AI: The New Marketing Playbook
AI is here to stay — so let's dive into the ways the financial services industry can take advantage.
In 2023, we are expected to create 120 zettabytes of data. (For those that don't know how to quantify a zettabyte of data, it would take 5 trillion books to contain this information. If we stacked these books on top of each other we could travel around the world 316,911 times.)
In other words, we're facing an explosion of data — and it's only just begun: 90% of all data in the world was just created within the last 2 years.
This eruption of data creates a challenge for brands — and especially in financial services.
In the past, customers walked into a physical branch or insurance agent's office, and there were only so many options — and only so many signals competing for the customer's attention. Now, customers can read online reviews, scan product comparisons, and determine the place to get the best offerings even before they leave their couch. We compete on so many fronts to win each customer's attention, trust, and loyalty.
Today, customers search for — and find — more information than ever across an exploding number of digital touchpoints. Financial services and insurance brands need to keep all of this information correct and consistent, while also structuring it in a way that allows them to personalize the answers to complex customer questions online.
The good news? With rapid advances in AI, businesses have a new tool in their toolbox — provided that they can learn how to properly take advantage. Today, large language models (LLMs) can answer sophisticated questions and provide information to resolve problems.
1. Answer more user questions — by structuring your information in a way AI can understand
In today's world, much of the data financial services organizations produce is designed for specific channels (social, platforms like Google Business Profile, etc.) — and it's not "meaningful" or intelligible for other channels like web, chat, voice, and more. There is no way to reuse this information for new purposes because none of the technology talks to other systems. (For example, banks power ATM data on Allpoint, but they don't have a great way to syndicate that information elsewhere, like on third-party sites such as Google.)
Instead, organizations need a meaningful way for AI systems to understand their business and answer complex questions — which is not possible by just indexing a webpage for keywords.
With the rise of conversational AI, the opportunity for organizations to speak to consumers and/or their employees will move beyond the indirect world of updating these third-party platforms, to a more direct, two-way interaction. But in order for this to happen — i.e., for conversational AI to be able to answer business-related questions — it needs to be able to draw on all of a business' facts and information in a centralized, organized place.
In other words, businesses will need to organize their information into their own CMS — which can then be combined with a conversational AI interface. The CMS serves as a platform that is designed to "feed" answers to AI systems that can actually "converse" with customers — while still allowing businesses to maintain human control and oversight over exactly where each answer originated. (When we talk about "AI systems" here, we're talking about the wide variety of first and third-party experiences, like social sites , voice assistants, mobile, web app, chat interfaces, and many more — that consumers interact with every day.)
Here's an illustration of information structured with bi-directional relationships so that AI interfaces can "understand" it and draw on it.
With this understanding, organizations can start to organize their information more effectively — storing it in a way that allows them to engage in conversations with customers across different channels. The future is technology that can talk back to people and other systems.
The good news? Once you have your information organized this way, managing and updating it is an order of magnitude easier to maintain than with the "old" way, with facts siloed across channels.
By storing all of the content about your organization in a CMS that is well connected via entity relationships, not only will AI models be better equipped to answer questions about your brand, but content updates will be seamless and easier than ever.
(For example, with Yext, if you delete a page — e.g. a Loan Officer Page — you won't have to clean up broken links to other webpages like branch pages. The identity provider system can notify the graph of updates, so humans don't need to delete or/add this type of content. Click here to learn more.)
AI can also answer hundreds of complex questions from a simple graph. For example:
1) Find a mortgage officer near me that is available now? (Intent to find a mortgage + location intent + time intent)
2) Best mortgage loan officer near me? (Mortgage intent + reputation intent)
3) Can you send me the driving directions of a branch? (Branch intent + location intent)
As you think about optimizing your digital experiences for conversational AI, below are some simple steps for the AI-empowered marketer:
1. Think about the questions your customers have — and what entities/content you need to be able to answer these questions:
Products/services: this could be credit cards, insurance products, or offerings like wealth management for high networth vs mass affluent
Branches: you could have different types of branches (ex. digital branch, micro branch, full service, advisor, speciality branches, popup branches)
FAQs: often this is how-to information like "resetting a password" or "signing up for online banking"
Financial professionals: mortgage, wealth, business bankers, insurance agent, etc.
Announcements: merger, new branch openings, catastrophic events (for example, when branches need to close down in certain areas during a hurricane)
Offers: they are usually associated with a product or territory
Disclaimers: broker checker, state licenses, SEC, FINRA, etc.
2. Map out what data you need to know about these people, places, and things: phone numbers, addresses, hours of operations, reviews, languages spoken, rates, product descriptions, images, license numbers, and lots more.
3. Identify where you will get all of this information: ERP, LOS, Core Banking Platform, or your CMS — to name a few options
Now that we understand all the facts about your business — and why they need to be structured in a way that can be combined with an AI interface — let's explore the value of new AI possibilities for marketers, like content generation.
2. Take advantage of content generation
If you take a look at the Harvard Business Review's definition, content generation refers to the process by which: "large language and image AI models can be used to automatically generate content, such as articles, blog posts, or social media posts. This can be a valuable time-saving tool for businesses and professionals who create content on a regular basis."
Essentially, AI models today are powerful enough to serve as advanced "writing assistants," creating informative, readable copy in a matter of seconds — provided they have the right inputs. (You probably already know this if you've ever played around with the likes of Jasper or ChatGPT).
That said, there are several important considerations for financial services organizations looking to adopt an AI content generation strategy. Two key starting points are:
Determining the types of content that suit an AI approach
Ensuring that you're basing content generation on fact-based inputs that you control
If your organization needs to spin up simple, digestible content quickly — but lacks the bandwidth or resources to do it the "old-fashioned" way — AI content generation is an incredible asset: it's quick, cost-effective, and scalable. Further, it can work with your current approach to creating content as an accelerator or a supplement — not a replacement.
There are certain types of content that are best suited for AI content generation. Content generation works wonders for creating copy for financial professionals' bios, event descriptions, FAQs, and sometimes even blog posts. (Essentially, shorter, informative assets that are fact-based, rather than opinion-based.)
The second piece of the equation is that the inputs used to generate the content — the names, dates, essential facts, and more — need to be 1. controlled by your business and 2. intelligible to AI systems (just like we talked about in section one — it's all connected).
It's with both of these pieces in mind that we launched Content Generation at Yext.
To get a little bit more specific there, let's look at some of the use cases within Financial Services for AI Content Generation.
Let's dive into one example: generating bios for financial professionals. Traditionally, this process can be tedious and time consuming.
But when your information is stored with Yext —in the way we described in section one — we have all of the information about the languages a professional speaks, the communities they serve, the products they are licensed to sell, their address, hobbies, and who they are targeting (like business owners, or high-tech individuals). These facts can be entered into AI Generative Models to generate a bio in seconds for thousands of financial professionals.
Example inputs: :
- Speaks Spanish, Zip code 95050, Award President club, Product - business insurance, Jane Smith, 12 years of experience.
"Jane Smith is a highly accomplished insurance agent, known for her fluency in Spanish and expertise in serving the Santa Clara market. With 12 years of experience in selling business insurance, Jane has earned a reputation for providing top-notch insurance solutions to her clients. She is also a member of the President Council, a prestigious group of business leaders."
This is just a simple example, and the capability exists for fine-tuning to a specific brand voice or to understand brand names. Click here to learn more.
One important caveat: All content generated should be reviewed before publishing, maintained in books and records, and have review dates based on the risk of the content. For example, information about products, services, or financial advice should be reviewed more regularly.
3. Scale your business with conversational AI
We've already outlined why your business needs to organize your information in a way that can be combined with an AI interface.
Which interfaces exactly? There are more every day, and no organization should limit itself to the technologies of today. That said, there are two present technologies that businesses should prioritize in order to deliver great conversational experiences: chat and search.
At their core, chat and search both enable businesses to provide answers to customers who are looking for information in an interactive way.
But they each excel in different areas — and they can best be used as compliments to each other. In fact, chat needs to rely on a search algorithm in order to find relevant, up-to-date information. (This is why, for example, Bing's chatbot can answer questions about current events while ChatGPT only knows about the world up until 2021.)
Search is better for browsing experiences. (For example, you wouldn't want to use chat as the primary interface to your branch/location locator. Those are visual browsing experiences, ideal for search.)
Search makes it easier to merchandise/hard-code results based on business logic. This is quite difficult to do in chat — though not impossible.
Search can have UI widgets like facets, filters, sorting, and maps that help your customers navigate content.
Search is better at personalization with zero-party data. As consumers land on your website and engage in survey-like experiences, based on their responses you can provide a curated list of the most relevant products, people, or content they are looking for.
The user inherently expects chat to be a slower, more conversational experience. A longer anticipated response time means you can use bigger, more powerful models to generate more detailed answers. (For financial services, there are use cases here for support, marketing, and workplace.)
Chat can take the history of a conversation into account. This means users can ask follow-up questions and dig deep into certain topics. (E.g. chat could help answer a customer's question about a branch they visited last month, or a credit card they asked about previously.)
Chat is capable of synthesizing multiple search results to provide an answer. For example, someone could ask "What's the difference between a high-yield savings account versus a money market account?", and chat could tell them the answer by looking at two different documents.
Additionally, many digital experiences in the future are going to blur the line between search and chat, combining the best aspects of both. Many search engines like Bing are already announcing new features that combine search and chat into a single interface, allowing users to have a free-flowing conversation about search results.
We've written before that the "right" technology to focus on between search and chat is… both. That's truer than ever: there are massive benefits to each that businesses need to start incorporating into their strategy.
Businesses can't afford to lose any opportunities to drive engagement based on poor experiences, and it's important that they leverage AI to communicate with customers the way they want. (Here's a fun stat: 74% of users say they prefer chat while looking for answers to simple questions.) Embracing conversational AI is critical to gain a competitive advantage.
Curious about Yext Chat? Click here to learn more.
Just like the 90s were the dawn of the web, today is just the beginning for AI. But savvy financial services marketers will embrace AI today to generate content and answer more customer questions.
At the end of the day, AI won't replace marketers — but marketers using AI technology to give their customers what they want will replace those who don't.