How to Audit Your Marketing Data for Accuracy and Insight

Every KPI you report and every decision you make depends on the quality of your data. If your marketing data is incomplete, inconsistent, or duplicated, your insights will always be misleading.

B2B teams rely on accurate data for pipeline forecasting, attribution, lifecycle tracking, and campaign optimisation. Yet for many organisations, the CRM is filled with gaps, old records, and conflicting information that reduce confidence in reporting.

A structured marketing data audit helps you clean your CRM, standardise your properties, remove clutter, and build a reliable data foundation that supports RevOps growth. This guide explains how to audit your data from top to bottom and turn it into a source of insight rather than friction.

Why Marketing Data Accuracy Matters

Accurate data is the engine behind every revenue decision. When your CRM is clean and structured, your reports are reliable, your teams are aligned, and your pipeline forecast becomes more predictable.

When your data is messy, you face challenges such as:

• Duplicate contacts inflating your lead volume

• Inconsistent lifecycle stages disrupting attribution

• Incorrect contact owners slowing follow up

• Missing company association preventing accurate segmentation

• Faulty timestamps causing misleading trend analysis

Data problems expand as systems grow. A regular audit ensures everything remains accurate and aligned with how your team works.

Understanding What a Marketing Data Audit Covers

A marketing data audit looks beyond simple record cleaning. It evaluates four key areas.

1. Data completeness

Do your contacts and companies have the minimum fields required for segmentation, qualification, and reporting?

2. Data accuracy

Are your property values correct, consistent, and aligned with your processes?

3. Data structure

Are your lifecycle stages, lead statuses, and custom properties used correctly?

4. Data integrity across systems

Do your integrations send correct values into HubSpot or your CRM without overwriting accurate fields?

Together, these ensure your reports reflect reality and your operations run smoothly.

Step 1: Map Your Data Sources

Begin your audit by identifying every system that sends data into your CRM.

Common sources include:

• Website forms

• Landing pages

• Email marketing tools

• Sales engagement platforms

• Event platforms

• Webinar systems

• Lead generation partners

• Paid ads integrations

• Manual uploads from sales

Create a list of all these sources and note what data each sends. This prevents accidental overwrites and helps you identify inconsistencies such as different naming conventions or conflicting lifecycle values.

Step 2: Review Your Data Properties

Your property structure influences how your CRM functions.

Check for property duplication

Often, the same field exists twice due to past imports or integrations. For example:

• Job title vs role

• Industry vs sector

• Lifecycle vs lead stage

Duplicate fields cause confusion and inaccurate reporting.

Check for incomplete or unused properties

Any property that is rarely used or contains outdated values should be deleted or merged.

Standardise property naming

Ensure names are clear, descriptive, and consistent across teams.

Step 3: Analyse Record Quality

This is the core of your audit. Review all contact and company data for accuracy and consistency.

1. Duplicate records

Use HubSpot duplicate management tools to identify and merge duplicates.

2. Missing mandatory fields

Define minimum requirements such as:

• First name

• Last name

• Email

• Company

• Lifecycle stage

• Lead source

• Industry

Check how many records fail this requirement.

3. Incorrect lifecycle stages

Check whether contacts progress through stages correctly or remain stuck in early stages.

4. Missing associations

Many CRMs contain contacts with no linked company.
This breaks attribution and segmentation.

5. Faulty timestamps

Check for incorrect dates in fields such as first conversion or lifecycle change which impact trend reports.

This section of the audit often reveals the largest issues.

Step 4: Validate Lead Sources and UTM Tracking

Attribution accuracy depends entirely on correct source tracking.

Review your lead source values

Ensure sources such as organic, paid, social, and referral are correct and consistent.

Check UTM parameters

UTM values should be standardised across campaigns to support consistent reporting in HubSpot or your CRM.

Correct any overwrites

Some integrations overwrite the original UTM values which corrupts your reporting. Fix rules inside HubSpot to preserve first touch and last touch values.

Step 5: Audit Forms and Data Capture Points

The biggest source of poor data quality is inconsistent or overly flexible forms.

Ensure all forms capture standard fields

These typically include:

• Name

• Email

• Company

• Job title

• Lead source

• Consent

Remove open text fields where possible

Instead use dropdowns and controlled values to maintain consistency.

Check each form integration

Ensure forms on landing pages, pop ups, and external tools send data into the correct fields.

Step 6: Review Integration Data Integrity

Every integration should follow clean data rules.

Check for syncing issues

Identify cases where integrations fail or send incomplete data.

Ensure no field overwrites

Sales engagement platforms or lead providers may overwrite lifecycle stages or owner fields.

Validate mapping templates

Review field mappings to ensure correct connections.

Data flow problems from integrations often impact CRM health the most.

Step 7: Evaluate Segmentation and List Accuracy

Segmentation depends on clean inputs.

Review active lists

Remove or update outdated lists that were created for old campaigns.

Check list filters

Filters based on incorrect or inconsistent fields will skew reporting.

Consolidate overlapping lists

Too many segments can introduce complexity and errors.

Step 8: Audit Reporting Dashboards

Your dashboards reflect the quality of your data.

Identify reports showing incorrect numbers

Common issues include:

• Mismatched lifecycle stages

• Double counting contacts

• Incorrect first touch attribution

• Inflated lead numbers due to duplicates

Outline which reports need correction, then link fixes to the earlier steps of your audit.

Step 9: Fix Data with a Structured Cleanup Plan

Once issues are identified, create a structured plan to fix them.

1. Standardise property formats

Align dropdowns, date formats, and naming rules.

2. Merge or delete unnecessary fields

This reduces clutter and improves performance.

3. Clean duplicate contacts and companies

Use automated tools and manual review for accuracy.

4. Reassign lifecycle stages

Move contacts into the correct stage based on real behaviour.

5. Update inaccurate values

Correct job titles, industries, and lead sources to improve segmentation.

A cleanup plan turns audit findings into measurable improvements.

Step 10: Build an Ongoing Data Quality Framework

Data quality is not a one time project. It requires ongoing governance.

Create a data owner role

Assign responsibility for ongoing maintenance.

Set quarterly audits

Review properties, integrations, and reporting every quarter.

Document data rules

Document property definitions, naming rules, and lifecycle logic for cross team alignment.

This ensures long term accuracy across marketing, sales, and operations.

Bringing It All Together

Marketing data is the foundation of every growth decision.
A thorough audit uncovers gaps, inconsistencies, and blind spots that prevent accurate reporting and forecasting.

When your CRM is clean and structured, your revenue insights become sharper, attribution improves, and sales follow up becomes more reliable.

Digitalscouts helps B2B organisations build robust data foundations that support RevOps growth.
From data audits to CRM optimisation and HubSpot automation, we ensure your data works as hard as your marketing efforts.