Sales teams lose roughly 17% of their time fixing data errors—that’s nearly one full workday every week. And companies lose an average of 12% of potential revenue to bad data.
Your CRM database holds some of your most valuable customer data, but data quality issues creep in fast.
So how do you actually get your CRM data back in shape?
These five CRM data cleansing tips will help you fix incorrect data, prevent future data issues, and keep your customer database accurate.
What is CRM data cleansing?
CRM data clean up (also called “CRM data cleansing”) is a set of processes that are used to improve the accuracy, validity, and consistency of the data in your CRM. Data cleanup can fix a multitude of problems, including:
- Outdated contact information
- Incomplete customer records
- Duplicate entries
- Missing data fields
- Typos and spelling errors
- Inconsistent formatting
- Extra spaces
- Invalid records
And more.
Why bad data hurts your CRM efforts
Your sales team wastes hours chasing outdated contacts. Your marketing and sales campaigns target the wrong segments. Your business intelligence tools spit out numbers no one trusts.
That’s what dirty data does to your CRM efforts.
According to Experian, up to 25% of CRM data becomes inaccurate every year, and duplicate records account for 15–20% of all data in the average organization.
Not great.
Just think about all the ways CRM data goes bad:
- Web forms get filled out with typos and errors
- New contacts get added with incomplete data
- Multiple teams add the same contact, creating duplicate records
- Customer data collected over the phone gets typed incorrectly
- Prospects enter fake contact info to avoid sharing personal details
- Formatting conventions get ignored or applied inconsistently
- Contacts get new titles, phone numbers, or email addresses—or leave the company altogether
- Companies merge, relocate, rebrand, or go out of business
No matter how well-protected you think your CRM setup is from bad data, it’s inevitable that some messiness will creep it’s way in.
And what does that bad data actually cost you?
- Wasted time as sales reps sort through inaccurate or missing data
- Inability to move prospects through the sales process efficiently
- Limited ability to segment audiences for marketing and sales campaigns
- Missed cross-sell and upsell opportunities
- Inaccurate forecasts and unreliable data driven decisions
- A disjointed customer experience that damages customer relationships
If you’re seeing these warning signs—duplicate records, missing fields, outdated contacts—it’s time for a CRM audit. Following CRM best practices starts with clean data.
The fix? Systematic cleanup.
5 tips for CRM data cleaning
A successful data cleaning process doesn’t require overhauling your entire database at once. Start with these five practical steps for CRM data cleaning.
1. Fix formatting issues and standardize formats
Improperly formatted data is one of the most common problems in any CRM. Phone numbers entered as 555-555-5555 in one record and (555) 555-5555 in another, state fields with CA, Cal, and California all mixed together, names with inconsistent capitalization, and so on.
Why does this matter?
Inconsistent formatting makes your search and segmentation unreliable.
A filter for California contacts won’t catch records entered as “Cal” or “CA.” Deduplication tools miss matches when the same data appears in different formats. And personalized emails with uncapitalized names create an unprofessional impression.
Start your data entry cleanup by standardizing formats across key CRM fields.
The most effective approach is to set property limits and use dropdowns instead of free-form text. If your state field only accepts two-character abbreviations, you’ll never have to correct “Mich” to “MI” again. Consistent data starts with consistent format rules, and building those naming conventions into your CRM prevents future cleanup headaches.
Once your formats are consistent, you can focus on consolidating your fields.
2. Consolidate and standardize data fields
It may seem counterintuitive, but more customer data isn’t always a good thing—because the more data you have in your CRM, the harder it is to keep clean.
Of course it would be nice to know every detail about every lead, prospect, and customer, but excess fields in your CRM create additional opportunities for data entry errors, formatting problems, and duplicate data.
Instead, think about the fields that add the most value and the CRM data you use most — then consolidate your CRM records down to the most essential fields and standardize on that list of data points. You can also minimize the data requested in your web forms to prevent data bloat and improve the rate of form completes.
Once you’ve set standards for CRM data fields, one way to avoid future inconsistencies is by defining different user roles with varying permission levels. This way, only high-level users can add or delete fields from a CRM record.
3. Merge duplicate records
Duplicate records are inevitable as your CRM database grows.
Leads get captured through outbound prospecting, web forms, industry events, social media, inbound calls, and more. Any inconsistency in CRM data entry can generate a duplicate record—and those duplicates add up fast.
What happens when customer data is scattered?
Duplicate data splits vital information between multiple contact records, limiting context for sales and support teams. You get unreliable forecasting, an incomplete view of customer history, and reps wasting time piecing together information that should be in one place. That scattered CRM data undermines everything your system is supposed to do.
Many CRMs offer built-in data matching tools, and third-party deduplication services can automate routine cleanup. But whether you’re merging manually or using automation, the goal is the same: one complete, accurate record per contact and account.
4. Create standard practices around data entry
In order to keep your data set clean and organized, you need to implement standard practices for CRM data entry and ongoing data quality.
Start by identifying your most common sources of bad data. As you worked through your CRM data cleanup, did you find a high volume of duplicate data? Consistent formatting issues? Old records that were never purged?
Understanding how your customer data can go bad will help inform your new standards and practices. Elements of your overall solution may include:
- Creating formal guidelines on naming conventions and data formatting
- Training sessions to educate and engage regular CRM users
- Reducing the number of free-form text fields in your CRM records
- Defining user roles and setting permissions to limit the number of people with editing ability
- Developing a process for purging records after an extended period of inactivity
5. Implement a schedule for ongoing CRM data hygiene
A one-time cleanup isn’t enough. Data decays at roughly 30% per year—contacts change job titles, companies rebrand or close, and human error creeps in no matter how tight your standards are.
How often should you clean?
We’d suggest at least monthly. Ideally you can build out a cadence where you’re running deeper reviews quarterly and/or annually as well.
- Monthly spot-checks catch obvious issues before they spread.
- Quarterly reviews dig deeper into outdated records and duplicate patterns.
- Annual audits give you a comprehensive look at your existing data and a chance to reinforce standards with your team through CRM onboarding refreshers.
In practice, the data cleansing process never really ends—but with the right CRM, maintaining that hygiene becomes much easier.
Which leads us nicely to…
How to maintain CRM data quality with Insightly
Clean data powers accurate forecasting, stronger customer relationships, and confident decision-making. The key is making sure your CRM system helps you maintain that data quality—not fight against it.
That’s where Insightly comes in.
Insightly includes built-in tools that make clean data easier to achieve and maintain:
- Import clean data with built-in migration tools
- Prevent errors at entry with field restrictions and property limits
- Connect to 2,000+ apps for automated data enrichment through AppConnect
When your data is accurate and usable, CRM adoption follows naturally—teams actually use what works. Get started with a free trial of Insightly CRM today, or request a personalized demo to see how it works for your team.
CRM data cleansing FAQ
Common questions about CRM data cleansing and keeping your database accurate.
How often should you run a CRM data cleaning process?
Most teams benefit from monthly spot-checks, quarterly deeper reviews, and annual comprehensive audits.
The right frequency depends on your data volume and how many sources feed your CRM—high-velocity sales teams with multiple lead channels may need more frequent actual cleaning cycles.
For a systematic framework, start with a CRM audit.
What’s the difference between CRM data cleansing and CRM data management?
Data cleansing is reactive—fixing errors, merging duplicates, removing outdated records. Data management is the broader discipline that includes data governance, access controls, ongoing maintenance, and long-term strategy.
Cleansing fixes problems; management prevents them.
For the full picture, check out our guide to CRM best practices.
Can you automate the cleansing process?
Yes, to a degree. Third party tools and native CRM features can handle routine cleanup tasks. Automated deduplication workflows catch duplicates as they’re created, and data enrichment services refresh firmographic data and job titles automatically.
If you’re using Insightly as your CRM platform, AppConnect lets you connect enrichment tools without code. That said, manual review is still needed for complex merges and judgment calls where data quality depends on context.
