The Top Applications of AI in Marketing
Artificial intelligence once seemed like it was reserved for science fiction; something people could imagine, but not actually use. However, AI has been around since 1955, and it is becoming increasingly commonplace. From Netflix’s recommendation algorithm to pocket-assistants like Siri, AI is an everyday tool—even if it doesn’t look like a robot.
One particular industry loving the AI trend is marketing. Many marketers and agencies are under the impression that as they expand their businesses’ or their clients’ digital reach, they will increase the conversion of some of those audience members to become customers. However, this does not always prove to be the case: Marketing Insider Group reports that for every $100 spent on driving internet traffic, only one dollar is used for converting said traffic to business. Likewise, surveyed marketers note an average conversion rate of less than 0.5%.
Increasing traffic does not necessarily mean growth. So, what is it that encourages consumers to become customers? Is it content? Calls to action? Searchability? How does the medium affect engagement? It can be challenging for human minds to discern the factors and make something of them—AI, on the other hand, can provide these insights quickly, as well as give actionable advice.
How Can Artificial Intelligence Benefit Marketing?
The impact of AI in marketing is extensive. How are marketers currently leveraging different applications?
1) Visual searching
Have you ever wanted to search for a product, but for the life of you could not remember what it was called? You type in whatever keywords you think are necessary, but the search engine pulls up a hodgepodge of disappointing results. Unlike text, visual searching can scan the internet for more accurate matches, using image data to connect you with what you are looking for.
According to Social Media Today, 90% of all information our brains receive is visual, so programmers are understandably intrigued to see if machines can similarly analyze images for shapes, lines, patterns, and colors. Pinterest and Google are notable pioneers in this space; 600 million visual searches occur on Pinterest every month, and image-based ads on the platform boast an 8.5 percent conversion rate. Visual searching is an example of how AI could greatly benefit consumers from their end—and the market is expected to reach $25.65 billion by this year.
2) Predictive analytics
Improved targeted advertising is desirable from marketers’ side of things. The consensus amongst marketers that Forrester surveyed is that predictive marketing can be defined as using AI to make more informed marketing decisions by anticipating which methods have a higher likelihood of success.
66% of respondents said that their marketing and consumer data comes from an overwhelming number of sources. With so much information to extract and no way to make sense of it manually, AI is necessary for deconstructing the components of a positive customer experience and predicting consumer behaviors that will allow marketers to act accordingly in a customer’s moment of need.
3) Enhanced personalization
Many consumers now expect personalization, which means marketers need to refine their efforts now more than ever. An Accenture study found that 43% of consumers in the United States are more likely to do business with companies that personalize customer experiences—as long as they do not compromise their trust. 91% of global customers from another Accenture survey noted that they are more likely to purchase a product from a company if it remembers their preferences.
Any company probably has too many customers to personalize each experience individually. AI, however, can suggest products to different shoppers based on their histories, generate targeted ads with improved relevancy, and even recommend content (to customers as well as marketers, such as social media posts that are likely to perform well). AI can aggregate data and act on it in ways human brains cannot, making it a practical tool in stepping away from generalized customer experiences.
4) Audience insight
You cannot market your products efficiently—or leverage certain AI applications effectively—if you do not know your audience. AI can assist with this as well: what are your customers’ preferences, even if they have not officially designated them? What channels do they use, and when are they online? What are their demographics?
Many analytics tools already provide such information, but AI-based resources can help you use this data appropriately to deliver the right message at the right time. When you know your customers inside and out—as long as the information they provide is consensual, of course—the need for marketing surveys is obsolete, and you can execute more effective campaigns and maximize customer retention.
5) Customer service via chatbots
Your business’s customer service representatives are human, so they can only respond to so many messages and interact with so many people at a time. From consumers’ point of view, many of them prefer not to interact with human service representatives at all if their questions are simple.
An AI phenomenon known as chatbots can help: they are quick, easy to use, and seemingly friendly, allowing customers to communicate with your business without picking up the phone. Chatbots are also time-savers for representatives so that they can tend to more serious issues and not worry about responding to queries when off the clock.
What are the Top Challenges Facing AI in Marketing?
Artificial intelligence is not without its shortcomings, though. As revolutionary as it may seem, it is still created by humans—and is thus subject to error.
Is AI Even Ready to Go?
Implementation itself is a barrier impeding mainstream adoption; while many applications incorporate machine-learning and the ability to act independently, there are still instances where they require human supervision and management. An Infosys study notes that 53% of surveyed organizations believe that developing their employees’ skills are vital before deploying AI internally. As such, these organizations feel that they require external help in order to benefit from it—and AI talent is notoriously scarce.
In addition, because humans still develop AI, it may inherit its creators’ biases. The founder of the Algorithmic Justice League and MIT scientist, Joy Buolamwini, published research that explored racial and gender bias in artificially intelligent facial recognition applications developed by major tech companies including Microsoft, IBM, and Amazon. When asked to guess a face’s gender, systems made error rates of:
- Less than 1% for light-skinned men
- 35% for darker-skinned women
If applied to a marketing context, biased AI attempting to use facial recognition to match people with products based on gender could result in offensive if not outright harmful encounters.
A Lack of Transparency
Another prominent issue AI in marketing faces is a lack of transparency. Applications that rely on deep neural networks can be immensely difficult to understand—and thus there is no straightforward way to follow said application’s decision-making process. How can we ethically and pragmatically employ artificial intelligence if we cannot fully understand how it arrives at the conclusions and predictions that it does?
As expert Yavar Bathaee writes for the Harvard Journal of Law & Technology:
“If an AI program is a black box, it will make predictions and decisions as humans do, but without being able to communicate its reasons for doing so. The AI’s thought process may be based on patterns that we as humans cannot perceive, which means understanding the AI may be akin to understanding another highly intelligent species—one with entirely different senses and powers of perception.”
Bathaee elaborates that if we cannot deduce an AI’s decision-making process, then we therefore cannot infer much about its creators’ intent or conduct—and even they might not foresee what conclusions the application will arrive at. This lack of transparency is known as the “black box problem;” AI’s inability to explain its deductions makes it a concept too impractical—and unnerving—to deploy comfortably.
The “black box problem” is relevant to marketing because marketers need to understand what their AI is telling them. If it predicts a high churn rate, what are the factors it is accounting for? If it says a B2B client is going to sever ties, why does it think so? And if an AI were to fail, how could marketers discern the reason if its calculations are hidden? Without a plain thought-map to read, AI can only help marketers improve their efforts to a limited extent—and possibly mishandle data without supervisors knowing.
The Future of AI in Marketing
AI’s place in the marketing world’s future is growing more substantial, but if we want its presence to be positive, it is essential to address its caveats. Experts like Joy Buolamwini are rightfully calling for complete transparency and accountability in AI; she notes that inclusivity in AI development and execution are integral to creating ethical applications that will not strip people of their rights under the guise of technological neutrality.
As for the black box problem, an innovation known as explainable AI (XAI) can illuminate the mystery of AI’s decision-making. Many developers are attempting to attach explanation capabilities to existing AI applications retroactively, but the results are proving unsatisfactory because the technology can only summarise predictions after they are made. The only current explainable machine learning technology that simultaneously provides both predictions and explanations is Similarity, which allows users to reap its analytic capabilities while following its thought processes.
According to Salesforce, 51% of 3,500 global marketing leaders use AI, and an additional 27% plans to start using it within the next couple of years. Business owners and marketers are realizing that AI is becoming increasingly necessary if they want a competitive advantage. However, AI never has the final say—that part still belongs to humans. It can predict patterns as accurately as possible, but people are ultimately the ones who decide what to do with said information. We need to be careful about AI’s shortcomings because marketers are the ones responsible for any consequences if they act on an AI’s recommendations.
Artificial intelligence is disrupting the marketing landscape. How it does so, however, depends on the technology itself and the humans that create and operate it. AI can make life easier for marketers as long as they can follow its conclusions, and it can make commerce easier for consumers, as long as they retain their right to data privacy.
AI is powerful—but for it to work the way we desire it to, it necessitates complete transparency.