Artificial intelligence (AI) is no longer restricted to science fiction, now being actively employed in several industries. The automotive industry, for example, is implementing AI to design self-driving cars for convenience, eliminating crashes caused by human error, and reducing carbon waste. In marketing, companies utilize AI to maximize profits and gain valuable information on consumer trends.
Why is AI used in marketing?
AI is valuable in marketing because it can synthesize vast amounts of information in an efficient manner, with tests showing it capable of outperforming humans in tasks such as financial trading. Using sophisticated algorithms, AI can swiftly analyze storehouses of consumer data to predict future market or individual consumer trends, for instance. AI can then create customer profiles or predict future spending, which in turn can generate more targeted advertisements. In other words, AI will use available information on a consumer, like past online transactions, to create potentially more relevant product offers. This is why if you have a history of purchasing clothing online, you might see more advertisements for clothing or even jewelry since AI might associate purchases of clothes with accompanying accessories.
Trends and Predictions for AI Use in Marketing
Hoarding of Consumer Data Creates Potential Privacy Threats
Consumer privacy is one area of concern in the use of AI in marketing, especially in an online age. Internet service providers (ISPs), websites, and mobile apps collect and share vast amounts of data on consumers. In the United States, there are currently few comprehensive laws that regulate the collection and selling of this data in comparison to other developed countries. With few caps on the amount of data collected and the length it can be kept, AI automatically increases the value of data for marketers.
As expert Ginger Zhe Jin explains in “Artificial Intelligence and Consumer Privacy,” AI creates more incentive for buyers to collect and store more consumer information. Namely, since sophisticated algorithms can quickly analyze large amounts of consumer data, the more data there is to analyze, the more valuable information there is for marketers to use. This encourages greater collection of data to be stored in larger places. While beneficial for data buyers, the existence of these large storehouses is a threat for consumer privacy: they are enormous potential sources of data for scammers and hackers to access.
Enacting new online consumer privacy laws might not have much effect on data breaches if they place the responsibility of ensuring privacy mostly on consumers. Since consumers are the ones more likely to be hurt by a security breach, there are fewer incentives for companies to change their practices unless required by law. Until there are more restrictions on how much data buyers can collect and/or how long it can be stored, it is likely that this largescale hoarding of consumer data will continue.
Assessing Potential Financial Risks
Developments in AI technology could lead to better identification of fraud or potential investment adviser misconduct. One 2017 report from the U.S. Division of Economics and Risk Analysis (DERA) claims that back testing showed AI to be five times better at recognizing potential fraud than at random, although this came at the risk of greater false alarms. This means that AI could help predict patterns of fraud or potential investment adviser misconduct. Yet given its current state of development and inability to enforce laws, AI still needs to be paired with human interactions for optimal use.
Optimizing Customer Service and Building Brand Loyalty
With the global AI market expected to exceed $70 billion by 2020, it is likely that companies will continue to pour more resources into developing AI technology to better customer experiences. AI, while not yet a perfect replacement for all human interactions, is becoming more common in customer service. Research from PwC’s Consumer Intelligence Series found that many people prefer a human-AI hybrid approach to customer service. 43% of millennials and 28% of business executives surveyed said they would pay for a hybrid service as opposed to a humans-only service. Conversely, 35% of those surveyed said their largest concern with using AI-only customer service (e.g., a chatbot) was losing “a human touch.” Until AI advances and becomes more widely accepted, some predict that blended approaches will be best for optimizing customer satisfaction.
Conversational interfaces and AI advertisements are useful for more than just customer service convenience, however: they can also help build brand loyalty through connecting directly to customers. Companies can now create more interactive ad campaigns to help build brand loyalty. Watson Ads, for example, are a sort of augmented intelligence that companies like Toyota and The Weather Channel have used to create immersive advertisements to better engage audience members, creating a more individualized customer experience in the hopes of connecting audiences to their brand.
Of course, AI development could quickly get out of hand without implementing ethical practices. IBM, which owns Watson, announced last year its long-term $240 million investment plans to create the MIT—IBM Watson AI Lab with MIT. This research will include attempts at advancing AI to deep learning, along with exploring ethical implications of AI use. The European Commission has also announced plans to invest in AI through 2020; part of this plan includes establishing ethical and legal guidelines. Hopefully future investments into AI technology continue this trend of incorporating ethics conversations, although we are still far from some comprehensive global standard of ethical AI use.
