SmartAsset AMP Case Study: Helping Advisors Create Smarter Marketing with Generative AI

Introduction

Most  case studies will walk you through a polished solution. I want to start by showing you how I think. My background in development sharpened my eye for detail and user experience, but moving into product has strengthened my focus on turning research and A/B testing into actionable insights. With the SmartAsset AMP platform, that user obsession means putting financial advisors at the center of the experience. SmartAsset’s mission of helping people make smart financial decisions connects directly to giving advisors the tools they need to succeed.

Advisors are balancing compliance, trust, and client relationships. Marketing is not everyone’s strong suit, and in a digital world we need to think creatively about how to meet users where they are and form stronger connections. The question is what details we can extract to help advisors reach their clients more effectively, take some work off their plate, and show them we understand their challenges.

This case study explores how a generative AI tool inside AMP could give advisors a faster, more effective way to create content and connect with clients, while also improving the experience for the end user.

Note: Discovery and information cited and used were taken from WalletHub, BBB.org, and Reddit.com, along with informational videos on the SmartAsset AMP platform from SmartAsset’s YouTube.

The Problem

Advisors using AMP have voiced a set of consistent frustrations:

  • Many feel leads are low quality or duplicated, which means wasted outreach. (BBB.org and Reddit.com)

  • Marketing content is time-consuming to create, and most advisors don’t have the skills or bandwidth to produce it consistently.

  • Compliance adds another layer of risk, so even strong ideas often stall before they reach clients.

  • Outreach can feel generic or overwhelming for end clients, who then tune it out or lose trust.

If advisors cannot market effectively, AMP under-delivers. And if end clients feel bombarded or disconnected from outreach, the entire referral loop breaks down.

Testing & Validation

After discovery, I would move to structured testing.

  • Quantitative A/B Testing

    • Control group: advisors use AMP’s current outreach features.

    • Test group: advisors use the AI-assisted content tool.

    • Metrics: content output volume, open and click-through rates, client engagement, and time saved in content creation.

  • Qualitative Follow-Up

    • Advisor interviews to confirm whether the tool actually saved time and whether it still felt authentic.

    • Client surveys to capture how outreach felt on their end: personal and useful, or still generic.

The Solution

The outcome of this discovery would be the addition of a generative AI content tool integrated directly into AMP. The tool would not just generate text, it would guide advisors through the entire process of creating content that is compliant, effective, and personalized:

  • Content Templates
    Starting points for emails, blogs, social posts, and newsletters tailored to financial planning scenarios.

  • Compliance Guardrails
    Drafts flagged for risky claims or language, with suggestions for adjustments and required disclaimers added automatically.

  • Personalization Engine
    Messaging that adapts based on advisor specialties and client profiles. A retirement-focused advisor would see different tones and calls to action than one serving younger professionals.

  • Performance Feedback
    Campaign analytics that show how content performs, highlight successful variants, and suggest optimizations over time.

  • Advisor Control
    Advisors always review and approve content before it is sent. The AI provides drafts, but the advisor’s voice leads.

Discovery Approach

The first step would be discovery, not assumptions. I would focus on structured onboarding interviews, surveys, and lightweight experiments:

  1. Workflow Mapping
    Walk through how advisors create content today. Where do they get stuck, what tools do they use, and what parts of the process do they avoid altogether?

  2. Pain Point Prioritization
    Ask advisors to rank their top frustrations: time spent, compliance barriers, lack of inspiration, or low engagement. This ensures the MVP targets the problem that matters most.

  3. Content Effectiveness
    Review examples of outreach and ask advisors what feels authentic versus what feels flat. Explore how they measure success today and what success would look like with stronger support.

  4. User Preferences
    Gather feedback from end clients on what builds trust versus what feels like spam. Quick surveys and follow-ups could show whether they actually engage with email, SMS, or blog content.

  5. Early A/B Testing
    Even during discovery, I would set up lightweight A/B tests. For example:

    • Compare advisor-written emails with AI-assisted drafts.

    • Test short versus long content versions.

    • Track open and click-through rates across both groups.

Why A/B Testing Matters
What users say and what they respond to are not always the same. A/B testing lets us measure behavior directly, uncovering which formats or tones truly engage clients. By pairing interviews with experiments, we can validate assumptions early and ensure we build a tool that reflects real-world use.

Expected Impact

  • Advisors generate more content in less time.

  • Compliance risks are reduced before campaigns go live.

  • Clients receive outreach that feels more relevant and less repetitive.

  • Advisors report higher satisfaction with AMP, improving retention.

  • Measurable lift in engagement metrics, such as a 20–30 percent increase in email open and click-through rates.

Conclusion

This case study is about more than just adding generative AI to a product. It is about listening closely to advisors, being obsessed with the user experience, and understanding where issues and pain points exist, where the platform and content are working well, and how we can better support advisors in reaching their clients. It is also about exploring how AI can be used to tailor content to the diverse and unique needs of our users.

At the heart of this work is a duty to keep the trust of our users while also demonstrating how the platform can help them succeed, whether that means saving for a major purchase or investing for the future. It is equally important to keep the end client in mind, since their trust and engagement ultimately define success. By grounding this greenfield project in discovery, A/B testing, and continuous feedback, SmartAsset can create a product that delivers actionable value to both financial advisors and end clients.