AI, Blog, Digital Marketing

Three Steps To Overcoming AI Marketing Fears

AI creates unknowns, and they loom large over marketers. The 2020 State of Branding Report found that 56% of surveyed marketers think AI could negatively affect their brands by diminishing creativity, reducing jobs, or impacting differentiation.

A recent Brookings Institute study shows that higher-skilled jobs like marketing specialists are most likely to be affected by AI. But read further, and it becomes clear that opportunity is much greater than risk.

The Wall Street Journal notes of the Brookings study, “It is possible artificial intelligence will allow some workers to dispense with time-consuming tasks such as data analysis, and focus on potentially more profitable activities, such as meeting clients. Those workers could become more productive and command higher wages.”

AI fails like hackers using a mask to unlock iPhone facial recognition are public reminders that AI carries risks. Driving cars also carry risks, yet millions of people drive every day. The difference is that we’re used to driving, and we’re more comfortable assessing the risks.

There are three important steps marketers can take to better assess AI risks and move forward with valuable projects with confidence so that AI won’t diminish creativity, reduce jobs, or impact differentiation.

  • Use Data Patterns To Increase Creativity

    While marketers fret over major AI mishaps, thousands of extremely valuable AI projects are kicking off every day. According to Adobe, top-performing companies are more than twice as likely to be using AI in marketing than their less profitable peers. And IDC reports that retailers alone invested nearly $6B in customer-focused AI in 2019.

    And the most profitable marketers are using advanced AI-driven tactics like predictive personalization. AI actually has the power to increase creativity because it can analyze a lot of data and provide a wider variety of insights and recommendations.

    Rather than think of using AI like trusting a self-driving car, think of it as small AI-assisted elements like parking assist and lane indicators. These smaller assists help the driver without replacing them.

    The first step marketers can take to embrace AI without fear is to think in terms of moving from simple data points to data patterns in order to increase productivity and creativity. Marketers trying to segment an offer might divide customers into high and low-value buckets and perform an A/B test.

    This would lead to two different marketing messages. But, the segmentation can be further improved with AI that can recognize patterns over time and develop a much wider range of segments and offer combinations. AI might determine that some spenders are driven by seasonal signals or signals like the location.

    Complex insights are difficult for a person to evaluate, but with AI, they become clear very easily. Marketers suddenly have a lot of knowledge that can drive very differentiated, personalized marketing that can be more creative and more effective.

  • Let AI Do the Worst Jobs First

    AI frees the marketing team to move on to more interesting projects that can be layered on top of AI. James Manyika, Chairman, and Director at McKinsey Global Institute said that AI will change marketing jobs, and will actually create more jobs, rather than take them away. Let AI categorize millions of data points into patterns. Let AI sort and recombine text and image elements for a test.

    When that AI identifies twenty messages that could work on a whole bunch of new customer segments, that requires more creative power, not less. When AI tests show that personalized newsletter content drives more conversions, it requires more content, which needs to be written and edited. Even AI’s written content needs a human touch!

    As AI takes away some of the manual tedium of marketing, new positions are being created such as specialists in marketing intelligence, marketing optimization, and experiential marketing, which all harness the power of AI. For marketers just kicking off AI-driven projects, it’s important to identify what the AI will do, but also what the people are freed to do once it starts working.

  • Let AI Be the Stage for Brand and Customer Interaction

    Think of an image for AI and I bet some kind of robot comes to mind. Marketers are by nature creative, uniquely minded individuals, and so the idea of adding a level of robotics to a marketing strategy feels very unsettling. But AI is not a replacement for a brand, or for customer experience, but rather the stage that supports the interactions between the two.

    Banks are some of the most bullish AI investors, and they actually lead the way in implementing AI into their customer experience. The main reason is that banking customers prioritize efficiency, accuracy, and security, three things where AI can help tremendously. As the nature of daily banking changes and the mundane transactions can be automated, banks are actually free to differentiate themselves more, not less.

    For example, Bank of American uses an AI-driven chatbot named Erica to interact with customers on their app, interacting with more than seven million users to date for over 50 million transactions. Retailer StitchFix uses AI to create personalized monthly shipments of new clothes, ensuring that customers get a highly differentiated product, not just another little black dress. The AI gets better over time as it incorporates feedback from individual customers.

    Adding AI in places that could use added insight, efficiency, and scale increases performance. But, what about those small risks that still exist? What if an AI-driven test sends an ad for a winter coat to someone who’s actually in Florida? These risks can be mitigated with a strong commitment to data quality being fed into technology and by employing a personal review of any outgoing and incoming interaction.

    AI might recommend content that doesn’t make sense or might create a segment that doesn’t actually respond as predicted. These small fails only affect an individual campaign or small group of customers, which is actually a less risky fail than a national campaign flop. And what’s better, the learnings can be fed right back into the system for better results next time.


    This article is written by TechFunnel (Team Writer) and originally published here


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