Most RevOps teams can't answer a simple question: is our CRM data good or bad?
They know it feels messy. They see bounced emails, misrouted leads, and duplicate records. But without benchmarks, there's no way to know where you stand or how much improvement is realistic.
This post gives you the reference tables. We compiled CRM data quality benchmarks across B2B companies of every stage - from seed to enterprise - so you can score your own database and set targets that actually make sense.
The CRM Data Quality Scorecard
A CRM data quality scorecard measures ten key metrics across five rating levels: email validity rate, phone accuracy, duplicate rate, field completion, data freshness, bounce rate, contact-to-account match rate, job title standardization, address completeness, and lead source tracking. Scoring "Average" or better across all ten puts you ahead of most B2B companies.
This is the table to bookmark. Ten metrics, five ratings, clear thresholds.
Use it to audit your CRM today. Score each metric, then focus on the ones rated "Poor" or "Critical" first.
| Metric | Excellent | Good | Average | Poor | Critical |
|---|---|---|---|---|---|
| Email validity rate | Over 97% | 93-97% | 88-93% | 80-88% | Under 80% |
| Phone accuracy rate | Over 85% | 75-85% | 60-75% | 45-60% | Under 45% |
| Duplicate rate | Under 3% | 3-5% | 5-10% | 10-20% | Over 20% |
| Field completion rate | Over 90% | 80-90% | 65-80% | 50-65% | Under 50% |
| Data freshness (updated in 90 days) | Over 80% | 65-80% | 50-65% | 35-50% | Under 35% |
| Bounce rate | Under 1% | 1-2% | 2-5% | 5-10% | Over 10% |
| Contact-to-account match rate | Over 95% | 88-95% | 75-88% | 60-75% | Under 60% |
| Job title standardization | Over 90% | 80-90% | 65-80% | 50-65% | Under 50% |
| Address completeness | Over 85% | 70-85% | 55-70% | 40-55% | Under 40% |
| Lead source tracking | Over 95% | 85-95% | 70-85% | 50-70% | Under 50% |
If you score "Average" or better across all ten, you're ahead of most B2B companies. If three or more metrics land in "Poor" or "Critical," you have an urgent data hygiene problem.
Quick Assessment
Don't have time for all ten? Start with three: email validity rate, duplicate rate, and field completion rate. These three metrics predict overall CRM health with surprising accuracy.
How to Measure Each Metric
Numbers only matter if you can actually pull them. Here's how to measure each metric in Salesforce and HubSpot.
Email validity rate
What it measures: Percentage of email addresses that are deliverable.
Salesforce: Export all contact emails. Run them through an email verification service. Divide verified emails by total emails.
HubSpot: Check Marketing > Email > Health tab for bounce rate trends. For a precise count, export contacts and verify externally.
Target: 93% or higher. Anything below 88% will damage your sender reputation.
Phone accuracy rate
What it measures: Percentage of phone numbers that connect to the right person.
Salesforce: Run a sample dial test on 100-200 records. Track connect rate, wrong numbers, and disconnected numbers.
HubSpot: Same approach - there's no built-in phone accuracy metric. Sample testing is the only reliable method.
Target: 75% or higher for direct dials. Main line accuracy can be higher but is less useful for outbound.
Duplicate rate
What it measures: Percentage of records that have one or more duplicates in your CRM.
Salesforce: Setup > Duplicate Management > run duplicate jobs. Or export and match on email, name + company, or phone number.
HubSpot: Contacts > Actions > Manage Duplicates. HubSpot flags likely duplicates automatically.
Target: Under 5%. Most CRMs without active deduplication run 10-25%.
Field completion rate
What it measures: Percentage of required fields that are populated across all records.
Salesforce: Create a report with the fields you consider required. Filter for blank values. Calculate (filled fields / total required fields).
HubSpot: Use the Data Quality Command Center in Operations Hub, or build a custom report filtering for blank properties.
Target: 80% or higher for your core fields (email, company, title, phone, lead source).
Data freshness
What it measures: Percentage of records updated within the last 90 days.
Salesforce: Report on "Last Modified Date" field. Filter for records updated in the last 90 days. Divide by total records.
HubSpot: Filter contacts by "Last activity date" or "Last modified date" within 90 days.
Target: 65% or higher. If less than half your database was touched in 90 days, stale records are dragging down every campaign.
Bounce rate
What it measures: Percentage of sent emails that hard bounce.
Salesforce: Pull from your marketing automation platform (Pardot, Marketo, etc.). Salesforce CRM doesn't track this natively.
HubSpot: Marketing > Email > Analyze tab. Look at hard bounce rate across recent campaigns.
Target: Under 2%. Anything above 5% triggers ISP filtering and hurts deliverability for all future sends.
Contact-to-account match rate
What it measures: Percentage of contacts correctly associated with their company account.
Salesforce: Report on contacts where Account is blank or mismatched. Spot-check a sample of 50-100.
HubSpot: Filter contacts where Company property is blank or doesn't match the associated company record.
Target: 88% or higher. Unmatched contacts break account-based reporting and routing.
Job title standardization
What it measures: Percentage of job titles that follow a consistent format.
Both platforms: Export all unique job title values. Count the ones that map cleanly to your standardized title list. Common problem: "VP Sales" vs "Vice President of Sales" vs "VP, Sales" all meaning the same thing.
Smart Agents can normalize job titles automatically - mapping variations to a clean, consistent format.
Target: 80% or higher. Unstandardized titles break lead scoring, routing, and segmentation.
Address completeness
What it measures: Percentage of records with full mailing address (street, city, state, country).
Both platforms: Report on address fields, filter for records with all components filled. Partial addresses (country only) shouldn't count.
Target: 70% or higher for companies with field sales or territory-based routing.
Lead source tracking
What it measures: Percentage of records with an accurate lead source value.
Both platforms: Report on Lead Source field. Calculate percentage that is blank, "Other," or "Unknown."
Target: 85% or higher. Without accurate source data, attribution is guesswork and marketing can't optimize spend.
CRM Data Quality by Company Stage
Benchmarks aren't one-size-fits-all. A seed-stage startup with 5,000 CRM records faces different challenges than an enterprise with 500,000.
Here's what "good" looks like at each stage.
| Metric | Seed / Series A | Series B-C | Enterprise |
|---|---|---|---|
| Email validity rate | 90-95% | 92-96% | 94-97% |
| Phone accuracy rate | 65-75% | 70-80% | 75-85% |
| Duplicate rate | 8-15% | 5-10% | 3-7% |
| Field completion rate | 60-75% | 75-85% | 82-92% |
| Data freshness (90 days) | 50-65% | 60-75% | 70-85% |
| Bounce rate | 3-5% | 2-4% | 1-2% |
| Contact-to-account match | 70-80% | 80-90% | 88-95% |
| Job title standardization | 50-65% | 70-82% | 80-92% |
| Address completeness | 40-55% | 55-70% | 70-85% |
| Lead source tracking | 65-80% | 78-88% | 85-95% |
Why the gap? Early-stage companies typically have:
- Fewer data governance processes
- More manual data entry from multiple sources
- Less investment in enrichment tools
- Higher tolerance for messiness (speed over accuracy)
Enterprise companies benefit from:
- Dedicated RevOps teams focused on CRM hygiene
- Automated enrichment and deduplication workflows
- Established validation rules at the point of entry
- Regular audit cycles
If you're Series B and your metrics look like Seed stage, you're behind. The data problems that felt manageable at 5,000 records become crippling at 50,000.
The Most Common CRM Data Quality Problems (Ranked by Impact)
Not all data quality issues are equal. Here they are, ranked by how much damage they cause.
1. Duplicate records. Duplicates corrupt every metric downstream. Pipeline gets inflated. Sales reps work the same lead without knowing it. Attribution breaks. Fix duplicates first - it improves every other metric.
2. Invalid email addresses. Every bounced email hurts your sender reputation. Above 5% bounce rate, ISPs start filtering your domain. The damage compounds and takes months to recover.
3. Missing or inconsistent job titles. Lead scoring, routing, and segmentation all depend on knowing who you're talking to. If 30% of titles are blank or unstandardized, your lead scoring model is guessing.
4. Stale records. B2B data decays at 22.5% per year. Records untouched for 12+ months are likely wrong. Stale data inflates your "total addressable contacts" while delivering nothing.
5. Incomplete field data. A contact with just a name and email is nearly useless for sophisticated outbound. Missing company, title, phone, and industry data limits what your sales and marketing teams can do.
6. Orphaned contacts. Contacts not linked to an account record are invisible to account-based workflows. They get missed in territory routing and account-level reporting.
7. Inconsistent formatting. "US" vs "United States" vs "USA" breaks geographic segmentation. "California" vs "CA" breaks territory rules. Small inconsistencies create big problems at scale.
Priority Check
If your duplicate rate is above 15%, stop everything else and fix that first. Deduplication is the single highest-ROI data hygiene activity. Every other metric improves when duplicates are gone.
Data Quality Improvement Timeline
What's realistic after you commit to data hygiene? Here's what to expect.
| Metric | Baseline (Day 0) | 30 Days | 60 Days | 90 Days | 180 Days |
|---|---|---|---|---|---|
| Duplicate rate | 15-25% | 8-12% | 5-8% | 3-5% | Under 3% |
| Email validity rate | 78-85% | 88-92% | 92-95% | 94-97% | 96-98% |
| Bounce rate | 8-12% | 4-6% | 2-4% | 1-3% | Under 1.5% |
| Field completion rate | 55-65% | 68-75% | 75-82% | 80-88% | 85-92% |
| Data freshness | 35-45% | 50-60% | 60-70% | 68-78% | 75-85% |
| Phone accuracy | 50-60% | 60-70% | 68-78% | 72-82% | 78-86% |
| Title standardization | 45-55% | 60-70% | 72-80% | 78-86% | 84-92% |
30 days: Biggest gains come from deduplication and email verification. These are mechanical - run the tools, merge the records, remove the bounces. Expect the most dramatic improvement here.
60 days: Waterfall enrichment fills gaps in field completion. Re-enrichment catches stale records. Validation rules prevent new bad data from entering.
90 days: Processes are established. Your team is maintaining quality, not just fixing it. Metrics stabilize in the "Good" range.
180 days: With continuous hygiene running, you're approaching "Excellent" on most metrics. The focus shifts from cleanup to optimization - fine-tuning scoring models and routing rules on top of clean data.
The timeline assumes dedicated effort. If you're cleaning data one afternoon a month, double these timeframes.
How to Build a CRM Health Dashboard
Benchmarks are useful. A live dashboard is actionable. Here's what to include.
Essential dashboard widgets
- Overall CRM Health Score - Weighted average of your ten benchmark metrics. Update weekly.
- Trend charts - Each metric over the last 90 days. Are you improving or decaying?
- Records by freshness - Pie chart: updated in 30/60/90/180/365+ days.
- Duplicate creation rate - How many new duplicates are being created per week? This measures prevention.
- Enrichment coverage - Percentage of records that have been enriched vs raw entries.
- Bounce rate trend - Week-over-week bounce rate. Spikes signal data decay.
Building it
Salesforce: Use Reports and Dashboards. Create report types for each metric. Schedule weekly refresh.
HubSpot: Use the Data Quality Command Center (Operations Hub) or build custom dashboards with calculated properties.
For either platform: Start simple. Six widgets. One page. Reviewed weekly at your RevOps team standup. Add complexity only after you've acted on the basics for a month.
Automated vs Manual Data Quality Maintenance
Both have a role. The key is knowing which tasks to automate and which need human judgment.
Automate these
- Email verification on entry - Every new lead gets verified before entering the CRM. No exceptions.
- Duplicate detection - Flag potential duplicates automatically. Block exact-match duplicates from creation.
- Field formatting - Auto-capitalize names, standardize phone formats, normalize country codes.
- Enrichment triggers - When a record is created or an email bounces, trigger waterfall enrichment automatically.
- Decay alerts - Notify owners when records go 90 days without updates.
Keep these manual
- Duplicate merging - Auto-detection is reliable. Auto-merging is risky. A human should confirm which record wins, especially for fuzzy matches.
- Account hierarchy decisions - When companies get acquired, determining the right parent-child structure requires context.
- Archival decisions - Which stale records to archive vs re-enrich depends on business relationships.
- Scoring model updates - Data quality improvements change the distribution. Recalibrate scores with human oversight.
The goal: automate the repetitive, rule-based tasks. Keep humans on the judgment calls. Most teams start with 80% manual and should aim for 80% automated within six months.
The Cost of Ignoring Data Quality
Think you can skip all this? The math says otherwise.
Bad data costs the average B2B company $12.9 million per year (Gartner). Even for smaller teams, the damage is real: wasted rep time, destroyed sender reputation, broken attribution, and lost pipeline.
We break down the full cost calculation - with formulas you can use for your own database - in our post on the true cost of bad sales data.
The short version: every month you delay CRM hygiene, the problem gets 2% worse. Data decay is relentless. The cleanup cost grows while your data quality declines.
If you need a step-by-step process for getting started, our guide on how to clean your CRM data walks through deduplication, standardization, enrichment, and ongoing maintenance in detail.
Frequently Asked Questions
What is a good CRM bounce rate?
Under 2% is good. Under 1% is excellent. If your bounce rate is above 5%, your sender reputation is already taking damage and deliverability is declining across all campaigns. Verify your email list before every major send and remove hard bounces immediately.
How often should I clean my CRM?
Quarterly is the minimum for any B2B database. High-velocity teams running active outbound should clean monthly or continuously. The most effective approach is automated hygiene - verification on entry, weekly deduplication scans, and quarterly deep audits.
What's the most important CRM data quality metric?
Email validity rate and duplicate rate have the highest downstream impact. Invalid emails directly hurt deliverability and waste outreach efforts. Duplicates corrupt pipeline reporting, confuse sales reps, and break attribution. If you can only focus on two metrics, pick these.
How do I calculate my CRM health score?
Assign a weight to each of the ten metrics in the scorecard above based on your business priorities. For most B2B companies: email validity (15%), duplicate rate (15%), field completion (15%), data freshness (15%), bounce rate (10%), phone accuracy (10%), contact-to-account match (5%), title standardization (5%), address completeness (5%), lead source tracking (5%). Score each metric 1-5 based on which column you fall in, multiply by the weight, and sum.
What data quality tools should a RevOps team use?
At minimum, you need email verification, deduplication, and enrichment. Many teams use separate tools for each. A simpler approach is a platform that combines all three - waterfall enrichment verifies, enriches, and deduplicates in a single workflow, pulling from 15+ data sources to maximize accuracy and fill rates.
CRM data quality isn't a one-time project. It's a practice. Use these benchmarks to score your current state, set realistic targets by company stage, and track improvement over time. Start with the highest-impact metrics - duplicates and email validity - and build from there. Automate your data hygiene with Cleanlist and turn your CRM from a liability into a clean, actionable growth engine.