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How gen AI will change bank marketing and the role of the marketer

Financial services marketing executive Abbas Merchant has been thinking a lot about fast-moving generative artificial intelligence (gen AI) and its role in changing how businesses engage with, inform and sell to consumers.

While marketers across industries no doubt feel excited, humbled and cautious about the efficiencies AI has and will continue to bring to their campaigns and product offerings, perhaps nowhere like financial services are the stakes this high. Regulation, reputation and fierce competition from fintech alternatives mean that AI, gen AI in particular, is no casual prospect for banks and credit unions.

BAI sat down with Merchant to explore the topic in a discussion drawn from his recent white paper. Plus, he offers marketing use cases to explore right now. Some answers have been edited for length and clarity. Please visit Merchant’s LinkedIn profile for the full white paper.

Artificial intelligence (AI) is not new to marketing, including financial services marketing. But with generative AI, a new world is opening up, and rapidly. What has changed in just a short time?   

AI has been used widely in many applications including but not limited to decisioning, targeting, ad serving, SEO, and attribution for a long time with strong results. In addition, AI has been effectively leveraged in optimizing content for email messages and display advertising (including message, imagery, call-to-action, and subject lines).

From personal experience, when I have combined AI (for content optimization) with multivariate testing, it has generated meaningful outcomes with 50% or greater increases in click-through or conversion rates. However, most of these applications have utilized “narrow AI,” which largely relies on classification and prediction capabilities. Nonetheless, AI has been very effective in improving our ability to predict consumer behavior through analyzing massive amounts of data and running permutations and simulations that have allowed us to evaluate numerous scenarios at a scale and with a speed that was not possible previously.

But now, the recent and rapid progress in the sophistication of gen AI, which utilizes large language models (also referred to as LLMs), vast amounts of data, and enormous computing power, has the promise to deliver an entirely new set of capabilities. It will allow marketers to automate parts of the creative process by transforming text into engaging audio, images, and videos that can evoke strong emotional connections within our target audiences. Until now, this was strictly limited to the domain of human creativity. Multimodal large language models (MLLMs) are already being introduced that have the ability to create content across multiple modalities including text, image, voice, and video. MLLMs can interpret and respond to a broader spectrum of communication by integrating multiple senses, such as visual, auditory, and more. This will enable interactions that are more natural, intuitive, and more human-like.

Thus, if AI allowed us to supercharge our left-brain applications (that require logic and calculations), then gen AI has the power to supercharge our right-brain functions (creative and artistic expertise). This new breed of AI (gen AI), when combined with automation and cloud computing power, is creating a wide set of new use cases that represent opportunities (and challenges) that marketers must now contend with.

We’d be wise to learn from the Greek myth about Icarus that it is important to understand the power and perils of new technology before deploying it and that once deployed, it is hard to control its implications. The recent developments in AI present us with similar opportunities and challenges.

You mentioned that innovations in AI bring both opportunities and challenges. What do you see are the big challenges financial services executives face?

AI and automation are giving us very powerful capabilities, yet our lack of ability to discern between fact and fiction is rampant. And the bad actors have access to these same powerful technologies and are using them to commit fraud. Therefore, marketers must address these challenges to maintain consumers’ trust.

Here are a few questions that we should consider to proactively manage risk and maintain trust in our brands:

  1. How can we be better prepared to proactively detect and prevent fraud in the first place?
  1. Then, how can we educate customers along with providing them with relevant and timely tools and insights so that they don’t fall prey to fraud and scams?
  1. And most importantly, how do we earn and maintain customers’ trust? This is critical so that they feel comfortable in sharing their information with trusted brands so that we can deliver more personalized solutions that better serve their needs.

Can you share a few examples of how marketers can use gen AI that go beyond a ‘chat’ application?

While there are many use cases, I will share two that I believe are achievable right now.

Use Case #1 – Content Development and Automated Deployment

Today, the majority of marketing resources and staff are dedicated to important but tactical and routine tasks. These include activities like writing content and copy, designing creative, managing campaigns, processing data, etc. These tasks are labor-intensive and time-consuming. In the near future, we will be able to streamline much of this work by leveraging the capabilities of new versions of gen AI technologies.

In particular, gen AI can be utilized to develop a good first draft of messages, content, copy, creative, and subject lines which will require our team to only review and refine, rather than generate it from scratch. Thus, instead of designing creative or writing content, which can be very time-consuming, marketers will spend much less time reviewing and refining the output created by gen AI technologies. The review and any necessary revisions by content creators and designers will be important to ensure that the content is accurate and reflects the tone, voice, and imagery consistent with the brand. This feedback loop is critical to help train the AI engine to ascertain the tone, voice, language, imagery, etc. that accurately reflects our brand.

This capability, when combined with marketing automation technologies (such as decisioning, dynamic content optimization, personalization, campaign automation, and testing), will allow us to do precision-targeted marketing at a scale that we have only dreamt of. Many marketing platforms already have native integration of gen AI within their applications such as Salesforce, Microsoft, and Sprinklr, to name a few. This will make gen AI capabilities more easily accessible and allow marketers to use it at scale rather than in one-off use cases.

Use Case #2 – Content Accessibility and Consumption

The social media channels (such as YouTube, Instagram, X, and TikTok) have trained users to consume information in short and engaging formats. Even those consumers who have the patience or motivation to read longer-form articles have a difficult time gleaning insights from these articles that are relevant to their specific situation. Therefore, it is no surprise that we see high bounce rates and short read times on many of these articles and web pages.

Imagine if we were able to feed these articles into a gen AI engine and make the content accessible in a question-and-answer format. In addition, these new technologies can convert long-form articles into easily digestible tips, infographics, and audio or video responses tailored to customers’ specific questions. Text-to-audio, image, and dynamic video conversion capabilities already exist (Google’s Gemini and GPT-4V by Open AI), but they will need to be integrated with the content management system to automate the creation and publishing of the content.

It will be important that our content creators and designers review, and stress test the responses produced by the gen AI engine to ensure that it is functioning appropriately (and not hallucinating) and provide feedback so that gen AI can become smarter. Further, real-time feedback from users, when combined with usage data on whether users prefer text, audio, or video formats can provide additional input to the gen AI engine so that it can, not only continuously improve its responses but also deliver in the formats that the users prefer.

This is an interesting use case as it represents a very low risk which is a major concern in the adoption of gen AI today. This is so because the content that the gen AI engine is trained on is already reviewed and approved by marketing, legal and compliance functions and represents the unique advice, guidance, and thought leadership of the organization.

In what ways can gen AI be leveraged for product innovation?

Gen AI platforms scour through vast amounts of shared social data, find patterns within different contexts, and provide relevant and valuable insights regarding a specific brand, product, service, or category.

These insights can be very valuable in understanding the challenges, concerns, preferences, and sentiments of specific segments of consumers as well as gleaning insights that can be useful in improving existing or developing new products or solutions. For example, a marketer can ask specific questions such as “What are the challenges faced by 25 to 35-year-old consumers in paying bills on time or their perceptions of a brand or a product’s performance,” etc. and so on.

This could be a very fast and easy way of doing consumer research and could augment the conventional approaches we use today.

How could gen AI change the role of marketers?

Instead of focusing on campaigns and creative briefs, the role of marketers will shift to creating strategic marketing briefs. The strategic brief will focus on key goals of marketing (not campaign goals) and outcomes marketers want to impact along with a specification of broad segments to target. The AI-powered marketing assistants will do all tedious tasks we do today and do them better by leveraging the data and optimization capabilities from design to targeting, channel selection, media placement, testing, measurement, attribution, and optimization all in a seamless manner. This will free up resources to focus on scaling up precision marketing efforts and strategic aspects of marketing.

It is important to note that tactical execution will continue to be important, as it is today. However, it will be enabled and assisted even more so by this new generation of technologies. The role of the content creators/writers, designers, and marketing operations will become even more important as we adopt gen AI, especially in curating, and validating content generated with assistance from gen AI.

What do you think are the implications of the efficiencies gained by adopting gen AI and automation?

One can argue that some organizations will take the efficiencies from leveraging these technologies as savings through staff reductions. While this may be true to some extent in the short term, this argument is not compelling in the long term. Technological transformations have spurred new industries and occupations that have created not only more jobs but also higher-paying jobs than before.

Our capitalistic motivations will compel us to invest more in marketing efforts that deliver attractive business results. Most importantly, today we already have the ability to demonstrate results from marketing more clearly and objectively than we have been able to in the past. This will continue to improve as we migrate more of our marketing to precision-targeted marketing efforts.

In conclusion, help us imagine the marketing departments of the future.

This shift will require CMOs to re-invent their operating model and re-train existing staff and or attract new talent that possesses a very different set of skills than today. This will also give rise to the notions of two related but distinct marketing command centers: 1) the strategic command center and 2) the tactical command center. The first will serve as the think tank and the second will operationalize the strategies and focus on a continuous stream of test-and-learn and optimization to drive greater results. This division will enable the CMO and marketing strategists to run through various scenarios and develop strategies that are based on a deeper understanding of customers as well as results of strategies and tactics and learnings from what has not worked.

This new order will value skills over credentials, a strategic mindset over a tactical mindset, curiosity over experience, and change over the status quo.

Abbas Merchant is a consulting senior financial services marketing leader. He most recently served as CMO and EVP of Regions Bank.

Originally Appeared Here

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