Performance boost
Person-days saved / month
Interactions tagged
Text tagging accuracy
A Fortune 500 specialty retailer with over 400 stores worldwide and 1 million + annual incoming customer contacts struggled with identifying, recording, and analyzing customers' Reasons for Contact (RFC). With growing call volumes, the client aimed to improve operational efficiencies without proportionately increasing staff. Manual categorization was time-consuming and error-prone, leading to inconsistent tagging.
Key challenges
Sluggish and imprecise manual tagging
Inconsistent RFC categorization across representatives and locales
Limited insights gleaned from untagged interactions
Operational inefficiencies hampering customer care quality
The solution
AI tagging with dCrypt
Streamlines categorization process
Real-time processing for rapid insights
100% coverage eliminates gaps
Enhanced data analytics
Consistent tagging across locations
Error detection ensures accuracy
Actionable customer insights
1
Deployment
dCrypt deployed
Legacy system integration
Automated tagging configured
2
Model configuration
Fine-tuned for Dutch
Cross-lingual methods
RFC taxonomy for scalability
3
Global scaling
40+ countries rollout
Parallel employee retrain
Ongoing monitoring & optimization
Optimizing operations
Performance lift
Eliminated manual errors
Reduced handling time
Substantial productivity gains
Accurate tagging
Interactions tagged
85% text model accuracy
65% voice model accuracy
Scalable RFC taxonomy
Cost savings
Person-days/month saved
Reduced training spend
Cut operational costs
Enhanced reporting efficiency
Omnichannel integration
Evolving tagging for AR/VR interactions
Dynamic RFC taxonomy
Real-time category evolution from data trends
Sentiment analysis
Emotional intelligence for attuned service
