Case Study of AXIS MAX LIFE

Turning Insurance Signals Into Action Across Renewals, Sales And Distribution.

Axis Max Life partnered with Fundamento to orchestrate AI-led customer and agent journeys across renewal recovery, cross-sell meeting generation and inactive-agent reactivation—connecting customer signals to the next best action at scale.

Written By

Team Fundamento

Published

Building Lifetime Value Across A Life-Insurance Relationship

Axis Max Life operates across a long customer lifecycle where value depends on more than policy acquisition. Renewals, cross-sell opportunities and advisor engagement all influence the quality and continuity of the relationship.

Fundamento was deployed across three distinct moments in that lifecycle—recovering renewal intent, creating cross-sell conversations and reactivating inactive agents.

The Same Insurance Relationship Can Create Three Very Different Opportunities.

Axis Max Life needed to act differently depending on where the relationship stood.

A renewal-due policyholder required a timely payment intervention. An RM-disengaged policyholder required a relevant cross-sell conversation. An inactive agent required understanding—not another generic sales pitch.

  • Renewal procrastination
  • Low engagement among RM-disengaged customers
  • Agent issues hidden behind inactive status
  • Pitch-first approaches not necessarily creating engagement
Opportunity What Was Broken?
Renewals Customers at risk of non-renewal required timely intervention
DSF Cross-Sell RM-disengaged policyholders were not being consistently engaged for additional opportunities
Agent Activation Inactive agents needed reactivation at scale
Understand why the customer or agent is disengaged—and adapt the conversation accordingly.

Signal OS™ Connected Customer Status To The Next Best Conversation.

Fundamento used customer and distribution signals to distinguish renewal-due policyholders, RM-disengaged customers and inactive agents, then applied journey-specific rules to determine the appropriate intervention.

Signal What It Revealed
Renewal Due Status Immediate persistency opportunity
RM Engagement Whether a customer had an active relationship touchpoint
Agent Activity Whether an agent had become inactive
Conversation Engagement Quality of customer / agent interaction
Issue Signals What was preventing agent reactivation
State Appropriate routing for agent activation
Architecture Layer Deployment Capability
Customer Signals Renewal due • RM engagement • agent activity
Decision Intelligence Journey-specific prioritisation
Journey Intelligence Renewal • DSF cross-sell • Agent Activation
Execution Layer AI Voice Agent + WhatsApp
Human Intelligence RM / agent-manager handoff
Customer / Agent Signal → Cohort → Journey Selected → AI Conversation → Intent / Issue Identified → Action → Human Follow-Through Where Required

Three Insurance Journeys. Three Different Definitions Of Success.

Journey After Fundamento Outcome
Renewals 61.6% recovery rate
DSF Cross-Sell 341 agreed meetings
Agent Activation 403 qualified agent leads
Renewal Due → AI Outreach → Payment Conversation → Recovery RM Disengaged → AI Outreach → Product Pitch → Customer Agrees → RM Meeting Inactive Agent → AI Outreach → Understand Issue → Qualification → Manager Follow-Through

The Conversation Changes With The Context.

Agent / Layer Role
Renewal Voice Agent Engages renewal-due policyholders
DSF Voice Agent Identifies cross-sell interest and books meetings
Agent Activation Voice Agent Re-engages inactive agents
A/B Script Logic Tests pitch-first vs issue-first engagement
State Routing Routes activation conversations appropriately
WhatsApp Layer Supports agent activation notifications

Script A: Cross-sell pitch

Script B: Ask the issue

Measure Script A Script B
Engagement 3.77% 4.69%
Qualified Leads 3.09% 3.96%
Issue-first conversations produced stronger engagement and qualification than pitch-first conversations.

AI Creates The Opportunity. People Complete The Relationship.

Fundamento automates the first interaction, but high-value insurance outcomes still require relationship owners and distribution teams to take the conversation forward.

AI Handles Human Teams Handle
Renewal outreach Renewal follow-through where required
Cross-sell conversation RM meeting
Meeting agreement Customer relationship
Agent issue discovery Agent-manager reactivation
Lead qualification Distribution follow-through

Capabilities Are Not Projected. They Are Measured. Renewal Recovery:-

61.6%

Recovery Rate

37% lower

Cost Per Conversion

Cross-Sell:-

341

Agreed Meetings

Agent Activation:-

403

Qualified Agent Leads

3.96%

Qualified Lead Rate — Script B

4.69%

Engagement Rate — Script B

Every Conversation Revealed What The Next Intervention Should Be.

Intelligence Created What It Enables
Renewal Signals Prioritise customers in the renewal window
RM Disengagement Identify cross-sell opportunities outside active RM engagement
Agent Inactivity Identify dormant distribution relationships
Issue-First Learning Improve agent reactivation conversations
Meeting Intent Convert cross-sell interest into RM action
In agent reactivation, understanding the problem created more qualified engagement than leading with the product.

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