Embedding Audience Intelligence Into the Quote Journey to Prioritize High-Value Prospects in Real-Time

By Garry Rosenfeldt, Senior Principal, Analytics, Insights & Strategy at Allant

A Fortune 100 multi-line financial services and insurance organization came to Allant with a strategic challenge: how to better identify which inbound prospects were most likely to represent deeper, more profitable long-term relationships. The company believed there was a meaningful difference between prospects simply requesting a quote and those more likely to become high-value, multi-policy households, but it needed a scalable way to operationalize that insight.

Rather than purchasing and managing a full third-party prospect universe internally, the organization worked with Allant to build a more efficient model. Using AMP+, Allant developed a custom scoring methodology designed to evaluate incoming prospects against a defined “protector” profile – a proprietary indicator of a consumer’s propensity to value broader insurance coverage, engage more deeply, and potentially hold more products over time. The score was delivered on a 0–100 scale, along with a confidence measure that indicated how much underlying data was available to support each score.

Allant’s solution combined advanced analytics, practical deployment, and governance support. Instead of requiring the client to license an entire third-party data universe, Allant scored the broader prospect universe in advance and then returned scores only when a prospect entered the client’s digital quote flow. That approach enabled a far more efficient commercial model: the client paid only for the scored records it actually used, rather than absorbing the cost of a full-file license. In the working session, the team contrasted an approximately $4,500 monthly usage model with a roughly $400,000 full-universe licensing approach.

Equally important, Allant helped the client navigate a highly complex internal operating environment. The engagement required extensive back-and-forth with the client’s internal analytics and governance stakeholders, including multiple rounds of refinement to the model inputs and thresholds. What could have taken the organization up to two years to move through internal controls was completed with Allant in roughly four months.

To make the solution actionable, Allant integrated the scoring methodology into an existing API framework already in use by the client. This made it easier for the organization to operationalize the score across internal systems without having to build the scoring engine from scratch. Even so, implementation on the client side required coordination across six separate IT teams, a reflection of the scale and complexity involved. Allant’s ability to deliver a ready-to-use score through an existing integration path significantly reduced the burden on the client’s internal teams.

The scoring model itself was designed to support real business decisions, not just analytics for analytics’ sake. Higher-scoring prospects could be directed toward a more valuable treatment path, such as faster access to agents, improved service handling, or more strategic cross-sell and upsell opportunities across personal insurance lines. Lower-scoring prospects could be handled differently based on the client’s internal business logic. While the precise downstream workflows varied by business unit, the core value was clear: Allant helped the organization move from broad, undifferentiated prospect handling to a more intelligent and prioritized engagement strategy.

Over time, the program expanded beyond the original use case. Additional business lines within the organization began adopting the scoring approach, and monthly score volumes increased substantially as the solution scaled. The client and Allant also established a monthly benchmarking cadence to monitor score history over time and refine the model as more learning became available. While the team noted that quantified performance metrics were still largely anecdotal at the time of the discussion, early feedback suggested that higher-scoring consumers were moving through the process faster and beginning relationships more deeply than others.

Why this matters

This use case highlights a broader truth for insurers and financial services providers: valuable audience intelligence does not have to live only in campaign planning. It can also be embedded directly into operational workflows, helping large enterprises know more about who they are engaging, prioritize resources more effectively, and create better pathways for profitable growth. As Allant’s team noted in the meeting, the real win was not just the model itself, but the ability to operationalize it efficiently, compliantly, and at enterprise scale.