The Integration of Artificial Intelligence in Digital Marketing and its Ethical Considerations
Ottawa, August 21 - I often find myself sitting at my desk creating content for hours, wishing that there was some sort of shortcut to increase my productivity. In the rapidly developing field of digital marketing, businesses have begun integrating artificial intelligence into their advertising efforts. This integration has induced an industry-wide shift that is revolutionizing how brands engage with consumers. However, this raises many questions about the ethics and legitimacy of designers who use this new tool in their work, creating an unnavigated struggle to keep the use of AI regulated. This journalist's goal is to explore these struggles and comment on how artificial intelligence is transforming digital marketing strategies fundamentally.
The role of artificial intelligence in marketing has increased over the past decade. Companies can now personalize and automate content, as well as use AI as a predictive analysis tool. Furthermore, artificial intelligence allows customization with the ability to recognize patterns from data through data mining, speech recognition, and facial recognition (Huang and Rust). For example, these tools are most commonly used as recommendation tools by streaming services such as Netflix, Disney+, Hulu, and Crave (Huang and Rust). Using personalization tools, AI can enhance customer engagement by recognizing and targeting consumers' emotions, enhancing the relationship brands have with their target audience (Huang and Rust).
Automated content has emerged as a powerful trend in digital marketing. The automation of content is being driven by the tools AI provides allowing brands to increase efficiency and scale content upwards. The automation process involves generating texts, captions, images, and videos, which all can be customized to meet the brand's standard and target audience (Islam et al.). There is a worry that with increased volume, AI-generated content will not align with brand values and might be lost. An argument against this thought is Google's use of GEN-AI and how it is revolutionizing content creation. Using GEN-AI as a case study the platform takes data given by businesses to create and enhance advertisements (Innovations in generative AI and marketing - think with Google). GEN-AI uses a streamlined advertisement management system the time it takes to make advertisements based on company data (Innovations in generative AI and marketing - think with Google).
As a predictive analytical tool, AI helps companies anticipate consumer behaviors, optimize marketing campaigns, and make decisions based on data. Predictive analytics can provide real-time insights. In a fast-paced digital world, trends emerge and dissipate rapidly, and AI’s ability to analyze data in real-time can be lucrative for many companies (AI & Predictive Analytics in Marketing - Expert Guide). This model was implemented in an email marketing campaign analyzing past purchase behaviors and browsing patterns, the AI system could predict which products individual customers were likely to be interested in. The information provided created highly personalized email content, resulting in a 20% increase in click-through rates and a significant boost in sales (AI & Predictive Analytics in Marketing - Expert Guide).
With all of the tools AI presents there are ethical considerations that must be acknowledged. Companies that use AI tools for digital marketing purposes must consider these ethical standards: transparency, data privacy, and Biases. Using AI for marketing solutions is a critical component of ethics.
It is this journalist's opinion that companies should be transparent about the capabilities and limitations as well as the risks involved with their AI tool. To achieve this transparency companies must be upfront about the data used to train their AI modes, algorithms, and decision-making process (Rivas and Zhao). In addition to remaining transparent, companies can not overstate the capabilities of their AI tools and provide realistic capabilities of what the AI model can and cannot do (Islam et al.). A recent incident occurred with Amazon showcasing how easily the use of AI can get out of hand. Amazon claimed they had a "Just Walk Out" checkout technology, which used cameras and sensors to scan each item (Amazon's just walk out stores relied on '1,000 people in India watching,' not AI). It turned out that the AI powering the “Just Walk Out” technology was one thousand employees from India sifting through camera footage (Amazon's just walk out stores relied on '1,000 people in India watching,' not AI), resulting in a prime example of when a company was not AI ethically to gain a competitive advantage, as it was practically not used at all.
Prioritizing data privacy is required for privacy law compliance. One common data privacy ethical recommendation is to allow users to have an opt-out option or delete history option, allowing users to have autonomy over their data (Rivas and Zhao). When dealing with personal data, companies to have a risk assessment to assess potential security breaches on consumers' data (Rivas and Zhao). The protection of personal data should be at the top of companies' lists when building an AI model, and building trust with its customers.
The final ethical consideration when building an AI model is to remove any possible biases. When building AI models a vast amount of data gets poured into creating one. When applying it to marketing solutions some biases can leak through. To keep the module ethical and thus applicable to real-world solutions, companies should actively identify and eliminate biases through monitoring and testing phases. Ensuring that content being created by AI models is inclusive, fair, and does not perpetuate any already existing biases (Staff Amazon's just walk out stores relied on '1,000 people in India watching,' not AI). The best way to ensure there are no biases is for companies to use a diverse and wide range of data (Staff Amazon's just walk out stores relied on '1,000 people in India watching,' not AI).
Vivienne Ming, executive chair and co-founder of Socos Labs once said "I think the future of global competition is, unambiguously, about creative talent, and I’m far from the only person who sees this as the main competition point going forward. Everyone will have access to amazing AI. Your vendor on that will not be a huge differentiator. Your creative talent though — that will be who you are... [use] AI as the tool that takes the busy work out. That is the company that wins in the end.” The integration of artificial intelligence in digital marketing is undoubtedly transforming the industry, offering tools that enhance personalization, automation, and predictive analytics. Adopting this new system into this industry comes with a variety of ethical dilemmas that should not be ignored. However, should companies address these issues, the implementation of artificial intelligence can greatly benefit any company that chooses to implement it.
Works Cited:
“35 Ai Quotes to Inspire You.” Salesforce, www.salesforce.com/artificial-intelligence/ai-quotes/. Accessed 20 Aug. 2024.
Digiquation. “AI & Predictive Analytics in Marketing - Expert Guide.” Digiquation, 19 Mar. 2024, digiquation.io/articles/ai-predictive-analytics-digital-marketing-guide/.
Huang, Ming-Hui, and Roland T. Rust. “A strategic framework for artificial intelligence in marketing.” Journal of the Academy of Marketing Science, vol. 49, no. 1, 4 Nov. 2020, pp. 30–50, https://doi.org/10.1007/s11747-020-00749-9.
“Innovations in Generative AI and Marketing - Think with Google.” Google, Google, www.thinkwithgoogle.com/marketing-strategies/automation/innovations-in-generative-ai-and-marketing/. Accessed 20 Aug. 2024.
Islam, Tasin, et al. “Transforming Digital Marketing with Generative AI.” MDPI, Multidisciplinary Digital Publishing Institute, 8 July 2024, www.mdpi.com/2073-431X/13/7/168.
Rivas, Pablo, and Liang Zhao. “Marketing with CHATGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology.” MDPI, Multidisciplinary Digital Publishing Institute, 10 Apr. 2023, www.mdpi.com/2673-2688/4/2/19.
Staff. “Amazon’s Just Walk out Stores Relied on ‘1,000 People in India Watching,’ Not Ai.” The Washington Times, The Washington Times, 4 Apr. 2024, www.washingtontimes.com/news/2024/apr/4/amazons-just-walk-out-stores-relied-on-1000-people/.