Since the advent of online marketing, measuring and analyzing metrics has become essential for financial services businesses seeking to attract new investors. 

Tracking metrics like click-through rates, cost-per-click, impressions, and conversions have been central components of achieving marketing success. They’ve provided financial service firms with the insights needed to decide when, where, and how to promote their content, solutions, and services to prospective clients. 

The issue: the days of tracking those metrics could be ending. The increased use of artificial intelligence (AI) in marketing — from developing creative to making ad-buying decisions to getting found online — will change everything, including how we track success. Because AI does not do or process things as humans do, many of the methods and metrics used by marketers to predict behavior and judge intent are likely to become much less critical.

If traditional metrics are less meaningful, what should wealth and asset managers track to guide their marketing decisions and determine success? This guide explains what to consider in the future.

How AI Is Changing Marketing in the Financial Services Industry

Marketers are used to technology forcing them to rethink how they do their jobs. Search engine evolutions, social media, mobile internet, and the ability to conduct more and more business online — and now artificial intelligence — have all brought massive change to the industry and how marketers operate.

Artificial intelligence is not influenced by the next flashy corporate marketing campaign and instead focuses on individual needs and intent.

Click rates — a metric reflecting the percentage of people served a page in social media or through Google Search and clicked on it — will be less valuable when a zero-click environment allows prospective investors to do things like book an appointment without ever visiting a site. 

Similarly, impressions, the metric indicating how often an ad or listing is viewed, could count for less when people aren’t looking at ads or traditional search engine results, and are exposed to your brand in AI-generated summaries.

Bounce rates give marketers insight into when they are presenting information on their websites that isn’t relevant to an audience, isn’t very good, or is delivered through a poor user experience, causing visitors to abandon. AI agents don’t bounce because they’re bored or frustrated. They ingest everything available and move on, guided by their algorithms and training. 

Just as earlier marketing shifts required significant change, these metrics and perhaps others, such as time spent on page and social engagement (likes and shares), could go away or their importance reduced. So, what should financial service businesses monitor instead?

The Factors that Matter Now and in the Future

AI Overview (AIO) Citations are links to source websites embedded within Google’s AI-generated search summaries. Their purpose is to help validate the information included in them. These citations boost brand visibility and authority, especially because they appear at the top of search results, signaling trustworthiness to users. Unlike traditional links, they often cite from a wider, more diverse range of web sources rather than just the top-ranked pages. Appearing among them is a sign of respect.

Measuring AIO Citations involves tracking how often your brand is linked to within Google’s AI-generated summaries using tools like Semrush, Ahrefs, or Averi AI. It allows you to monitor visibility, citation frequency, and competitor activity. Key metrics include:

• Citation frequency: The total number of times your website domain is cited in AIOs in meaningful queries that could drive traffic to your site from financial professionals or prospective investors.
• Citation position: Whether your citation is first, in the middle, or at the end of the AI summary. Similar to traditional search engine results pages (SERPs), higher positions typically earn more traffic.
• Share of voice (SOV): This metric reflects how often your brand is cited compared with competitors for the same queries.
• Citation source diversity: The number of different pages from your site cited in AI summaries. This is an indicator of content quality, breadth, and depth.
• Content type performance: This will help you understand whether AI prefers your blog posts, lists, videos, or frequently asked questions (FAQs). Knowing this can help you optimize your content production.
• Brand mention sentiment: The tone, whether positive, neutral, or negative, of AI-generated brand mentions. For instance, is it referring to something your company published that it recommends, or to something to stay away from?
• AI referral traffic: Website traffic coming from AI sources such as ChatGPT, Gemini, or Bing Chat.
• AI conversion rate. Percentage of new contacts, investments, appointments, or any other results initiated through AI-based sources. This can help you determine whether your AI marketing efforts are performing better or worse than more traditional ones.
• Client or relationship acquisition cost efficiency: This metric can help determine if AI-generated website traffic is more efficient than traffic from standard marketing sources.

This is an initial list of AI marketing metrics you can track now with the right software. We will continue to offer up more as additional ones emerge.

AI Marketing Metrics: The Future

With the importance of traditional digital marketing metrics like impressions, clicks, click-through rates, cost per click, and bounce rates slipping because of the rise of AI, you must shift to the world of AI-powered marketing data.

Here are some other things to consider.

Focus on metrics that reflect content quality and structure. AI is more likely to prefer information it finds easy to parse and interpret. This means that measures such as how well-defined your schema is, and how scannable the information is, are worth tracking. AI prefers content that uses proper heads and subheads, is relatively easy to read and understand, and is scannable and not too dense. Monitoring these things will also help make your material more visible and actionable with AI.

Other critical things to track are indicators that signal brand authority and trustworthiness. Verified investor reviews, citations by respected financial industry resources, and compliance with recognized standards and certifications (such as accessibility for all website visitors, including those with limited vision or hearing) could become increasingly important factors. 

And as AI becomes more sophisticated and is able to understand brands more deeply, reliability is likely to be another factor in brand prominence in AI summaries. This means metrics that point to a business that values long-term client relationships and has low levels of churn could be useful to track.

Of course, it will also be essential to be able to tell how much marketing traffic and conversions happen through AI and whether it is of good quality.

Another important concept to measure and understand will likely be query match — an indicator of how closely your content matches the questions users are asking online. If your pages provide direct answers to common questions, AI agents could consider them more relevant. Tracking broader query levels will become more critical than monitoring simple keywords.

What is important is to shift from monitoring metrics that track human response exclusively to those that track AI response and AI influence on humans.

This is an evolving area. Get started with the tips and direction in this guide and stay tuned as we provide additional insights on the influence artificial intelligence will have on wealth and asset manager marketing in the weeks, months, and years ahead.

Want to learn more about how AI could help power your marketing? Schedule time to meet with Dan Sondhelm and the team today.