Artificial intelligence (AI) is term that covers a lot of technologies such as cognitive computing, machine learning, deep learning, and, occasionally, robotic process automation (RPA). One modern aphorism states, “AI sells but machine learning delivers.” Marketers are interested in both selling and delivering and most of them are convinced AI-related technologies represent the future of marketing. Tod Loofbourrow (@todatmit), CEO and chairman of ViralGains, writes, “Here’s a quick reality-check for the next artificial intelligence pitch you hear: Ask what the company’s solution optimizes for. If the answer is along the lines of ‘anything you need,’ that should raise a red flag. AI doesn’t work that way, but it’s ad tech’s favorite new buzzword, so you can understand why marketers say they’re prioritizing a technology that few understand.”[1] In previous articles, I have underscored the importance of knowing exactly what business problem needs addressing so the right technology can be used. This is as true for marketing as it is for any other business process.
Marketing challenges addressable by AI
Loofbourrow asserts AI-related technologies, like deep learning, are great for recognizing cats on the Internet; but, he believes the advertising sector has more complex challenges. “Advertising challenges,” he writes, “largely relate to issues of culture and human psychology. These aren’t the narrow and deep problems of cat recognition and automated driving — because people are complicated, and our goals are both fluid and difficult to articulate. … But advertising does present some goals that are ripe for AI disruption. The key is that you need a clear, unambiguous, and consistent business goal to tie to AI. Purchases are the ideal business goal because they’re binary — people either buy, or they don’t. With purchases as a fixed starting point, machines can discern patterns that elude humans, and from those patterns advertisers can mine a wealth of actionable insights. Taking it one step further: purchase intent also represents a good AI challenge; but, of course, there are more variables in this instance than in measuring a purchase, so it quickly becomes more complex.” When I read or hear the word “complex” I immediately think cognitive computing may be able to help. Below are some of the ways cognitive technologies can help marketers:
Targeted Marketing. Marketers are always trying to improve conversion rates and getting to know customers better is the best way to achieve that goal. Cognitive computing platforms can help marketers gain a 360° view of potential buyers. Cal Ó Donnabháin (@CalODonnabhain), asks, “Isn’t it an all-time wish for every company to see through the minds of their customers and find out their preferences?”[1] He asserts artificial intelligence can help marketers better understand their customers’ preferences. He explains, “Artificial Intelligence, for all the right reasons, is being employed by many companies to find out exactly what their potential customers are looking for and give it to them at the right time. On the other hand, AI is also used by companies to create a whole new set of target customers that sometimes they never knew existed!”[2] Loofbourrow adds, “The success of an advertising campaign depends on a strong understanding of business objectives, a great match between the ad creative and the intended audience, and a clear understanding of the context in which consumers see your ad. Failure in any of these areas will lead to the failed use of AI. “
Lead Generation. The staff at Business Matters writes, “Machine learning helps to drill deep into user’s data and runs causal models from observed data to foretell if a client could be a potential buyer.”[3] Carl Landers (@lead2rev), Chief Marketing Officer at Conversica, adds, “Companies use AI to identify sales prospects, nurture customer relationships and, ultimately, drive sales. This approach to lead management is built on a traditional funnel that drives suspects to engaged leads, to Marketing Qualified Leads (MQLs) and then into Sales Qualified Opportunities (SQOs), leveraging technology to improve quality and conversion rates throughout.”[4]
Product Pricing. Chris Pitt (@Pitty_C), head of marketing, Vertical Leap, writes, “Some say marketing is an art. Sure, being creative with your slogans, copywriting and other marketing collateral is important but the ‘art’ component should not extend to areas where it does not belong. Creatively brainstorming your product pricing strategy or your PPC spending is never a good idea — science needs to applied.”[5] Cognitive computing can help with pricing strategies, including trade promotion optimization. Pitt adds, “Discounts, loyalty bonuses and promos — customers love these perks. Businesses, not so much as consistent ‘low balling’ can negatively impact the bottom lines and dilute the brand value. … Machine learning comes to the fore again. Regression techniques in machine learning allow you to estimate how every possible price configuration will impact your revenue and sales forecasts. After churning available data, the algorithm will choose to display the optimal price based on different variables.”
Recommendation Services. Most people are familiar with recommendation services. They see pop up ads while Surfing the Internet that correspond to recent searches and companies like Netflix recommend movies to watch based on past viewing habits. Done right recommendation services can be very helpful to consumers. Done wrong it can become annoying or creepy.
Content Optimization. The Business Matters staff writes, “Machine learning systems can see the content that’s attracting more user attention helping you to make better decisions as far as buying online content is concerned.”
Media Buying. Pitt notes reinforcement machine learning “algorithms are now being tested for cross-channel marketing solutions and real-time bidding in display advertising.”
Concluding thoughts
Loofbourrow concludes, “As with any tool, the key to AI is how and where advertisers put it to use. If well executed, it can be a powerful tool that revolutionizes the way advertising works. If not, it will lead to the continued spread of irrelevant ads and breakdown of consumer trust. The choice is ours as to which way we decide to go.” Pitt adds, “Marketing analytics has become granular, tracking each and every insight imaginable — from social media sentiment expressed by some micro group on Twitter to your website’s technical performance, benchmarked against fifty other competitors.” With myriad ways to leverage cognitive technologies, successful marketers will identify specific business challenges and figure out how cognitive technologies can be applied to overcome them. As the Business Staff puts it, “From improved optimization, laser focused targeting, better placement of Ads, and ultimately lower costs, the gains that machine learning brings to businesses can be huge.”
Footnotes
[1] Tod Loofbourrow, “Marketers Know AI Is the Future, But Do They Understand AI Today?” MarketingProfs, 30 August 2018.
[2] Cal Ó Donnabháin, “Targeting the Right Customers using Artificial Intelligence,” Irish Tech News, 18 may 2018.
[3] Staff, “Machine learning & digital advertisement,” Business Matters, 10 October 2018.
[4] Carl Landers, “The Future of Lead Engagement is Now, Thanks to Artificial Intelligence,” MarTech Advisor, 17 October 2018.
[5] Chris Pitt, “How machine learning is helping marketers get the edge,” The Drum, 16 October 2018.
