Codrin is the Chief Marketing Officer at Digital Authority Partners. He has spent over a decade creating successful digital strategies for both Fortune 500 companies and startups. Expertise: Product Management, Artificial Intelligence, Healthcare Digital Transformation, Analytics, Marketing
Are your marketing strategies taking advantage of the massive leaps in technological innovation to reach your customers when—and where—it matters? In the arms race that is modern marketing, failure to capitalize on advances in emerging technologies such as artificial intelligence can result in customer engagement levels far behind your tech-enhanced competitors.
For decades, the standard model for targeted advertising has been to find indicators of behavioral similarities (a subscription to a magazine in a particular subject matter, for example) and produce mass content that appeals to the perceived needs of your targeted demographic.
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But in today’s hyper-personalized world, this “good for the goose, good for the gander” approach no longer cuts it. Yet, the human effort required to craft truly personalized, large-scale marketing and advertising has made it virtually impossible to target buyers with surgical-like precision.
Thanks to artificial intelligence, however, we now have the tools to do just this—making it easier than ever to find, connect with, and convert prospects.
Just as importantly, artificial intelligence is allowing for the streamlining of marketing processes, lifting the prohibitive costs that once impeded efforts to enhance 1:1 communication and individualized content.
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In this article, we’ll cover the various ways companies are using artificial intelligence to improve both the precision and effectiveness of their marketing campaigns for better 1:1 communication, taking a glimpse into how AI will transform the way businesses interact with customers in the years to come.
Artificial intelligence is a subset of computer science that revolves around creating software capable of learning and improving its performance over time. Though computers are very good at quickly gathering large volumes of data, knowing what to do with that information and how to use it to pursue of a specific goal has traditionally been reserved solely for the human mind.
In the 1950’s, computer scientists began searching for ways to create software capable of problem solving and mimicking the ways humans think. Early AI programs assisted with “simple” tasks such street mapping and, by 2003, the technology that would serve as the bedrock for cyber-helpers like Alexa or Siri had been put in place.
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Today, AI’s algorithms are able to continuously learn and adapt to various “inputs”—whether it be the human voice, x-ray images, or other forms of data. And the potential of AI to transform every part of business, from production to customer service, is enormous.
Companies are already using AI to track customer movement, predict demographic compatibility, anticipate purchases, and to provide top-tier customer service. In fact, through a variety of AI offshoots such as machine learning, deep learning, and natural language processing (NLP), the technology is already responsible for powering the everyday technologies that we know and love, including Siri, Cortana, PayPal, Google Maps, and more.
This of course begs the question: If AI technology is so powerful, how can I use it to advance my company’s marketing strategy?
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With the introduction of AI, companies have gained the ability to not only interact directly with individuals, but to transform the data they leave behind into a blueprint of how to better serve them in the future.
As AI algorithms continue to evolve and advanced computational power becomes more commercially-available, companies are progressively increasing the role of artificial intelligence in their marketing strategies. Often used to interpret the messages lurking beneath oceans of data, artificial intelligence lends a key competitive edge by providing keen predictive insights to correctly anticipate the behavior of customers.
Predictive analytics uses vast quantities of data to forecast future outcomes. Companies such as Target have shone the spotlight on predictive analytics to masterfully predict the purchasing patterns of buyers. In fact, in 2012, the retailer made headlines when they were able to correctly predict the pregnancy of a teenager based off of her purchasing habits, before she had even discovered she was (unexpectedly) expecting.
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This incident reveals both the precision of the algorithms used by marketing companies, as well as the continued need for human oversight during the usage of these burgeoning technologies.
Deep learning enables a computer to “learn” to recognize patterns in images, text, voice, and other data to extract valuable information. No conversation about the use of deep learning is complete without mentioning the way that social networking giant, Facebook, is employing a sophisticated object-recognition engine using user-submitted photos from Instagram.
While the company asserts that the information gleaned isn’t being used to better understand user behavior, their recent open-sourcing of the platform opens up the gates to the possibility that other companies can leverage this predictive technology as well.
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It's not just mainstays of the tech industry, however, that are using artificial intelligence to enhance the customer experience. With the help of intelligent chatbots , companies are providing customers with intuitive, responsive, and dynamic 1:1 communication that addresses their needs and helps them identify helpful services.
Last year Shell, the multinational oil and gas company, introduced a new chatbot known as “Shelly” to help customers identify which enhancements are most effective for their vehicles.
Not only does Shelly assist with promoting 1:1 customer engagement for the brand, but, according to Mansi Madan Tripathy, Managing Director of Shell India, Shelly’s intelligent assignment of products reduces downtime, cutting customer operational costs by 25-30%.
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Tripathy also asserted that Shelly's use of sensor-driven data consolidation will enhance both the safety and operational efficiency of fleets that integrate the new technology into their company's physiology.
As AI’s prevalence grows, so too will the expectations from customers that companies replicate the quality of service it provides. In the same way that businesses are expected to have websites, online shopping, and other omnichannel avenues for purchasing, customers will expect intuitive, relevant communication from companies.
Moreover, the efficiency AI enables simply cannot be matched by humans. And even if humans could match their robo-brethren, the resource-intensive nature of this work favors companies that use the skills of their workers for non-programmable tasks.
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It’s necessary for modern marketers to begin exploring the various ways in which AI can advance their organizational objectives, and begin to actually use them. Waiting to adapt after an industry-wide paradigm shift can leave companies scrambling to catch up.
Companies are changing the way customers experience their brand, with the incorporation of intelligent chatbots, predictive analytics, and other personalized offerings providing more intuitive customer experiences.
Ultimately, customers will defer to companies that provide the most intuitive, valuable services at the lowest costs. Artificial intelligence will become the key tool that allows companies to not only meet their customers’ needs, but their own.
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We recently discussed how Shell is using chatbots to enhance their customers' experience. Let’s now take a look at how Shell, or any other company in the oil and gas industry, could continue to integrate AI into their marketing strategy to further optimize something used by most customers at least once a week: the gas station.
Many companies already offer customer incentives to encourage use of brand-specific cards, both at and away from the pump. These loyalty cards provide companies with an invaluable window into the behaviors of individual consumers, and the data generated by buyers often holds the key to predicting their future purchasing patterns.
In fact, AI has already demonstrated its ability to draw predictive insights from transactional data using sophisticated algorithms that far outperform previous marketing models.
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In the past, customers were targeted with advertisements that matched prior purchases with upcoming promotions (for example, people who had purchased from iTunes found themselves targeted with promotional materials for U2's newest album).
This new generation of AI-driven marketing technology, however, allows marketers to employ sophisticated algorithms that convert customer purchasing data into indicators of which actual products they may be interested in, bringing custom targeting to a whole new level.
British Bank HSBC , in partnership with Maritz Motivation Solutions, conducted a loyalty program experiment in which 75, 000 clients were sent rewards points that could be redeemed for a host of products and services. Some of the clients had their rewards plan generated by AI, while others used conventional promotional emails.
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The results echoed an overwhelming preference for the AI-rendered suggestions. Roughly 70% of the participants who ultimately redeemed their rewards package preferred the robo-enhanced recommendations.
The research uncovered that AI-powered predictive analytics can be used to acquaint customers with what they want, before they even know it exists.
Even more astonishingly, voice-recognition software enhanced with sentiment analysis can be installed at gas station pumps to both assist clients with their orders and to detect their emotional state.
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Depending on the emotions detected, screens at gas pumps could provide product recommendations that best suit a particular mood. Consequently, AI will enable marketers to surmount hurdles presented by the dissonances between their marketing strategy and their customers’ emotions.
Similarly, voice-recognition software can be used to hasten the speed of customer orders, allowing for a faster flow of customer traffic. Instead of requiring clients to manually select the grade and quantity of gas they need, transactions could be expedited by implementing
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