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Collect 200 leads from LinkedIn, organize in Excel sheet with name, email, and company
Great job
Lead generation + structured Excel organization is a great combo — having name, email, company and social accounts all in one clean sheet makes outreach so much faster. Did you build this with manual research or a scraping tool?
Well done. If I remember correctly, webscraping on LinkedIn is difficult
Great job Sajib
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BEFORE: Client had messy Excel inventory with 500 rows, duplicates (A12 / a12), blanks, wrong prices like "$ 2.5" and "N/A", and different warehouse names (KER / ker / Kericho).
AFTER: I did:
Removed 12 duplicate SKUs
Standardized item names to Title Case
Fixed QTY and Prices to 2 decimals
Unified Warehouse to Kericho
Made sheet ready for pivot dashboard
Result: Clean, ready-to-use inventory in 2 days using Excel.
Tools: Excel, Remove Duplicates, Text to Columns, TRIM, PROPER
👟 24+ Years Footwear Expert → Now Helping USA Shoe Brands 3X ROAS with Facebook & Instagram Ads
I just launched my new project: Facebook & Instagram Ads for Shoe Brands Only!
🚫 Why ONLY Shoe Brands?
I was Ex-Head Commercial of Top 3 Footwear Factory in Bangladesh. I know shoe buyer psychology, not just ads.
🎯 Recent Result: USA Hemp Sneaker Brand
ROAS 1.2x → 3.2x in 30 Days
CPC $0.80 → $0.35
Revenue $15k → $38k/mo
Most agencies run generic ads. I build:
✅ Shoe-buyer only audiences (Sneakerheads, not random interests)
✅ UGC Try-on + Factory Story Creatives
✅ Shopify + Subscription Funnel for Repeat Buyers
If you own a Shoe Brand (Sneakers, Boots, Sustainable Shoes like 8000Kicks), drop your store URL - I'll send you FREE 5-min audit with 3 mistakes losing you money.
Let's make your shoe brand the next 8000Kicks 🚀
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footwearfootweardesignShopifyGoogle SheetsMicrosoft ExcelLead GenerationMarket ResearchProduct Research
Good Luck!
Behind every clean chart is a messy spreadsheet 🧹
This is what a real data cleaning job looks like before it becomes an elegant bar chart — duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
