By now we’ve seen all the headlines and think pieces about technology rendering humans obsolete, as well as the rebuttals saying robots will never replace intrinsic human creativity. Today, the most likely path lies somewhere in the middle. Marketing is one industry where applying machine learning techniques to human intuition go hand-in-hand. As Acquia’s CMO Lynne Capozzi 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.” However, employees are often reluctant to accept artificial intelligence (AI) solutions because they cannot understand how a machine produced an answer. The lack of transparency into machine learning behavior can cause issues of trust that need to be overcome before a business can successfully adapt and use AI technology to its full potential.
Bridging the AI trust gap in marketing involves 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 the AI-driven insights. To tap into the full potential offered by AI technology, marketers first need to understand the advantages these tools offer and how to take ownership of all the new information at their fingertips.
How Machines Shift Marketing Priorities
Digital transformation is not a total elimination of old ways, but an 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 basic hammer, computer algorithms can help today’s marketers and digital leaders improve their customer understanding and give them more time to dedicate to producing impactful business results.
Marketers are in the business of creating relationships with people and 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. Today, businesses spend countless hours simply collecting, validating and organizing datasets before any real analysis can even begin.
Powerful algorithms cut down on 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. All of that can now be done much more efficiently through predictive technology. AI lets marketers devote more time to strategy and creative thinking so they can develop meaningful programs.
The Value of Data Science in Marketing
To progress the integration of advanced machine learning techniques across the entire organization there needs to be regular collaboration between marketing departments and data scientists who can break down complex AI concepts and provide insight in layman’s terms. By demonstrating a need to understand and analyze their customer data, marketers can propel the democratization of AI tools and gain ownership over this information.
We’ve already seen how customer data platforms (CDPs) powered by machine learning 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.
Overcoming the AI Trust Gap with Testable Outcomes
Perhaps the most important solution to eliminating distrust and achieving positive institutional change is testing 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, the RFM approach is not a statistically valid method for predicting outcomes due to its limited number of variables and dependence on past results to predict future behavior. Instead, machine learning provides multidimensional segmentation that can recommend 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 superiority of 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.
Our Acquia CDP team performed this experiment with a popular linen and home brand who had been spending over 40 million dollars a year mailing catalogs to a wide pool of past buyers. Predictive algorithms were able to identify high-value customers in their database to re-target direct mail efforts to the most engaged customers and those with the greatest potential to purchase.
Compared to the RFM segment, the algorithms selected over 25% fewer direct mail candidates. Yet, the AI segment generated 38% more conversions. These results shifted their business operations by prioritizing long-term retention over customer acquisition. They realized that applying the algorithm’s recommendations meant they could save 7-8 million dollars in shipping and printing costs while generating higher returns and building trust and loyalty with their core audience.
While machine learning is used to suggest possible outcomes, human judgment is the final arbitrator in how that data is prioritized and acted upon. As leaders begin to recognize 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 and executes the customer experience.
For a deeper look at how machine learning can advance marketing goals, get our e-book: Artificial Intelligence and Customer Analytics: A Guide for Marketers.