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Marketing and Artificial Intelligence

June 28, 2021


Artificial intelligence (AI) is having the same kind of impact in the marketing sector as it is in other economic sectors. In fact, the staff at CIO Review asserts, “Digital marketing is one the central applications of AI, and it holds exceptional future opportunities in the field.”[1] The staff continues, “AI promises predictive analytics, better customer experiences, and targeted marketing that will undoubtedly offer businesses higher ROI. AI is an essential ingredient in digital marketing capable of decreasing business operation costs, delivering improved customer experience and streamlining marketing tasks.” AI is changing the marketing world at such a dizzying pace that Aabroo Saeed, a writer and social media manager, laments, “Digital marketing trends are changing every day and there is no way to keep track of them.”[2] Nevertheless, she insists marketers need “to keep an eye on these trends because they are going to shape the upcoming days of the digital marketing industry.” Ingrid Burton (@ingridvdhburton), CMO at Quantcast, agrees marketers need to understand AI. She explains, “Marketers today have more data available to them than ever before. Applying artificial intelligence to that data is necessary to driving marketing effectiveness in today’s competitive digital world. When properly harnessed, AI provides insights that help achieve lower customer acquisition costs, greater lifetime spending per customer and better revenue outcomes in general.”[3]


How AI Improves Marketing Efforts


Before marketers can take full advantage of AI in their marketing efforts, Burton believes they need to know what AI can help them achieve. She explains, “[Marketers] should try to understand the advantages that AI provides. Let’s break down those advantages into three Ps: Patterns (AI can quickly detect patterns in vast amounts of data, allowing marketers to detect common customer attributes and understand segments of consumers); Preferences (AI can discern customer preferences, helping marketers serve up the right content for a given audience); [and] Predictions (AI can provide a better view of what might happen next, helping marketers determine things such as the next best offer or who will be the next new customer ahead of their competition). … By focusing on the three P’s, marketers can better understand how to apply AI to their marketing objectives, leverage its tremendous value and ultimately better understand their customers.” Louis Columbus (@LouisColumbus), a marketing and product management leader, suggests several ways AI and machine learning are improving marketing.[4] They include:


1. Marketing Performance. According to Columbus, “70% of high-performance marketing teams claim they have a fully defined AI strategy versus 35% of their under-performing peer marketing team counterparts. … 36% of marketers predict AI will have a significant impact on marketing performance this year.” Burton insists better performance is the result of better insights. She explains, “Making sense of massive data sets is difficult or impossible to do manually, especially for marketers that lack deep analytics or data science experience. But AI can provide clear, streamlined insights from data.”


2. Ad Generation. Columbus reports a recent study by Advertiser Perceptions found, “32% of marketers and agency professionals are using AI to create ads, including digital banners, social media posts and digital out-of-home ads.” Matthew Berman (@TheMattBerman), President of Emerald Digital, explains that AI can also help determine which ads to show consumers. He writes, “Programmatic advertising uses AI to help automate decisions about what ads to show to which people, so advertisers can save time and money on the process. AI can target customers whose behavior and demographic information matches based on information collected through cookies or other processes.”[5]


3. Customer Segmentation. Columbus writes, “High-performing marketing teams, and the CMOs who lead them, invest in AI and machine learning to improve customer segmentation. They’re also focused on personalizing individual channel experiences.”


4. Predictive Analytics. According to Columbus, “Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders. Combining all available data sources, including customer sentiment analysis using supervised machine learning algorithms, it’s possible to improve demand sensing and demand forecast accuracy.” Burton adds, “By using AI and ML to create models, marketers can gain a better view of consumer behavior in the future. Think about it as having a sort of crystal ball: an opportunity to identify the next best offer, next best action, next new customer, etc.”


5. Media Mix. AI can help you determine where to best spend your marketing dollars. Amazon marketers explain, “A media mix is the combination of communication methods in which brands can reach their desired audiences. … A brand’s media mix is important for total ROI and testing new campaigns. Having a diverse mix of media means a brand isn’t putting all its marketing or advertising budget in one place. So, if one method is ineffective, the other methods can help balance out the total ROI.”[6]


6. Actionable Insights. Columbus reports a survey conducted for Drift by the Marketing Artificial Intelligence Institute found the top three current uses of AI in marketing are: Accelerating revenue growth and improving performance (41%); getting more actionable insights from marketing data (40%); and creating personalized consumer experiences at scale (38%). Better insights mean better decisions. Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), report, “Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”[7]


7. Personalization. Burton writes, “When applied to large data sets, ML not only helps isolate patterns, but also discern preferences. Ideally, digital consumers would provide their consent and preferences so that they are served the products and services that actually interest them. But that isn’t occurring as much as you’d think. … AI is only as powerful as the data that feeds it. That’s one reason consumer consent is such an important topic for marketers to familiarize themselves with. … The more organizations can clearly prompt consumers to authorize use of their data while explaining how that data will result in a better experience, the more set up for the future those companies will be.” Columbus adds, “For high-achieving marketing organizations, achieving personalization-at-scale is their highest and most urgent priority based on Salesforce Research’s most recent State of Marketing survey. And McKinsey predicts personalization-at-scale can create $1.7 trillion to $3 trillion in new value.”


8. Campaign Management. Columbus reports, “Campaign management, mobile app technology and testing/optimization are the leading three plans for a B2C company’s personalization technologies.”


9. Financial Performance. According to Columbus, “Successful AI-driven personalization strategies deliver results beyond marketing, delivering strong results enterprise-wide, including lifting sales revenue, Net Promoter Scores and customer retention rates. When personalization-at-scale is done right, enterprises achieve a net 5.63% increase in sales revenue, 10.26% increase in order frequency, uplifts in average order value and an impressive 13.25% improvement in cross-sell/up-sell opportunities.”


Concluding Thoughts


“As AI matures,” Berman concludes, “marketers will grow more effective. Marketers will be able to easily use data to target consumers with the right message at the right time, and will be able to optimize and personalize ads to the individual. AI will help us to analyze data, learn from it to provide users with a better experience. We stand at the precipice of change.” Burton reiterates the fact that marketers don’t need to be AI experts, they just need to understand the basics to leverage AI and machine learning solutions embedded in many marketing systems. “Leveraging AI doesn’t have to be a byzantine process that’s only accessible to seasoned data scientists,” she writes. “Marketers and all business professionals can gain significant value from AI without being deeply technical. … AI and ML tech is already embedded into a number of marketing and advertising technology platforms and tools out there today.” As Saeed pointed out, the real challenge is keeping up with the changes technology is introducing in the marketing sector.


[1] Staff, “Revolutionizing Digital Marketing with AI,” CIO Review, 21 May 2019.
[2] Aabroo Saeed, “14 Predictions for the Digital Marketing Industry That Will Come True by 2022,” Digital Information World, 15 August 2019.
[3] Ingrid Burton, “Why Marketers Should Understand AI,” Dataversity, 27 April 2021.
[4] Louis Columbus, “10 Ways AI And Machine Learning Are Improving Marketing In 2021,” Enterprise Irregulars, 3 March 2021.
[5] Matthew Berman, “The Best & Worst Examples Of AI Use In Digital Marketing,” The Marketing Insider, 19 March 2021.
[6] Staff, “What is a media mix and why is it important?” Amazon, 24 February 2021.
[7] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.

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