AI Agents

Context

Built for insurance within the Renewals & Upsell stack and used by renewals, retention, and digital teams, this agent focuses on one thing: deciding when and what to propose as cross-sell or upsell, so offers feel relevant rather than spammy. It runs around renewal and at key in-life moments, turning portfolio and behaviour signals into concrete “this is a good moment to offer X” triggers.

What it does

The agent looks at each customer’s current portfolio (products, covers, limits, deductibles, add-ons), recent claims and interactions, and simple life/behaviour signals (e.g., telematics usage where available, address changes, household composition markers, channel behaviour). Based on your product and suitability rules, it identifies gaps or natural extensions—missing cover on obvious risks, under-used benefits that suggest a different configuration, or bundles that would fit how the customer actually lives or drives—and generates a small set of cross-sell / upsell suggestions with reasons. These triggers are pushed into renewal journeys, agent desktops, and digital channels so offers show up at the right moment with a clear “why this makes sense for you now” explanation.

Core AI functions

The core capability is propensity-plus-rules. The agent combines simple propensity or pattern models (who tends to buy what, after which events) with explicit product and suitability constraints, then scores potential offers for each customer and selects the few that pass both filters: commercially attractive and suitable. It focuses on producing ranked triggers with reason codes (“recent home purchase + no contents cover”, “high mileage + no breakdown add-on”) rather than generic product lists.

Problem solved

Today, cross-sell and upsell are often driven by broad campaigns and static segments: everyone with product A gets offered B at renewal, regardless of whether it fits their profile. That leads to low take-up, offer fatigue, and a real risk of mis-selling if unsuitable products are pushed just to hit targets. Meanwhile, genuine opportunities—clear coverage gaps, strong bundles, or natural upgrades—are missed because no one systematically looks for them. This agent replaces that blunt approach with targeted, case-by-case triggers that respect both opportunity and suitability.

Business impact

The main impact is higher quality growth: more additional premium from customers who actually need and value the extra cover, with less noise and fewer complaints about irrelevant upsell. Renewal and retention teams can have more constructive conversations (“here are two options that better match your situation”) instead of pure price defence, and digital journeys can surface one or two meaningful offers instead of long, random lists. Over time, that improves ARPU and product penetration while supporting good-outcome / Fair Value expectations.

Integration and adjacent use cases

Integration complexity is medium: the agent needs access to policy and product holdings, basic customer and behaviour signals, simple life-event markers, and the product/suitability rule set, then writes triggers and reasons back into CRM, renewal journeys, and digital channels.

Common combinations in this stack:

• Premium Change Impact Explainer to pair cross-sell / upsell suggestions with a clear “why your premium changed” narrative;

• Usage-Based Data Interpreter (Telematics) to feed interpreted driving/usage patterns into the trigger logic so offers reflect real behaviour;

• Product Suitability Re-Matcher to ensure the base product configuration is right before any add-ons or upgrades are proposed; and

• Lapse Risk Predictor to prioritise cross-sell / upsell efforts on customers at higher risk of churn, using more relevant offers as part of the retention play rather than generic discounts.