which may be due in part to the fact that the gross margins in retail media far exceed the gross margins of any other retail category (70–90% for onsite media, and 20–40% for offsite media). Greater margins equal opportunities for further development.

Such developments in the industry include an increasing number of competitors to the big players. In fact, the majority (80%) of advertisers are already spending (or plan to spend) on RMNs other than or in addition to Amazon.

These advertisers look to other networks to gain access to unique, highly targeted audiences, more competitive CPMs, and other advantages such as greater return on ad spend (ROAS), closed-loop sales attribution, and a greater variety of ad formats.

However, the opportunities in RMN haven’t escaped another big player — Microsoft is planning to build a “most complete” omnichannel RMN to help retailers monetize onsite inventory.

Another area of development in the RMN industry is the use of data clean rooms (DCR) to minimize risks on its first-party data.

Read more: Understanding data clean rooms

DCRs can help retailers connect datasets from multiple organizations within a safe, secure environment, and provides transparent control, ownership, and flexibility for retailers to partner with other parties with valuable data sets without risking customers’ privacy or security.

With more active adoption of RMNs by advertisers, there will also likely be a push for standardization, particularly when measuring return on investment (ROI). As such, these changes will also push for better reporting and overall measurement capabilities.

Speaking to MarTech about the growth of retail media, Rachel Tipograph (founder and CEO of ecommerce platform MikMak) says “but for all of this growth, brands expect accountability through the standardization in the measurement of ROI. The industry is buzzing about all of the inefficiencies more and more every day. I believe 2023 is when action will finally start to be taken.”

Moving to a data-first approach

Building and maintaining an RMN requires harnessing huge volumes of data. Without a solid ground of foundational data practices, retail advertisers can quickly find themselves buried under messy, chaotic, and disorganized raw data.

By unifying data from multiple sources, such as ad revenue data, and transforming it into clean, verified, and easy-to-query data, they can make more confident business decisions based on real-time, large-scale analysis.

In our next post, we’ll discuss the importance of automating data operations as retail media advertisers strive to find more innovative ways of harnessing their ever-increasing data sets.