You Have All the Data. Now What? Why Conversational Data Access Changes Everything

You’ve done the hard work.

You unified your interaction data in the Engagement Data Hub—voice, chat, email, social are all in one place (see part one in this blog series: Why Your CX Team Needs Contextual Data Where They Actually Work—Not Where It Lives).

You’ve integrated critical third-party data: network operations, point of sale, CRM, marketing campaigns (see part two: Elevate CX and Contextual Data with Third-Party Integrations). Verint’s platform is capturing, enriching, and storing a complete picture of your customer journey.

But here’s the uncomfortable question: When someone asks you, “Why did repeat contacts spike 23% in the Southeast last week?”—can you get an answer in minutes instead of days?

Not your data scientists. Not your IT team. You, or your operations managers, business analysts, and CX leaders who need to understand what’s happening and take action.

The “I’ll Get Back to You” Problem

Let’s look at what happens in most contact centers when someone needs to answer an urgent business question.

Monday 9 a.m. Leadership Meeting:

Executive: “Our repeat contact rate jumped 23% last week in the Southeast region. What’s driving it?”

Operations Manager: “Let me pull that data and get back to you.”

What happens next:

• Monday 10 a.m.: Manager submits IT ticket: “Need report showing repeat contact drivers by region for last 7 days.”
• Monday 3 p.m.: IT responds: “Can you be more specific? Which metrics? What granularity?”
• Tuesday 9 a.m.: Manager clarifies: “Repeat contacts within 48 hours, grouped by issue category, filtered to Southeast region.”
• Tuesday 4 p.m.: IT: “We’ll need to join three data sources. Estimate: 3-5 business days.”
• Friday 11 a.m.: Report delivered as CSV file with 47 columns.
• Friday 2 p.m.: Manager spends 3 hours in Excel trying to find the answer.

Following Monday 9 a.m. Leadership Meeting:

Executive: “So what’s driving the Southeast spike?”

Operations Manager: “Based on my analysis… it looks like network-related technical support calls, but I’d need to dig deeper to confirm…”

What Your Existing Solutions Already Do Well

Before we go further, let’s be clear about what Verint Platform already solves:

Workforce Management handles:

• Contact volume forecasting based on historical patterns
• Schedule optimization and adherence
• Capacity planning for campaigns and events
• Real-time staffing adjustments

Quality Management delivers:

• Agent performance tracking and scoring
• Automated coaching recommendations
• Compliance monitoring
• Interaction evaluation at scale

These are structured, recurring analyses with defined processes and proven ROI.

But what about everything else? The ad-hoc business questions that don’t fit neatly into WFM forecasts or QM scorecards:

• “Why are customers who start with chat 2.3x more likely to call back?”
• “Which issue types correlate with customer churn in our premium segment?”
• “What’s the financial impact of fixing the bot-to-agent context transfer gap?”
• “How did last week’s marketing campaign affect contact volume by region and channel?”

These questions require exploratory analysis across multiple data sources. They’re unpredictable, urgent, and business-critical—but they don’t happen often enough to justify pre-built dashboards.

This is where most organizations hit a wall in unlocking their data insights potential.

The Real Cost of “I’ll Get Back to You”

When your team can’t answer business questions quickly:

• Decisions get delayed: “We’ll revisit this next month when we have better data.”
• Assumptions become strategy: “It’s probably seasonal… let’s assume 20% increase.”
• Problems go undetected: “We should look into that spike… when someone has time.”
• Opportunities get missed: “I wonder if we could have predicted that…”

What Conversational Data Access Actually Means

Verint Data Insights Bot doesn’t just give you access to your unified data. It gives you a conversational interface that lets you ask questions in natural language and get answers in seconds—not days.

Instead of:

• Writing SQL queries
• Submitting IT tickets
• Waiting for pre-built dashboards to be created
• Exporting CSVs and analyzing in Excel

You simply:

• Ask a question in plain English
• Get an answer in seconds
• Drill deeper with follow-up questions
• Save the view for recurring analysis.

It’s conversational because it works like you are talking to a data analyst—but the response is instant.

Real Questions, Real Answers, Real Time

Let me show you what this looks like in practice.

Scenario 1: The Root Cause Question

Question: “Why did repeat contacts spike in the Southeast region last week?”

Traditional Approach:

• 10-day cycle through IT ticket queue
• Multiple clarifications and iterations
• CSV export requiring manual analysis
• Partial answer with caveats

Data Insights Bot Response (seconds, not days):

Analysis Complete

Repeat Contact Rate: Southeast Region (Last 7 Days): ↑ 23% increase vs. prior week

Primary Drivers:

Network-Related Technical Support: 847 repeat contacts (+340%)

• Correlation: NOC data shows fiber infrastructure issues
• Affected areas: Atlanta metro, Charlotte, Jacksonville
• Peak: Wed-Fri (coincides with outage reports)
• Average time to repeat: 8.4 hours

Billing Inquiries: 234 repeat contacts (+67%)

• Correlation: Payment processing system maintenance (Thurs 2-6 a.m.)
• 156 customers experienced payment failures
• Pattern: Late fee disputes after system downtime

Order Status: 112 repeat contacts (+23%)

• Correlation: Carrier delays (FedEx^®1 weather disruptions)
• Customers calling multiple times for shipping updates

Recommended Actions:

• Priority 1: Proactive outreach to network-affected customers
• Priority 2: Reverse late fees from payment system maintenance
• Priority 3: Send proactive shipping updates for delayed orders

Estimated Impact: 65% reduction in repeat contacts if executed today

Follow-Up Question: “Show me key differences between network-related repeat contacts and other repeat contacts.”

Data Insights Response (nearly instant):

Network-related repeat contacts:

INSIGHT: Agents lack visibility to network outage data

• Spending 12-16 minutes troubleshooting infrastructure issues
• Cannot resolve (external problem)
• Customers calling back when issue persists

RECOMMENDATION: Surface NOC data in agent desktop

• Expected handle time reduction: 8-10 minutes
• Expected repeat contact reduction: 70%

Total time from question to actionable insight: seconds, not days.

The data was always there—unified interaction data from the Engagement Data Hub, enriched with network operations data from Integration Studio. But without conversational access, it would have taken 10 days to extract this intelligence.

Scenario 2: The Integration Analysis Question

Question: “Why do chat customers call back 2.3x more than voice customers?”

Traditional Approach:

• Pull chat transcripts (separate report)
• Pull voice call data (separate report)
• Identify customers who used both channels (manual matching)
• Analyze issue types (separate categorization)
• Look for patterns (Excel pivot tables)
• Build presentation (PowerPoint)
• Timeline: 2-3 weeks of analyst time

Data Insights Bot Response (seconds, not weeks):

Root Cause Analysis: Chat vs. Voice Repeat Contact Rate

• Chat Customers: 34% repeat within 7 days
• Voice Customers: 15% repeat within 7 days
• Ratio: 2.27x

Root Causes Identified:

ChatBot to Agent Escalation Without Context (41% of chat repeats)

• Chatbot collects information but doesn’t pass context to agent
• Customer must re-explain everything
• Root: Integration Studio not passing chatbot context

Without network data, agents can’t identify if it’s a known network issue, leading to generic troubleshooting that doesn’t solve the problem—forcing customers to call back.

Partial Issue Resolution (33% of chat repeats)

• Agent resolves immediate symptom but not root cause
• Customer returns when underlying problem persists
• Root: Knowledge base gaps for complex issues

Channel Preference Mismatch (26% of chat repeats)

• Complex issues require verbal explanation
• Customer switches to voice for clarity
• Root: No proactive channel routing based on issue complexity

Follow-Up Question: “What’s the financial impact of fixing the chatbot context transfer?”

Data Insights Bot Response (nearly instant):

Financial Impact Analysis

Current State:

Transfer without context: customers must re-explain their issue, and if they’re transferred multiple times or the context is lost, many abandon the call and call back hoping for better service.

• 3,444 repeat contacts/month from lost chatbot context
• Cost per contact: $12
• Annual cost: $495,936

With Bot Context Transfer:

• Expected reduction: 75%
• Annual savings: $371,952
• Implementation Cost: $23,000 (one-time)²
• Payback period: 0.7 months
• First-year ROI: 1,517%

Total time from question to business case: seconds, not weeks.

This analysis required joining interaction data, chatbot transcripts, customer journey data, and financial metrics across multiple systems. Your data foundation made it possible. Conversational access made it fast.

Why This Capability Is Different

Verint offers conversational data access capabilities that set it apart in the CX platform market.

Traditional CX Platform Approaches:

Verint Data Insights Bot Approach:

Conversational interface built specifically for CX business users:

• No SQL required
• No separate BI platform needed
• No waiting for IT
• No data scientist required

This is possible because:

• Unified data foundation – Engagement Data Hub stores interaction + third-party data with identity resolution
• Normalized data model – Engagement Data Management ensures quality and consistency
• Natural language processing – Understands CX business context and terminology
• Intelligent query generation – Translates questions into optimized queries automatically
• Contextual recommendations – Suggests related questions and deeper analysis

Building Business-Specific Views

The conversational interface isn’t just for ad-hoc questions. Once you’ve asked a question and gotten valuable insight, you can save that view for recurring analysis—creating custom dashboards that complement your systems.

Operations Dashboard:

• “What are my top 3 repeat contact drivers today?”
• “Which issue categories have >20% volume increase this week?”
• “Show me sentiment trends by channel and issue type.”

Executive Dashboard:

• “What’s our overall CX health score across all channels?”
• “What’s driving increases in customer effort?”
• “Which customer segments are at highest churn risk and why?”

All pulling from the same unified data in Engagement Data Hub. All answering questions in seconds. All customized to what each role needs to see.

The Value Equation

Quantified Impact (Based on Early Adopter Results)

Analyst Productivity:

• Time saved per question: ~90% (days → seconds)
• Questions answered per week: ~10x increase (4 → 40)
• Strategic analysis capacity: ~4.7x increase

Business Decision Speed:

• Average time to insight: ~95% reduction (days → hours)
• Decisions made with data vs. assumption: ~8x increase

ROI Calculation (Representative Example for a Contact Center with 200 Agents):

Returns:

• Analyst time savings: $180K/year
• Faster decision making (prevented issues): $300K/year
• Reduced IT burden: $45K/year
• Opportunity capture: $269K/year

Total Annual Value: $794K

First-Year ROI: 1,988%

The Bottom Line

You’ve invested in unifying your interaction data across channels. You’ve integrated critical third-party data from network operations, POS, CRM, marketing, and fulfillment systems. You’ve built the complete customer journey picture.

Now make your data work for you. Verint Data Insights Bot lets your operations managers, business analysts, and CX leaders:

• Ask questions in plain English
• Get answers in seconds, not days
• Drill deeper with follow-up questions
• Build custom views for recurring analysis

Conversational data access transforms unified, integrated data from “nice to have” into “competitive advantage.”

Your data is ready. Your team is ready. The question is: Are you ready to empower them with real-time, conversational analytics?

This concludes our three-part series on making CX data actionable:

• Part 1: Why Your CX Team Needs Contextual Data Where They Actually Work—Not Where It Lives
• Part 2: Elevate CX and Contextual Data with Third-Party Integrations
• Part 3: Why Conversational Data Access Changes Everything

Ready to see conversational data access in action? Learn more about Verint Data Insights and the CX Automation Platform at verint.com.

¹ FedEx is a registered trademark of Federal Express Corporation.

² Actual costs vary based on data sources, user count, and integration complexity.