How to Import and Cleanse Prospect Data in Vidu for Better Outreach

Let’s be honest: most prospect lists are a mess. Duplicates, typos, missing fields, weird formatting—you name it. If you’re using a tool like Vidu to run outreach campaigns, junk data isn’t just annoying, it actively hurts your results. This guide is for anyone who’s tired of wasted effort and wants a no-nonsense, step-by-step way to import and clean up prospect data in Vidu, so you can actually reach the right people with the right message.

Why Clean Data Matters (and Why Most People Skip This)

Before you even touch an import button, let’s get real about why this matters:

  • Bad data wastes your time. You’ll end up emailing the same person twice, or worse, hitting dead addresses.
  • Your emails look sloppy. “Hi ,” or “Dear John johnson@example.com” isn’t a great start.
  • Personalization falls flat. If fields are blank or wrong, your dynamic templates will embarrass you.

Plenty of people try to fix this as they go, but honestly, it’s way easier to do upfront. A few minutes now saves hours (and headaches) later.

Step 1: Get Your Data Ready Before Import

You probably have your prospect data in a spreadsheet or CSV. Don’t just upload it blindly—take a beat to prep it.

What to Check in Your File

  • Headers: Make sure each column has a clear name (like first_name, email, company). Vidu tries to map columns, but junk headers confuse it.
  • No merged cells: Every cell should have only one piece of info.
  • Consistent formatting: Emails should be emails, names should be names, etc.
  • Remove extra tabs/sheets: Only include what you need.

Pro tip:

If your data came from LinkedIn scraping or an export from another CRM, expect mess. Run a quick scan for weird symbols, odd capitalization, or missing emails.

Quick Data Hygiene Steps (Do This Before Import)

  • Delete obvious duplicates (sort by email and scan).
  • Fix capitalization (use Excel’s PROPER() for names).
  • Fill in blanks where you can, or decide how you’ll handle them later.
  • Save as UTF-8 CSV—this avoids headaches with special characters.

Step 2: Importing Data into Vidu

Vidu’s import is pretty straightforward, but there are a few things to watch out for.

How to Import

  1. Log in to Vidu
    Head to your dashboard.

  2. Navigate to the Prospects Section
    Usually, there’s an “Import” or “Add Prospects” button. Click that.

  3. Upload Your File
    Select your clean CSV. If the preview looks weird, stop and check your formatting.

  4. Map Your Fields
    Vidu will try to match your columns to its fields. Double-check these—automation isn’t perfect.

    • Don’t just click “Next.” Make sure first_name isn’t getting mapped to company_name by accident.
    • If you have custom fields (like “Industry” or “Personal Note”), create those in Vidu first.

  5. Choose Import Settings

  6. Decide if you want to overwrite existing records or skip duplicates.

  7. If you’re not sure, choose “skip duplicates”—better safe than sorry.

  8. Start Import
    Let it run. For big lists, this might take a few minutes.

What Can Go Wrong

  • Encoding errors: If your CSV isn’t UTF-8, names with accents or symbols will get mangled.
  • Field mismatch: If you see empty fields after import, your columns probably didn’t line up.
  • Duplicate records: Vidu tries to dedupe by email, but if you have multiple emails for one person, you’ll get repeats.

Honest take:

Don’t expect miracles—Vidu’s import tool is good, but not magic. Garbage in, garbage out.

Step 3: Cleansing Data After Import

Even with prep, some mess always slips through. Luckily, Vidu has built-in tools to help you find and fix issues.

Use Vidu’s Data Tools

  • Deduplication:
    Use the “Find Duplicates” feature (usually in your prospects list). It’ll flag likely matches—review before merging, since it’s not always right.
  • Bulk Edit:
    Filter for common problems (like missing first name), then update those in bulk. This is faster than editing one by one.
  • Validation:
    Some CRMs validate emails automatically. If Vidu flags bounced emails or obvious fakes (“test@test.com”), purge or correct them.

What to Fix (and What to Ignore)

Worth your time: - Fixing missing or obviously wrong emails and names. - Standardizing company names (e.g., “IBM” vs. “International Business Machines”). - Removing role-based emails like “info@” or “sales@”—these rarely get responses.

Not worth your time: - Obsessing over every missing LinkedIn profile. - Tracking down every phone number if you only use email.

Pro tip:

Set a time limit. Give yourself 30 minutes to clean up, then move on. Done is better than perfect.

Step 4: Segmenting for Smarter Outreach

Once your data’s tidy, don’t just blast everyone. Good segmentation makes your outreach actually work.

How to Segment in Vidu

  • By Title or Role:
    Tag or filter by job titles so you can tweak messaging (“Hi VP of Marketing” vs. “Hi IT Manager”).
  • By Industry or Company Size:
    Use custom fields or tags to separate prospects. One-size-fits-all emails flop.
  • By Engagement:
    After initial outreach, use Vidu’s tracking to tag people who opened or replied, so you can follow up differently.

Honest take:

Most people skip segmentation and wonder why their response rates suck. You don’t need 20 segments—just start with 2-3 that matter.

Step 5: Ongoing Data Hygiene

Prospect data decays fast. People change jobs. Emails die. Set up a simple routine:

  • Monthly dedupe: Run Vidu’s duplicate finder.
  • Quarterly clean: Filter for bounces or unsubscribes and remove them.
  • After every new import: Do a quick scan for obvious problems.

Don’t let “we’ll clean it later” become “we never clean it.”

What to Ignore (For Now)

There’s always a new tool or plugin promising “AI data enrichment” or “instant perfect lists.” Most are mediocre, expensive, or both. Unless you’re running massive campaigns, focus on:

  • Clean, accurate basics: name, email, company, title.
  • Reliable segmentation.
  • Regular maintenance.

You can always add fancy enrichment later—but if your core data sucks, it won’t help.

Wrapping Up: Keep It Simple and Keep It Moving

Clean data isn’t glamorous, but it’s the foundation for outreach that actually works. Don’t get bogged down chasing perfection. Prep before you import, fix the big stuff after, and spend your time on outreach, not endless data tweaks. The best approach? Start small, keep improving, and let your results guide what you clean up next.