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Agentic AI for Loyalty Program Activation & Engagement Agentic AI for a Leading Tile Manufacturing Company

Discover how DGTL and Emvo.ai implemented Agentic AI for a leading tile manufacturer to automate product enablement, and loyalty programs—driving higher recovery, engagement, and ROI. Read the full case study.

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Company Overview

A leading tile manufacturing company in India with a large base of dealers and masons enrolled in its loyalty program. While enrollment numbers were high, active participation and redemption rates remained a challenge, limiting the program’s effectiveness and ROI.

Business Challenge  

Despite a well-structured loyalty program, several key gaps impacted performance:

1. Zero-Scan Inactivity

  • Many users were enrolled but never scanned products

  • Lack of engagement broke the loyalty cycle at the entry level

  • No visibility into why users remained inactive

2. Redemption Inertia

  • Users accumulated points but did not redeem them

  • Reduced perceived value of the loyalty program

  • Missed opportunity to drive repeat engagement

3. Lack of User Visibility

  • Field teams were unable to manually reach thousands of users

  • No clear distinction between:

    • Disinterest

    • Lack of awareness

    • Technical issues

4. Hidden Technical Friction

  • App-related issues (e.g., scanning failures) went unreported

  • Users silently dropped off without feedback

  • No structured mechanism to identify and resolve blockers

Objective 

To create an intelligent loyalty engagement system that could:

  • Reactivate inactive (“zero-scan”) users

  • Drive higher participation and redemption rates

  • Identify and resolve technical and behavioral barriers

  • Provide real-time visibility into user intent and engagement stages

  • Scale engagement without increasing manual effort

The Solution  - DGTL's Agentic AI Approach

DGTL in association with Emvo..ai , implemented a multi-agent loyalty engagement framework, where each AI agent handled a specific user journey—ensuring continuous, personalized, and outcome-driven engagement.

Loyalty Activation & Engagement Agents

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1. Activation Agent – Driving First Engagement

  • Identified users with zero scanning activity

  • Initiated outbound voice calls to understand barriers

  • Guided users step-by-step on how to scan products

  • Triggered WhatsApp tutorials post-call for reinforcement

 

Outcome: Converted dormant users into active participants by simplifying onboarding and education.

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2. Redemption Agent – Unlocking Program Value

  • Monitored user point balances in real time

  • Triggered nudges for reward redemption

  • Educated users on available benefits and rewards

  • Increased perceived value and stickiness of the program

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Impact:Encouraged users to experience tangible benefits, improving overall engagement.

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3. Smart Escalation System – Resolving Friction Instantly

  • Identified users facing technical issues

  • Classified issues as:

    • Awareness gap

    • Behavioral gap

    • Technical problem

  • Created high-priority tickets for customer support teams

  • Ensured faster resolution and reduced drop-offs

Key Features 

  • Real-Time API Integration for last scan date and point balance

  • Granular Intent Classification (Not Aware, Forgot, Needs Help, App Issue)

  • Context-Aware Conversations adapting dynamically to user responses

  • Hinglish Language Support for natural communication

  • WhatsApp Integration for tutorials and follow-ups

  • Deterministic Logic ensuring accurate reward communication

Performance & Result

20%

Reactivation of Inactive Users

5%

Improved

Granular

Hidden Technical Issues Identified

Loyalty Engagement & Participation​​

 

User Insights for Better Decision-Making

The loyalty program evolved from a passive rewards system to an active engagement engine, where users are continuously guided, supported, and nudged toward meaningful participation.

The system not only increased engagement—but also delivered actionable insights into user behavior and friction points.

Conclusion 

With DGTL in association with Emvo.ai, the organization moved from assumption-driven engagement to data-driven activation.

By combining intelligent outreach, real-time insights, and automated support, the loyalty ecosystem became more responsive, scalable, and impactful.

This is not just loyalty automation—it’s intelligent user activation at scale.

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