Adobe’s new marketing AI helps customers cut ad spend by 50% while increasing sales by 10%

It is a trader’s dream. Maybe even a midnight fantasy: increase sales while reducing advertising and marketing expenses. But Adobe’s AI marketing means that at least one big cosmetics customer is actually living that dream.

And you could also, theoretically, with a little caveat.

Do you spend at least $ 50 million a year on advertising?

Adobe today announced that 80% of its cloud marketing customers are using AI to increase marketing ROI by personalizing the customer experience, creating better ads and emails, and speeding up processes. Director of Product Strategy Ali Bohra told me last week that Adobe is using AI to solve one of marketing’s toughest problems: knowing what drives sales. The framework is called Media Mix Modeling (or Market Mix Modeling) and it’s literally a 50-year-old technique that measures all campaigns, all paid media, all organic social and web activity – in short , all actions – and tells marketers how well they are doing their job in driving demand.

Although old, media mix modeling is experiencing a kind of revival as it is a macro-level methodology that is generally safe for privacy. In other words, it doesn’t necessarily rely on the methods of surveillance capitalism to hunt down the people and devices that government regulation and technological innovations increasingly limit.

But it depends on the scale.

“We measure all campaigns, not just Adobe, not just paid media,” says Bohra. “We look at the incremental value of each contact. Only Adobe has done this… only Adobe has enough scale in the market.

It’s very likely that cloud marketing competitors like Oracle, Salesforce, Neustar, and dozens of smaller vendors wouldn’t agree. And even Facebook and Google provide tools for marketers to gauge the effectiveness of their customers’ spending.

But it is true that Adobe has a massive scale.

The company’s Experience Cloud marketing, commerce, and analytics suite of tools collects billions of data points from thousands of sources, including 80 of the 100 largest retailers in the United States.

So there is certainly important data that feeds into the technology of the company.

However, there is another challenge to media mix modeling (MMM): it takes considerable time and scale. Because MMM needs to account for historical variance, seasonal fluctuations, and external factors in customer behavior, the more scale you have and the longer it runs, the better its information will be. Existing Adobe customers who already have data in Experience Cloud will reap the benefits in just two weeks, says Bohra. New clients will need three to four months of ramping up to start generating valuable information. And clearly, years of data would be better, so the system would know how to account for summer, winter, and vacation variations for your business and specific products.

But the rewards are there.

Adobe claims that NVIDIA has increased paid registrations by 500% using its AI capabilities, which Adobe groups together under the product name “Sensei”. Pitney Bowes has reduced over 200 order anomalies per week to just one, and Under Armor manages millions of marketing assets with Adobe technology.

Plus, of course, the big cosmetics company that cut expenses by 50% while increasing sales by 10%.

The company started working with Adobe’s MMM solution six months ago and has clearly seen amazing results.

It should be noted, of course, that there are several ways to achieve this. If a business indulges in indiscriminate ad spending, it is very likely to fuel the bad guys who operate large scale ad fraud operations. So, a simple optimization is likely to reduce much of the fraudulent activity, and advanced technology can reduce even more. Additionally, massive ad spend focusing on top-of-the-funnel metrics without tracking clicks and visualizing them until customer acquisition and actual sales are also unlikely to be effective.

None of those 100% require an AI.

But AI can clearly make it easier, faster, automated, and most importantly, smarter. Plus, modeling the media mix is ​​notoriously complex and easy to fail, so doing it with a provider that literally has all the data you need as well as the smart systems to run it for you is a major bonus.

One interesting thing about Adobe’s AI: the black box problem.

Or rather its absence.

The problem with the AI ​​black box, of course, is the frequent inability of companies to understand why their AI made a particular decision or performed a certain action, which poses problems as to whether it optimizes itself. correctly or not.

“Adobe’s AI is designed not to be a black box,” says Bohra. “We want to make the marketer smarter… so they know why things are happening. “

For example, Adobe Sensei generates propensity to buy scores among customers, looking at what influences a higher propensity to buy. But the result isn’t just a probability or a percentage – it’s a list of the top key influencing factors that build that score, such as weather, location, and purchase history.

Ultimately, this has tremendous value.

And there’s huge value in marketing effectiveness scoring that doesn’t need to track and monitor people’s clicks and app installs and time spent on pages. Adobe customers have first-party data – after all, if you order something, the company needs to know who to send it to – but they don’t share data between brands or track people between apps and websites owned by multiple companies.

So, the impending death of the third-party cookie that worries marketers?

Bring it on.

“Everyone is trying to figure out what the world will be like without cookies,” says Bohra. “We started investing in this over five years ago. “

However, new Adobe customers may still have to wait for these features.

Despite the long testing period, the marketing mix modeling is still in beta and will generally be available next year, according to Adobe.

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