How to Clean and Export Scraped Data from Scrapestorm to Your CRM System

If you’ve just scraped a big pile of data using Scrapestorm and now you want to get it into your CRM, you’re in the right place. This guide’s for anyone who’s tired of wrestling with messy exports, weird formatting, and manual cleanups that eat up your entire afternoon.

We’ll walk step-by-step through the whole process: cleaning up what Scrapestorm gives you, getting it into a format your CRM actually accepts, and making the import as painless as possible. If you’re hoping for a one-click magic solution… sorry, but you’ll save yourself a lot of headaches by doing it right the first time.

Step 1: Get Your Data Out of Scrapestorm

First, you need to actually extract your data in a usable format.

  • Open Scrapestorm and run your scraper (if you haven’t already).
  • Export Options: Scrapestorm lets you export as CSV, Excel, JSON, or direct to a database. For most CRM imports, CSV is the safest bet.
  • Export Your Results: Click “Export” and pick CSV. Save it somewhere easy to find.

Pro tip: If your data set is huge, break it into smaller chunks. Many CRMs (especially the web-based ones) choke on big files.

Step 2: Open Your Data and Assess the Mess

Before you even think about importing, open your CSV in Excel or Google Sheets. Don’t trust that everything scraped perfectly.

  • Look for obvious problems: Blank rows, gibberish characters, columns with the wrong data, weird date formats.
  • Check for duplicates: Scrapestorm is fast, but it isn’t always smart about scraping the same thing twice.
  • Scan for missing fields: Did your scraper miss addresses, emails, or names? Now’s the time to find out.

What usually goes wrong: - Scrapestorm sometimes pulls in hidden site elements or advertising content. - Dates and phone numbers often come out in weird formats. - Multi-line fields can spill into multiple rows, messing up the CSV structure.

Step 3: Basic Cleaning – Fix the Big Stuff

Let’s get your file into shape. You don’t need fancy tools—Excel or Google Sheets are usually enough.

A. Remove Garbage Columns and Rows

  • Delete any columns you don’t need: If your CRM doesn’t use “Meta Description” or “Scrape Timestamp,” delete them.
  • Kill blank rows: Filter them out or just highlight and delete.

B. Standardize Field Names

  • Rename columns to match your CRM’s import template. If your CRM wants “First Name” instead of “Name,” make the change now.
  • Tip: Download a sample import template from your CRM to compare.

C. Tidy Up Dates, Numbers, and Text

  • Convert dates to your CRM’s required format (often YYYY-MM-DD).
  • Strip out weird symbols from phone numbers (“+1 (123) 456-7890” → “11234567890”).
  • Trim whitespace: Excel’s TRIM() function is your friend.

D. Remove Duplicates

  • In Excel: Use the “Remove Duplicates” tool (Data tab).
  • In Google Sheets: =UNIQUE(range) or use “Remove Duplicates” under Data.

Don’t obsess over perfection—just aim to get rid of things that will break your import.

Step 4: Advanced Cleaning – Fix the Sneaky Stuff

If your data’s pretty rough, you might need to do a bit more. Here’s what to watch for:

A. Split or Merge Columns

  • Did “Full Name” come out as a single column, but your CRM wants “First Name” and “Last Name”? Use Excel’s “Text to Columns” tool.
  • Sometimes addresses get smashed together. Split them up if you can, but don’t stress if you can’t get it perfect—the world won’t end.

B. Normalize Values

  • Standardize country codes, states, or categories so your CRM doesn’t create a new field for every typo (“CA,” “California,” “Calif.” all become “CA”).
  • Use Find & Replace or formulas like =UPPER() to make things consistent.

C. Handle Special Characters

  • Watch out for emojis, foreign characters, or formatting from web pages.
  • Save your CSV as UTF-8 encoding to minimize issues, especially if you scraped non-English sites.

D. Fill in the Blanks (Or Don’t)

  • Decide if you want to import incomplete records (like leads with no email). Some CRMs will reject them; others will just import what’s there.
  • Consider using formulas to fill in defaults (e.g., if “State” is blank, set it to “Unknown”).

Step 5: Match Your Data to Your CRM’s Requirements

Every CRM is different, but most want data served up on a silver platter. Here’s how to avoid the classic mistakes:

  • Download your CRM’s import template—don’t guess at field names or formats.
  • Line up your columns so they match the template exactly, both in order and in naming.
  • Double-check required fields—if “Email” is mandatory, make sure every row has one.

What to ignore: Don’t bother trying to import every single possible field on the first try. Stick to the essentials—name, email, phone, company, whatever your team actually uses.

Step 6: Export Your Cleaned Data

Once your sheet looks good:

  • Save As: Export your file again as CSV (preferably comma-separated, not tab).
    • In Excel: “Save As” > CSV (UTF-8).
    • In Google Sheets: File > Download > Comma Separated Values (.csv).
  • Check your file size: Some CRMs have a 5MB or 10MB limit per import. Split into chunks if needed.

Step 7: Import into Your CRM

Now for the moment of truth.

  • Go to your CRM’s import tool. Usually found under “Settings,” “Contacts,” or “Data Management.”
  • Upload your CSV.
  • Map your columns. Most CRMs try to match column names automatically, but double-check.
  • Run a test import with 5–10 rows first. Seriously, don’t skip this. You want to catch issues before they snowball.
  • Check the results: Did things land in the right fields? Are emails and names showing up where they should?

If the import fails: Read the error messages. They’re usually cryptic, but they’ll point to column mismatches, missing required fields, or bad formats.

If it works: Import the rest.

Step 8: Spot-Check and Clean Up in Your CRM

Don’t assume everything went perfectly.

  • Look up a few imported records.
  • Check for weird artifacts: extra quotes, broken formatting, missing info.
  • Fix anything obvious before you move on to the next data set.

What Actually Matters (And What Doesn’t)

Look, cleaning scraped data is not glamorous. But it’s the difference between having a CRM full of junk and actually being able to use your contacts. Here’s the real talk:

  • Don’t chase perfect data—it doesn’t exist. Clean enough is good enough.
  • Automated tools help, but they won’t fix everything. Scrapestorm can get you 80% there, but you’ll always need a human eye.
  • Import only what you’ll actually use. More fields = more headaches.

Keep It Simple, Iterate, and Don’t Stress

The first time you clean and import data from Scrapestorm, it’ll probably take longer than you want. The second time, it’ll be faster. You’ll figure out which columns you actually care about, and which ones you can ignore. Keep your process simple, save your templates, and don’t be afraid to throw out junk data.

Every CRM import is a little different, but the basics never change. Clean, test, import, and repeat. You’ll get better at it—just don’t expect miracles from your tools, and don’t trust your data until you’ve checked it yourself. Good luck!