See how the Emarsys customer engagement platform helps marketers deliver personalized, 1:1 experiences across multiple channels and drive business outcomes.
See how the Emarsys customer engagement platform helps marketers in a range of industries deliver personalized, 1:1 experiences that deliver measurable results.
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In today’s world, the reality of e-commerce is this: there’s an overwhelming amount of pressure to personalize every customer interaction. The challenge is how to do it. Allen Nance, CMO at Emarsys, recently spoke about the role of AI in e-commerce at HUBDAY Retail in Paris.
If you want to compete, as Allen describes, you have to meet the rising consumer demands for personalized brand interactions at every place. The issue is that creating and scaling these kinds of personalized experiences is not possible to do via drag-and-drop. The answer? Artificial intelligence.
But even AI has all sorts of misnomers, myths, and false marketing surrounding it. In truth, AI should give you data you don’t currently have. Marrying the marketer and machine — so you can be more creative more of the time — is the means to achieving personalization.
“#AI is the creation of #data you DON’T have based on a #bayesiannetwork,” says @AllenNance CLICK TO TWEET
“I have never met a marketer who said, ‘My 2019 strategy is to send less personalized messages to fewer people’ — no one says that! Everyone is trying to do more personalized marketing… the problem is that we’ve moved beyond the human’s capacity to do personalization… you cannot drag-and-drop your way to personalization. We’ve moved into a world where it is physically impossible. There’s too much data, too many channels, too many choices… if we marry the marketer with the machine, we will unleash a creative renaissance where you get back hours in your day for strategy, content, and creative.”
AI is not “all-knowing,” though, and marketers need to remain mindful of that. An AI engine is excellent at ingesting prior engagement data, identifying probabilities of an individual responding to a certain offer, and weighting revenue and cost per incentive. If marketers let the machine do what it’s good at, they’ll get back to doing what they’re good at — and simultaneously revolutionize their roles in the process.
