Home » Artificial Intelligence » Cognitive Computing and the Transformation of Marketing

Cognitive Computing and the Transformation of Marketing

February 8, 2018


The specter of artificial intelligence (AI) or some its variants (like cognitive computing and machine learning) haunts workers in numerous fields. They fear automation is coming for their jobs. Marketing is no different. Michelle Huff (@michelle_huff), Chief Marketing Officer at Act-On Software, writes, “Artificial intelligence strikes some people as scary. It’s only a matter of time, they fear, before AI replaces many of the tasks performed by humans — and takes their jobs. Marketing is one of the areas where AI and machine learning are making inroads.”[1] How deep are those inroads? Ben Lamm (@federallamm), co-founder and CEO of Conversable, predicts, “Most brand experiences will be delivered through AI by 2025. The only question is whether your brand will still exist.”[2] Should marketers be worried about their jobs? Huff writes, “I’m frequently asked whether these technologies will eventually automate the entire marketing process. My response: No way. Stop worrying and start getting excited.” Are Lamm’s and Huff’s views at loggerheads? As I explain below, probably not.


Cognitive Computing in Marketing


Although both Lamm and Huff use the term AI, I believe cognitive computing (a subset of AI) will end up being the preferred term in the future. Cognitive computing is often touted as a tool that augments human work and decision-making. Huff certainly sees AI that way. She explains, “We already love marketing automation, right? It puts computers to work performing a variety of manual tasks that we don’t like to do in the first place (think of the soul-crushing email list management process). It also helps us save time, target more effectively, and optimize all stages of the customer experience. AI can push it even further.”


Lamm goes even further in his explanation of why cognitive computing will be essential in the coming years. “Forget the nuts and bolts of the technology for a second,” he writes. “Pay attention to what your customers are doing. Today’s internet is a lot like American culture after the interstates were built: we’re scattering across a vast expanse of real estate and there are no discernible patterns in the chaos. AI brings order to this chaos. It creates brand consistency wherever digital volatility once maddened marketers. Just as marketers are growing weary, AI is bringing brand marketing back from the precipice of irrelevance.” Note he doesn’t say that AI will replace marketers.


Cognitive Computing and Customers


Marketing is, and always has been, about reaching the right customer with the right message at the right time. Historically, that has been difficult. Targeted marketing probably started with merchants shouting at promising looking customers as they walked by their stalls. When mass media outlets developed, retailers targeted customers by selecting the sections of newspapers, the genre of magazine, and the types of radio and/or television programs in which they would place their advertisements. Cognitive computing has changed how marketers can target customers. Don Fluckinger (@DonFluckinger), executive editor of TechTarget, notes “retail AI” is making targeting easier. He explains, “Retail AI … examines data points of consumer behavior and, combined with rules-based marketing automation, cashes in on sales by predicting consumer behavior in future transactions.”[3]


Analysts from Zaius insist identifying potential customers or understanding current customers is not as easy as some people would have you believe. They call the process “identity resolution.” They explain, “Identity resolution is the process of stitching together data to fully understand who your buyers are and how they interact with your brand online — across devices, channels, and browsers. … It may sound outrageous, but your company’s most loyal customers have, on average, six completely different identities in your marketing systems, including your ESP. It’s not because your customers are purposely trying to confuse you or constantly adding in fake names and numbers. It’s simply the nature of how buyers interact with e-commerce brands online.”[4] Cognitive computing systems can help with identity resolution. As a result, assert Zaius analysts, “You can track every customer’s multiple identities and unify them. That way, you know exactly when an individual customer visits your website, clicks on an ad, or makes a purchase. That type of knowledge isn’t just a nice-to-have, either. Unifying customer data allows you to truly personalize and customize every marketing interaction. Instead of batch-and-blast emails, you can personalize and target your marketing based on loyal customers’ most recent interactions with your brand.”


Alessia Civita, a Senior Consultant at SDG Group, asserts companies aren’t leveraging the data they have to take advantage of retail cognitive computing. She explains, “Most companies currently have access to an incredible amount of consumer data collected from a wide range of sources, from online browsing, day-to-day interaction with customer service and brand social media channels to actual purchases both in online or physical stores. Unfortunately, companies often lack the resources to leverage this huge amount of information in order to spot behavioral patterns and turn them into actionable insights. … This is where cognitive computing comes in.”[5] She suggests there are a couple things companies should concentrate on. “First of all,” she writes, “it is important to identify the aspects to focus on.” She recommends companies understand how their public image is perceived; how their products are performing; and so forth. “Secondly,” she writes, “it is important to make sure the cognitive system you are using can contextualize and interpret data. The difference in wording between a genuinely happy customer’s tweet and a frustrated one’s could be as subtle as the addition of an emoji. … An advanced sentiment analysis system is going to be able to infer the actual tone of a post and help you interpret the true meaning of your customers’ posts.” Sarcasm is sometimes difficult to discern even by humans.




Zaius analysts conclude, “The possibilities are truly endless once you have the right data driving your e-commerce marketing.” Whether you conducting targeted mobile campaigns or offering up personalized website recommendations, cognitive computing can make a difference. Rachel Arthur (@rachel_arthur), Chief Intelligence Officer at The Current, provides impressive anecdotal evidence of how cognitive computing can increase sales. She writes, “Product recommendations for e-commerce sites are not new in concept, but the suggestions they present to shoppers are increasingly getting smarter thanks to the algorithms behind them. The result of delivering more relevant product ideas? Higher spend of course.”[6] She reports when Jewelry.com leveraged “omnichannel personalization technology” it resulted in “revenue increases per visitor of 39% from the homepage, 13% from product pages, and 18% from cart pages. The key, according to the team, was not just to focus on the usual ‘most popular’ or ‘similar to current item’ suggestions, but instead to turn to machine learning to automatically select the most effective strategy for each user.” Civita adds, “Like all advancements in technology, no matter how powerful your IT systems are, or how much data you can obtain from your customers, what is really going to make a difference is your company’s attitude towards these new tools.” Are you ready to let cognitive computing work for you?


[1] Michelle Huff, “AI is enhancing the roles of marketers in progressive companies,” Venture Beat, 27 September 2017.
[2] Ben Lamm, “Why AI Is the New Battleground for Brand Marketers,” AdWeek, 7 January 2018.
[3] Don Fluckinger, “Retail AI predicts consumer behavior for targeted marketing,” TechTarget, January 2018.
[4] Zaius, “Why Marketers Should Care About Identity Resolution: Your Customers Have Many Faces,” MarketingProfs, 14 November 2017.
[5] Alessia Civita, “How to Understand Consumer Behavior by using Cognitive Computing,” SDG Group, 9 November 2017.
[6] Rachel Arthur, “Machine Learning: Jewelry.Com Drove Revenue +39% By Personalizing Its Homepage Recommendations,” Forbes, 21 December 2017.

Related Posts: