In a business landscape marked by fierce competition and the diminishing effectiveness of traditional lead-acquisition approaches, marketing teams are going through a profound transformation in terms of both marketing strategies and technological innovation.

Within this landscape, many forward-thinking chief marketing officers (CMOs) have recognized the transformative potential of generative AI (GenAI). In fact, a recent BCG report indicates that 70% of CMOs have already integrated GenAI into their practices, with an additional 19% currently in the testing phase.

Marketing teams may face challenges in adopting GenAI, however, and this article will look at innovative strategies that merit consideration for overcoming these issues.

Content Creation

One of the most well-known use cases of GenAI revolves around creating content, such as images, text and audio. To succeed with GenAI in creating content, though, will require both human and machine intelligence and establishing new standards for content generation.

CMOs, for example, must establish boundaries regarding when it is acceptable to use GenAI for public-facing content, which may differ from how they use GenAI for internal content development. AI-generated outputs may be incoherent or inaccurate at times, or they might not meet a company's privacy guidelines.

Because of these concerns, the human touch remains a vital component of the content-creation process. Humans still must ensure the refinement and quality of the end product.

Tailored Product Recommendations

GenAI also creates an expansive opportunity for CMOs to expedite their product experimentation and launch marketing and sales campaigns.

Within the insurance industry, for example, leaders have access to invaluable data insights that can be harnessed to segment their target audience. This segmentation serves as the bedrock for informed decisions regarding the creation of new products or product bundles, tailored to the unique needs of each segment.

With GenAI, for example, marketers have now acquired the capability to dynamically test and fine-tune insurance product offers, concentrating their efforts on an audience that exhibits a higher probability of embracing these new products.

But this will take a carefully thought out strategy that should look something like this:

1. Collect Customer Data

• Aggregate zero-party, first-party and third-party data. This gives insights into customer preferences, historical behaviors, demographic attributes and reviews.

• Employ advanced machine learning to dissect this data reservoir, which can reveal customer intent and help to predict their latent needs.

2. Segment Customers

• Organize customer data into segments founded on the information amassed in the initial step.

• Use these segments as the foundation for tailored and targeted campaigns.

3. Design Tailored Campaigns• Craft campaigns and outreach strategies that align with the specific needs and preferences of each customer segment.

• Personalize content to ensure relevance and timeliness.

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