Eliminating contact data quality issues
Ensure Data Quality
Data Challenge
Contact data quality issues in the Customer Relationship Management (CRM) system are preventable at the point of entry. Most systems accept an incomplete or malformed record without warning, and the damage compounds with every downstream process that depends on it.
Each contact record spans name, title, company, email, phone, address, and segmentation fields. A blank job title means the lead scoring model cannot classify the contact. An email typo bounces on the first campaign send and degrades sender reputation. A company name entered as free text instead of linked to the existing account creates a duplicate that splits the customer’s history.
The errors follow predictable patterns. A representative creating a contact from a business card skips fields below the fold. A marketing coordinator importing a list maps “job function” to “job title,” producing records with wrong data in the wrong field. A company entered as “IBM Corp” instead of linked to the existing “IBM” account goes undetected until a territory dispute surfaces months later. These are not knowledge failures. The CRM does not enforce completeness at the point of entry, and classroom training cannot close a gap that reopens with every new hire, every list import, and every system update.
Our Solution
Userlane is a software adoption platform that works inside browser-based applications. For contact management workflows, it catches data quality issues where they happen: inside the CRM, during the task itself.
Catching incomplete or malformed records before they are saved
Validators check each contact record as the user completes it. A missing job title, an email that fails format validation, a company name entered as free text when a matching account exists: each is flagged before the record is saved, not discovered during the next data cleanse.
Guiding complex contact management workflows
In-app guidance walks sales and marketing staff through multi-step contact scenarios inside the CRM: merging duplicate records, linking contacts to accounts across subsidiaries, updating segmentation fields after a role change, bulk-updating contacts after a company acquisition. It replaces the CRM data entry guide and the request to the CRM administrator.
Tracking contact data quality over time
The HEART Score, Userlane’s application health metric, tracks the share of contact records created or updated correctly on the first attempt. The CRM operations team sees the breakdown by source, team, and field: where data quality improved and where issues remain.
Measure. Act. Prove.
Every deployment follows the same cycle. Measurement comes first, intervention second, proof third. The cycle repeats with each new workflow.
Measure
Userlane records every contact creation and update before any intervention starts. The data shows where data quality problems concentrate, so the team acts on evidence, not assumptions.
Validators observe. Every contact action is recorded: which fields are completed, which are skipped, where users enter free text instead of selecting from controlled values. Nothing changes for the sales or marketing team. The CRM works exactly as before.
A pattern emerges. Contacts created from the mobile app skip the industry field at five times the rate of desktop entries. Email format errors concentrate in one region where a legacy naming convention persists. New representatives create duplicate company records for the first month after onboarding. The HEART Score puts a single number on the workflow’s health.
The team sees the real problem. Not “everyone needs CRM data entry training.” One entry channel needs help with one specific field. The intervention writes itself.
Act
Validators and guidance deploy only where the measurement found issues. Targeting the intervention is what makes the result provable.
Validators activate. Only where issues concentrate. A missing job title, a malformed email, an unlinked company name: each is flagged before the record is saved. Teams with clean contact data see no change.
Guidance meets staff in the workflow. Complex scenarios (duplicate merges, subsidiary linking, segmentation updates, post-acquisition bulk changes) get contextual help inside the CRM. No data entry guide, no CRM admin request. The help is where the work is.
The intervention stays proportional. High-issue channels get support. Low-issue channels are left alone. New representatives get onboarding help that experienced staff never see.
Prove
The same measurement that found the problem now tracks whether the fix worked. A rising data quality rate, confirmed against the organization’s own campaign and sales data, closes the loop.
The HEART Score moves. The same measurement that found the problem now tracks the fix. Validator pass rates by source, team, and field show whether the intervention worked.
Results track against the target. The goal the team set before the intervention tracks automatically. The quarterly review gets a before-and-after built from live data, not a separate report.
The cycle starts over. The CRM vendor releases a new contact management update. The segmentation fields change. The HEART Score flags a dip. Measure, act, prove runs again. The infrastructure is already there.
Proven Impact
When Validators and in-app guidance replace classroom training and data entry guides on contact management workflows, the results show in the organization’s own data: fewer malformed records reaching campaigns and pipeline reports, lower support volume from sales and marketing teams, and shorter time to competency for new staff.
Up to 97%
Task completion rate across enterprise deployments
Up to 75%
Reduction in training time for new staff
Up to 48%
Fewer support requests after deployment
Up to 60%
Lower training costs vs. classroom methods
Frequently Asked Questions
