This is the second in our three-part series on the realities of the build vs. buy decision for contact center analytics. Contact centers sit on goldmines of data, yet transforming raw call events into actionable insights remains one of the most underestimated challenges in business intelligence. What begins as “just a few dashboards” often evolves into a complex engineering project with never-ending maintenance requirements.
In this article, we’ll unpack why contact center data presents unique technical challenges and the true costs of in-house development. Our final installment will explore organizational success factors and how to evaluate specialized solutions against your specific needs.
Moving Beyond Basic Reporting
Traditional reporting tells you what happened yesterday. True analytics capabilities deliver insights that drive immediate fixes and long-term strategic shifts. The journey typically unfolds across three stages.
From Descriptive to Predictive
Most contact centers start with descriptive analytics, tracking call volumes, handle times, and abandonment rates. This answers the question, “What happened?” but offers limited actionable insight.
The next step is diagnostic analytics, which examines why performance fluctuates by analyzing call patterns, customer journeys, and the root causes of service variations. This deeper understanding enables targeted improvements.
The most advanced organizations leverage predictive analytics to anticipate volume spikes, customer behaviors, and emerging issues before they escalate. This shift from reactive to proactive management fundamentally changes how resources are allocated and decisions are made.
Balancing Real-Time and Historical Perspectives
Effective contact centers need both immediate operational visibility and longer-term pattern recognition:
- Real-time dashboards enable supervisors to spot developing queue issues, monitor agent availability, and make immediate adjustments when service levels drop.
- Historical analytics reveal trends, seasonal patterns, and systemic issues that require strategic intervention.
Organizations that successfully implement this dual approach often see significant improvements in key performance indicators. In practice, platforms that combine real-time monitoring with historical trend analysis—like those used in many modern contact centers—support more agile decision-making and long-term process improvements.
Metrics That Actually Matter
While contact centers track countless data points, specific metrics consistently drive meaningful improvement:
Customer Experience Indicators
- First Contact Resolution: Are customers getting answers without callbacks?
- Customer Satisfaction and Effort Scores: How do customers feel about their experiences?
- Net Promoter Score: Would they recommend your service?
Operational Efficiency Measures
- Average Handle Time: How efficiently are issues resolved?
- Transfer Rates: Are customers reaching the right people initially?
- Occupancy Rates: Are agents spending appropriate time with customers?
Service Performance Metrics
- SLA Adherence: Are service targets consistently met?
- Abandonment Patterns: When and why are customers giving up?
- Queue Distribution: How is wait time distributed across customer segments?
Agent Performance Indicators
- Quality Scores: Are interactions meeting standards?
- Schedule Adherence: Are agents available when needed?
- After-Call Work Time: How efficiently are interactions documented?
Transformation happens when organizations stop passively monitoring these metrics and start implementing interventions based on the insights they reveal.
Turning Insights into Outcomes
Data creates value only when it drives action. The most successful organizations follow a consistent pattern:
- Identify patterns or anomalies through analytics
- Investigate the root causes behind the findings
- Develop specific interventions targeting these causes
- Implement changes to operations, technology, or processes
- Measure results to verify improvement
- Refine approaches based on outcomes
Organizations that formalize this cycle create engines of continuous improvement. Those who treat analytics as passive monitoring systems rarely achieve meaningful change.
Real-World Transformation Examples
Turning Data into Results: A Lighthouse Works Story
When properly implemented, contact center analytics can deliver measurable business results. Brightmetrics reports that its real-time analytics enabled Lighthouse Works to reduce abandonment rates by 35%, significantly improving overall contact center performance. The key to these improvements is using analytics to identify patterns and opportunities that aren’t visible through standard reporting.
Making the Most of Metrics: SCAN Health Plan’s Contact Center Upgrade
SCAN Health Plan, a not-for-profit Medicare Advantage provider with a 90% member satisfaction rate, implemented Brightmetrics to optimize their Genesys contact center operations. Before this implementation, SCAN struggled with an inefficient analytics system requiring multiple software tools and significant manual effort to extract and interpret data.
After implementing advanced analytics, SCAN gained instant visibility into real-time and historical data, enabling it to track agent activity, optimize call queues, and rapidly identify issues. The intuitive dashboards and drill-down capabilities helped them analyze trends, monitor agent performance, and proactively address gaps.
The results demonstrated the power of analytics-driven improvement: Data-driven resource allocation helped distribute workloads efficiently, minimizing service delays. Aligning resources with demand and offering targeted coaching improved agent retention. Operational efficiency increased as the easy-to-use platform reduced data processing time. Regulatory compliance was maintained through real-time monitoring features.
Organizational Factors for Analytics Success
Technical capabilities alone don’t drive transformation. Organizations achieving the greatest impact typically share several characteristics:
Leadership Commitment
Executive sponsors ensure analytics initiatives receive adequate resources and attention. Leaders who actively engage with data foster cultures where decisions consistently follow evidence rather than intuition.
Cross-Functional Collaboration
Contact center insights often require action across departments:
- Operations teams addressing service delivery
- Training departments developing agent skills
- Technology teams refining systems
- Product teams improving offerings based on customer feedback
Organizations establishing cross-functional governance for analytics achieve a broader impact by addressing issues across departmental boundaries.
Building Data Literacy
Contact centers seeing the highest impact invest in developing data skills across all roles:
- Agents understand their performance metrics
- Supervisors analyzing team trends
- Managers identifying improvement opportunities
- Directors connecting operational data to strategic goals
This transforms analytics from a specialized function to an organizational capability embedded in daily operations.
Embracing Ongoing Evolution
Successful organizations view analytics as a journey rather than a destination. They continuously refine their metrics, methods, and implementation approaches to address changing business requirements.
Technology Requirements for Effective Analytics
While human and process elements are critical, technology forms the foundation for analytics success. Effective solutions must provide:
Comprehensive Data Integration
Contact centers typically generate data across multiple systems:
- ACD/IVR platforms tracking routing, and handling
- CRM systems capturing customer information
- Quality management tools assessing interactions
- Workforce management systems tracking scheduling
- Knowledge platforms supporting agent guidance
Analytics solutions must integrate these sources to provide complete visibility across customer journeys and operations.
Real-Time Processing Capabilities
Operational contact centers need immediate insight for tactical intervention. Solutions must process streaming data with minimal delay to enable timely responses to developing situations.
Accessible Visualization
Contact center teams operate in fast-paced environments where access to information determines usage. Effective solutions provide role-specific dashboards, drill-down capabilities, alert mechanisms, and mobile access, enabling quick comprehension and action.
Self-Service Exploration Tools
Frontline leaders need independent analytical capabilities without relying on technical specialists. Solutions should enable ad-hoc queries, custom views, personalized alerts, and flexible sharing options that accelerate insight-to-action cycles.
Build vs. Buy Considerations
Organizations implementing analytics face a fundamental choice between custom development and specialized platforms. Key factors influencing this decision include:
Available Technical Resources
Building effective analytics requires sustained commitment from teams with expertise in data engineering, analytics development, and ongoing maintenance. Organizations without these dedicated resources often find specialized solutions that deliver faster results with lower risk.
Implementation Timeframes
Custom development typically requires months or years to mature, while specialized solutions can deliver impact within weeks. Organizations facing immediate performance challenges often benefit from purpose-built platforms.
Ongoing Maintenance Requirements
Contact center environments constantly evolve through platform changes, business reorganizations, and new channels. To address these changes, custom solutions require continuous updates, creating permanent resource commitments that specialized platforms typically include in their service offerings.
Analytics as a Strategic Capability
Contact center analytics have evolved from tactical reporting tools to strategic capabilities that transform operations. Organizations leveraging these capabilities effectively gain competitive advantages through enhanced customer experiences, optimized resources, faster problem resolution, and improved agent engagement.
The journey requires investment in both technology and organizational readiness. As contact centers evolve toward increasingly complex, omnichannel environments, analytics capabilities will continue differentiating leaders from laggards.
The future belongs to organizations that transform data into actionable insights, driving continuous improvement across every aspect of customer engagement and operational performance.
Explore the Build vs. Buy Series
Part 1: The Technical Reality of Contact Center Analytics: What IT Leaders Need to Know
Part 3: The Hidden Costs of Building Contact Center Analytics In-House
Download the eBook: Build vs. Buy: Making the Right Call on Contact Center Analytics
