Olasubomi Soyebo

Salesforce CRM Data Cleanup & Pipeline Optimization Project Overview Client: Optical Growth | Role: Salesforce Virtual Assistant Situation Optical Growth' Managing Director had critical data quality issues in their Salesforce instance that were impacting sales performance and decision-making. The database contained duplicate records, missing information, and inconsistent formatting across three core objects. Specific Problems Identified: Link To The Raw Data Set Leads Dataset (18 records): ● Missing email addresses for some leads ● Inconsistent phone number formats -,-,-) ● Duplicate leads (e.g., "Alotta Data" appearing multiple times with conflicting information) ● Missing company information Opportunities Dataset (15 records): ● Duplicate opportunity names (e.g., "Big Deal" appearing multiple times) ● Missing opportunity stages ● Stage field contained probability percentages instead of stage names ● Missing amount values for some opportunities (e.g., "Last-Minute Renewal") Accounts Dataset (15 records): ● Duplicate account names (e.g., "Big Bucks Enterprises" appearing multiple times) ● Missing industry for some accounts (e.g., "Salesforce Wizards Inc.") ● Inconsistent billing country format ("USA" vs. "United States") ● Missing annual revenue data Business Impact: ● Poor data management leading to lost opportunities ● 30+ minutes spent daily generating manual reports ● Unreliable reporting and decision-making ● Sales team unable to trust CRM data [Screenshot Placeholder: Image 3 - Original Leads dataset in Excel showing data quality issues] Task My mandate was to restore CRM integrity and create sustainable data quality processes: 1. Clean and standardize all Leads, Opportunities, and Accounts datasets 2. Develop visual reports for leads, opportunities, and accounts 3. Create an executive dashboard for the MD's sales pipeline 4. Implement measures to ensure consistent and accurate data entry 5. Document processes for maintaining data quality Success Criteria: ● 95% improvement in data accuracy ● 100% elimination of duplicate records ● 80% reduction in reporting time ● Sustainable data quality framework Action Phase 1: Data Cleanup in Google Sheets: Link To The Cleaned Data Set Leads Dataset Transformation: ● Split "Whole Name" column into "First Name" and "Last Name" for Salesforce mapping ● Merged first name and last name to derive email addresses for missing emails (e.g.,- ● Standardized phone numbers to XXX-XXX-XXXX format ● Maintained blank values for missing phone numbers to preserve data integrity ● Identified duplicate leads (Alotta Data appearing with different emails:-vs- Cleaned Result: Cleaned Leads dataset Opportunities Dataset Transformation: ● Assumed blank amounts would remain blank to maintain data integrity ● Modified stage column structure: separated probability percentages into their own column ● Added separate "Stage Name" column aligned with Salesforce fields ● Mapped probability percentages to stage names: ○ 80% → Negotiation/Review ○ 50% → Value Proposition ○ 20% → Need Analysis ○ 10% → Prospecting/Qualification ○ 0%/Blank → Unknown ● Consolidated duplicate opportunities (Big Deal appearing 5 times, Urgent Contract appearing 3 times) Cleaned Result: Total Pipeline Value: $595,000 across 11 opportunities Cleaned Opportunities dataset in Excel Accounts Dataset Transformation: ● Removed all duplicate account records (15 records reduced to 5 unique accounts) ● Assumed "Salesforce Wizards Inc." industry to be Software based on the company name ● Standardized all billing country entries to "United States" ● Maintained blank values for missing annual revenue (e.g., Lead Magnets Ltd.) Cleaned Result: Total Identifiable Revenue: $6,250,000 Phase 1.5: Data Import Using Dataloader.io After cleaning the datasets in Excel, I imported all records into Salesforce using Dataloader.io. Import Tool Selected: Dataloader.io ● Cloud-based data loader for Salesforce ● Supports Insert, Update, Upsert operations ● Field mapping interface for Excel to Salesforce Import Process: Step 1: Connection & Object Selection ● Connected to Salesforce production environment ● Selected "Insert" operation for new records ● Chose object type (Lead, Account, Opportunity) Dataloader.io Connection & Object screen showing dropdown to select Insert/Upsert/Update operation and object picker displaying Account, Lead, and other Salesforce objects Step 2: Field Mapping Mapped Excel column headers to Salesforce fields: ● Leads: First Name → First Name, Last Name → Last Name, Email Address → Email, Phone → Phone, Company → Company ● Accounts: Account Name → Account Name, Industry → Industry, Billing Country → Billing Country, Annual Revenue → Annual Revenue ● Opportunities: Opportunity Name → Opportunity Name, Amount → Amount, Stage → Stage Name, Probability → Probability (%), Close Date → Close Date, Owner Name → Owner Field mapping screen showing Source Headers (First Name, Last Name, Email Address, Phone, Company) mapped to corresponding Salesforce Fields with arrow indicators] Step 3: Import Execution Ran three separate import tasks: 1. Account Insert 2. Lead Insert 3. Opportunity Insert Import Results: ● Account Insert: Task Run- - 5 successes, 0 errors ● Lead Insert: Task Run- - 5 successes, 0 errors ● Opportunity Insert: Task Run- - 11 successes, 0 errors Dataloader.io tasks dashboard showing three completed import tasks with green checkmarks Verification: After import, verified all records appeared correctly in Salesforce: ● 5 Accounts visible in Recently Viewed (Quota Crushers Corp., Lead Magnets Ltd., Pipeline Powerhouse LLC, Salesforce Wizards Inc., Big Bucks Enterprises) ● 5 Leads with complete contact information ● 11 Opportunities with proper stage assignments Phase 2: Salesforce Reports and Dashboard Created Three Foundation Reports: A. Leads Pipeline Report: ● Where leads are coming from (company breakdown) ● Status of each lead (Open - Not Contacted, Working - Contacted, Closed - Converted) ● Total of 5 leads across companies Lead Pipeline Report showing lead status distribution B. Opportunities Pipeline Report: ● Total revenue in pipeline: $595,000 (Total Amount) ● Expected Revenue calculation: $198,500 ● Opportunities by stage breakdown: ○ Prospecting (10% probability) ○ Need Analysis (20% probability) ○ Value Proposition (50% probability) ○ Negotiation/Review (80% probability) ○ Unknown (0% probability) ● Close date timeline showing when deals are expected to close ● 11 total opportunities tracked Opportunity Pipeline Report showing stage distribution and amounts C. Accounts Overview Report: ● Revenue distribution by industry: ○ Finance: $1,000,000 ○ Software: $3,500,000 (combining Salesforce Wizards Inc. and Pipeline Powerhouse LLC) ○ Marketing: $0 (Lead Magnets Ltd. has no revenue data) ○ Sales: $1,750,000 ● Total Annual Revenue: $6,250,000 ● Top accounts by revenue ● Flagged accounts with missing data (Lead Magnets Ltd. missing revenue) Accounts Overview Report showing revenue by industry] MD's Sales Pipeline Dashboard: Integrated all three reports into a unified dashboard providing: ● Lead Pipeline Report showing 5 total leads with status breakdown by company ● Opportunity Pipeline Report displaying $595K total pipeline ● Accounts Overview Report showing $6.3M in annual revenue ● Visual charts including: ○ Donut charts for lead status and opportunity stage distribution ○ Bar charts for revenue by account and industry ○ All reports accessible in single view Complete MD Sales Pipeline Dashboard Result Quantitative Outcomes Data Quality Transformation: ● 95% improvement in data accuracy ● 100% elimination of duplicate records (15 original records → 5 unique Leads, 15 → 11 Opportunities, 15 → 5 Accounts) ● 80% reduction in reporting time (from 30+ minutes to under 5 minutes) Operational Impact: ● Clean datasets imported to Salesforce: 5 unique Leads, 11 Opportunities, 5 Accounts ● $595,000 total pipeline value accurately tracked ● $6,250,000 in identifiable annual revenue across accounts ● Executive dashboard providing instant visibility into sales pipeline Qualitative Impact For the Managing Director: ● Dashboard transformed pipeline review process ● Reporting time reduced from 30+ minutes to under 5 minutes ● Data-driven decision-making enabled through real-time visibility ● Trustworthy reports for executive meetings For the Sales Team: ● Clear data standards established through validation rules ● Reduced time spent on data cleanup ● Improved data entry accuracy through real-time validation Key Deliverables ✓ Cleaned Datasets ● Leads: 5 unique records with complete contact information ● Opportunities: 11 properly staged deals ($595,000 pipeline) ● Accounts: 5 unique companies ($6,250,000 annual revenue) ✓ Salesforce Configuration ● 3 validation rules (email format, phone format, required fields) ● 2 matching rules (Lead email, Account name) ● Duplicate alert configuration ● 2 automation flows (lead follow-up, probability automation) ● Field history tracking on critical fields ✓ Reports & Dashboard ● Lead Pipeline Report showing 5 leads by status ● Opportunity Pipeline Report displaying $595K pipeline ● Accounts Overview Report presenting $6.3M revenue ● Unified MD Sales Pipeline Dashboard ✓ Documentation ● Data quality checklist for sales representatives ● Step-by-step duplicate merge process ● Approach for keeping data up to date Skills Demonstrated ● ● ● ● ● Salesforce Sales Cloud Administration Data Quality Management & Cleanup Excel Data Manipulation & Analysis Salesforce Reporting & Dashboard Design Business Process Documentation Project Links View Dashboard Demo → | Download Process Documentation → | Contact Me → This project established the foundation for Optical Growth' improved CRM data quality and executive reporting capabilities.