Indiana Farm Bureau Uses Machine Learning to Enhance CRM with Predictive Lead Scoring

improving lead response time in Microsoft Dynamics CRM

Written By // Rob Wellen

Insurance
1,000 - 5,000
Indianapolis, IN

Indiana Farm Bureau Insurance knew that lead response time was critical to the success of their business. The company was employing a variety of strategies to target new customers but needed a better way to pull insights to help target the right prospects at the right time.

To significantly improve lead response time by developing a lead scoring model that could automatically assign a lead quality score to the thousands of leads coming in through Microsoft Dynamics CRM.

Indiana Farm Bureau Insurance (IFBI) was initially founded in 1934 to serve the unique liability insurance needs of farmers. Since then, the company has expanded to address the changing needs of their community by protecting people from all walks of life through insurance products for auto, home, business, and farm, as well as other banking and financial services products.

While the company remains dedicated to their founding mission of helping their local community protect what matters most, they also recognize that progress and innovation are key to supporting that mission in an increasingly competitive marketplace.

Meeting the Growing Needs of the Business and Community

In an industry where customers are actively encouraged to shop around for new policies year-after-year, Indiana Farm Bureau Insurance knew that lead response time was critical. As the firm continued to grow, it was becoming increasingly difficult for agents to keep up with the demand; and because not every lead is created equal, the company’s agents felt they were spending too much time following up on unqualified leads rather than cultivating relationships with real prospects.

While the company’s existing systems and processes were still profitable, access to advanced analytics and machine learning had become more accessible to companies of all sizes within the past few years. They knew that critical answers could be found through the massive amounts of data they had collected in Microsoft Dynamics CRM and other third-party sources, and they were looking for a partner who could bring the depth of knowledge necessary for incorporating advanced analytics into its Enterprise Data Warehouse and expediting their journey toward becoming a data-driven organization.

IFBI was happy to learn that SWC Technology Partners was not only one of just thirty Microsoft AI Inner Circle Partners in the world, but they were also located right in their backyard of Indianapolis. The company knew they were in good hands with a local partner who shared in their values and vision, while still having insider access to the top Microsoft data scientists in the world.

Machine Learning for Predictive Lead Scoring

SWC set out to develop a predictive lead scoring model that could automatically assign a lead quality score to the thousands of leads coming in through CRM. The algorithm would use data from their Microsoft Dynamics CRM database, as well as other sources, to identify a score based on a variety of pre-defined attributes. In order to develop a lead scoring model, SWC worked with various members of the Farm Bureau’s marketing and IT teams to identify what characteristics and requirements were needed to qualify a lead.

After understanding the business goals, SWC collaborated with Indiana Farm Bureau Insurance to build a predictive analytics model utilizing CRM and other 3rd party data sources. With the right data gathered and organized, SWC’s data analytics team used Azure Machine learning and a predictive analytics algorithm to accurately predict which leads were most likely to convert. The system would then automatically deliver a score to each lead so agents could quickly and easily sort through thousands of requests and respond to the top prospects in a timely manner.

We leverage our data to drive operational processes and timely decision making. The sheer amount of data available for analysis is often times the most daunting. Using advanced analytics tools such as Azure Machine Learning predictive modeling provides key insights that might otherwise elude us. Partnering with SWC data analytics experts helped us position our data for more informative analytics. Fine tuning the model was key as some initial insights did not provide optimal business value.

– Hong Gao, Director of Data Analytics at IFBI

Plans for the Future

With the successful launch of their predictive analytics model, Indiana Farm Bureau Insurance has seen a positive result in their pilot utilization of the model that significantly improved their lead response time. Predictive lead scoring has empowered the company’s agents to focus on building relationships with the leads that matter, leading to higher customer satisfaction and faster business growth overall.

Looking ahead, IFBI is excited about expanding a partnership with SWC – with plans to further integrate their predictive analytics model for even faster insights within Microsoft Dynamics CRM and develop visualizations for easy analysis through Power BI dashboards.

Understanding the marketplace is fundamental to our customer acquisition strategy. The dynamics across the state can vary widely based on a number of factors. Our sales teams employ a variety of strategies to target new customers and this new predictive model can provide key insights helping us target the right prospects at the right time. SWC was a great partner as they listened to our feedback to improve the overall scoring algorithm. IFBI needs tools that can learn as demands change, and Azure AI can certainly grow with us.

– Chris Coffin, Director of Marketing Systems at IFBI

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