How AI Generates Irresistible, Tailored Bank Marketing Messages

Imagine a marketing message so perfectly tuned to your interests and needs you feel like it was written just for you. It’s not impossible to imagine that becoming the standard. Generative artificial intelligence can create content that goes beyond mimicking language and instead analyzes your preferences and communication style to create hyper-personalized content.

That level of hyper-personalization can be more persuasive. A recent study by Billion Dollar Boy. found that 60% of consumers prefer generative AI content over traditional content, and 81% of content creators saw more favorable engagement. That may be an eye-opening top-line number, but there are a few caveats: consumers recognize the benefits of generative AI in content quality and diversity.

Generative AI, with its ability to learn, predict and create tailored content at scale, is changing how consumers interact with brands, including banks. For bank marketers, this poses a question: How can the power of AI be harnessed to enhance customer engagement while navigating the potential pitfalls of this technology?

The Power of Personalization at Scale

Generative AI excels at analyzing vast amounts of customer data to deliver incredibly high levels of personalization. It uses transactional history, past interactions and even psychographic data to infer a customer’s personality and motivations. Then, with that information, crafts messaging that feels tailor-made. That level of deep personalization factors into why consumers may find AI-generated content more persuasive; it can create a stronger sense of connection and increase engagement.

“What AI brings to the table is the ability to personalize at scale. And that personalization can be anything from individual preferences and current situations to things that worked in the past,” Jakub Piotrowski, vice president of product at Bud Financial, told The Financial Brand. “We’ve been able to derive deep financial insights about individual customers so that any communication has the right context.”

For bank and financial marketers, that ability to personalize at scale is a new frontier. Previously, marketers could only get so far by segmenting customers and delivering slightly more personalized messages. With generative AI, deep personalization allows for targeted product offers, tailored financial advice and even matching the consumer’s tone through communications. For example, if the customer is annoyed or enthusiastic, AI can respond in a way that matches them, making interactions feel more empathetic.

This deep personalization is only possible with clean, well-organized data. Providing AI with accurate information about customer habits, goals and preferences can create messages that feel customized to the individual customer.

AI as a Tool to Augment Human Creativity

Instead of replacing human creativity, generative AI can become a powerful tool to augment and enhance it. Human-AI collaboration is key. AI can analyze data with unparalleled efficiency, providing bank and financial marketers with information and possibilities. However, marketers still need to leverage those insights, make strategic decisions and add empathy and emotional touchpoints that resonate with customers. AI is an incredible tool for precision marketing and improving efficiency, but it still needs oversight and accountability.

“This is an evolving area,” says Parmar. “So, the approach is machine-led, human-augmented; there’s always someone double-checking the processes and output.” Parmar notes that while generative AI is in the driver’s seat, so to speak, human governance still needs to be involved. It helps ensure a focus on building trust, identifying potential bias and maintaining ethics.

Additionally, human involvement in training, fine-tuning the models and prompt engineering is also critical; marketers need to set constraints and guardrails around what banks and financial institutions want marketing to communicate for better precision and control over the message.

“Generative AI is best applied to augment humans to become better at crafting and scaling quality,” says Magnus Revang, CPO of Openstream. “Marketing still needs to adhere to the fundamentals of relationship building no matter the tooling and still answer questions about what problems we can solve, what we do better than anyone else, and how can we generate demand and awareness as efficiently as possible.”

Ethical Considerations and Trust

While AI offers significant potential for bank marketing, it is important to address the pitfalls and challenges. Biases within data or AI’s interpretation of information could lead to inaccurate or misleading messaging, impacting trust and leaving customers with a negative impression of the brand.

“A side-effect of this is that generative AI is always confident in its wording. Confidently correct or incorrect, we want clear, concise output, not riddled with caveats and vagueness. Thus, it’s not surprising we prefer AI content,” says Revang. “However, while they predict what you want to hear. Nobody talks about what you need to hear or what is correct. People don’t necessarily prefer the truth or a complex, nuanced answer. Hence, there is a great risk that these models will oversimplify or even lie to please you.”

Clean data is the foundation of a successful AI implementation and goes hand-in-hand with human involvement in creating models. The quality of data directly impacts the quality of any AI output. Biased or inaccurate data can lead to AI systems that generate incorrect or even harmful information. So, cleaning and organizing data is critical for banks and financial brands.

“Surprisingly, financial institutions tend to underuse their myriad of data points. Fixing that and adding all useful context is essential for any large-scale AI application, says Piotrowski. “In our field, this translates to, for example, looking at financial transactions and trying to add multiple categories, merchant, location, regularity, and more tags to them so that an LLM can fully understand the individual customer and pick the right context.”

That context is important. Explaining how AI reaches certain conclusions or recommendations helps provide transparency, which is essential for building consumer trust.

Best Practices for Bank Marketers

Banks and financial institutions can use generative AI in various ways, but to capture AI’s full potential while ensuring responsible use, banks should adopt a strategic approach. Parmar and colleagues provide suggestions in Navigating the Gen AI era: Insights and strategies for the future of bank marketing, including:

• Pilot AI in specific marketing projects with clearly defined metrics to allow for evaluation and iteration to grow over time.
• Invest in data quality and prioritizing data management systems.
• Proactively develop guidelines for AI use in various marketing contexts and address potential biases and ethical concerns.
• Include collaboration with input from stakeholders in marketing, compliance and IT to ensure alignment on messaging, security and ethical concerns.
• Position AI as a tool to improve customer experience and service by providing tailored insights and personalized offers versus just promoting products.

Generative AI has the potential to impact how banks engage with customers. Tapping into the power of personalization means AI can deliver more relevant, helpful and persuasive content. However, success lies in taking a human-centric approach and not losing sight of building genuine connections with customers.

Reference source: The Financial Brand

Compiled by the DTSVN author group - Digital transformation solutions for the Finance and Banking industry.

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