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TL;DR

  • What it costs: Bad data drains revenue, time, and morale — reps spend nearly 20% of their week (a full day) researching prospects or correcting contact info instead of selling.
  • Why it compounds: Data decays at about 30% per year, so an unmaintained CRM becomes nearly a third useless within 12 months, forcing teams to work 30% harder just to stay in place.
  • What breaks: Bad data burns domain reputation through high email bounce rates, causes lead-routing errors (wrong rep, wrong territory), and erodes reps’ trust in the CRM.
  • How to fix it: Automate data entry with real-time enrichment, shorten forms to just an email address, and run ongoing “clean as you go” workflows to keep records fresh.

Bad data isn’t just a nuisance for your CRM. It’s a drain on your revenue, your time, and your team’s morale.

Most companies know their data isn’t perfect. But when you’re busy hitting targets, it’s easy to ignore a few duplicate records or outdated job titles. The problem is that these small errors compound. By the time you notice the impact, you’ve already lost significant pipeline.

Here is how bad data actually costs you, and how to fix it.

The hidden costs of subpar data

When your data is inaccurate, your entire go-to-market system slows down. You aren’t just losing contact info; you’re losing the ability to move fast.

  • Wasted sales time: The average rep spends nearly 20% of their week researching prospects or correcting contact info. That is one full day every week where they aren’t selling.
  • Burned domains: Sending emails to outdated or “guessed” addresses leads to high bounce rates. If your bounce rate stays high, email providers flag your domain. Once your deliverability drops, even your best prospects won’t see your messages.
  • Missed windows: If an inbound lead arrives with a missing phone number or an old job title, your speed-to-lead suffers. By the time a rep finds the right info, the prospect has already moved on to a competitor.

The friction between Sales and RevOps

Bad data doesn’t just impact outreach; it breaks the relationship between the people doing the work and the people building the systems.

For RevOps, bad data means lead routing breaks. If a company’s size is listed incorrectly, a Tier-1 account might end up with a junior rep, or a local lead might be routed to the wrong territory.

For sellers, bad data leads to a lack of trust in the CRM. When a rep realizes the last five phone numbers they dialed were wrong, they stop using the CRM and start building their own spreadsheets. Once that happens, you lose all visibility into your pipeline.

The data decay tax

Data isn’t static. It decays at a rate of about 30% per year.

People change jobs, companies scale, and titles shift. If you aren’t actively refreshing your database, nearly a third of your CRM becomes useless every 12 months. This creates a “tax” on your growth, so you have to work 30% harder just to stay in the same place.

How to stop the drain

You don’t need a massive overhaul to improve your data quality. You just need to change how data enters your system.

Automate the entry point

Stop asking reps to manually type in data. Use enrichment to fill in the blanks automatically. When a lead enters your CRM, Lusha can instantly add verified emails, direct dials, and firmographic details. This keeps your records complete from day one.

Shorten your forms

Longer forms lead to higher drop-off rates. By using real-time enrichment, you can ask for just an email address. Lusha fills in the job title, company size, and industry in the background. You get better data, and your prospects get a better experience.

Clean as you go

Data decays at a rate of about 30% per year. People change jobs, companies scale, and titles shift. Setting up a regular enrichment workflow ensures that your existing database stays fresh, so your AEs aren’t reaching out to “ghost” accounts.

Stop paying the “bad data tax”

Reliable data doesn’t just support your pipeline—it keeps your entire team aligned. When RevOps trusts the data, routing is accurate. When Sales trusts the data, they spend more time talking to buyers.

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