Audit Template: Do You Have Too Many Analytics Tools? (Spreadsheet + Scoring System)

Are you paying for complexity? Prune your martech scientifically with a scoring spreadsheet

Every quarter someone on your team emails a new tool that will “fix” reporting, personalization or attribution. By 2026 most teams have dozens of subscriptions—and no single person owns the bill or the data trust problem. The result: rising costs, fragmented data, and dashboards that can’t be trusted. This guide gives you a practical, repeatable spreadsheet template and scoring system to evaluate each tool on usage, cost, overlap and tracking fidelity so you can prune the stack with evidence, not opinions.

Quick summary: What you get and why it matters

• One spreadsheet template with inventory, feature matrix, normalized scoring and a net-priority score.
• Automations recipes for pulling MAU, billing and GA4/BigQuery metrics into the sheet.
• Decision thresholds and recommended actions (retain, consolidate, retire, negotiate).
• 2026 context: how AI proliferation, privacy shifts and investment pressure change the calculus.

Why a scoring spreadsheet beats subjective audits

Subjective debates (“Marketing needs this” vs “It’s redundant”) waste cycles. A structured spreadsheet forces teams to assign measurable inputs: active users, monthly cost, overlap percentage, and a data-quality score. That produces a transparent rubric and an auditable path for retirements—crucial when finance and legal need justification.

2026 trends that make tool pruning urgent

• AI-first martech: Hundreds of AI-enabled tools launched in 2024–25. Many add incremental value but also new data pipelines and model inputs—raising risk and cost.
• Data trust pressure: Enterprise surveys in late 2025 and early 2026 found that siloed, low-trust data limits how companies scale AI (see industry reports from major vendors).
• Privacy & measurement shifts: Cookieless environments, newer Consent frameworks and clean-room requirements increase the cost of maintaining redundant event tracking.
• Cost optimization imperative: Macroeconomic focus on margins means every stray subscription draws scrutiny from procurement and finance.

Core framework: What your spreadsheet contains

Build 4 sheets inside one workbook:

1. Tool Inventory — one row per tool with costs, team usage and contract terms.
2. Feature Matrix — binary feature flags so you can compute overlap.
3. Scoring Engine — normalized metrics and weighted net score.
4. Actions & Timeline — retire/keep/consolidate and negotiation calendar.

Columns for the Tool Inventory sheet

• Tool Name
• Category (Analytics, A/B, CDP, Email, Personalization, Tag Manager, ETL)
• Monthly Cost
• Annual Cost (formula =MonthlyCost*12)
• Active Users / MAU (source: internal auth logs or GA4/BI)
• Active Teams (number of teams using it weekly)
• Primary Use Case (short text)
• Contract Remaining (months)
• Data Sensitivity / Compliance Flag
• Tracking Fidelity (1-5) — defined below
• Integration Difficulty (1-5)
• Strategic Value (1-5)
• Net Score (computed)

Feature Matrix (how to measure overlap)

Create columns for core capabilities (e.g., Email Send, Segmentation, Event Ingestion, Attribution, Session Replay, Personalization, ML Scoring). Mark each tool with 1 if it has the capability, otherwise 0. Overlap is a simple Jaccard-like metric: shared features vs union of features.

The scoring system: weights and formulas

The goal: a single normalized net score (0–100) that balances cost, usage, overlap and data trust. Below is a recommended weight set you can adjust by company priorities.

• Usage (MAU & Active Teams) — 25%
• Cost Impact (annual) — 25%
• Overlap (feature redundancy) — 20%
• Tracking Fidelity / Data Trust — 15%
• Integration Difficulty / Technical Debt — 10%
• Strategic Value / Roadmap Fit — 5%

How to normalize numeric fields in Google Sheets

For each numeric input convert into a 0–1 score so you can weight-sum them. Use min-max normalization for bounded distributions and invert cost (higher cost => lower normalized score).

Examples (assume values in columns):

=IF(MAX($C$2:$C$100)=MIN($C$2:$C$100),0,(C2 - MIN($C$2:$C$100)) / (MAX($C$2:$C$100) - MIN($C$2:$C$100)))

For cost (invert so higher cost reduces score):

=1 - IF(MAX($D$2:$D$100)=MIN($D$2:$D$100),0,(D2 - MIN($D$2:$D$100)) / (MAX($D$2:$D$100) - MIN($D$2:$D$100)))

Overlap score (compute from the feature matrix)

For each tool compute the percent of its features that are duplicated by at least one other tool. Simple formula per tool:

OverlapPercent = COUNT(shared features with other tools) / COUNT(total features the tool claims)

Normalize OverlapPercent to 0–1 (higher means more redundant, worse).

Net score formula

= (UsageNorm * 0.25) + (CostNorm * 0.25) + (1 - OverlapNorm) * 0.20 + (TrustNorm * 0.15) + (IntegrationNorm * 0.10) + (StrategicNorm * 0.05)

Multiply by 100 to get a 0–100 score. Higher scores indicate tools worth keeping or consolidating into core platforms. Lower scores are candidates for retirement or negotiation.

Scoring for qualitative items: Tracking fidelity & Integration

These are subjective but crucial. Define clear rubrics so raters agree:

• Tracking Fidelity (1–5)
  • 5 — Full event coverage, auto-validation tests pass, low send errors
  • 3 — Partial coverage, occasional mismatches vs canonical events
  • 1 — Low coverage, many missing/duplicated events
• Integration Difficulty (1–5)
  • 5 — Plug-and-play connectors with clean schema mapping
  • 3 — Needs middleware or engineering support for updates
  • 1 — Custom API + manual maintenance required

Automation recipes: save time and avoid manual errors

These are practical ways to keep the sheet current.

1) Pull monthly active users (MAU) from GA4 / BigQuery

Use the native BigQuery export from GA4 and the Google Sheets BigQuery connector (or Supermetrics) to pull MAU for each product or site. Query example (BigQuery SQL):

SELECT event_date, COUNT(DISTINCT user_pseudo_id) as mau
FROM `project.dataset.events_*`
WHERE event_date BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) AND CURRENT_DATE()
GROUP BY event_date;

Schedule this query daily to refresh the MAU column. If you need an edge/backups strategy for export artifacts, plan for export storage and ingestion costs up front.

2) Pull billing automatically (Stripe / Chargebee)

Automate cost data with an API call. Example Apps Script to fetch a vendor invoice amount (pseudo):

function fetchVendorBilling() {
  const url = 'https://api.stripe.com/v1/invoices?customer=acct_xxx';
  const options = {
    method: 'get',
    headers: { Authorization: 'Bearer ' + PROPERTIES.getProperty('STRIPE_KEY') }
  };
  const response = UrlFetchApp.fetch(url, options);
  const json = JSON.parse(response.getContentText());
  // parse and write monthly amounts to sheet
}

If your finance team uses a centralized billing system (SAP, Netsuite), export the subscription list monthly and VLOOKUP into the sheet. Use an orchestration tool like FlowWeave or similar to schedule and monitor the billing syncs.

3) Feature overlap via a feature-flatten script

Maintain a feature matrix and run a script (or formula) to count overlaps. A simple formula to count shared features for Tool A vs others:

=SUMPRODUCT((FeatureMatrixRowA=1)*(SUM(FeatureMatrixOtherToolsRange)=1))/SUM(FeatureMatrixRowA)

That gives you the percent of Tool A’s features that exist elsewhere. You can orchestrate that flatten script in your automation platform (see orchestration examples).

Decision thresholds & playbook

Convert scores into actions. These thresholds are a starting point—adjust to your risk tolerance and vendor relationships.

• Score 75–100: Keep. Consider consolidating complementary tools to reduce integration surface area.
• Score 50–74: Evaluate. If strategic value <3, negotiate pricing or reduce seats.
• Score 25–49: Target for retirement within 3–6 months. Build migration plan for data and features.
• Score 0–24: Immediate retirement candidate. Validate legal/contract exit costs first.

Practical pruning workflow (30–60 day cycle)

1. Run the scoring spreadsheet and review the bottom 20% of tools.
2. Map dependencies: which dashboards, automations or API calls will break? Build a migration checklist.
3. Engage stakeholders (marketing, analytics, IT, legal). Present the net-score and cost impact.
4. Negotiate vendor exits or seat reductions (use competition as leverage).
5. Execute decommission plan and run validation tests for key metrics post-retirement.

Case study: How a mid-market team saved $120k a year

Acme Media (fictional) ran the audit. They had 28 active tools. After populating the template and running the net scores they found:

• 3 tools scored below 30 (duplicate personalization, an underused session replay tool, and a legacy attribution layer)
• 5 tools scored 30–50 and were consolidated into their CDP + native email solution
• The audit surfaced $10k/month in direct subscriptions that could be canceled and another $5k/month negotiable

Result: $120k annual savings, a 40% reduction in duplicate event sends, and a 35% improvement in data completeness measured six weeks after decommissioning.

Risks and how to mitigate them

• Data loss — export historical data before canceling a tool. Use clean-room or S3 dumps for traceability.
• Hidden dependencies — use SQL search across BI and product code to find API calls to the vendor before retirement.
• Contract penalties — legal must sign-off on cancellations if early-termination clauses exist; loop in procurement early for renegotiation levers.
• Stakeholder resistance — present the scoring rationale and offer a 90-day rollback plan if key metrics are impacted.

Advanced strategies for 2026 and beyond

Use these tactics once you have the baseline spreadsheet in place to build a future-proof martech stack.

• Model the total cost of ownership (TCO) — include integration hours, engineering SLAs, and data-cleaning time, not just subscription fees.
• Introduce a tool gate — any new tool request must pass a mini-audit: feature gap, expected MAU, integration effort and exit plan.
• Consolidate around data-first platforms — favor tools that support canonical schemas and native BigQuery/Delta Lake exports to reduce data trust issues.
• Automate validation tests — implement daily checks that compare canonical events vs vendor events and flag drift (critical for AI models). See audit patterns in audit-ready pipelines for provenance ideas.

"A tool that saves 5% of a campaign's time but costs 20% of your optimization budget is not an optimization — it's a tax." — common refrain among analytics leaders in 2026

Template delivery & next steps

To accelerate your audit:

• Download the spreadsheet template (Inventory, Feature Matrix, Scoring Engine) and import it into your Google Drive.
• Schedule a 60-minute cross-functional review to validate qualitative scores (tracking fidelity, integration difficulty).
• Connect MAU and billing sources using the automation recipes above; refresh the sheet monthly. Use an orchestration or automation platform like FlowWeave to keep automations reliable.

Final checklist before you hit the retire button

• Have you exported historical data and access logs?
• Do all dashboards that depend on the tool have replacement queries or are marked as deprecated?
• Is legal and procurement aligned on contract exit costs?
• Have you communicated a rollback plan to stakeholders?

Conclusion — prune deliberately, prioritize trust

In 2026 the real competitive edge is not how many AI martech products you try, but how well you manage data trust and operational complexity. Use this spreadsheet approach to make pruning a repeatable, defensible process. Reduce cost, eliminate duplicated tracking, and focus engineering capacity on data quality and useful automations.

Call to action

Ready to run your first audit? Download the free Tool Audit Spreadsheet Template and scoring guide, or book a 30‑minute consultation with our dashboard strategists to run a live audit and migration plan for your stack.