You will get a clean, organized and validated Excel or CSV dataset

Let a pro handle the details

Buy Data Entry & Cleaning services from Claudenilson, priced and ready to go.

Let a pro handle the details

Buy Data Entry & Cleaning services from Claudenilson, priced and ready to go.

Project details

You will receive a clean, accurate, and analysis-ready dataset that saves time and improves the reliability of your business decisions.

As a Data Analyst with experience in SQL, Python, Excel, and data visualization, I specialize in transforming messy datasets into structured, high-quality data that can be confidently used for reporting, dashboards, business intelligence, and analytics projects.

My data cleaning services include:

• Duplicate detection and removal
• Missing value handling
• Data validation and quality checks
• Standardization of formats, dates, and text fields
• Error and inconsistency correction
• Outlier identification
• Dataset restructuring and preparation for analysis

I pay close attention to detail and document every cleaning step, ensuring transparency and reproducibility throughout the process.

Whether you're preparing data for Power BI, Tableau, Excel reporting, SQL analysis, or machine learning projects, I will deliver a reliable dataset that is ready for the next stage of your workflow.
Data Tool
Microsoft Excel
What's included
Service Tiers Starter
$25
Standard
$50
Advanced
$100
Delivery Time 2 days 3 days 5 days
Number of Revisions
123
Optional add-ons You can add these on the next page.
Fast Delivery
+$15 - $30

Frequently asked questions

Claudenilson J.Status: Offline
Claudenilson J.Status: Offline
E-commerce Data Strategist | Fix Retention Leaks & Maximize ROAS
Recife, Brazil - 4:09 pm local time
Stop driving your e-commerce business blind. I help e-commerce store owners and startup founders turn raw, messy transactional data into clean profit margins.

Most founders rely heavily on aggressive discounts and expensive ad campaigns, destroying their net margins without knowing why their revenue has flatlined. I engineer the data pipelines and automated dashboards that expose exactly where your business is leaking money.

🏆 DEEP-DIVE CASE STUDIES IN MY PORTFOLIO:

The Retention Bottleneck Study: I led a growth diagnostic for a retail startup to analyze why $772M in revenue had flatlined. Using SQL and Cohort Analysis across 2.2M+ records, I uncovered that 61.7% of acquired users were one-time buyers, and proved that securing a second purchase increased customer Lifetime Value (LTV) by 124% [🔗].

The 42k+ Product Pricing Elasticity Study: I audited a global consumer electronics catalog with 42,000+ active items using Python (Pandas/Seaborn). I mapped the exact threshold where discounts drive conversion vs. where they erode profit, proving that moderate discounts (5% to 8%) coupled with specific trust signals optimized revenue potential [🔗].

🎯 WHAT I WILL DELIVER FOR YOUR E-COMMERCE:

- Shopify & GA4 Audit: Cleaning your data pipeline and fixing broken tracking events so your dashboard numbers actually match your bank account.

- Automated Power BI / Looker Studio Dashboards: Building real-time executive views for Cohort Retention, True ROAS, and Product Margin Leaks.

- Data-Driven Action Plans: Translating complex database tables into clear commercial strategies.

I have a background in business intelligence and extensive experience working in international, fast-paced environments using English daily.

If you want a professional who doesn’t just build charts, but tells you exactly where your next $10,000 in profit is hidden, let’s look under the hood of your data.Available for Fixed-Price growth audits and long-term data strategy consulting. Let's chat!

Steps for completing your project

After purchasing the project, send requirements so Claudenilson can start the project.

Delivery time starts when Claudenilson receives requirements from you.

Claudenilson works on your project following the steps below.

Revisions may occur after the delivery date.

Dataset Assessment

Review the dataset structure, identify data quality issues, and define the cleaning strategy.

Data Cleaning

Handle missing values, remove duplicates, standardize formats, correct inconsistencies, and validate data quality.

Review the work, release payment, and leave feedback to Claudenilson.