Acrisure: Increasing +20% net new revenue with an AI-First CRM

Brennan Martin -
💡
Founding Product Designer for Acrisure where I led the product strategy to develop a multi-tenant platform that utilized AI/ML to drive net new revenue by enhancing the prospecting capabilities of over 3,000 global insurance agents.

🚨 Problem Statement: Acrisure needed to enhance new business growth in a scalable manner by empowering producers to prospect more intelligently using advanced technology.

🧢 My Role: Principal Product Designer & Manager, responsible for the comprehensive design process to create a solution that enhances producers' abilities to identify and engage with potential clients effectively.

✨ The Solution: Developed an AI-driven multi-tenant platform that supports producers in effectively identifying and engaging with potential clients, thereby leading to increased new business growth.

🕑 Timeline: 2020 - 2022

Introduction

As the Principal Product Designer at Acrisure, I addressed the challenge of driving net new revenue by equipping insurance producers with advanced tools to enhance their prospecting efforts. The aim was to utilize AI and ML technologies to streamline and optimize the prospecting process across a decentralized global network of over 500 agencies.

Problem Statement

With over 3,000 insurance sales agents across the globe, I was tasked to help increase new business growth in a tech-enabled and scalable manner in an effort to replenish sales pipelines and help our producers prospects more intelligently with artificial intelligence.

Old Experience

Producer on their existing inefficient experience
“There’s really no structure to our process, we really just go website to website, to website to find new businesses and it's just so tedious. It's ridiculous.”

Challenges

Disparate Processes: Producers employed various, unstructured methods to prospect, leading to inefficiencies and inconsistent outcomes.

Lack of Data Integration: Without a centralized data system, producers struggled to access and use actionable business intelligence.

Time-Consuming Prospect Identification: Producers wasted excessive time on manual tasks to identify potential clients, which was often unproductive.

Opportunities

Tech-Enabled Optimization: Utilizing AI and ML to streamline and enhance prospecting activities could significantly boost operational efficiency.

Data-Driven Decision Making: Centralizing data to provide producers with actionable insights could enable them to identify and prioritize high-value leads more effectively.

Enhanced User Experience: Developing a user-centric platform with guided processes and strategic recommendations could minimize cognitive load and focus efforts on productive activities.

Hypothesis

By implementing a unified, AI-driven multi-tenant platform to streamline and enhance prospecting activities, we hypothesize that producers will see a significant increase in efficiency and effectiveness in identifying and engaging with potential clients. This will lead to a measurable increase in net new revenue growth as producers focus on high-potential leads and tailor their strategies based on actionable data insights.

Solution

Designed a comprehensive AI/ML-driven platform to enhance producers' ability to prospect more effectively.

This included:

Contextual Prospecting Tools: Enabled producers to search and filter potential clients based on specific attributes and insights, enhancing the quality of leads and engagement strategies.

Data-Driven Insights: Leveraged benchmarking data and claims information to provide producers with actionable insights, assisting them in tailoring their approach based on industry trends and client-specific data.

"Opinionated UX": Implemented a dynamic user experience that guided producers through a structured process of identifying and engaging with leads, minimizing time spent on unproductive tasks and focusing on high-potential opportunities.

Results and Impact

The new platform led to a 20% increase in new business growth for 240 weekly active producers.

Key improvements included:

Enhanced Efficiency: Reduced the time producers spent on identifying viable leads by providing a streamlined, data-driven prospecting process.

Increased Engagement: Improved the quality of interactions with potential clients by using AI-generated insights and recommendations.

Business Growth: Achieved a significant uplift in revenue, demonstrating the value of integrating AI into producers' workflows.

Next Steps

  • Plan to further refine the AI models based on continuous user feedback.
  • Aim to expand the platform's capabilities to include predictive analytics for even more precise targeting of potential clients.

Conclusion and Reflections

A few closing thoughts on this project:
  • Co-Create as much and as early as possible
  • Bad Data Is Bad - “Garbage in ➡️ Garbage out”
  • Ensure AI researchers are close to the research data and designs
  • Learn how the UX can help train the AI model
  • Work closely with the customer success teams to ensure the release notes and benefits are relayed accurately
  • Building relationships is more important than solutioning (especially for exploratory projects)

Future efforts will focus on enhancing the platform's predictive capabilities and continuing to improve the user experience based on detailed feedback and usage patterns.

Interested to hear more?

Schedule an interview with me here!