Marketing Data Audit Worksheet
A step-by-step framework for auditing scattered marketing data, building unified reporting, and surfacing the metrics that HR Tech buying committees actually care about.
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How to use this worksheet
Work through each section in order. The audit typically takes 2–3 hours for the inventory, then 1–2 weeks to implement the unified view. Don’t try to fix everything at once — the goal is to know what you have, what’s missing, and what to build first.
This worksheet is built for HR Tech marketing teams that have outgrown spreadsheets but haven’t yet built disciplined reporting. That’s most companies between Series A and Series C.
Section 1: Data source inventory
List every place marketing data currently lives. Be thorough — the ones you forget are usually the ones causing the most confusion.
| Source | What it tracks | Who owns it | Last time someone looked at it | Confidence level (High/Med/Low) |
|---|---|---|---|---|
| CRM (e.g., Salesforce, HubSpot) | ______ | ______ | ______ | ______ |
| Marketing automation | ______ | ______ | ______ | ______ |
| Google Analytics / web analytics | ______ | ______ | ______ | ______ |
| Ad platforms (LinkedIn, Google) | ______ | ______ | ______ | ______ |
| Email platform | ______ | ______ | ______ | ______ |
| Event/webinar tools | ______ | ______ | ______ | ______ |
| Social media analytics | ______ | ______ | ______ | ______ |
| Spreadsheets (be specific) | ______ | ______ | ______ | ______ |
| ______ | ______ | ______ | ______ | ______ |
| ______ | ______ | ______ | ______ | ______ |
The “spreadsheet graveyard” check
Count the spreadsheets in your inventory. If you have more than three that people actively reference for marketing decisions, you have a data fragmentation problem. That’s not a judgment — it’s a symptom that your reporting infrastructure hasn’t scaled with your team.
Number of active spreadsheets: ______
Which ones contain data that doesn’t exist anywhere else?
Those are your highest-priority items to consolidate. Data that only lives in a spreadsheet is one accidental deletion from gone.
Section 2: The zombie report audit
Most marketing teams produce reports nobody reads. Identify them so you can stop wasting time.
For each recurring report your team produces:
| Report name | Frequency | Who receives it | Who actually reads it | Decision it informs | Verdict: Keep / Kill / Redesign |
|---|---|---|---|---|---|
| ______ | ______ | ______ | ______ | ______ | ______ |
| ______ | ______ | ______ | ______ | ______ | ______ |
| ______ | ______ | ______ | ______ | ______ | ______ |
The test: If a report doesn’t directly inform a decision or action, it’s a zombie. Kill it. Your team’s time is better spent on reports that change behavior.
Common zombie reports in HR Tech marketing
- Monthly “website traffic” reports that nobody acts on
- Social media follower counts sent to leadership
- “Campaign recap” decks that get filed and never referenced
- Weekly lead counts without source attribution or quality assessment
Section 3: Metrics that matter — by audience
The metrics your marketing team tracks internally are different from the metrics your leadership team needs to see, which are different from what the board cares about. Align them.
Tier 1: Board / Executive level
These connect marketing to business outcomes. Report monthly or quarterly.
| Metric | Current value | Source of truth | How it’s calculated | Confidence |
|---|---|---|---|---|
| Pipeline generated by marketing | ______ | ______ | ______ | ______ |
| Marketing-sourced revenue | ______ | ______ | ______ | ______ |
| Customer acquisition cost (CAC) | ______ | ______ | ______ | ______ |
| Pipeline velocity (days to close) | ______ | ______ | ______ | ______ |
Tier 2: Marketing leadership level
These explain why Tier 1 numbers are moving. Report weekly or biweekly.
| Metric | Current value | Source of truth | Confidence |
|---|---|---|---|
| MQLs by source | ______ | ______ | ______ |
| MQL → SQL conversion rate | ______ | ______ | ______ |
| Cost per MQL by channel | ______ | ______ | ______ |
| Content engagement by funnel stage | ______ | ______ | ______ |
| Email engagement (not just opens) | ______ | ______ | ______ |
Tier 3: Practitioner level
These help individual contributors optimize their work. Track daily/weekly.
| Metric | Current value | Source of truth | Confidence |
|---|---|---|---|
| Ad spend by campaign | ______ | ______ | ______ |
| Landing page conversion rates | ______ | ______ | ______ |
| Email deliverability / bounce rates | ______ | ______ | ______ |
| Organic keyword rankings | ______ | ______ | ______ |
| Social post engagement rate | ______ | ______ | ______ |
What HR Tech companies specifically get wrong
HR Tech companies often track “leads” without distinguishing between an HR Director downloading a whitepaper (high intent) and a student researching a class project (zero intent). If your MQL definition doesn’t include job title, company size, and behavioral signals, you’re counting noise as signal.
Your current MQL definition: ______
Does it filter for HR buyer titles? Yes / No
Does it filter for company size/stage? Yes / No
If either answer is no, fix that before optimizing anything else.
Section 4: Data quality assessment
For each Tier 1 and Tier 2 metric, assess the data quality:
| Metric | Do two people get the same number? | Is historical data available? | Can you explain the calculation to a new hire? | Quality grade (A/B/C/F) |
|---|---|---|---|---|
| ______ | ______ | ______ | ______ | ______ |
| ______ | ______ | ______ | ______ | ______ |
| ______ | ______ | ______ | ______ | ______ |
The “two people” test is critical. If your VP of Marketing and your demand gen manager pull “pipeline generated” and get different numbers, you have a definition problem — not a data problem. Align on definitions before building dashboards.
Section 5: Gap analysis and action plan
Based on Sections 1–4, identify your top gaps:
Data gaps (metrics you should track but can’t today):
Quality gaps (metrics you track but don’t trust):
Reporting gaps (data exists but isn’t surfaced to the right people):
90-day action plan
| Week | Action | Owner | Definition of done |
|---|---|---|---|
| 1–2 | Align on MQL definition and Tier 1 metric calculations | ______ | Written definitions reviewed by marketing + sales |
| 3–4 | Consolidate critical spreadsheet data into primary system | ______ | Spreadsheets identified in Section 1 migrated or connected |
| 5–6 | Kill zombie reports, redesign keeper reports | ______ | Report list from Section 2 actioned |
| 7–8 | Build Tier 1 dashboard with trusted data only | ______ | Leadership can self-serve Tier 1 metrics |
| 9–10 | Add Tier 2 metrics to dashboard | ______ | Marketing team has weekly operational view |
| 11–12 | Establish data review cadence | ______ | Monthly data quality check scheduled |
Building the business case for this work
If you need to justify spending time on a data audit to your leadership:
The cost of bad data: Every decision made on inaccurate metrics is a gamble. If your CAC calculation is wrong by 20%, every budget allocation downstream is misinformed.
The time cost: Estimate how many hours per week your team spends pulling, reconciling, and formatting data manually: ______ hours/week × $______ loaded cost/hour = $______ /year in reporting labor.
The ask: “I need [X hours/weeks] to audit our marketing data and build a reporting framework we can trust. The output is a dashboard that replaces [Y] manual reports and gives us confidence in the numbers we’re using to make budget decisions.”