CRM Support Optimization Project

Executive Summary

Following a CRM system rollout, support teams were overwhelmed by recurring issues—70% of tickets were due to login and data format errors. I designed a resolution playbook, onboarding templates, and dashboards that helped reduce escalations and standardize workflows across 3 teams.

Key Results

• Reduced resolution time from 3.2 to 1.4 days
• 38% drop in login-related tickets within 3 weeks
• Standardized templates adopted across 3 regions

Solution Highlights

The resolution flow enabled frontline users to self-triage common CRM issues such as login failures.

Deliverables

• CRM Resolution Playbook (PDF)
• Data Entry Template (XLSX)
• Power BI Dashboard (available on request)

Learn More

• System Architecture
• Execution Breakdown
• Appendix

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System Architecture

This diagram outlines the full project pipeline: extracting CRM ticket data, identifying error patterns through SQL, visualizing trends in Power BI, and delivering targeted tools—a resolution playbook and Excel-based data entry template—to reduce repeat issues and improve onboarding consistency.

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Execution Breakdown

Dataset & Structure

The dataset includes 500+ CRM ticket records extracted from internal support systems. Key fields:

Field	Type	Description
ticket_id	String	Unique ticket identifier
rep_id	String	Sales representative ID
region	String	Region of rep (e.g., Quebec, Ontario, Alberta)
language	String	Rep language (French or English)
issue_type	String	Error category (login_issue, format_issue, etc.)
description	Text	Free-text error description
date	Date	Ticket submission date

Issue Types (6 total):

• login_issue (e.g., wrong ID, password reset loop)
• format_issue (e.g., wrong postal code format)
• report_error (e.g., empty KPI dashboard)
• data_sync_error (e.g., submitted record not showing)
• field_missing (e.g., blank required fields)
• multi_language_conflict (e.g., accented characters causing issues)

SQL Segmentation and Analysis

• Used SQL to group tickets by region/language/issue type
• Flagged ticket clusters by week number using date functions
• Filtered format_issue in French regions for deeper review

SELECT region, language, issue_type, COUNT(*) AS count
FROM crm_tickets
GROUP BY region, language, issue_type;

Region	Language	Issue_Type	Count
Quebec	French	format_issue	60
Quebec	French	login_issue	57
Quebec	French	report_error	10
Ontario	English	login_issue	58
Ontario	English	format_issue	49
Alberta	English	login_issue	51
Alberta	English	data_sync_error	6
...	...	...	...

Key Insights:

• Identified top 2 issues account for ~70% of total volume
• French-speaking regions had 22.5% higher format error rate
• Login issues clustered in the first 2 weeks after CRM rollout

Visualization and Reporting

Used Power BI to build interactive dashboards:

• Issue type frequency by week and region
• Breakdown by language and recurrence rate
• Escalation trend vs resolution time