As consumers increasingly take the digital path to purchase, reaching them with the right offer at the exact moment they are making purchasing decisions is critical. This new age truism brought artificial intelligence (AI) into the marketing arena in a big way. Wesley Mathew, head of marketing at Meltwater, writes, “It’s not uncommon for artificial intelligence to come up in marketing conversations as a future focus. However, the irony is that many marketers are already using aspects of AI now, and don’t even realize it.”[1] He goes on to describe some of the ways AI is currently being used in marketing. “Examples,” he writes, “include everything from targeted and retargeted ads to propensity modelling, predictive analysis and chatbots. If you specialize in marketing or content creation, you’ve likely used email sequences based on user behavior or specifically targeted certain markets through popular media platforms. There are elements of artificial intelligence in each of these practices.” He concludes, “There’s no doubt that machine learning, AI and big data will continue to form a firm foundation for reaching consumers.” Nevertheless, he notes, “AI technologies can be costly to build and the initial price-tag might scare you. However, in the long run, we know that these bits of tech pay off. By automating a number of processes, machine learning can do the hard work for you.”

Does your marketing effort need AI?

Initial costs of implementing AI solutions and fear-mongering headlines about AI overlords taking over the world can discourage marketers from investing in cognitive technologies. As Mathew observed, however, “These bits of tech pay off.” If you wonder whether AI technologies can pay off for your marketing efforts, Or Shani (@Or_Shani), founder and CEO of Albert Technologies Limited, suggesting asking four questions.[2] They are:

Question 1. Does this problem really require AI? Shani notes, “Brands should have a defined problem set or desired outcome in mind before considering AI. Their challenge or objective should guide their AI journey — and reveal whether they actually need AI or not.” In other words, a business case needs to be made before investing in any technology. If you are unsure, a proof of concept project may be help.

Question 2. What does the machine do — versus what does your marketing team do? The answer to that question, Shani asserts, may rely on answers to three clarifying questions: “Do you want the AI to step-analyze data and provide you with recommendations you can execute on your own? Or do you want it to take actions on your behalf in pursuit of KPIs? What level of automation is sufficient to solve your workflow and scaling challenges?” If providing real-time recommendations to consumers isn’t important, then providing the marketing team with insights may be what is required. Shani notes, “An AI that helps with decisioning vs. an AI that makes and acts on the decisions in real time operates at two very different altitudes.”

Question 3. Will it play well with other systems and data? Shani observes, “AI requires massive data sets to perform best, so marketers will want to be able to integrate it with other systems, giving it access to many datasets created by their efforts.” A good cognitive computing system can handle both structured and unstructured data and can usually integrate with legacy systems.

Question 4. Who’s building it? Shani writes, “AI is very challenging to build, so it’s important to understand the DNA of the vendor’s team.”

The staff at CIO Review notes, “Modern businesses rely majorly on social media marketing to source leads for their marketing strategies.”[3] Here’s the rub. The number of social media outlets continues to grow making the marketing landscape ever more complex. AI can help. The CIO Review staff asserts, “Enterprises can use marketing automation to engage with social media platforms to prevent draining their resources. It allows businesses to post the same message across various media platforms from a central dashboard interface.” AI can provide other marketing benefits as well.

How AI can benefit marketing

We live in an age of personalization and targeted marketing. Karl Wirth (@wirthkarl), CEO and Co-Founder of Evergage, explains, “Today’s marketers are striving to deliver a relevant message to their customers.”[4] To accomplish that objective, marketers need to develop the right message for the right product and deliver it to the right consumer at the right time. While that may sound like an impossible task, Wirth explains, “While humans can’t communicate with large volumes of customers individually at scale, machines can.” He is referring to capabilities embedded in cognitive computing systems and he suggests five ways those capabilities can help marketers. They are:

1. Recommend the most relevant products or content. “Machine learning can synthesize all the information you have available about a person, such as his past purchases, current web behavior, email interactions, location, industry, demographics, etc., to determine his interests and pick the best products or the most relevant content. … And machine learning-driven recommendations are not limited to products and content. You can recommend anything — categories, brands, topics, authors, reviews vs. tech specs, etc.”

2. Automatically spot important customer segments. “Segmentation remains a valuable tool for marketers. With segmentation, you create groups of prospects or customers based on meaningful differences to better understand those groups. … A machine can help you identify segments you didn’t realize you had, and you can use that information to speak to those segments in a more meaningful way.”

3. Identify and act on potential problems. “Your marketing campaigns generate a lot of data. … All of those interactions generate immense volumes of data — so much data that a human can’t look at it all in a timely manner. It may not always be immediately obvious to you when something is wrong — when a link is broken or a promotional code doesn’t work. Algorithms can sift through all of that data, predict what should happen, and notify you if something doesn’t seem right.”

4. Deliver individually relevant experiences and offers. “A machine learning algorithm [can pick the most appropriate experience from a selection of available] experiences, in the moment, that it thinks will deliver the best results for each individual based on all the information it has available. It will learn from each of those interactions to inform the next decision it makes. The same approach can be taken with promotions and offers.”

5. Decide how to communicate with each person. “Instead of a batch and blast approach to email where you simply send everyone the same email every day, you can use a predictive score generated by machine learning to determine if sending this next email to this particular person will cause them to open, ignore, click or unsubscribe. If so, you don’t send it. Instead, you can wait until you have something more relevant to him or her.”

Concluding thoughts

Wirth concludes, “Machine learning offers the potential for marketers to interpret and act on large amounts of information in a scalable way. In a world where we constantly accumulate more data than we know what to do with — and where we desire to build individual relationships with our customers at scale — this is an exciting development.” Wirth only touches on the many ways AI can be used in marketing. Shani reminds us, however, that exuberance can’t be allowed to blind reality. He explains, “AI comes with a price tag, so it’s important for marketers to know exactly what they’re paying for.” Generally, it’s not difficult to make a business case for AI in marketing once all of the benefits are identified. Like most endeavors, doing your homework pays off in the long run.

Footnotes
[1] Wesley Mathew, “AI in marketing: separating the fact from the fiction,” The Drum, 9 April 2019.
[2] Or Shani, “Do You Really Need Artificial Intelligence? How To Decide,” The Marketing Insider, 31 December 2018.
[3] Staff, “How automated Marketing can help Business,” CIO Review, 5 March 2019.
[4] Karl Wirth, “5 Ways Marketers Can Gain an Edge With Machine Learning,” Entrepreneur, 26 March 2019.