Data Driven Contact
Center Analytics
Services
Collect, process and analyze data
generated during customer interactions on the go→
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Stats That Prove the Impact
of Contact Center Analytics
- 42% of CX leaders identify enhancing data capabilities for analytics, intervention, and reporting as their top priority for the next 1–2 years.
Source – Genesys
End-to-End Contact Center Analytics Services
Inspect Current Call Center Processes
- Input & Output Metrics Review: Detailed analysis of inbound and outbound call data to identify bottlenecks.
- Operational Challenges: Pinpoint key operational inefficiencies, such as high handle times, low first-call resolution, customer dissatisfaction rates with contact center speech analytics and sentiment analysis call center tools.
- Omni-Channel Data Aggregation: Integrate and aggregate data from different sources like calls, emails, chats, social media to provide a unified view of customer interactions through a call center metrics dashboard.
Design and Redesign For Automated Contact Center Processes
- AI-Driven Redesign: Route incoming calls to the most suitable agent based on customer queries, historical interactions using AI.
- Data-Driven Decision Models: Use historical data to design processes that minimize customer churn and improve resolution rates through call center analytics.
- Feedback-Driven Process Evolution: Use feedback data in analytics to automate processes based on agent and customer input.
Proactive Performance Management
- Real-Time Anomaly Detection: Call center data analytics detects irregular patterns in call center metrics like sudden spikes in call abandonment rates or unusually high agent idle times.
- Workforce Optimization Analytics: Use call center analytics to assign agents based on call volume forecasts, peak hours, agent skill sets.
- Resolve Issues Causing Delays: Dataplatr uses call flow analytics to identify IVR drop-off points, menu delays, and repetitive options, optimizing IVR design. It also analyzes handover patterns within customer interactions using conversation transcripts and call flow data to pinpoint redundant transfers and extreme agent switches.
Implement Advanced Contact Center Analytics
- Predictive Analysis for Performance: Use ML techniques to predict call volumes, agent performance and customer sentiment.
- Customizable Dashboards: Build dashboards that allow managers to monitor KPIs like CSAT, NPS, etc, enabling quick decision-making.
- Root Cause Analytics: Use contact center analytics services to trace recurring escalation triggers, such as long response times, agent knowledge gaps to reduce escalations.
Who Needs Contact Center Analytics
Services?
For Companies Building Automated Call Centers
Key Challenges:
- Low customer satisfaction with post-purchase support.
- Healthcare firms handling patient inquiries who need to track service quality.
- Manual calls management is a barrier in increasing the number of calls.
For Companies Revamping Existing Call Centers
Key Challenges:
- Unable to get deeper insights into agent performance and identify staffing gaps.
- Need to determine trends in inquiries and improve first-call resolution rates.
- Need to understand call data issues such as long wait times or inadequate agent training.
How Generative AI Optimizes Call Center Data
Every leader we surveyed says their organization plans to use
generative AI in customer
service—and 67% say they've already begun
Personalized Coaching For Representatives
- In traditional settings, SMEs often would be reviewing agent performance manually. Contact center analytics services with Gen AI can track agent’s performance and provide personalized coaching by identifying areas of improvement. It can also replicate agent’s top-performing behaviors to improve team performance.
Knowledge optimization
- Generative AI continuously learns from historical customer interactions to update knowledge databases and refine response strategies so that AHTs and escalation are lowered.
Script Generation
- Generative AI optimizes scripts based on customers feedback to create engaging conversations. Go to option for Contact centers firms that use scripts for agents which can also be somewhat prohibitive with a “one size fits all” approach.
Moving Beyond Traditional Metrics: What Your Contact Center Can Achieve Today
Modern Analytics-Enhanced Metrics
Net Promoter Score (NPS)
Customer Effort Score (CES)
Customer Satisfaction (CSAT)
Emotions and Sentiment Analysis
Ability to Predict and Improve Customer Outcomes
Insights into Agent Performance and Training Needs
Traditional Metrics
Average Time to Answer
First Call Resolution (FCR)
Call Transfer Rate
Average Handle Time
Cost per Call (CPC)
Average Hold Time
Our Clients
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KYLE MERWIN
CO-owner
200+
Projects Completed
90%
Certified Data Professionals