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Data Driven Contact
Center Analytics
Services

Collect, process and analyze data generated during customer interactions on the go→
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contact center analytics services
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

Call Center

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.
Contact Center
call center analytics reporting

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.
Advanced Contact Center Analytics

Who Needs Contact Center Analytics
Services?

For Companies Building Automated Call Centers

    • Low customer satisfaction with post-purchase support.
    • Healthcare firms handling patient inquiries who needs to track service quality.
    • Manual calls management is a barrier in increasing the number of calls.
call center data analytics

For Companies Revamping Existing Call Centers

    • Contact center companies who aren't able to get deeper insights into agent performance and identify staffing gaps.
    • Who wants to determine trends in inquiries and improve first-call resolution rates.
    • Who wants to understand call data such as long wait times or inadequate agent training.
call center analytics

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.
call center analysis
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.
contact center analytics
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.
voice analytics call center

Technologies Powering Contact
Center Analytics

Scan voice conversations for specific keywords or phrases that demonstrate customer needs.

Use historical call data to forecast future call volumes using ML algorithms.

Understand customer dissatisfaction and urgency to guide interactions.

Collect data from both live chat and AI chatbots to use terms customers already know like using "card declined" instead of "payment error," for faster issue resolution.
Speech Analytics
Predictive Analytics

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

Contact Center Analytics Reference Architecture

Contact Center application

Our Clients

User

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KYLE MERWIN

CO-owner

200+

Projects Completed

90%

Certified Data Professionals

Reach out to us to optimize your call center processes, enhance customer satisfaction, and improve overall performance.

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