Salesforce teases future of Marketing Cloud: What we learned

The EVP & GM of Marketing Cloud at Salesforce, opened Dreamforce’s Marketing Keynote with:

“We’ve never done something at Dreamforce that lets you move forward into our products. But we’re gonna do that today. You can’t go out and buy what I’m about to show you in a few minutes… but we want to give you a glimpse into what we’re building, from our labs, from our engineers.”

– Steve Hammond

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As a marketer, how could I not be excited? I was going to see something unveiled for the first time.

And yes, it hit harder. Because I somehow ended up in the first row of the Marketing Keynote (see yellow arrow and SaltClick cap)—gleefully shuffled forward by blowup racoon and goat mascots.

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Why did Salesforce give us a glimpse into what’s cooking in Marketing Labs?

They did it to get marketers like me excited about AI. They also took the opportunity to inspire a captive in-person and online audience—showing us the value of connected systems. How they could streamline things and finally make personalization faster, less costly, and scalable. Next up is Kelly Eliyahu, a Product Marketing Director at Salesforce. She took the stage, rocking a lab coat to show us what could be possible in the future of Marketing Cloud.

In her demo, Kelly’s a marketer at Cumulus, a fictional consumer and commercial bank. The business’s goal? To be as data-driven and efficient as possible.

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Salesforce Marketing Cloud and AI

In this new vision of Marketing Cloud, Kelly has automated AI insights to alert her when there are opportunities to improve her KPIs. But for those insights to reach Kelly in real time, the interface needs to extend to where she (and many other marketers) are already working—in this example, Slack.

In this scenario, Einstein Copilot has surfaced a new opportunity. It’s recommending a cross-sell credit card opportunity to Cumulus’s university segment to increase customer lifetime value (CLTV). Next, Kelly asks Einstein to create a new campaign—starting with a data-driven campaign brief based on the AI insight that popped up in her Slack. The brief is interactive and editable. To illustrate this, Kelly asks Einstein how to expand the campaign beyond college students.

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Einstein recommends a new target audience of parents of college students.

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Easing anxiety about AI

Kelly, the marketer of this fictional bank, knows her audience, and this insight tracks with her. We repeatedly see AI as an assistant during the demo, but the marketer is in the driver’s seat. I believe this is intentional to quell people’s fears about AI taking their jobs (or more dystopian futures).

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Once gut-checked by a human (Kelly), we move on to the next step. Einstein’s insight is added to the brief, which then automatically regenerates.

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AI grounded in customer data

Next, Kelly takes us to the Preview tab. It’s like a central command center for this cross-sell credit card campaign. Here, we can get segment insights, see the content created for each persona, and see product affinity, channel preferences, and more. What we see in the Preview is based on the details in the brief Kelly created and grounded in customer data. Satisfied with the campaign, Kelly moved to content creation.

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AI-driven personalized content

Within the content workspace, marketers can see AI-curated images and content. The content here is based on assets from Cumulus’s brand and creative teams. It also includes AI-generated images, copy, and variants for each target persona.

Kelly describes the variants as the ability to experiment or ABM test at scale, showcasing the value of such a solution if Salesforce can deliver.

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Who AI-Driven Personalization Could Serve (First)

AI personalized content could benefit mega brands with multiple brands under their banner. A great example featured prominently during Dreamforce is Salesforce’s shining marketing star, Williams-Sonoma. Other potential brands that could benefit from this promised innovation include Unilever, Nestlé Global, Colgate-Palmolive, JP Morgan/Chase—the list goes on. And the reality is that mega brands will pilot these innovations like these. Why? Because personalization like this saves them time and money, giving them unparalleled insight into what resonates (and what doesn’t) with their different target personas. That’s millions of dollars. And, with VUCA (volatile, uncertain, complex, and ambiguous) global markets, matching those permutations quickly and efficiently enables them to problem-solve and deliver tailored solutions like scale.

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How Salesforce AI can boost customer satisfaction

Starbucks, for example, is dealing with long wait times for complex drinks. Imagine if they could offer “alternatives to” recommendations when people are time-crunched. These recommendations would be based on the consumer’s past purchases and lookalikes who made successful swaps. These additional options could help marketers learn more about their target personas. For instance, if a customer is into fall favorites like the Pumpkin Spice Latte (PSL) but doesn’t have the time to wait for a hot drink or a PSL Frappuccino, then maybe a faster recommendation could surface based on real-time actionable data demoed repeatedly in sessions featuring Salesforce’s Data Cloud. Or, maybe they’d see a message like “Hey, we’re swamped right now. The wait time for this drink is 8 minutes” via SMS or the Starbucks app.

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But this level of real-time insights requires pristine data, in-store modernization, and intelligent hardware for baristas like smart espresso machines, blenders, and state-of-the-art POS systems. It also requires training. Now, let’s move on to the content review process.

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AI content collections and collaboration

Once AI has created the variants (visuals and copy) for Cumulus Bank, Kelly will need to preview the content collections. This is where she can dig into the content and see what the campaign will look like in other channels, such as email, ads, WhatsApp, and so on.

Satisfied with the results, Kelly presses save, and voila!—she’s almost done. If Salesforce delivers on the promise of Einstein Copilot (and finds a way to make the price point accessible), this could be huge. It’s personalized content creation at scale at a fraction of the cost and time. Time will tell, but based on the “oohs” and “ahhs” in the room, it was hard to mask marketers’ genuine excitement. Even if the details of how, when, and how much haven’t quite been ironed out yet. Hopefully, we’ll learn more with the Winter 24’ Release.

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AI approval in the Flow of work (aka Slack)

Happy with the AI-assisted work, Kelly submits the content for approval to Steve (who plays the marketing lead). Using an automated workflow in Slack, Steve can review the assets and automatically approve or reject them.

For the sake of time, Steve approves everything.

Personally, I would have liked to see the rejection process. For instance:

• If Steve disliked an image, would Kelly get a notification?
• Could he choose another image for the AI Content Collection and replace it himself?
• Could he drop a comment on why the image was rejected?
• Could Steve give Kelly clarity on next steps?

Fast forward. The campaign is up and running.

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The assets have been in market for a couple of weeks. Kelly gets real-time campaign updates in Slack when experiments are complete. From Slack, she can click on results to get a snapshot of how different elements of an experiment are performing—like subject lines and banner ads—and see the campaign’s impact on her goals.

In the Cumulus example, the KPI Kelly was trying to increase was Customer Lifetime Value, which, of course, increased due to her efforts.

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AI-driven insights with Einstein Copilot

She goes full circle, explaining that marketers like her would continually receive automated AI insights with new recommendations. I’m still trying to figure out how or who would set these up and whether or not these capabilities would require additional Salesforce tools, but still, having that kind of insight into our flow of work is a game-changer.

In Kelly’s example, she gets an Einstein Recommendation for another cross-sell opportunity for auto loans to recent college graduates. Essentially, the process we just saw would repeat itself. ‘

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Personalization at Scale in Generation AI

AI and Data Cloud bring new promise to marketing personalization. The big takeaway? The marketer isn’t cut from the squad. Instead, they’re more like the coach, training AI to do faster and better work. While excited about these developments, I need to see pricing and explore use cases from successful brands piloting these technologies. But despite my reservations, I can’t help but feel inspired. On that note, I can’t wait to see the stuff you’re cooking up in Marketing Labs come to life, Salesforce.

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