How to Track Marketing Tool Usage Automatically in Sheets

Stop losing money to underused MarTech: automate tool usage tracking in Sheets

Hook: If your finance team keeps paying subscriptions that your marketers never open, or your ops team struggles to prove ROI for specialty tools, you need an automatic usage dashboard — not another manual spreadsheet. This guide shows how to instrument user logins, API calls, and spend, stream them into Google Sheets or Excel, and build an automated dashboard that flags underused platforms and triggers alerts.

The why — short version (2026 context)

In late 2025 and early 2026 the MarTech landscape accelerated: hundreds of AI-driven point tools, new budget features in ad platforms (see Google’s January 2026 total campaign budgets), and stronger privacy controls all increased the risk of bloated stacks and wasted spend. Industry analysts call this marketing technology debt — the recurring cost, complexity, and lost productivity from underused platforms. The fastest way to reduce that debt is a repeatable, automated usage measurement system that connects every login, API call, and dollar to a simple score.

“Marketing stacks with too many underused platforms add cost, complexity and drag.” — MarTech (Jan 2026)

What this guide covers (and what it assumes)

• What telemetry to collect: logins, API requests, billing/spend.
• How to ingest that telemetry into Google Sheets or Excel automatically (micro-apps, Zapier, Apps Script, Power Query).
• Designing a master usage schema and formulas to compute health metrics.
• Rules to flag underused tools and automated alerts (Slack/Email/Zapier).
• Scale and governance best practices for 2026.

Target audience: marketing ops, small business owners, finance and procurement owners who need a commercial-ready implementation — templates and automation patterns you can copy.

Step 1 — Inventory your stack and identify telemetry endpoints

Before you build anything, create a clear inventory and map where usage signals live.

1. Inventory spreadsheet: One row per tool with columns: Tool Name, Category, Owner, Monthly Cost, Billing Source, Login Source (SSO/Vendor), API Available (Y/N), Billing API (Y/N), Events Webhook (Y/N).
2. Telemetry sources to look for:
   • Logins: Identity provider (Okta, Azure AD, Google Workspace) audit logs, vendor login logs, SSO assertion records.
   • API calls: Vendor API usage endpoints (e.g., /usage, /requests), rate-limit headers, or proxy logs if you route calls through your middleware.
   • Spend: Billing APIs (Stripe, Paddle, Chargify), vendor billing exports, credit card feeds, or ad platform spend APIs (Google Ads, Meta Ads).
   • Events: Webhooks for feature usage or activity (created leads, sends, sequences started).
3. Permissions: Confirm you can access these endpoints. For SSO logs, ask IT for audit access; for vendor APIs you might need admin API keys or a service account.

Step 2 — Design a simple, normalized usage schema

Keep your schema tidy so you can slice and join. Use one master sheet per telemetry type and a small dimension table for tools.

Suggested sheets (Google Sheets naming)

• Tools (tool_id, name, category, owner, monthly_cost, active_since)
• Logins (event_id, tool_id, user_id, timestamp, source_ip, region)
• API_calls (event_id, tool_id, endpoint, method, response_ms, timestamp)
• Spend (invoice_id, tool_id, amount_usd, period_start, period_end, billing_provider)

Why normalize? You’ll compute aggregates (MAU/30, API calls/day, cost/month) by joining these sheets with simple formulas and QUERY() or Power Query. Normalized tables also make automations safer because each new event is just an appended row.

Step 3 — Ingest telemetry automatically

Choose one of three practical ingestion patterns depending on what endpoints you have access to.

Option A — Zapier / Make (best for non-coders and vendor webhooks)

1. If the vendor supports webhooks, configure the webhook to hit Zapier or Make.
2. Create a Zap: Webhook Trigger → Formatter (normalize timestamps) → Google Sheets 'Create Spreadsheet Row'.
3. For logins from SSO providers, many IdP systems (Okta, Azure AD) can post to webhooks or stream to SIEM; use a webhook forwarding service if necessary.

Pros: fast to implement, reliable for small to medium volumes. Cons: cost and rate limits at scale.

Option B — Apps Script (Google Sheets) for periodic API pulls

When a vendor has an API but not webhooks, schedule a time-driven Apps Script that authenticates and appends rows.

// Example Apps Script (simplified)
function pullVendorApi() {
  const ss = SpreadsheetApp.openById('YOUR_SHEET_ID');
  const sheet = ss.getSheetByName('API_calls');
  const token = PropertiesService.getScriptProperties().getProperty('VENDOR_TOKEN');
  const url = 'https://api.vendor.com/v1/usage?from=' + getLastPulledDate();
  const res = UrlFetchApp.fetch(url, {headers: {Authorization: 'Bearer ' + token}});
  const data = JSON.parse(res.getContentText());
  const rows = data.items.map(i => [i.id, i.tool_id, i.endpoint, i.method, i.latency, i.timestamp]);
  if (rows.length) sheet.getRange(sheet.getLastRow()+1,1,rows.length,rows[0].length).setValues(rows);
}

Notes: store credentials in Script Properties, respect rate limits, and include incremental pulls (since last timestamp) to avoid duplication.

Option C — Power Query for Excel / Sheets add-ons

Excel users can use Power Query to call APIs and load normalized tables on refresh. Good when finance owns the workbook and needs scheduled refresh via Power BI or Microsoft 365.

Step 4 — Compute the core metrics in your sheet

Use simple formulas to produce comparable signals across tools. These are the metrics to surface on your dashboard:

• MAU30 — unique users with a login in the last 30 days.
• RecentLogins7 — logins in last 7 days to catch acceleration/deceleration.
• APIcalls7 / day — average daily API calls in last 7 days.
• Spend30 — spend in last 30 days.
• CostPerActive — Spend30 / MAU30.

Sample formulas (Google Sheets)

/* MAU30 for tool_id in cell A2 */
=COUNTUNIQUE(FILTER(Logins!C:C, Logins!B:B=A2, Logins!D:D>=TODAY()-30))

/* Spend30 */
=SUMIFS(Spend!C:C, Spend!B:B, A2, Spend!D:D, ">="&TODAY()-30)

/* CostPerActive (handle divide-by-zero) */
=IF(B2=0, "—", C2/B2)

Step 5 — Build a usage score and flag underused tools

A single metric rarely tells the whole story. Create a compact score that combines adoption (MAU), activity (API calls), and economics (spend).

Normalized score approach

1. Normalize each metric to 0–1 across tools (min-max or percentile).
2. Score = w1 * normalized_MAU + w2 * normalized_API + w3 * (1 - normalized_CostPerActive)
3. Choose weights (example: w1=0.5, w2=0.3, w3=0.2).

Then set rules:

• If Score < 0.25 and Spend30 > $500 → Flag = "Underused — High cost"
• If MAU30 < 5 for 60 days → Flag = "Dormant"
• If APIcalls7 < threshold and feature-dependent → Flag = "Low integration"

Example formula to flag (pseudo Google Sheets)

=IF(AND(Score<0.25, Spend30>500), "Underused - High cost",
 IF(MAU30<5, "Dormant",
 IF(APIcalls7<10, "Low integration", "OK")))

Step 6 — Automate alerts and ops workflows

Detecting underused tools is only useful if someone acts. Automate notifications and lightweight governance workflows.

• Slack alerts: Use Zapier or Apps Script to post when a Flag appears. Example Zap: Google Sheets New/Updated Row → Filter (Flag contains "Underused") → Slack Message to #martech-savings.
• Weekly digest: Create a summary sheet with all flagged tools and trigger a weekly email via Apps Script or Zapier Digest.
• Deprovision workflow: When a tool is flagged, trigger a Zapier multi-step: add a row to a 'Decom Requests' sheet → notify owner → create a ticket in your service desk (Jira/Trello/Asana).
• Auto-archive: If a tool is Dormant for 90 days and owner approves (one-click), schedule cancellation reminders to finance.

Practical implementation examples

Example A — Bring Okta login events into Sheets via webhook + Zapier

1. In Okta, create an inline hook or event hook for user.session.start.
2. Target Zapier webhook URL. In the Zap: Webhook → Formatter (convert timestamp) → Google Sheets Append Row (Logins sheet).
3. Map user_id, email, tool_id (if Okta apps mapped to tool IDs), and timestamp.

Example B — Pull vendor billing via Apps Script

Many vendors publish invoices via API. Schedule Apps Script to pull invoices daily and append to Spend sheet. Keep a high-water mark in Script Properties to only fetch new invoices.

Example C — API proxy counts API calls centrally

If you have middleware (a small proxy or serverless function), add a lightweight header to count calls per tool: X-Proxy-Tool-ID, then forward metrics to a central webhook that writes to Sheets. This is especially useful for internal APIs and single-tenant integrations. For teams leaning into infra-as-code, pair your proxy with IaC templates to keep deployments repeatable and auditable.

2026 trends & future-proofing your tracking

Expect these developments in 2026 and plan accordingly:

• More standardized usage endpoints: Vendors are rolling out consumption APIs as a competitive feature; build around /usage or /metrics endpoints when available.
• Privacy-first telemetry: Identity providers moved to event sampling and privacy-preserving telemetry in late 2025, so some login detail may be coarser — rely on aggregated counts where necessary. Also consider compliance patterns from teams running compliant infra when handling sensitive telemetry.
• Consolidation & budget automation: Google’s total campaign budgets reduce manual ad spend tweaking; still track spend and ROI to validate consolidation decisions. For advice on ad placement and exclusions, see guides on account-level placement exclusions.
• AI-driven recommendations: Expect vendor consoles to suggest consolidation; your job is verifying those recommendations with cross-tool usage and cost-per-active metrics. Complement vendor suggestions with independent AI-driven recommendation checks.

Governance, accuracy & common pitfalls

Accuracy matters. Here are the most common mistakes and how to avoid them.

• Counting bots as users: Filter API keys, service accounts and monitoring IPs from user counts.
• Duplicate events: Implement idempotency (event_id) and use last-pulled timestamps.
• Rate limit surprises: Use incremental pulls and exponential backoff in scripts.
• Misaligned timezones: Normalize to UTC and store timezone info in the dimensions table.
• Over-automation: Don’t auto-cancel; instead trigger an approval workflow with owner sign-off.

Real-world mini case study

Marketing ops at a 120-person e‑commerce company deployed this pattern in Q4 2025.

• They instrumented Okta, Stripe invoices, and three vendor APIs with Apps Script and Zapier webhooks.
• Within 6 weeks they discovered 5 subscriptions totaling $3,400/month with MAU30 < 3. Using a Score & Flag rule they prioritized two tools for consolidation and saved $1,700/month after negotiation and one cancelled vendor.
• They automated a weekly digest to the VP of Marketing and set an approval step with finance before cancellation.

Lesson: the automated dashboard made the problems visible and created a low-friction governance path to act.

Checklist & quick start template

1. Export your tool inventory into a Tools sheet.
2. Decide ingestion method per tool (Webhook / API pull / Manual import).
3. Create Logins, API_calls, Spend sheets with the columns listed earlier.
4. Build MAU30, APIcalls7, Spend30 formulas and a Score column.
5. Create Flag rules and a Zap/App Script to notify Slack or email on new flags.
6. Run bi-weekly review meetings with owners for flagged tools.

Advanced strategies (when you’re ready to scale)

• Move event storage to BigQuery or Snowflake for high-volume telemetry and use Sheets as a reporting front-end with connected sheets. If you need resilient infra patterns at scale, review guides on resilient cloud-native architectures.
• Use a central identity metric: link user_id across tools via hashed corporate email to measure cross-tool adoption.
• ML-based anomaly detection: use short-term models to detect sudden drops in API calls or logins that warrant immediate investigation. Also correlate with macro signals (see a Q1 2026 macro snapshot) when spikes align with market events.
• Inventory and tools audit: periodically compare your list to industry roundups and marketplaces for consolidation opportunities (see a tools & marketplaces roundup).

Final recommendations — implement in 4 weeks

Here’s a pragmatic 4-week rollout plan:

1. Week 1: Inventory + pick top 10 costliest tools.
2. Week 2: Wire up logins (SSO) and billing (Stripe/CC) to Sheets.
3. Week 3: Add API telemetry for 3 highly integrated tools and build Score columns.
4. Week 4: Create flagging rules and automate Slack digest + create governance playbook.

Closing — why automated usage tracking is non-negotiable in 2026

With MarTech proliferation and new budget automation features in ad platforms, the only reliable way to keep cost and complexity under control is continuous, automated measurement. By instrumenting logins, API calls, and spend into a normalized spreadsheet and layering simple scores and alerts, teams can reclaim wasted spend, reduce noise, and make buying decisions based on repeatable data — not guesses.

Get started now: Download our ready-to-use Google Sheets template with the Tools, Logins, API_calls, Spend sheets, example Apps Script snippets, and Zapier recipes — and cut unnecessary tool spend in weeks, not months. Need a custom audit or automation setup? Contact our team for a 30-minute implementation session.

Sources & further reading: MarTech (Jan 2026) on tool sprawl; Search Engine Land (Jan 2026) on Google total campaign budgets. These trends underline why usage-driven governance is critical this year.

Call to action

Download the free tracking template or schedule a quick audit to discover your top underused tools — start saving in 30 days. Click to get the template and a step-by-step setup guide tailored to Google Sheets, Zapier, and common IdPs.

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