How to run a SaaS usage heatmap for your hotel: Identify the platforms that deserve your spend

Cut the subscriptions that quietly bleed margin: run a SaaS usage heatmap for your hotel

Most hoteliers know they pay for too many cloud tools — but deciding which subscriptions to keep, optimize or cancel is a risk-heavy guess without a repeatable method. If your stack is causing integration headaches, rising OTA commissions, and rising labor costs, a usage heatmap that combines login metrics, feature adoption and revenue impact will give you objective, actionable answers.

Top takeaway (read first)

Create a two-dimensional heatmap where the X-axis is actual usage (logins + activity), the Y-axis is business impact (revenue uplift, cost avoidance, or operational criticality), and color/intensity encodes feature adoption depth. Use a composite scoring formula to rank subscriptions, then apply a 4-step rationalization playbook: Keep, Optimize, Consolidate, Sunset.

Why this matters now (2026 context)

By early 2026 the hotel tech landscape is tighter: cloud-native PMS providers matured their APIs in late 2024–2025, AI ops tools surfaced for task automation, and consolidators began bundling channel management, CRS and guest engagement. That means vendors expect higher ARPU and buyers face bigger renewal leverage. At the same time, labor inflation and OTA pressure have kept margins thin — wasted subscriptions are now a visible drag on RevPAR. A data-driven heatmap is the fastest way to reclaim margin and simplify operations.

Who should run this and how often

Owners, general managers, revenue managers, and tech leads should collaborate. Assign a project owner (typically the operations or revenue director) and a technical lead with API or log access. Run a full heatmap quarterly and a lightweight check monthly around renewals.

Overview of the method

1. Collect data: logins, session length, feature calls, billing, contracts, and revenue attribution.
2. Normalize metrics to comparable scales.
3. Calculate three scores: Usage Intensity, Feature Adoption Depth, and Revenue Impact.
4. Map onto a heatmap: X-axis = Usage Intensity, Y-axis = Revenue Impact, Color = Feature Adoption Depth (or risk/cost).
5. Prioritize subscriptions using quadrant logic and run the rationalization playbook.

Step 1 — Collect the right data (sources and metrics)

Focus on objective telemetry and commercial records. Here’s a practical list of data to gather for each subscription:

• Authentication and login metrics: unique users, daily/weekly/monthly active users (DAU/WAU/MAU), average sessions per user. Pull from SSO (Okta, Azure AD), vendor logs, or local identity providers.
• Activity traces: API calls, feature clicks, workflows completed (check-ins processed, invoices raised, rate updates). Use vendor APIs or exported activity logs.
• Feature adoption: list of key features used and adoption % of relevant staff. E.g., percent of front-desk staff who use mobile check-in, percent of revenue team using dynamic pricing module.
• Financials: annual subscription cost, unit pricing (per seat, per property), contract length, renewal dates, and overage charges.
• Revenue and savings attribution: incremental revenue linked to the tool (direct bookings via a booking engine, upsell conversion from a guest-app), cost avoidance (reduced nights of labor), or time saved (FTE hours saved × loaded cost).
• Integration and data flow: degree of integration with PMS, channel manager, CRS, accounting — note manual workarounds that add labor cost.
• Security/compliance risk: if tool holds PII or payment data, factor in compliance cost and risk tolerance.

Step 2 — Normalize and score

Raw metrics are heterogeneous. Normalize each into a 0–100 scale to compare.

Usage Intensity score (0–100)

• Weight DAU/MAU (40%), average session length (20%), and active workflows per user (40%).
• Example: a tool with high MAU but short sessions (quick look-ups) gets lower weight than a tool used for full workflows like check-ins.

Feature Adoption Depth score (0–100)

• Measure breadth (% of available relevant features used) and depth (% of staff using advanced features) — weigh breadth 50%, depth 50%.
• This is your color dimension: a shallow-adoption tool may be rarely used but still strategically important.

Revenue Impact score (0–100)

• Combine direct revenue (booking engine conversions, upsell value), cost savings (automation FTE hours × loaded cost), and risk avoidance (estimated cost of non-compliance or outages). Weight direct revenue 50%, cost savings 30%, risk avoidance 20%.
• Use a 12-month lookback with seasonality adjustments for hotels with strong peaks.

Step 3 — Build the heatmap

Choose a visualization tool: Google Sheets, Excel, Power BI, Tableau, or a BI built into your PMS. Structure the chart as follows:

• X-axis: Usage Intensity (0–100)
• Y-axis: Revenue Impact (0–100)
• Color/size: Feature Adoption Depth (0–100). Optionally encode annual cost as bubble size.

Interpretation:

• Top-right (high usage, high impact) = mission-critical — keep and protect.
• Bottom-right (high usage, low impact) = high operational drag — optimize and renegotiate.
• Top-left (low usage, high impact) = niche strategic tools — consider license consolidation or targeted training to increase adoption.
• Bottom-left (low usage, low impact) = candidates for sunsetting.

Step 4 — Practical scoring formula (example)

Use a simple composite for priority ranking. For each tool, compute:

Priority Score = (0.5 × Revenue Impact) + (0.3 × Usage Intensity) + (0.2 × Feature Depth)

Sort descending. Tools with Priority Scores above 70 are high-priority to retain; 40–70 need attention; below 40 are candidates for sunsetting.

Step 5 — Validate with qualitative checks

Numbers can lie if you miss context. Do these quick qualitative steps for any tool flagged for action:

• Interview 3–5 power users for use-cases and workarounds.
• Map manual steps that the tool replaces — count minutes saved and error rates reduced.
• Check contract lock-ins, minimum seat terms, and exit costs.
• Assess integration pain: how many custom connectors or daily exports are needed?

Case example — 60-room boutique chain (concise)

Context: four properties under one operator. They run 8 SaaS subscriptions: PMS, channel manager A, channel manager B, booking engine, guest app, RMS, accounting sync, and a workforce scheduling tool. Annual spend: $210k.

Data snapshot:

• Channel manager A: Usage 82, Feature Depth 70, Revenue Impact 88, Cost $48k — top-right quadrant (keep and negotiate enterprise discount).
• Channel manager B: Usage 22, Feature Depth 18, Revenue Impact 12, Cost $36k — bottom-left (sunset; backup channel manager retained).
• Guest app: Usage 56, Feature Depth 30, Revenue Impact 60, Cost $24k — top-left (boost training and marketing to increase adoption for upsells).
• Workforce tool: Usage 74, Feature Depth 62, Revenue Impact 18, Cost $14k — bottom-right (high usage, low revenue impact; but saves FTE hours; negotiate seat pricing or replace with cheaper scheduler module in the PMS).

Result: by consolidating Channel manager B and negotiating Channel manager A, the chain recovers 18% of annual spend the first year and reduces nightly manual reconciliation tasks by 45%.

From insight to action: the rationalization playbook

Once the heatmap is complete, follow this structured playbook:

1. Keep: Mission-critical tools in the top-right. Protect integrations, increase monitoring, and set SLA terms for uptime.
2. Optimize: High usage/low impact tools — optimize workflows or reduce licensing tiers. Consider training to shift use to higher-impact features.
3. Consolidate: Low-usage/high-impact tools — either increase adoption across properties or fold functionality into a primary platform if feasible. Evaluate migration cost vs. ongoing subscription cost.
4. Sunset: Low-usage/low-impact. Plan a decommission: export data, notify stakeholders, and soft-delete after a trial period to avoid lost data or workflows.

Negotiation levers and contract tips (practical)

• Use your heatmap as a negotiation tool: show usage and impact when asking for seat discounts or bundled modules.
• Push for usage-based pricing where possible — aligns vendor value to you. If the vendor resists, negotiate capped overages.
• Request API access and data export clauses in contracts for portability. Avoid proprietary lock-in without exit data flows.
• Ask for phased pricing tied to adoption milestones — e.g., discount until adoption reaches X% across properties.

Integration, migration and hidden costs

Always model the total cost of change (TCC): migration engineering hours, data cleanup, retraining, and temporary dual-run operations. A cheap tool can be expensive if integration requires nightly CSV exports and manual matching. Add TCC as a one-time cost when evaluating consolidation.

Security and compliance factors

Embed security into the heatmap: mark tools that handle credit cards, guest PII, or sensitive compliance logs. Even low-usage tools may have high remediation risk: keep them only if they meet your security standards or if remediation costs are justified by impact. See our recommended compliance checklist for handling payment and data risks.

Automation and tooling to scale this process

To operationalize the heatmap, implement an automated monthly pipeline:

• Ingest SSO logs and vendor APIs into a central analytics store (BigQuery, Snowflake, or your BI).
• Use scheduled SQL transforms to compute scores (cloud pipelines).
• Publish dashboards with annotated snapshots and renewal calendar triggers.
• Integrate with procurement and finance for automated alerts on high-cost/low-usage renewals.

Metrics to track after rationalization

• Savings realized vs. forecasted (%)
• Change in manual touchpoints (hours/week)
• Time-to-value for replacements (days)
• Adoption rate change for consolidated features (% of staff using feature in 90 days)
• Impact on RevPAR and direct booking conversion

2026 trends you should factor into decisions

• AI-first vendor features: In late 2025 many vendors shipped AI modules — measure whether these are production-ready or marketing features. If AI automation reduces manual rate updates or guest communications, quantify savings before paying premium fees.
• Consolidation and bundled stacks: Vendor consolidators are bundling channel management, CRS, and guest apps. Bundles may reduce integration overhead but can increase lock-in risk — use the heatmap to test bundle value.
• Privacy and regional compliance updates: New local privacy rules and stronger consumer data rights introduced in 2025–2026 mean compliance costs may rise for tools handling PII. Factor remediation risk into revenue impact.
• Shift to usage-based and consumption pricing: The market is trending away from per-seat licenses — negotiate to pay for active transactions instead of seats where possible.

Common pitfalls and how to avoid them

• Relying on logins alone — a user can log in and not use critical features. Always combine login metrics with workflow completions.
• Ignoring seasonality — normalize revenue impact with seasonality factors.
• Underestimating migration costs — always compute TCC before deciding to consolidate.
• Skipping stakeholder interviews — numbers may miss human factors that keep a tool necessary.

Implementation checklist (30–60 day plan)

1. Day 1–7: Project kickoff, assign owner, list subscriptions, collect contracts.
2. Day 8–20: Pull logs & financials, normalize metrics, compute scores.
3. Day 21–30: Build heatmap, conduct 5–10 stakeholder interviews, draft rationalization plan.
4. Day 31–45: Negotiate with vendors for high-priority items, plan migrations for sunsetting tools.
5. Day 46–60: Execute pilots for consolidation, run training, update procurement guardrails.

Final recommendations

Make the heatmap a governance artifact. Tie it to procurement approvals, renewal reminders, and quarterly tech reviews. Over time you’ll see two big wins: reduced subscription drag on margins, and a simplified tech stack that reduces operational errors and speeds up guest-facing innovation.

Closing thought

In 2026 the difference between an optimized hotel and a mediocre one is not just occupancy — it's the ability to spend selectively on tools that meaningfully drive revenue or reduce cost. A repeatable, data-driven SaaS usage heatmap gives you that selective lens.

Call to action

If you want a ready-to-use template and a sample scoring workbook tailored for hotels, download our free PDF heatmap workbook and a checklist for vendor negotiations. Or contact our team to run a pilot heatmap on your portfolio and identify 6–12 months of immediate savings.

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