Marketing in the Age of Generative AI

Generative AI is bringing the future of marketing into the present, write Debdutta Choudhury and Raul V Rodriguez.

 In recent years, marketing automation has reached great heights thanks to the development of web-based technologies and social media tools. Artificial intelligence has also made several advanced manual processes obsolete, helping the marketer make sense of large amounts of data and make better-informed decisions.

Generative AI is poised to take this to the next level with its ability to generate text, audio, video, images, code, simulations, and more content. This system was popularized recently by Open AI’s ChatGPT, which is based on GPT 3 and 4 large language models. ChatGPT, Bard, and other generative AI systems are raising the game for the marketing industry in that they enable the development of more customized content.

Content creation is one of the more significant ways generative AI can be applied to marketing. Traditionally, marketers have relied on human creativity and labor to develop engaging and persuasive campaigns. Now, generative AI algorithms can generate tailored content that is both relevant and appealing to the target audience by analyzing vast amounts of data, including customer preferences, online behaviors, and market trends. For example, generative AI can be used to create personalized product descriptions or social media posts that cater to individual customer preferences. This level of customization and personalization can significantly enhance the effectiveness of marketing efforts, leading to higher engagement and improved return on investment (ROI). In addition, generative AI is transforming the visual aspects of marketing with realistic and high-quality images that create visually appealing advertisements and virtual environments that allow marketers to bring their ideas to life in previously unimaginable and newly creative ways.

Moreover, generative AI can analyze customer data and generate visual representations of customer segments or personas. This visualized data helps marketers refine their targeting and messaging strategies based on customer preferences and behaviors. To that point, by better understanding different customer segments, marketers can create more visually captivating campaigns that resonate with each particular intended audiences.

Another area where generative AI is making a significant impact is customer engagement and interaction. Chatbots powered by generative AI can simulate human-like conversations, providing real-time support and assistance to customers. These AI-driven chatbots can understand customer queries, provide relevant information, and even recommend products or services based on individual preferences. By leveraging generative AI in customer support and engagement, businesses can improve response times, enhance user experiences, and build stronger relationships with their customers.

Nicolaj Siggelkow and Christian Terwiesch of Wharton suggest three ways to use generative AI to reimagine the digital customer experience:

  • Focus on the customer, not the technology: Companies should focus on solving the customer pain points with the new technology using a three-pronged approach of Recognition, Request, and Respond that considers customer needs, uses algorithms to understand expectations, and then creates solutions. For example, generative AI algorithms can use past health data to determine a patient’s requirements and generate appropriate reports, send them to relevant doctors, and request an appointment based on urgency.
  • Focus on the learning: The entire cycle of Recognition, Request, and Respond should be repeated many times over to train and refine large language models.
  • Use technology to complement your capabilities, not substitute for them: Technological gains can be rapidly replicated by competition. Therefore, such technology should be used as an add-on to a firm’s competitive advantage, as a way to further sharpen their competencies.

Generative AI can be leveraged in the entire sales journey with interventions from the top of the funnel all the way to post-sales engagement. Traditional AI models have already demonstrated lead generation through web scraping – but now with generative AI, a more refined segmentation and targeting can be achieved. New patterns in very large datasets can be identified, creating new segments based on unique trends and once trained, generative AI can completely automate this process of identifying niche segments and providing them with hyper-customized content.

During the sales process, generative AI can assist the team in creating customer pitching documents including presentations and proposals based on past data and industry trends. Generative AI also can provide real-time predictive insights to the sales team during a deal negotiation process, and teams can follow up with customers via automated emails and support.

In fact, a recent McKinsey survey found that marketing leaders feel that generative AI will have the most impact in marketing processes when it comes to lead identification, A/B testing, SEO strategies, and personalized outreach. Other important factors are dynamic content, cross selling, analytics, customer journey maps, automation of workflows, and hyper-personalized sales training.  Essentially, executives feel that much, if not all, of their industry will soon be transformed by generative AI.

There are, of course, some challenges that come with using generative AI in marketing:

  • Data privacy: Generative AI tools often require access to large amounts of customer data and this can raise privacy and security concerns .
  • Accuracy: Because generative AI tools are not always accurate, this can lead to incorrect or irrelevant content.
  • Cost: Generative AI tools can be expensive to purchase and use, making them inaccessible to smaller businesses.
  • Environmental impact: Because generative AI is extremely power hungry, especially during the initial training phase, its use poses a sustainability challenge.
  • Bias: Any AI system has a built-in bias of its training datasets. One major concern, for example is that English is being pushed as the language of many large language models and this affects the digital capabilities of non-English speaking population.
  • Ethical: Generative AI’s ability to generate content indistinguishable from human-created content raises concerns about misinformation, manipulation, and intellectual property rights.

Considering all of the above, marketers must approach the use of generative AI in a responsible manner and ensure transparency and authenticity in their communications with customers.

Looking ahead, the future of marketing in the age of generative AI is brimming with possibilities. The continued advancements in this field will likely lead to increasingly sophisticated and nuanced marketing strategies. With AI-powered tools assisting marketers in creating compelling content, predicting consumer behavior, and optimizing campaigns, businesses will be better equipped to deliver personalized experiences and build meaningful connections with their target audiences in ways previously unattainable. The future of marketing is here, and generative AI is at the forefront of this transformative journey.


© IE Insights.


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