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Targeted Marketing and Cognitive Computing

May 31, 2017


“It’s not a coincidence that artificial intelligence is rapidly colonizing marketing tools designed for business sales,” writes Barry Levine (@xBarryLevine). “It’s a necessity.”[1] That’s a message you hear more often regardless of whether you are talking about B2B or B2C targeted marketing. “There has been a limited amount of practical applications of AI and cognitive computing to date,” write Dan Telling (@TellingDaniel) and Jeremy Waite (@jeremywaite). “There is, however, little doubt amongst marketers that these technologies have the potential to create seismic changes in the way businesses interact with their customers.”[2] Targeted marketing is founded on the belief that companies can analyze big data to better understand what their customers want and when. Analyzing the oceans of data being generated each day obviously requires some form of AI capability. Nevertheless, “while there is much fascination with the potential for cognitive,” Telling and Waite note, “there is still an element of nervousness from many organisations, especially when it comes to AI.”


Targeted Marketing and Cognitive Computing


“Marketing, is all about solving efficiency issues, improving intelligence gathering, and understanding so that we can predict and react faster than anyone else,” writes Logan Rosenstein, an employee at NVIDIA.[3] That’s why cognitive computing, a subfield of AI, is such a good fit. I define cognitive computing as the combination of Semantic Reasoning (i.e., AI (including machine learning), natural language processing, and ontologies) and Computational Intelligence (i.e., advanced mathematics). Cognitive computing platforms can gather, integrate, and analyze both structured and unstructured data — an absolute essential in the marketing arena. Using that analysis cognitive computing systems can optimize processes, identify issues and interests, predict campaign effectiveness, and provide other valuable insights to decision makers. It’s little wonder, then, that Telling and Waite conclude, “The next few years will see organisations start to get to grips with what AI and cognitive computing can offer.” Great targeted marketing begins with data that helps marketers personalize the messages they create; especially, for consumers on the digital path to purchase.


Nikki Baird (@nikkibaird), Managing Partner at Retail Systems Research, notes, “Personalization is only as good as the data about products and the site’s ability to influence a shopper. Shoppers who are logged in and whose behavior can be tied to past purchase history are likely to get the most relevant recommendations. But a lot of guesses can be made based on behavior alone. As long as a personalization engine can piece together similarities between how you shop compared to how other people before you have shopped, and has good access to product data, it can make pretty good guesses as to what other products you might also like to see and how to organize them.”[4] Where do you find a “personalization engine”? It’s just another name for cognitive computing. Baird explains, “Throw in some Artificial Intelligence or Machine Learning … and the dynamic of engaging with shoppers on a site will change.” She continues:

“At its most sophisticated, personalization looks at browsing behavior, makes guesses as to what underlying attributes are most important, and then shows you other products, ordered by their attribute relevancy to what it’s guessing you’re looking for. The more you interact, the more information it can use to make guesses, testing and discarding or revising according to how you respond to what it offers up. This is a basic version of machine learning. The next level requires a bit more sophistication. We’ve already seen that AI is capable of developing its own internal reference systems to help make connections from things it learns. For eCommerce that means instead of relying on the attributes already in use to describe products, AI could create its own attributes as a way of understanding what makes products similar to each other for a shopper. … Given enough learning, AI could even tell you the order you need to present those filters, according to which ones are ultimately most important to conversion rate.”

Levine adds, “Artificial intelligence is more than a stylish trend. It goes beyond rules, providing the ability to understand content or language, find patterns that can be applied to the future, digest all kinds of information and make reasoned decisions.” Some analysts insist cognitive computing capabilities are essential for the survival of most companies. In an interview with David Weldon (@DWeldon646), Brandon Purcell, a Senior Analyst at Forrester Research, stated, “There is a clear need for AI. Increasingly empowered customers are demanding personalized experiences, and the only way to meet their expectations at scale is through artificial intelligence. AI provides the ability to understand customers and anticipate their needs, then deliver optimized experiences across channels and touch points. Companies that don’t embrace AI are likely to fall behind.”[5] Reporters at i-SCOOP add, “If you look at all the AI-driven or AI-enabled sales tools out there and the ways they are used the two main areas where sales gets impacted revolve around 1) the automation of repetitive, mundane and less value-generation tasks and 2) the use of AI to, let’s put it simply, know more to sell more, faster and better.”[6] What company doesn’t want to sell more, faster and better?


Bad Targeted Marketing can Hurt a Company


Although it may sound like targeted marketing using cognitive computing capabilities is a silver bullet solution, things can go wrong. Simon Gwynn (@simongwynn) reports a UK study by YouGov for the Chartered Institute of Marketing found, “61% [of respondents] have been sent marketing material about a hobby or interest they don’t have, while 35% receive promotions for offers in areas that they neither live in nor visit. Half of the respondents reported that the marketing they received was never relevant to them, and 55% believe most of the organisations behind the marketing obtained their contact details without their consent.”[7] Ouch! There is a saying in the computer field — garbage in, garbage out. In other words, if data being analyzed is insufficient or inaccurate then the results are going to be flawed and inaccurate — no matter how advanced the analytics platform may be. So what’s so bad about poor targeted marketing? According to Gwynn, a brand’s reputation can be badly damaged. You might recall the story several years ago about Target sending a teenage girl emails about baby-related products. Her father was outraged. He wrote Target saying, “My daughter got this in the mail! She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” Turns out, she already was. There was a “creepy” factor associated with that story that, for a while, marred Target’s reputation. Anytime a consumer believes a company is misusing personal data, there is likely to be some negative fallout.




A few years back, the website crowdsourcing.org called targeted marketing “the Holy Grail of marketing!!” It went on to say, “Targeted marketing can be key to a more successful business and making the promotions, pricing and services even more efficient. … Attracting new customers or new subscribers to existing services or launching a new service can be an expensive proposition. Giving the ability to service providers to target the right customers with promotions or additional services, it is essential to identify subscribers who are most likely to respond positively to a product.” Half-a-decade later, marketers are still trying to come to grips with how best to use data to target consumers without crossing the line from being helpful to creepy. Cognitive computing platforms can help because they can provide context to the data. Even with the latest technology, however, companies need to be sensitive and responsible when dealing with a consumer’s personal data.


[1] Barry Levine, “Why B2B needs artificial intelligence,” Martech Today, 26 April 2017.
[2] Dan Telling and Jeremy Waite, “Taking steps towards cognitive computing,” MarketingTech, 28 April 2017.
[3] Logan Rosenstein, “The Growing Role Of A.I. And Machine Learning In Marketing And Customer Engagement,” Forbes, 3 May 2017.
[4] Nikki Baird, “Three Ways Artificial Intelligence Will Transform Online Shopping,” Forbes, 30 April 2017.
[5] David Weldon, “Providing personalized experiences at scale with AI,” Information Management, 20 April 2017.
[6] Staff, “Artificial intelligence in sales: usage, impact, examples and evolutions,” i-SCOOP, April 2017.
[7] Simon Gwynn, “Badly targeted marketing leads to distrust in brands,” Campaign, 27 April 2017.
[8] Kashmir Hill, “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did,” Forbes, 16 February 2012.

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