Shop Email Data Extraction

@AwaisChaudhry

Hello, I’m Muhammad Awais. I will parse 14,000 emails from Google Workspace, across years and varied templates, and extract customer and order data into a single tidy CSV or Google Sheet. The approach uses a robust Python parser to read .mbox, identify order blocks in different HTML layouts, normalize customer info (name, contact, shipping and billing addresses, VAT/EAN if present) and order details (SKUs/products, line totals, and date). I’ll validate a sample subset against the originals and deliver a reproducible workflow plus a ready-to-use script and a clean, sortable sheet. The result will be accurate, auditable, and easy to extend if templates change. What is the preferred final format for the output sheet (CSV or Google Sheet), and should you want a versioned export log for each run? Best regards,

@ZohaibRoy

⭐⭐⭐⭐⭐ Efficiently Parse and Organize Your Order-Confirmation Emails ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see that you're looking for someone to parse and organize 21,000 order-confirmation emails. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects involving data extraction and organization. I will create a neat spreadsheet from your emails, ensuring accuracy and easy sorting. ➡️ Why Me? I can easily parse your order-confirmation emails as I have 5 years of experience in data extraction and organization. My expertise includes parsing emails, data formatting, and spreadsheet creation. Not only this, I have a strong grip on data analysis tools, ensuring your data is accurate and well-organized. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you! ➡️ Skills & Experience: ✅ Data Parsing ✅ Email Extraction ✅ Spreadsheet Creation ✅ Google Sheets ✅ CSV Formatting ✅ Data Validation ✅ Data Analysis ✅ Attention to Detail ✅ HTML Understanding ✅ Project Management ✅ Time Management ✅ Communication Skills Waiting for your response! Best Regards, Zohaib

@OutsourceMan

⭐⭐⭐⭐⭐ CnELIndia, led by Raman Ladhani, will deliver an accurate, fully validated order dataset from your 14,000 Google Workspace emails. Step 1: Secure Intake & Structuring We ingest your .mbox archives, index messages, and group by storefront/template era (custom HTML and Shopify). Step 2: Intelligent Parsing Engine Using Python and structured HTML parsing, we build adaptive extractors that detect order blocks across layout changes. Rules + pattern recognition ensure reliable capture of customer details (name, contact, shipping/billing, VAT/CVR, EAN) and order data (SKU/product per cell, totals, order date). Step 3: Data Normalization Standardize formats (dates, currency, addresses), deduplicate, and validate mandatory fields. Step 4: Accuracy Control Automated cross-checks plus manual QA sampling before delivery to ensure analytics-ready precision. Step 5: Delivery Clean CSV or Google Sheet—one row per order, structured for sorting, filtering, and analysis. We ensure scalability, confidentiality, and near-100% extraction accuracy.

@Tanmoy236

Hello! I specialise in structured data extraction from complex email archives, and with over 9 years of experience in data processing and automation, I turn messy .mbox files into clean, analytics-ready spreadsheets with high accuracy. Here’s how I can help: 1. Parse your full Google Workspace .mbox archive (14,000+ emails) across multiple storefront templates 2. Detect and normalize order data despite layout changes (custom HTML + Shopify standard templates) 3. Extract complete customer details: name, email, phone (if available), shipping address, billing address, VAT/CVR, and EAN where present 4. Capture order-level data: order date, product names or SKUs (structured clearly), item totals, and total amount spent 5. Standardize all records into one clean, sortable CSV or Google Sheet (one row per order) 6. Deduplicate and validate entries to ensure accuracy before final delivery 7. Provide a brief data-structure summary so your analytics team can plug it in immediately Accuracy is my priority—before final delivery, I run consistency checks (totals, date formats, missing fields) to minimize discrepancies during your spot-check review. Do you prefer one column containing all purchased SKUs comma-separated, or separate structured columns per item line? The scope and timeline can be aligned once I review a small sample of the .mbox structure.

@belalihabibullah

Hello, I can help you extract and structure your order data from the .mbox email archive with high accuracy. I have experience parsing large email datasets across multiple templates, including varying HTML layouts like Shopify and custom systems. I will identify the relevant data blocks from each message and compile everything into a tidy, analytics-ready spreadsheet. I’m happy to review a sample before starting. Best regards, MD

@asifdwan

Hi! I’ve worked on large email-parsing projects like this and can reliably extract structured order data from mixed HTML templates, including older custom layouts and newer Shopify formats. Approach: • Parse the .mbox archive using Python (mailbox + BeautifulSoup) • Detect template variations and normalize fields across formats • Extract customer + order data into a unified schema • Clean and validate VAT/CVR/EAN fields where present • Output a single tidy CSV (or Google Sheet) ready for filtering I’ll include validation checks to ensure totals, dates, and product lines align with each original email. Accuracy and consistency will be prioritized so your analytics remain trustworthy. Happy to review a small sample of emails to confirm field patterns and timeline.

@sujonmax2

Hi, I have extensive experience parsing large email archives and extracting structured order data across changing HTML templates, including Shopify formats. I can process your 21,000 .mbox messages, identify key data blocks regardless of layout variations, and consolidate everything into a clean, analytics ready spreadsheet. I’ll implement validation checks to ensure totals, dates, and customer details align exactly with source emails. Accuracy and consistency are my priority. Ready to begin and deliver a fully verified dataset. Regards sujon

@justmian876

With my extensive background in data-driven projects and web scraping, I am more than equipped to take on your Shop Email Data Extraction task. My team at BN-Droids Digital Services has successfully managed complex data extraction assignments, spanning across multiple time frames and platforms. We've interacted with HTML layouts of all kinds, ensuring we adapt easily to these changes over the years in your storefront systems.

@petermyvw

Let me extract and structure your order data into CSV from the 21,000 or so emails in that .mbox archive. I have experience parsing & extracting data from emails using Python. So you can rely on me to carry out the task. I'm happy to review a sample CSV output for say, an order that has more than 1 item in it, just to see if we are on the same page regarding the format of the CSV you desired. Let me know if you'd like me to help.

@WelcomeClient

Hi There, Ready right now I'm ready to parse each message, identify the relevant blocks no matter the template, and push the results into a single, tidy spreadsheet (CSV or Google Sheet) that I can sort and filter easily. I will show you sample for your satisfaction and project accuracy then we will go to start, so please contact me and share more details thanks. Check My Profile: https://www.freelancer.pk/u/WelcomeClient I would like to work on this project and can complete with 100% accuracy within the time frame. https://www.freelancer.pk/projects/excel/business-profit-loss-reporting-excel/reviews https://www.freelancer.pk/projects/data-entry/copy-listings-from-website-another/reviews Thanks, Umer

@nkrsasnaa

Dear client, I am a python developer and can parse all the mails and extract the required data from it and bring all the data in a spreadsheet before you. I had done similar work of parsing emails before which I can show you. Let's connect. Thanks!

@vishal1145

Your Shopify template migration means older emails likely store product data in <table> tags while newer ones use <div> structures - if your parser treats them the same way, you'll get incomplete SKU extraction or miss line items entirely. That's 14,000 rows of bad data feeding your analytics. Before I architect the extraction logic, two quick questions: Do any of these storefronts use dynamic pricing (discounts, bundles, multi-currency)? And are VAT/CVR/EAN fields consistently labeled across templates, or do some emails bury them in footer text without clear identifiers? Here's the technical approach: - PYTHON + BEAUTIFULSOUP: Build template-agnostic parsers that detect structural patterns (order tables, address blocks, totals) instead of relying on fixed CSS selectors that break across versions. - REGEX + NLP FALLBACKS: Extract VAT/CVR/EAN numbers using pattern matching first, then apply named entity recognition for edge cases where labels vary (e.g., "VAT No." vs "Tax ID"). - PANDAS DATAFRAME VALIDATION: Cross-check extracted totals against line-item sums to flag parsing errors before export - catches missing products or currency conversion issues automatically. - GOOGLE SHEETS API: Push results directly into a Sheet with frozen headers and conditional formatting so you can spot anomalies (duplicate orders, missing addresses) during QA. - MBOX CHUNKING: Process emails in 1,000-message batches to handle memory constraints and generate progress logs - you'll know exactly which orders failed parsing. I've built similar extraction pipelines for 3 e-commerce clients migrating legacy data, including one with 8 years of mixed WooCommerce/Magento emails. Let's schedule a 15-minute call to review a sample .mbox file - I need to see the actual HTML variance before committing to a timeline.

@inertiasoftltd

Hello, As an expert in data analysis, JavaScript, and Python, I'm the perfect fit for your Shop Email Data Extraction project. I understand that you need to extract detailed customer and order information from different versions of order-confirmation emails - a task that demands a high level of accuracy. Over my 8 years of experience, I have fine-tuned my data extraction skills to ensure precision even when dealing with HTML templates that have evolved over time, like in your case. Additionally, my strong command over CMS systems, including Shopify which you mentioned having used for your storefronts, will streamline the extraction process. This will enable me to easily identify and parse relevant blocks from each message despite the differing templates used throughout the years. Moreover, my proficiency in Python puts me at an advantage for sorting and filtering the extracted data into a single, comprehensive spreadsheet. I will ensure every row represents one order with all the required customer details (including VAT/CVR & EAN numbers if applicable) and order details (products purchased per SKU or Product name). Accuracy and completeness are my top priorities in this project since this data is crucial for your analytics feeding process. I guarantee a flawless output that you can test by spot-checking sample orders against original emails. Let's collaborate closely on this project to deliver an exceptional result on time. Thanks!

@johnallenvillan1

Hello, I specialize in data extraction and analysis. With over 5 years of experience in data management, web scraping, and data processing, I am well-equipped to tackle your project efficiently. I will meticulously parse each order confirmation email, extract relevant details, and compile everything into a comprehensive spreadsheet for easy sorting and filtering. By employing a combination of JavaScript, Python, and Excel, I guarantee accurate results that align perfectly with your analytics requirements. I look forward to delivering the final spreadsheet to you promptly. How can I assist you further with this project?

@mattafareed4848

Hello I can accurately parse your .mbox archive of 14,000 order-confirmation emails across varying templates (including legacy custom HTML and Shopify formats), extract all required customer and order data fields with reliable pattern detection, normalize the results into a clean, de-duplicated CSV or Google Sheet (one row per order), and ensure high accuracy through structured validation so your analytics can rely on it confidently. Regards Muhammad

@maximg10

Hello. Thanks for your job posting. ⭐Shop Email Data Extraction⭐ I'm the developer you're looking for. I can successfully complete your project. Let's chat for a more detailed discussion. Thank you. Maxim

@suganyav12

Hi there, Myself Suganya hope you are doing good. I carefully gone through the project and clear with the instructions. I will perfectly get the Customer details and order details from 14,000 order-confirmation emails stored in Google Workspace. Good in basic python and other data collection method. Kindly consider this straightforward project for me. Thanks

@SamirBanna

Hello, I’m a Senior Software Engineer with extensive experience in Python automation and web scraping & C# WindowFormApp and WFP. I’ve carefully reviewed your requirements and I can deliver a reliable, production-ready solution — not a quick workaround. ✅ Clean and maintainable code ✅ Clear communication ✅ On-time delivery I’d be happy to discuss your project details and propose the best technical approach. Best regards, Samir

@moustafa1

Hi, I have multiple specialized programs that can extract data directly from MBOX files, which means I can process your entire Google Workspace archive efficiently. Regardless of the email template—whether older custom layouts or newer Shopify formats—I can identify the relevant blocks and normalize them into a consistent structure. Deliverables: A tidy CSV or Google Sheet with one row per order. Each row will include customer details (name, contact info, addresses, VAT/CVR/EAN numbers if present) and order details (products purchased, SKU/product names, item totals, order date). Verified accuracy through spot-checks against the original emails. My focus will be on reliability and clarity, ensuring the final dataset is ready for sorting, filtering, and analysis. Best Regards, Moustafa