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Editorial

How Marketers Can Overcome the AI Trust Gap

4 minute read
Omer Artun avatar
Lack of transparency into machine learning behavior causes issues of trust. Businesses must overcome these issues before it can adapt and use AI technology.

By now, we've all read the headlines about AI rendering humans obsolete, as well as the inevitable rebuttals that robots will never replace human creativity. Like most things, the answer lies somewhere in the middle, with marketing being a prime example of an industry where machine learning (ML) techniques and human intuition go hand-in-hand. As our CMO (and CMSWire contributor) Lynne Capozzi recently wrote, “You need to have both the tech-supported insight and creative inspiration to design relevant campaigns based on the real-time needs and interests of your audience.”

The challenge here is how to help employees accept these new AI-based solutions, even if they don’t fully understand how a machine produced an answer. Lack of transparency into ML behavior causes issues of trust that businesses must overcome before they can adapt and use AI technology to its full potential.

It will take a combination of changing employees’ perspectives, changing the process by which marketers comprehend customer behavior, and ultimately, changing the way teams interpret and act upon those insights to bridge that gap. To tap into the full potential offered by AI, marketers need to first understand the advantages these tools offer and how to take ownership of the new information.

How Machines Shift Marketing Priorities

Digital transformation isn't an elimination of old ways, but rather a critical evolution. Just as automation boosted factory workers’ productivity during the Industrial Revolution or power tools gave an unprecedented advantage to construction workers far beyond the hammer, computer algorithms can help today’s marketers and digital leaders improve customer understanding and free up more time to dedicate to producing stronger business results.

Marketers are in the business of building and nurturing relationships at scale. Part of this involves learning how to meet human needs. However, people are so uniquely complex that trying to understand the values and desires of individual consumers becomes impossible when dealing with millions of potential buyers around the globe. Businesses today spend countless hours simply collecting, validating and organizing datasets before any real analysis can even begin.

Algorithms powered by AI cut down the number of repetitive tasks in a typical workday. The traditional way of doing things involved long hours spent identifying subjects, determining lead ratios and qualifying prospects. Predictive technology can do all of this much more efficiently. AI allows marketers to devote more time to strategy and creative thinking so they can develop meaningful programs.

Related Article: How Machine Learning Is Upending Marketing

The Value of Data Science in Marketing

For advanced ML techniques to progress across the entire organization, marketing departments and data scientists need to collaborate on a regular basis. The data scientists can break down complex AI concepts and provide insight in terms marketers can understand. By demonstrating a need to understand and analyze customer data, marketers can propel the democratization of AI tools and gain ownership over this information.

Learning Opportunities

We’ve already seen how customer data platforms (CDPs) powered by ML can transform enormous amounts of consumer data into straightforward, comprehensive dashboards. CDPs do this by framing their results in a customer-centric way. By creating that connection between hard data and customer experience, marketers can better grasp these takeaways and transform their data into real-time actionable insights.

Related Article: What Ever Happened to the Sexiest Job of the 21st Century?

Comparing Old Predictive Approaches With New

One of the most compelling ways to eliminate distrust and achieve positive institutional change is to test the old method against the new. For example, RFM was an early method for predicting customer behavior, traditionally used by catalog marketers to calculate a customer’s likelihood to buy.

However, due to its limited number of variables and dependence on past results to predict future behavior, the RFM approach is not a statistically valid method for predicting outcomes. Instead, ML provides multi-dimensional segmentation that recommends outcomes based on the entire customer lifecycle. The machine algorithms follow an alternative, more intricate process than those in the RFM model. Therefore, the final results must be analyzed from a different perspective. To help organizations accept the newer methods, it’s useful for teams to conduct A/B tests by observing both the outcomes of what segments RFM users believe you should target and what segments are chosen by the AI.

While ML is used to suggest possible outcomes, human judgment clearly is the final arbitrator in how that data is prioritized and acted upon. As marketing leaders begin to view AI as a powerful value driver and employees have more opportunities to engage with these technologies, AI will become instrumental in how every digital marketer thinks about building better experiences and creating more personalized interactions.

About the author

Omer Artun

Omer Artun is the Chief Science Officer at Acquia. He was previously the founder and CEO of AgilOne, which was acquired by Acquia in December 2019.