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AI is embedded in the platforms marketers use every day. In fact, recent research shows 88% of marketers use AI in their daily work. Consumers are also using AI tools to compare coverage, understand plan benefits, and evaluate plan options before ever visiting a health plan’s website.

Yet many health plans struggle to find the best ways to use AI in light of their concerns around compliance, privacy, and overall organizational risk. The good news?! AI doesn’t need to lead to a large-scale transformation or increased risk. Instead, health plans can apply AI in quieter ways to improve how existing marketing systems perform, primarily by strengthening the signals those systems rely on. Below are several examples of how this can be put into practice.

Improving media performance within platform constraints

One challenge health plans face when marketing, especially during the Annual Enrollment Period (AEP) and Open Enrollment Period (OEP), is controlling costs. Budgets are typically predefined; yet the cost to reach and convert prospects continues to rise, driven in part by increased competition during a compressed enrollment window. As a result, marketing teams must deliver strong performance within increasingly constrained conditions.

While today’s advertising platforms are heavily integrated with machine learning, audience targeting is limited. For instance, platforms like Meta frequently apply Special Ads Category restrictions to many health insurance campaigns. As a result, common targeting options like age-based audience selection may be removed. Plus, geo-targeting is often restricted to the county level. While these limitations are designed to protect consumers, they also make campaign optimization more challenging.

Fortunately, most ad platforms can be optimized toward conversion objectives (such as traffic, leads, or purchases), so choosing the right objective is critical. Without careful management, these platforms will identify opportunities to increase spending in pursuit of more conversions — often beyond what pre-established budgets allow. Therefore, it’s key to improve the conversion signals these algorithms rely on.

Improving attribution across multiple channels

Member enrollment rarely occurs after seeing one ad. Prospects often require multiple touches before making a decision (see “The Rule of 7”). Often, a prospect engages with an ad, visits a website and reviews key site content (such as the provider directory or formulary), receives a direct mail piece, and makes a phone call to ask questions before enrolling. In more complex journeys, a significant portion of influence occurs before a conversion is ever recorded. This can make it difficult to accurately attribute conversions across the full member journey.

To solve this challenge, GA4 is rolling out cross-channel conversion reporting. The reporting feature allows marketers to get a unified view of customer journeys across paid (and organic) marketing channels. This helps marketers understand how touchpoints work together, identify patterns across interactions, and determine which combinations of channels and messages are most closely associated with driving conversion events.

Generating actionable learnings before enrollment periods

In our industry, campaigns targeting people who are “turning 26” or “new to Medicare” during a Special Enrollment Period (SEP) often receive less attention than AEP- or OEP-related campaigns. However, SEP campaigns also offer valuable opportunities for teams to test messaging and channels without the same level of competitive pressure as AEP or OEP.

AI can help identify early indicators of intent — not just final conversions. For instance, AI embedded within GA4 (called Analytics Insights) can help measure visit recency and frequency, scroll depth, conversion path exploration, repeated site visits, and shifts in search behavior. These metrics aren’t new, but being able to query specific requests and effortlessly segment audiences would’ve been challenging for previous versions of the tool (i.e., GA3).

Through the use of AI, these modeled KPIs provide critical insights, such as identifying which campaigns are driving higher-quality leads or influencing future enrollment decisions, even if the conversion occurs weeks or months later. These insights, learned during a less-critical enrollment period, can help create an ongoing “learning loop” that can be applied during AEP or OEP when the stakes are higher.

Ensuring your website content is discoverable

As AI continues to change how people search for and evaluate health insurance options, the role of a health plan’s website is also evolving. More and more, people are asking questions within tools like ChatGPT, Gemini, or voice assistants (e.g., Siri, Alexa, etc.) and receiving summarized responses pulled from multiple sources. In many cases, this results in what is often referred to as a “zero-click search,” where the user gets the information they need without ever visiting the site itself.

This shift doesn’t reduce the importance of your website, but it does change how it should be structured and maintained. Rather than just being a destination, your website also functions as a “source of truth” these platforms use to generate answers. As a result, it’s especially important to ensure your content is clear, well organized, and easy for both users and machines to interpret.

In practice, many of the same SEO best practices still apply. This evolving approach is often referred to as Generative Engine Optimization (GEO) — see AI is Reshaping How Seniors Shop for Medicare. Can Your Marketing Keep Up? for more information. Write content in plain language, update it regularly, and structure it in a way that reflects how your audience asks questions. For example, build out FAQ-style content that directly answers common questions about coverage, benefits, provider access, and costs to make it easier for AI-driven tools to identify and surface your information. Similarly, use structured data, also known as schema markup, to help platforms better understand and categorize your content, which can increase the likelihood that it’s included in generated responses.

For health plans, this represents a practical opportunity to ensure your information is consistently and accurately represented, whether a member is visiting the website directly or encountering it through an AI-driven experience.

Identifying early signals of member attrition

AI also has applications within member retention. When paired with CRM data, it can help surface subtle shifts in engagement across channels long before a member actually leaves. Patterns might include declining open or click-through rates on core communications, reduced portal logins over a defined period, or changes in how often a member interacts with their plan.

For example, a member who suddenly visits their “coverage details” page more often right before AEP or OEP may be signaling dissatisfaction. Another potential signal is a member who consistently ignores wellness and preventive care reminders. In both cases, AI can flag these patterns so marketing and member service teams can coordinate outreach while there’s still time to salvage the relationship.

Recognizing these signals sooner allows teams to respond more thoughtfully and deliberately. Instead of increasing communication volume, they can trigger a single, relevant intervention: a call from a member service representative or a personalized email. After all, the goal is not more communication — it’s better communication.

Applying AI with discipline — and with DMW’s help

As AI becomes more embedded in marketing platforms, its most meaningful contributions in our industry will likely occur behind the scenes.

Even so, for health plans operating within complex regulatory environments, those subtle improvements can still yield measurable marketing results.

At DMW, we focus on helping health plans apply AI in ways that are measurable, compliant, and grounded in how marketing actually operates — enhancing performance without introducing unnecessary risk. Reach out today.