MOLOCO Leverages Machine Learning for Ad Creative

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The programmatic advertising company MOLOCO is bringing its machine learning algorithms to the creative space with the launch of a new solution that’s been dubbed Dynamic Creative.

When Dynamic Creative launches this morning, MOLOCO will become one of the first players in the industry to apply algorithms to creative, not just to make creative design and production easier, but also for more efficient production of ads across the programmatic ecosystem. 

Dynamic Creative will design ad creatives for each placement in real-time by analyzing factors including the marketer’s best-selling items, the context of the ad placement, and traits of the individual viewing the ad. MOLOCO will then deploy the ad programmatically to its network of more than 10 billion global devices across in-app ad networks, marketplaces, and exchanges. 

Dynamic Creative integrates with an advertiser’s catalog of products, so it can design ad creative based on the name, price, or image of top-selling items. Campaigns can also be set up to dynamically predict the ideal price to be advertised in order to achieve any target campaign metric, with bids, creative, and pricing adjustable in real-time. 

“Competition is fierce, and many e-commerce businesses are coming up against the limits of what can be achieved through traditional forms of digital advertising,” says Anurag Agrawal, vice president of product at MOLOCO.

This morning’s launch of Dynamic Creative comes on the heels of an April announcement that MOLOCO had raised funding at a $1 billion valuation, making this an especially important product release for the company.

By leveraging MOLOCO’s proprietary machine learning engine, Agrawal says the new feature can be used to tailor campaign creative to individual user preferences in real-time, leading to higher returns for advertisers on their user acquisition and re-engagement campaigns. It does this without relying on third-party data, using only first-party and contextual data to train performance models at the campaign level.

“Unlike other traditional retargeting engines that only show the exact items that users have expressed direct interest in, MOLOCO’s Dynamic Creative campaigns leverage our proprietary machine learning engine to preempt a user’s interest based on previous shopping behaviors,” Agrawal says. “Using a variety of signals, we can predict and surface the products most likely to lead to conversions across the entire programmatic ecosystem, delivering a better customer experience in the process.”

Agrawal says the Covid-19 pandemic has rapidly accelerated the transition to a commercial world that’s driven by e-commerce, making now the ideal time to launch a product like Dynamic Creative. Recent privacy restrictions have also increased the need for this type of product, which relies exclusively on first-party and contextual data. 

“We’ve made major investments in contextual data models over the past few years and now use as much contextual data as possible in our campaign optimization,” Agrawal says. 

Early users like LOTTE ON’s Jeong Eun Kang say Dynamic Creative is a “game changer.” LOTTE ON has reportedly been able to significantly boost ad performance and user LTV by generating personalized ads that recommend the most relevant offers based on user preferences and contextual data.

Agrawal also points to GS SHOP as another early partner that saw positive results using the Dynamic Creative feature. GS SHOP drove a 4,000% return on ad spend through Dynamic Creative campaigns that surfaced products based on past user shopping behavior. 

“Automating the creation of ad creative allows marketers to dynamically pair user interest with trends in marketplace activity … Dynamic Creative can recognize that signal right away as being a valuable opportunity and start to surface the product more often in paid ads to capitalize on real-time market trends, leading to more conversions overall,” Agrawal says. “Thanks to our proprietary machine-learning engine, we’re also able to simultaneously suppress visibility of popular products for users that are least likely to purchase, ensuring that marketers get the highest possible returns on their budget.”

Stephanie Miles is a senior editor at Street Fight.

Stephanie Miles is a journalist who covers personal finance, technology, and real estate. As Street Fight’s senior editor, she is particularly interested in how local merchants and national brands are utilizing hyperlocal technology to reach consumers. She has written for FHM, the Daily News, Working World, Gawker, Cityfile, and Recessionwire.