What is Data Accuracy?
Picture this: you’ve just purchased 1,000 contacts from a reputable data provider. You upload them to your email tool, craft the perfect outreach sequence, and hit send. Three days later, you check your campaign stats and your heart sinks. Bounce rate: 18%. Half your emails went to people who left their companies months ago. Your sender reputation just took a hit, and you’ve wasted money on leads that were never going to convert.
This is the reality when data accuracy goes wrong.
Data accuracy measures how correct and current your contact information actually is. It’s not just about having an email address—it’s about having the right email address, for the right person, at the right company, right now. In B2B sales and marketing, this distinction matters more than almost anything else.
The Four Dimensions of Data Accuracy
When we talk about accurate contact data, we’re really talking about four separate but related concepts. Deliverability measures whether an email can actually reach an inbox. You want at least 90% of your emails to be deliverable after verification. Currency tells you how fresh the information is—ideally, your data should be less than 30 days old. Completeness checks if all the required fields are filled in, which should be true for 90% or more of your records. Finally, correctness verifies that the information matches reality, with a target of 85% or higher.
Here’s a real example. You might have Sarah Johnson’s email address, and it’s technically deliverable, so your deliverability is 100%. But if Sarah left her VP of Marketing role six months ago to join a different company, your currency and correctness just failed. The record is complete but useless.
Why Accuracy Matters More Than You Think
Bad data doesn’t just waste your time—it actively damages your business. When emails bounce, email service providers notice. Send too many emails to invalid addresses, and suddenly your entire domain gets flagged as a potential spammer. Now even your valid emails start landing in spam folders. It’s a spiral that’s hard to recover from.
Beyond deliverability issues, bad data means you’re reaching out to the wrong people. Imagine sending a personalized pitch about marketing automation software to someone who hasn’t worked in marketing for eight months. Best case scenario, they ignore you. Worst case, they report you as spam out of annoyance.
Good data, on the other hand, transforms your entire outreach operation. Your emails actually get delivered. People respond because you’ve contacted the right person about something relevant to their actual job. Your sales team spends time talking to prospects instead of chasing down correct contact information. And perhaps most importantly, your cost per acquisition drops because you’re not burning budget on dead-end leads.
Accuracy Benchmarks: What’s Actually Good?
Let’s talk numbers. When someone sells you a contact list with “95% accuracy,” what does that actually mean? And more importantly, how does that stack up against reality?
Breaking Down Quality by Data Type
Email addresses should be your most accurate data point. After running them through a verification service, you should see 95% or higher validation rates for quality data. Between 90-95% is acceptable. Anything below 90% means you’ve got a serious quality problem with your data source.
Phone numbers are trickier. Direct dial numbers—actual desk lines or mobile phones—should connect about 80% of the time or better. Between 70-80% is acceptable, but below 70% means you’re mostly getting switchboard numbers or disconnected lines.
Job titles should be accurate at least 90% of the time, with 85-90% being the acceptable range. Company information, like email deliverability, should be right 95% or more of the time.
How Different Data Sources Stack Up
Not all data providers are created equal. From our experience working with hundreds of campaigns, here’s what we typically see: ZoomInfo delivers 90-95% accuracy, making them one of the more reliable premium sources. Apollo ranges from 85-92%, which is solid for the price point. Lusha tends to hit 85-90%. LinkedIn, when you’re looking at current profile information, is 95% or higher—but that’s because you’re seeing what people actually put on their profiles right now.
On the other end of the spectrum, purchased email lists typically deliver 60-80% accuracy at best. And that old CRM data you’ve been sitting on? It’s probably only 50-70% accurate if you haven’t touched it in a while.
The Age Problem
Here’s something most data providers don’t advertise: contact data has an expiration date. Fresh data, meaning zero to 30 days old, should be 95% accurate or better. After one to three months, that drops to 90-95%. Between three and six months, you’re looking at 85-90%. Six to twelve months old, and accuracy falls to 70-85%. After a year, you might as well flip a coin—accuracy drops to 50-70%.
Think about your own career. How many colleagues have changed jobs in the past year? How many got promoted or moved to different departments? That’s the reality playing out across your entire contact database.
The Brutal Reality of Data Decay
Contact data doesn’t just sit there staying accurate. It actively degrades, month after month, whether you’re using it or not.
Why Your Data Is Dying Right Now
The average B2B professional stays in a job for about 2-3 years. That sounds stable, but it means roughly 3% of your database changes jobs every single month. And job titles change even more frequently—someone might stay at the same company but get promoted from Manager to Director, making your old title data obsolete.
Companies themselves are constantly in flux. They get acquired and consolidated under new domains. They shut down entirely. They migrate to new email systems, invalidating every address you had. Teams restructure, eliminating entire departments and roles that existed just months ago.
Email addresses face their own set of challenges. Companies upgrade to new email systems. They migrate domains after rebrands or acquisitions. Accounts get deactivated when people leave. Security teams implement new policies that change email formats.
Who Moves the Most?
Data decay isn’t evenly distributed across all contacts. C-level executives have annual turnover rates of about 15-20%, making them relatively stable—though good luck getting past their gatekeepers. VPs turn over at 20-25%. Directors at 25-30%. Managers at 30-35%. Individual contributors have the highest turnover at 35-40%.
This creates an interesting dynamic. The senior people you most want to reach are more likely to still be in their roles, but they’re also the hardest to contact directly. The individual contributors who are easier to reach are also the most likely to have moved on since you got their information.
How to Actually Measure Your Data Accuracy
Theory is great, but you need concrete numbers to know if your data is performing. Here’s how to measure accuracy at each stage of your outreach.
Before You Hit Send
Start with email verification. Take your list, run it through a verification tool like NeverBounce or ZeroBounce, and see what comes back. A typical run might look like this: out of 1,000 emails, you get 850 marked as valid (85%), 100 as invalid (10%), 30 as risky (3%), and 20 as unknown (2%).
That 85% valid rate tells you something important about your data source. If you’re consistently seeing below 90% validation, it’s time to find a better provider.
During Your Campaign
Your bounce rate is your real-time accuracy gauge. Track both hard bounces—emails to addresses that don’t exist—and soft bounces from temporary issues like full inboxes. You should be targeting less than 2% bounce rate. If you’re seeing 5% or higher, you have a data quality problem that needs immediate attention.
Here’s the thing about bounce rates: they’re more honest than any vendor’s accuracy claims. A provider might tell you their data is 95% accurate, but if your campaign bounces at 8%, the real-world accuracy is nowhere near that number.
After Your Campaign
Calculate your actual accuracy with this simple formula: take your total delivered emails, subtract your bounces, divide by emails sent, and multiply by 100. Then compare this to what your verification tool predicted.
If you’re doing better than the verification predicted, you’ve found a good data source. If you’re doing worse, something went wrong—either your data source has quality issues, or your verification tool isn’t catching problems it should.
Five Strategies to Improve Your Data Accuracy
Knowing you have bad data is only useful if you do something about it. Here are five strategies that actually work.
Strategy One: Verify Everything, Every Time
This seems obvious, but you’d be surprised how many companies skip this step. Before any campaign, export your contact list, run it through a verification service, remove the invalid addresses, and only send to the valid ones. This simple step can save your sender reputation and dramatically improve your campaign performance.
Don’t just use any verification tool—use ones with proven track records like NeverBounce, ZeroBounce, MillionVerifier, or Bouncer. The few cents per contact you spend on verification is nothing compared to the cost of damaged sender reputation.
Strategy Two: Never Rely on a Single Source
Use multiple data sources and cross-reference them. Start with a primary provider like Apollo or ZoomInfo. Use LinkedIn to verify that people are still in the roles your primary source claims. For your most important target accounts, do manual research to confirm everything is current.
When the same email address shows up in multiple sources, you can be more confident it’s accurate. When different sources give you different emails for the same person, that’s your cue to dig deeper and figure out which one is actually current.
Strategy Three: Enrich and Validate Together
Don’t just collect data—validate it as you go. Start with basic contact information, enrich it with additional fields from multiple sources, validate the most important fields (especially email), and assign each contact a confidence score based on how well everything checks out.
This layered approach catches inconsistencies early. If your data provider says someone is a Director but LinkedIn shows them as a Manager, you’ve got a red flag that needs investigation.
Strategy Four: Treat Freshness as a Feature
Set up regular maintenance cycles. Re-verify any list that’s older than 30 days before you use it in a campaign. When contacts engage with your emails or show other activity signals, use that opportunity to refresh their data. Remove bounced addresses immediately—don’t let them sit in your database. At minimum, refresh your entire database quarterly.
Think of contact data like produce at the grocery store. You wouldn’t buy milk and expect it to stay fresh for six months. Your contact data has an expiration date too.
Strategy Five: Track Quality by Source
Create a simple scorecard for each data source you use. Let’s say you send 500 emails from Apollo and get 15 bounces—that’s 97% accuracy. You send 200 emails from manually researched contacts and get 2 bounces—99% accuracy. You send 300 emails from an old purchased list and get 45 bounces—85% accuracy.
Now you know to invest more in manual research and Apollo, and to stop buying those cheap lists. Your data becomes a feedback loop that gets better over time.
The Quality vs. Cost Calculation
Here’s where it gets interesting. Cheap data isn’t actually cheap when you factor in the real costs.
The Real Economics of Data Quality
Low-quality data might cost $0.05 per contact with a 15% bounce rate. That’s $0.06 per delivered contact. Medium-quality data costs $0.15 per contact with a 5% bounce rate, working out to $0.16 per delivered contact. High-quality data costs $0.30 per contact with a 2% bounce rate, which is $0.31 per delivered contact.
Looking at these numbers, cheap data seems appealing. But this calculation misses three critical factors: the reputation damage from all those bounces, the wasted outreach to wrong or outdated contacts, and the opportunity cost of missing your actual target because you contacted someone who left the company months ago.
When to Invest in Quality
Pay for quality data when you’re running cold email campaigns where deliverability is everything, when you’re targeting enterprise accounts where every contact counts, when you’re sending high volumes that could trigger reputation issues, or when your sender reputation is critical to your business.
You can accept lower quality data when you’re just testing a new market and learning, when the outreach is low-stakes, when budget is genuinely constrained, or when you need a temporary list for a one-off campaign.
But here’s the truth: in almost every scenario, quality data performs better enough to justify the extra cost.
Building Your Data Accuracy Workflow
Let me walk you through what a proper data accuracy workflow looks like in practice.
Before Outreach: The Setup Phase
Start by getting data from your chosen source. Immediately run email verification on the entire list. If less than 95% comes back as valid, question whether this source is worth using at all. Remove all invalid addresses. Spot-check 5-10 titles on LinkedIn to verify accuracy. Flag any catch-all addresses, as these often bounce even when verification tools mark them as valid. Only then should you import your clean list into your outreach tool.
This might seem like a lot of steps, but each one catches problems before they can damage your campaign performance.
During Outreach: The Monitoring Phase
Check your bounce rate daily. If you see it creeping above 2%, pause the campaign and investigate immediately. Something is wrong—either your data source degraded, your verification missed issues, or you’re hitting a specific segment with worse data quality. Remove bounced addresses from your list as soon as they occur. Track performance by data source so you can identify which providers are performing well. Adjust your sourcing strategy based on what the numbers tell you.
After Outreach: The Learning Phase
Calculate your actual accuracy rate for the campaign. Compare it to what your verification tool predicted. Evaluate each data source’s performance. Update your processes based on what you learned. Document your findings so you don’t repeat the same mistakes.
This closed-loop system means every campaign makes your next campaign better.
Key Takeaways
Data accuracy isn’t some technical detail to ignore—it’s the foundation that everything else in your outreach is built on. Target at least 90% email deliverability after verification, and don’t settle for less. Remember that your data is decaying at 2-3% per month whether you’re using it or not, so freshness matters. Always verify emails before sending, no matter how trustworthy your data source claims to be. Monitor bounce rates as your real-time quality signal—they’ll tell you the truth when vendors won’t. And finally, pay more for quality data because it’s worth it when you factor in all the hidden costs of bad data.
Quality data isn’t expensive—bad data is.
Ready to Fix Your Data Quality Issues?
We’ve built data quality systems for hundreds of B2B campaigns, and we’ve seen every data problem you can imagine. If your bounce rates are too high, your response rates are too low, or you’re just not sure if your data is performing the way it should, let’s talk.
Book a call with our team, and we’ll help you diagnose your data quality issues and build a system that actually works.