70% improvement in NPS
32% improvement in Customer Sentiment
36% reduction in Average Monthly Complaint Tickets
Our client, a major Australian-based Franchise Group with over 110 franchisees, was facing a unique challenge. While franchisees in major locations were meeting targets, those in other locations (that formed the bulk of the portfolio) were lagging behind. These underperformers reported declining Net Promoter Scores (NPS), an increase in customer complaints and support calls, and a perceived drop in brand value as indicated by consumer sentiment.
The client wanted to rapidly identify these performance gaps and implement effective solutions, without dedicating weeks to data collection and analysis.
With a user base spread across Australia, the client needed a way to unify customer feedback from all locations and channels (including online reviews, chats, calls, tickets and surveys) to discern overarching patterns at the group level, while also pinpointing specific issues at individual franchise locations.
The end goal of course was to identify the driving factors behind increasing customer dissatisfaction at each underperforming location and implement targeted improvements to fix them.
After connecting voice-of-customer data from 70 individual franchisees with Eclipse, the client was able to:
Eclipse AI empowered the client’s team with a unified view of customer feedback, trends, and sentiments. This bird’s-eye view, combined with granular insights, enabled the client to strategise at the macro level and concurrently detect inefficiencies at specific locations.
Leveraging actionable recommendations from Eclipse AI, the client introduced a standardised Customer Management training program. This initiative aimed to ensure that all staff, particularly those in customer-centric roles, upheld the brand’s service quality benchmarks. The real-time tracking of customer feedback also allowed them to gauge the efficacy of their initiatives continuously.
In four months of executing the suggested improvements, the client achieved:
Unify omnichannel Voice-of-Customer data, analyse it at scale using AI and extract insights to improve performance metrics
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