Real-time Customer Satisfaction Monitoring

Overview

AI-powered referral system that leverages customer behaviour patterns, social connections, and engagement metrics to automatically identify and incentivise high-potential brand advocates, maximising referral effectiveness while reducing program management costs.

Problem

Manual identification of potential brand advocates is time-consuming and often inaccurate.
Static referral rewards fail to account for individual customer value and influence.
Poor tracking of multi-channel referral attribution leads to missed opportunities
Limited ability to predict and optimise referral program ROI.
Difficulty in scaling personalised referral campaigns across different customer segments.

Solution

Utilises machine learning to analyse customer behaviour, purchase history, and social influence.
Automatically segments customers based on their referral potential.
Dynamically adjusts referral rewards based on customer lifetime value and influence score.
Provides predictive analytics for referral success probability.
Implements real-time A/B testing of referral messaging and incentives.
Automates multi-channel referral tracking and attribution.
Personalises referral campaigns using natural language generation.