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AI for Marketing: Myth vs. Reality

Ryan Greene

Head of Product Marketing

Behavioral Data Broke Marketing

Before digital, marketing was pretty straightforward. Marketers used customer profiles—derived from transaction and demographic data—to segment and run campaigns across a limited number of channels (primarily direct mail and email).

But as consumers moved increasingly online and organizations updated their technology to track behaviors across web, email, mobile apps, and more, the amount of behavioral data exploded. Brands sought to use this data for customer analytics and personalization, but the scale and scope of the data went beyond the marketing team’s capabilities.

Artificial Intelligence vs. Human Intelligence

Enter artificial intelligence (AI), which promised to mine all this rich behavioral data, predict future outcomes, and deliver the optimal customer experiences—all in real time. Unfortunately (or fortunately if you value job security), this is not the case.

AI is extremely good at automating and scaling analytical tasks normally done by humans. But it does not set goals or understand business context, and it certainly does not have empathy. These qualities are vital to designing meaningful customer experiences, which is why human intelligence and intuition still have a role in marketing. Even CDP marketing still requires these human inputs to guide the analysis while AI is still developing more advanced capabilities.

Artificial Intelligence + Human Intelligence

Augmenting human intelligence with AI is the fastest way to scale personalization efforts across your brand. And since nobody has a monopoly on algorithms, it all comes down to how you are able to operationalize AI into your marketing processes. The following framework can serve as a guidepost to integrate AI into your processes.

AI can’t sift through data and then tell you what to do. This is not possible. And if someone is telling you this is what they can do, don’t believe it. You must bring your own ideas and intuition. AI will help validate or debunk these hypotheses, but it won’t come up with strategies for you.

Algorithms don’t set goals. Only a human can select the best metric for AI to optimize. And then, algorithms must be constantly monitored to ensure model conditions and quality have not changed. Poor goals can lead to unintended outcomes. For instance, a media site optimizing for clicks could quickly become overrun with click-bait material, harming brand reputation.

In most cases, AI is a black-box algorithm, which means it’s a human’s job to provide business context and interpret the results. A model that is effective in one business context does not translate into another context, which is why out-of-the-box models don’t work. Take Netflix for example—the algorithm they used to forecast DVD order demand was not effective in predicting streaming preferences. If you don’t understand how AI is making decisions, the outcomes will be less credible and you won’t be able to improve or extract learnings from the model.

This is where AI excels. It automates and scales operational tasks and decisions that were once done by humans. AI needs to be monitored to make sure that it is still performing under the expected conditions and at a high quality, but this is where the human + machine benefits really come to life.

In closing, humans will continue to play a critical role in a brand’s journey to creating personalized experiences at scale. So for better or worse, AI may not replace you, but it definitely won’t save you.

To schedule time with an industry expert for tailored guidance and an action plan that meets your objectives, visit our Meet The Experts page to learn more.

Written By

Head of Product Marketing

Ryan Greene is an expert on the intersection of customer data and digital strategies who has led big data marketing initiatives within the retail and financial services industries. Ryan joined ActionIQ because he believes business stakeholders are most effective when they have user-friendly solutions to extract intelligence and value from all their data.



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