Leveraging Business Intelligence for Customer Experience

Why Business Intelligence Matters for Contact Centers

Companies using business intelligence are 5× more likely to reach faster decisions than those that do not.

As data expands in volume and complexity, the ability to harness insights becomes a key differentiator. BI helps teams turn existing data into actionable insights to reduce churn, increase customer satisfaction, drive sales, grow pipeline, improve first contact resolution, and uncover hidden insights about products and services.

In its simplest terms, Business Intelligence describes insights gained from data. It includes tactics and tools for examining business data and turning it into actionable insights that guide strategic and tactical decisions.

Get to know data

• BI and AI: distinct but complementary — AI focuses on computer-based intelligence; BI emphasizes smarter business decisions through analysis and visualization.
• BI and Big Data: BI includes tools/methods to collect, store, analyze, and present data; big data analytics is broader and can include data mining, predictive modeling, and text analytics.

Business Intelligence and your customers

In CX, BI enables clearer understanding of customers and journeys across marketing, sales, and service. Organizations can analyze buying patterns and build detailed profiles to develop better products and experiences.
This page focuses on achieving a 360° view of customers and contact center intelligence (even though BI can apply across the enterprise).

Chapter 1 — Why achieving a 360° view of customer data can be difficult

The goal is to bring all relevant data together to better understand customers, journeys, and business processes.

74% of employees feel unhappy or overwhelmed when working with data. The answer isn’t simply “more data,” but timely, actionable insights from all relevant data.

Common structural blockers

• Multiple departments with different technologies, systems, and databases (often worsened by M&A).
• CRM data spread across multiple applications and databases.
• Interaction data dispersed across point solutions (ACD, IVR, chat, email, web, mobile, social, outbound, surveys, help desk, etc.).
• Data quality issues and a lack of tools/expertise to keep up with complexity and volume.
• Overreliance on structured data while excluding unstructured data (texts, recordings) due to processing difficulty.

Frustrations CX leaders often share

• Not enough information to report to leadership.
• Conflicting reports across applications that calculate metrics differently.
• Agents struggle to get all the information they need.
• Lack of visibility into the full customer journey.
• Need to combine KPIs across multiple call center platforms and drill into KPIs to understand sources.

Tip for getting started: survey teams to understand issues they have with current reporting. The first step to a 360° view is breaking down silos to get to a single source of truth.

Chapter 2 — The impact of big data

Two types of data:

Structured data is quantitative, pre-defined, and easy to search/manipulate (e.g., address, phone number, items purchased). It’s at the core of CRM reporting.
Unstructured data is qualitative and harder to process, but provides deeper insight into behaviors, experiences, and preferences. It is often estimated to represent over 80% of enterprise data.

The term “big data” refers to combining structured databases with less-structured materials captured by systems and applications.

High-value unstructured data examples

• Customer reviews & feedback: reviews, social comments, feedback forms.
• Social media posts: posts, tweets, comments revealing trends and sentiment.
• Contact center transcripts: rich for pain points, FAQs, and service quality analysis.
• Open-ended survey responses: detailed qualitative feedback.

These can be leveraged with advanced analytics, NLP, and machine learning to extract meaningful insights.

Tips for actioning:

• Find all sources of data that are part of the customer experience.
• Make an ideal list of what you want to analyze (contact center performance, routing/containment/bots, speech/quality/sentiment, agent performance & workforce engagement/management, and customer experience).

Many organizations struggle with limited technical staff, budget, or time to keep up with the breadth and depth of data.

Chapter 3 — Getting to a single source of truth

Enterprises run on dozens (or dozens upon dozens) of systems — HR, CRM, marketing, customer service, support tickets, and more — but the information is typically siloed. Without a single source of truth, teams only see part of the story while making decisions that impact the entire company.

Why CX application reporting falls short

Vendor reporting often summarizes and filters based on predefined criteria. Even when connectors exist, limitations appear when you need to join unrelated sources, blend structured and unstructured data, create custom metrics/visualizations, or combine real-time and historical context.

A common pitfall: metric mismatch

Similar KPIs can be calculated differently across applications (e.g., Average Call Time), producing conflicting reports even for the same date/time range.

Advantages of unified data:

• A unified data lakehouse can store vast data across many parameters and sources.
• A unified data warehouse improves the ability to drill in, blend, and improve data quality.
• With a lakehouse + BI layer, teams can achieve a single source of truth and make better decisions using correct, relevant reports.
• Folding in existing stand-alone applications is often “low-hanging fruit” thanks to connectors and cloud-based open APIs.
• Connecting CRM, marketing, campaign, billing, collections, case management, and operational systems enables a true 360° view.

Tip for actioning: find a BI technology vendor that can use unified data to provide the intelligence and customer insights you need.

Chapter 4 — Looking through the lens of people, process, and technology

People & Roles to consider when adding BI to your tech stack:

• Data Scientist: turns messy and neat data into useful insights and clear explanations.
• Business Intelligence Analyst: manages BI tools/vendors, protects data integrity, and produces insight reports on performance and trends.
• Data Integration Expert (ETL): extracts/transforms/loads data into warehouses and ensures performance before go-live.
• Business Users: need easy-to-use dashboards and tools to get answers without deep technical work.

Process builds data maturity. Maturity goes beyond collecting/analyzing — it requires BI functions that turn raw data into action.

Core BI functions: reporting, analysis, monitoring, and prediction.

In customer experience, purpose-built BI technology for the contact center should enable you to:

• Bring data out of silos with a vendor-agnostic framework.
• Combine real-time and historical data to view trends over time.
• Use an enterprise BI layer to ingest/analyze data for slicing, visualizations, and alerts.
• Deliver a UI designed for business users to solve problems without needing specialty BI analysts.

For cloud migrations: maintain existing contact center reports. Continuity can make or break adoption during the transition.

Components of a BI-in-CX application

• Enterprise data platform purpose-built for contact center data requirements.
• Open data lakehouse architecture combining data-lake scalability with transactional powers needed for BI.
• Unified structured + unstructured data with an intelligence layer to join data.
• Ability to bring third-party data in and push data via REST APIs.
• Ability to run complex API calls to sync real time with platforms like CCaaS.
• Reusable, scalable, secure platform with disaster recovery.

Conclusion

With so many reporting and BI tools available, many still don’t meet the needs of the contact center.

SuccessKPI positions its approach as leveraging a data lakehouse and AI engine built for contact center data needs, plus a UI designed for business users — enabling leaders to use data to learn insights and take action without code.

Example outcome

Unified datasets were described as mission-critical for the CDC in deploying a 20k-seat contact center. By blending data across calls, chat, CRM, SMS, time tracking, and HCM, they achieved a single source of truth that enabled:

• Onboarding and training 21,000 agents in 8 weeks
• 100% AI-based call scoring yielding a 30% reduction in QM costs
• ML-based call scores and dispositions supporting reliable mission-critical reporting

Next steps: Ensure your BI-in-CX vendor can (1) deliver a single source of truth, (2) provide data visualization templates, and (3) provide the purpose-built data lakehouse needed to put data to work.