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How To Help Your Merchants Boost ROAS on Retail Media

Christie Zhang

December 8, 2022

In 2021, time spent in mobile shopping apps rose to nearly 100 billion hours globally. Moreover, mobile commerce topped $359 billion in 2021, and will reach $728 billion by 2025, accounting for more than 40% of all e-commerce sales.  

As a digital marketplace, you have a natural edge given that consumers prefer to shop, manage their finances and pay bills online. Machine learning (ML) is already a major boon to merchants. For instance, Bucketplace, a leader in the home living category in Korea, gained significant sales and merchant performance after implementing Moloco, an ML-based retail media adtech solution:

  • Some advertising merchants doubled sales through sponsored ads
  • Compared to other platforms, advertisers achieved 3x ROAS using Moloco
  • Onboarded over 10% of all merchants to advertise within three months

See the Bucketplace case study for more details

Turn your First-Party Data into Revenue

As a marketplace, you have a treasure trove of voluntary data on your shoppers: what they view, how they shop, what and how often they buy, and items in their baskets at the time of the purchase.

And as we’ll see below, there are numerous other attributes that affect a shopper’s likelihood of purchase, and ML is exceedingly good at finding and leveraging these attributes.

How ML changes the game for advertising

Traditionally when launching a campaign, marketers start with a hypothesis of who the likely buyers are and then find proxies for targeting (e.g. set target audience as women who buy household cleaning supplies, then advertise to this audience on apps). Then they test those hypotheses by serving ads to the audience, measuring the response, updating the targeting criteria and assigning another goal. Despite providing complete control, this process means complete reliance on the marketer’s expertise and manual upkeep.

ML targets, tests, and iterates too — but for every shopper continually and in real time. ML models are trained using human defined input and output data. In the case of retail media, this means serving the most relevant ad for every shopper based on their behavior, thus enabling merchants to reach and convert their audience. At Moloco, we do this for our marketplace customers by leveraging two datasets: their product catalogs and shopper event data. Let’s dive into how ML transforms your data into sales.

ML for Marketplaces

ML not only drives purchases, but also product discovery. Imagine a shopper adding waxless candlesticks in her cart in late November, signaling that she’s preparing for the holidays. ML taps into purchase behavior five steps ahead — by serving ads for other party products like no-snap party favors, rather than candlestick holders that the shopper won’t need prompting to purchase.

This kind of intelligent recommendation empowers shoppers to discover products that they didn’t know they needed, leading to higher satisfaction and more purchases, and improving ROAS for your advertising merchants. Shoppers visit your platform more for the relevant shopping experience, while merchants grow their ad spend to drive more sales. Fueled by machine learning, retail media can drive innate e-commerce growth, on top of a new revenue stream. 

Learn more about Moloco for Marketplaces.

Christie Zhang

Product Marketing Manager

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