01
First repeat-call rate
Measured the share of initial inquiries followed by an additional customer contact.
Business Intelligence
BigQuery + Tableau
2026
Understanding repeat customer calls.
Under-
standing
repeat
customer
calls.
A business intelligence project designed to identify repeat customer support patterns across markets, issue types, and subsequent contacts.
Repeat calls can signal unresolved customer issues and opportunities to improve first-contact resolution.
I consolidated customer-support data from three markets in BigQuery, analyzed repeat-contact behavior, and built an interactive Tableau story for stakeholder exploration.
01
Overview
What drives
repeat calls?
The Google Fiber customer service team wanted to understand how often customers contacted support again after their initial inquiry and how effectively issues were being resolved on first contact.
The analysis focused on repeat-call frequency, differences across markets, issue types generating additional contacts, and how customer calls continued through subsequent contact stages.
Business Question
How often do customers contact support again after their initial inquiry, and how do repeat-call patterns vary across markets, issue types, and subsequent contacts?
02
BI Workflow
From raw calls
to decision support.
The project moved from stakeholder requirements and data preparation through SQL transformation, analysis, visualization, and stakeholder communication.
Each step supported a single objective: creating a consistent view of repeat-call behavior that could be explored across markets, issues, and time.
01
Plan
Defined stakeholder needs, business questions, metrics, and dashboard requirements.
02
Prepare
Loaded three market datasets into BigQuery and reviewed the reporting structure.
03
Transform
Consolidated market records into one reporting table using SQL and UNION ALL.
04
Visualize
Built Tableau views for repeat-call trends, issue types, markets, and contact depth.
05
Share
Organized findings into an interactive story and stakeholder-facing summary.
03
Data Pipeline
Three markets.
One reporting table.
Customer-support records were provided as three separate market datasets with matching structures.
I loaded each source into BigQuery and combined them into a unified target table to create one consistent data source for Tableau.
01
+
Market 1
02
+
Market 2
03
→
Market 3
04
fiber_target
01
Date Created
Initial customer-contact date
02
Contact Stages
Initial through eighth customer contact
03
Issue Type
Customer-support inquiry category
04
Market
Service-market identifier
04
Analysis Strategy
Measuring where
repeat calls persist.
The analysis distinguished between the first repeat contact and the broader sequence of subsequent customer calls.
Metrics were evaluated across issue types, markets, and time to identify both high-volume repeat-call drivers and areas with elevated repeat-call rates.
02
Repeat-call volume
Compared the overall volume of repeat contacts across issue types and markets.
03
Contact depth
Tracked how customer contacts continued from the initial inquiry through later call stages.
05
Dashboard Design
Three views.
One customer story.
The final Tableau story was organized into three stakeholder views: Repeat Call Overview, Repeat Calls by Issue, and Repeat Calls by Market.
Line charts, bar charts, a market-by-issue heatmap, and repeat-contact depth views allowed stakeholders to move from high-level performance to specific repeat-call drivers.
01
Overview
Summarizes market rates, issue volume, and repeat-call trends over time.
02
Issue Analysis
Identifies which inquiry types generate the greatest repeat-call volume and rate.
03
Market Performance
Compares market trends and how customer contacts continue across repeat-call stages.
06
Key Findings
Market 3 emerged
as the priority.
Market 3 recorded the highest repeat-call rate and showed the clearest opportunities for further investigation.
Issue-level analysis also revealed that overall call volume and repeat-call rate did not always point to the same operational problem.
15.3%
Market 3 first repeat-call rate at its February peak.
35.2%
First repeat-call rate for Type 1 inquiries in Market 3.
Type 5
Issue type generating the highest overall repeat-call volume.
Market 2 consistently maintained the lowest repeat-call rates during the three-month period, while Market 3 remained substantially higher.
The contrast suggests that market-level operating conditions and specific issue types should be investigated together rather than evaluating overall volume alone.
07
Business Recommendations
Focus intervention
where calls repeat.
The findings provide a clear starting point for investigating customer-service processes associated with repeat contacts.
Teams should prioritize areas where both repeat-call volume and repeat-call rate indicate unresolved customer needs.
01
Investigate Market 3
Review service processes and inquiry handling in the market with the highest repeat-call rate.
02
Review Type 5 volume
Identify why Type 5 generates the greatest volume of subsequent customer contacts.
03
Prioritize Type 1
Examine Type 1 inquiries in Market 3, where the first repeat-call rate reached 35.2%.
08
Next Steps
Monitor performance
over time.
The dashboard can support continued monitoring of repeat-call patterns as new customer-service data becomes available.
Future analysis could incorporate longer time periods, additional service dimensions, and operational context to better understand the causes behind repeat contacts.
Business Impact
Repeat-call analysis turns customer-service activity into actionable signals for improving first-contact resolution.
Project Files
Explore
Interactive Tableau Story
Explore the complete interactive repeat-call dashboard published in Tableau Public.SQL / BigQuery Pipeline
View the SQL used to consolidate three market datasets into the unified reporting table.Executive Summary
Stakeholder-facing overview of findings, recommendations, and analytical workflow.BI Project Planning
Project documentation covering stakeholders, requirements, strategy, and dashboard mockup.Selected Work
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