Bad CRM data, such as outdated contact lists, hurt B2B sales performance because leads are not reachable without a person on the sales team manually editing missing or inaccurate fields. This wastes time and creates distrust in the CRM data. This can look like a phone number that’s now inactive, or a position that is no longer held at the company listed; these and similar cases of stale data are the most common reasons why B2B sales teams struggle with even connecting to leads, let alone converting them.
For companies that rely on legacy contact databases, the stakes rise even higher. With no knowledge of prior validation ever being carried out on available data, lists can be questionable at best. All of this, while paying a regular subscription fee every month or year, to purchase said lists.
What can B2B sales teams do, to clean bad CRM data and keep it clean?
There are two ways that B2B sales teams can clean stale and non-functional data from their CRMs.
1. Manually calling leads, and updating them one by one
In this approach, sales teams work through lists of contacts by calling companies, sending emails, checking LinkedIn profiles and visiting company websites, to confirm whether the information in the CRM is still accurate.
They may discover that a contact has changed jobs, a company has been acquired, a phone number is disconnected, or an email address no longer works. This is then manually reflected on their record.
Although manually contacting and updating old leads in your CRM improves rates of data accuracy, it is also highly time-consuming. This is because it requires a substantial amount of time and labor. In large databases containing tens or hundreds of thousands of contacts, manual cleanup quickly becomes unrealistic.
Also, CRM data naturally decays over time. People change jobs, companies restructure, and contact details become outdated constantly. Even after a successful cleanup effort, the database can begin deteriorating again within months.
Inconsistency is also common in manual processes; different team members may update records differently, skip fields, or fail to document changes thoroughly, leading to uneven data quality across the organization.
So while manual verification can produce highly accurate data, it is difficult to scale without additional automation and data governance processes in place.
2. Automatically updating leads at scale
Automated CRM data cleanups use software and third-party integrations to continuously verify, enrich, correct, and standardize records without requiring team members to manually update every contact.
These systems can automatically:
- Verify whether email addresses are still deliverable,
- Detect job changes and company moves,
- Update company information and firmographics,
- Flag duplicate records,
- Standardize formatting across records,
- Remove inactive or invalid contacts,
- Sync data from external databases and enrichment providers,
- Trigger workflows when records become stale.
Instead of relying on periodic cleanup projects, automation enables ongoing CRM maintenance in real time or at scheduled intervals. Data cleanup tools can also validate and refresh thousands (if not millions) of records far faster than any manual team could. This is especially important for outbound sales organizations that rely on large prospect databases.
Automation also improves consistency. Rules and workflows ensure that records are formatted uniformly, duplicate entries are handled systematically, and required fields remain populated across the CRM.
In addition, automation reduces the burden on sales teams, for keeping CRM databases clean. Rather than spending valuable selling time correcting records, team members can instead focus on prospecting, relationship-building, and closing deals.
Why automation alone cannot fix bad CRM data
As a data cleanup and enrichment partner for B2B sales teams, YourICP estimates that over 500 hours of GTM team time is wasted annually because of poor and/or incomplete data. With contact lists having a tendency to bloat (especially for organizations that experience a high volume of inbound leads and inquiries), automating data cleanup and enrichment processes is a viable solution.
However, automation also has its pitfalls. Poor integrations, outdated enrichment providers, or incorrect matching logic can introduce new inaccuracies into your original data. In some cases, automated updates may overwrite valid information with incorrect data.
There is also the risk of creating a false sense of confidence; even highly automated data cleanup processes still require human oversight, governance, and periodic audits to ensure data quality remains high.
Additionally, data validation isn’t the same as data verification:
- Data validation is confirming whether a contact detail, such as a phone number or email address, is active and functional,
- Data verification is confirming whether a lead is actively available at the company their current record says they work for, while also being part of your ICP.
Many data cleanup tools only offer data validation, and not data verification. This is something you need to confirm as an organization, before embarking on an automated initiative to clean your CRM data.
How should you start cleaning your CRM data?
1. Audit your current CRM data
Assess the current state of your database, by identifying common issues affecting sales performance, such as duplicate or missing records and inconsistent naming conventions. This initial audit helps quantify the scale of the problem and highlights which data issues are causing the biggest operational bottlenecks.
Many organizations also assign a sales operations or RevOps team to oversee CRM governance and monitor data quality metrics over time.
2. Standardize your data entry rules
Establish clear standards for naming conventions and other necessary classifications. Consistency makes reporting more accurate and ensures everyone across sales, marketing, and customer success works from the same information. If you are partnering with a data cleanup and enrichment solution such as YourICP, this standardization will come in handy as cleanup workflows can be oriented to this standardization, for your updated CRM data.
3. Enrich data that is missing any important fields
Once the database is cleaned, focus on enriching key fields such as:
- Job titles,
- Direct phone numbers,
- Industry information,
- Company size,
- Buying roles,
- Geographic data.
While data cleaning and enrichment solutions like CleanICP can help facilitate and augment this process, organizations should also encourage team members to update records regularly after customer interactions.
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