Overview

After your free database health scan completes, MarketingSoda Refine™ generates a detailed report showing the overall health of your HubSpot database. This guide explains every section of your report and helps you turn insights into action.

Report Structure

Your report is divided into six sections, displayed vertically on a single page:

Report Header -- When the scan was run and how many contacts were analyzed.
Overall Health Score -- Your composite score and letter grade.
Grade Distribution -- How your contacts break down by grade.
Quality Dimensions -- Scores for each of the seven quality dimensions.
Top Issues -- The most impactful problems found.
Recommendations -- What to fix first, in priority order.

Overall Health Score

The circular gauge at the top of your report shows two things:

Composite Score (0-100)

This is the average quality score across all scanned contacts. A score of 100 means every contact is perfectly complete, accurate, fresh, valid, consistent, unique, and enriched. In practice, most B2B databases score between 45 and 75.

Letter Grade

Your letter grade is derived directly from the composite score:

Grade	Score	What It Means
A (Excellent)	90-100	Your database is in top shape. Minimal issues detected. Your campaigns, routing, and reporting can be trusted.
B (Good)	75-89	Solid data quality with room for improvement. Most campaigns will perform well, but some segments may have gaps.
C (Fair)	60-74	Notable issues that are likely affecting campaign performance and sales efficiency. Action recommended.
D (Poor)	40-59	Significant problems. Your data is likely causing bounced emails, missed opportunities, and inaccurate reporting.
F (Failing)	0-39	Critical issues. Your database needs immediate attention. Most automated workflows and campaigns are unreliable.

Grade Colors

The report uses consistent colors throughout:

Green for A grades (score 90+)
Blue for B grades (score 75-89)
Yellow/Amber for C grades (score 60-74)
Orange for D grades (score 40-59)
Red for F grades (below 40)

Grade Distribution Chart

The grade distribution chart shows how many of your contacts fall into each grade bucket.

How to Read It

Each bar represents one letter grade (A, B, C, D, F).
The height of the bar corresponds to the number of contacts with that grade.
The total of all bars equals the total contacts scanned.

What It Tells You

Concentrated at the top (A/B): Your database is generally healthy, with most contacts in good shape.
Spread across all grades: Mixed quality -- some records are great, others need work. This is common.
Concentrated at the bottom (D/F): Widespread data quality problems. Most contacts need attention.
Bimodal (high A count and high F count): Your database has two distinct populations -- recently enriched or new contacts alongside old, neglected records.

Actionable Insight

The percentage of contacts graded C or below is a key metric. If more than 40% of your contacts are C/D/F, your campaigns and reporting are likely being materially affected.

Quality Dimensions Breakdown

This section shows seven horizontal progress bars, one for each quality dimension.

How to Read Each Bar

Label on the left shows the dimension name.
Score on the right shows the dimension average (0-100).
Bar fill is proportional to the score, with color matching the grade for that score.

The Seven Dimensions Explained

Completeness (Score 0-100)

Measures how many important fields are filled in across your contacts. A score of 100 means every contact has all key fields populated. Fields checked include: first name, last name, email, phone, job title, company, city, state, country, LinkedIn URL, seniority, and department.

Low score means: Many contacts are missing critical fields like job title, company name, or phone number. This limits your ability to segment, personalize, and route leads effectively.

Accuracy (Score 0-100)

Measures whether the data in your contact records appears to be correct and internally consistent. This includes email format validation and cross-referencing related fields.

Low score means: Some contact data may be incorrect -- wrong email formats, company names that do not match other records, or data that fails basic verification checks.

Freshness (Score 0-100)

Measures how recently your contact records have been updated. Records not modified in over 6 months receive lower freshness scores.

Low score means: Many contacts have not been updated recently. In B2B, approximately 34% of contact data becomes outdated every year due to job changes, company moves, and phone number changes.

Validity (Score 0-100)

Measures whether field values conform to expected formats. For example, does the email field contain a properly formatted email address? Does the phone number field contain a valid phone format?

Low score means: Some records contain malformed data -- emails without proper domains, phone numbers with incorrect digit counts, or other formatting issues that will cause failures when used.

Consistency (Score 0-100)

Measures whether data follows standardized patterns across your entire database. For example, are states always abbreviated the same way? Are country names consistent?

Low score means: Your data uses inconsistent formats. Some contacts might have "California" while others have "CA" or "Calif." This breaks segmentation, reporting, and automation rules.

Uniqueness (Score 0-100)

Measures how many duplicate contacts exist based on shared email addresses. A score of 100 means every contact has a unique email address.

Low score means: Your database contains duplicate contacts. Duplicates waste enrichment credits, create confusing experiences for sales reps, and inflate your contact metrics.

Enrichment Coverage (Score 0-100)

Measures how many contacts have professional enrichment data such as LinkedIn URLs, job titles, company names, seniority levels, and department information.

Low score means: Many contacts are missing professional data that would enable better targeting, personalization, and lead scoring. These contacts are harder to segment and prioritize.

Top Issues Found

This section highlights up to five of the most significant problems found during the scan.

How Issues Are Selected

Issues are dimensions where the average score falls below 80. They are sorted from worst (lowest score) to best, and the top five are shown.

What Each Issue Card Shows

Field	Description
Issue title	A description of the problem (e.g., "Low completeness score")
Dimension	Which quality dimension is affected
Severity badge	High (score below 40), Medium (40-59), or Low (60-79)
Contacts affected	Number of contacts scoring below 60 in that dimension
Average score	The average score for that dimension across all contacts

If No Issues Are Found

If all seven dimensions score 80 or above, you will see a congratulatory message instead of issue cards. This means your database is in excellent shape.

Recommendations

Recommendations appear below the top issues and provide specific, actionable steps to improve your data quality.

How Recommendations Are Generated

For each dimension scoring below 80, MarketingSoda Refine generates a targeted recommendation. These are ordered by impact (worst dimensions first).

What Each Recommendation Includes

Field	Description
Priority number	1 = most impactful, numbered in order
Title	A clear action (e.g., "Improve data completeness")
Description	Specific steps to take
Impact badge	High, Medium, or Low -- based on how much improvement is possible
Dimension	Which quality dimension this recommendation addresses

Recommendation Examples

Dimension	Recommendation
Completeness	Fill in missing contact fields like job title, company, and phone number to improve targeting and personalization.
Accuracy	Cross-reference email addresses and company names against authoritative sources to reduce bounce rates.
Freshness	Review contacts not modified in over 6 months and verify their information is still current.
Validity	Correct malformed email addresses, phone numbers, and other fields that fail format validation.
Consistency	Normalize inconsistent field values like state abbreviations, country names, and phone number formats.
Uniqueness	Merge or deduplicate contacts that share the same email or appear to represent the same person.
Enrichment Coverage	Use data enrichment tools to fill LinkedIn URLs, job titles, and other missing professional data.

How to Prioritize Fixes

The 80/20 Rule for Data Quality

Most databases can see dramatic improvement by focusing on just one or two dimensions:

Start with High-severity issues. These have the biggest impact on your operations.
Fix completeness first if it is low -- missing data affects every other dimension.
Address uniqueness early -- duplicates skew all other metrics.
Freshness requires ongoing effort -- set up a process, not a one-time fix.

Quick Wins

Completeness: Use HubSpot's bulk edit to fill in common missing fields.
Validity: Export contacts with malformed emails and correct them.
Uniqueness: Use HubSpot's built-in merge tool for obvious duplicates.

Longer-Term Improvements

Accuracy and enrichment coverage: Consider data enrichment tools to fill gaps.
Freshness: Set up automated re-verification workflows.
Consistency: Implement data standardization rules on import and entry.

Each report has a unique URL that you can share with colleagues. The URL format is:

https://marketingsoda.ai/en/audit/results/{report-id}

Anyone with the link can view the report -- no login required. The report contains only aggregate data, never individual contact information.

Report Expiry and Re-Running

Report Expiry

Reports are available for 12 months from the date of the scan. After expiry, the report page displays a message with an option to run a new scan.

Running a New Scan

You can run a new scan at any time:

Visit marketingsoda.ai/en/audit.
Connect your HubSpot again.
A fresh report will be generated with current data.

Running a new scan does not delete your previous reports. Each scan creates an independent report.

Benchmarks: How Do You Compare?

While every database is different, here are typical ranges based on industry data:

Dimension	Typical Range	Common Pain Point
Completeness	50-80	Job title and phone number most often missing
Accuracy	60-85	Company names frequently inconsistent
Freshness	40-70	Stale records accumulate over time
Validity	70-90	Email validity is usually high; phone less so
Consistency	50-75	State/country formatting highly variable
Uniqueness	80-95	Most portals have some duplicates
Enrichment Coverage	30-60	Often the lowest-scoring dimension

Industry fact: The average B2B company loses an estimated $15 million per year to bad CRM data (IBM). Approximately 34% of B2B contact data becomes outdated annually.

Understanding Your Audit Results