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Quick Wins1 monthMarketing

CRM Full of Duplicates, Can't Track Customers

An automotive marketing company's Salesforce CRM had become virtually unusable due to years of inconsistent data entry across multiple teams. With 60% duplicate records, fragmented customer information across disparate systems, and zero data quality controls, sales and marketing couldn't trust their own data. Customer journeys were impossible to track, and multiple sales reps would unknowingly contact the same prospect.

Data QualityCRMQuick Win

The Challenge

The company was facing several critical challenges:

Key Issues:

  • 60% duplicate records: More than half the database consisted of duplicate companies, contacts, and opportunities
  • Fragmented customer data: Single customers existed as 5-10 different records with slight name variations
  • Inconsistent entry practices: Sales reps, marketing, and operations all entering data differently with no standards
  • Multiple disparate systems: Data scattered across Salesforce, marketing automation, and internal databases with no reconciliation
  • Zero data quality controls: No validation, no deduplication rules, no data governance
  • Sales confusion: Multiple reps unknowingly pursuing the same prospect, causing embarrassing duplicate outreach
  • Impossible tracking: Couldn't follow customer journey when one customer had 10 different record versions
  • Unreliable reporting: Pipeline reports worthless when opportunities duplicated across records
  • Lost opportunities: Deals falling through cracks because history fragmented across multiple records

Business Impact:

The company couldn't trust their own data. Every report was questioned, every customer interaction risked confusion, and leadership had no clear visibility into their actual pipeline.

The Solution

Built Python-based deduplication solution using machine learning to identify and merge duplicate records across Salesforce and connected systems. Created master customer table with clean, standardized company names, opportunities, and deals.

Our Approach:

  • Phase 1 - Assessment (Week 1): Conducted comprehensive data audit, analyzed Salesforce structure, identified duplicate patterns, interviewed sales/marketing teams
  • Phase 2 - Deduplication Engine (Weeks 2-3): Built Python-based ML solution with fuzzy string matching, address normalization, contact email matching, phone standardization, ML scoring for match probability
  • Phase 3 - Master Data Model (Week 3): Created clean foundation with master customer table, standardized naming conventions, consolidated opportunities, validation rules
  • Phase 4 - System Integration (Week 4): Reconciled data across marketing automation and internal databases, established data flow rules, implemented ongoing duplicate detection

The Results

Eliminated 60% duplicates across the CRM. Sales team can now properly track complete customer journeys without confusion. No more embarrassing duplicate outreach or conflicting account ownership.

Duplicates Eliminated
60% reduction
Across entire CRM database
Email Deliverability
35% improvement
Reduced bounce rate, better campaign performance
Single Customer View
One record
Each company now has one authoritative record
Complete History
100% consolidated
All opportunities, activities, notes merged
Lookup Speed
3min → 15sec
Sales team finds correct record immediately
Timeline
4 weeks
From assessment to production

Additional Results:

  • Customer journey tracking: Can now follow prospects from first touch to closed deal
  • No more duplicate outreach: Sales reps see complete contact history
  • Accurate pipeline reporting: Leadership has reliable forecast visibility
  • Reduced friction: No more debates about "which record is the real one"
  • Preventive measures: Validation rules catch duplicates at entry, team trained on standards

Technical Details

Architecture:

Python-based deduplication engine with Salesforce API integration

Technology Stack:

  • Python 3.x for core deduplication logic
  • Salesforce API for data extraction and merge operations
  • Machine Learning: scikit-learn for fuzzy matching and duplicate scoring
  • Libraries: pandas (data manipulation), fuzzywuzzy (string matching), recordlinkage (ML matching)
  • Salesforce validation rules and duplicate detection
  • Data governance framework with monthly audits

Technical Highlights:

  • Fuzzy string matching: Caught variations like "ABC Corp" vs "ABC Corporation" vs "A.B.C. Corp"
  • Address normalization: Recognized same company at different locations/spellings
  • Contact email matching: Linked records through shared email domains
  • Phone number standardization: Matched despite formatting differences
  • ML-based scoring: Trained model to calculate match probability, flagging high-confidence duplicates
  • Intelligent merge: Chose most complete/recent record as master, combined all historical data
  • Audit trail: Logged every merge decision for review and potential rollback
  • Quality metrics: Duplicate rate (before: 60%, after: <2%), average records per company (3.2 → 1.0)
  • Ongoing monitoring: Weekly duplicate detection reports, alerts, data quality dashboard

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