What is your guest data
actually costing you?

Room revenue. Rooms multiplied by 365, multiplied by occupancy, multiplied by ADR. Stays are room nights divided by average length of stay.

Missed ancillary and upsell. Properties running personalised, data driven pre-arrival typically attach incremental ancillary worth 2.5 to 4.5 percent of room revenue on top of what they already sell. How much of that a property actually captures depends on two separate things: how unified guest profiles are, and how automated pre-arrival outreach is. The two compound rather than substitute for each other. A unified profile with no automation still only reaches about 35 percent of available attach, and full automation layered on fragmented profiles caps out around 33 percent. Capture only reaches its high point, about 82 percent, when both are in place together. With fragmented profiles and no automation, capture sits around 15 percent. Nothing here ever models 100 percent capture, since some share of guests decline any offer regardless of how well it is targeted.

Unrecognized returning guests. Fragmented profiles are modeled at a 16 percent duplication rate, partial at 8 percent, unified at 3 percent, based on 2025 to 2026 hotel identity resolution benchmarking (Alliants and Revinate data cited in industry reporting puts typical duplication around 6 percent industry wide, with the most neglected estates running as high as 16 percent). A duplicated returning guest is a guest your team treats as a stranger. Only half of those are counted as an actual revenue event, because plenty of guests return regardless of whether you recognize them. The remainder is valued at one stay at your ADR and length of stay.

Manual reporting and re-entry. Hours per week multiplied by 52, multiplied by your loaded hourly cost. This is the one number here that is not an estimate. It is arithmetic on what you entered.

Distribution and config drift. Rate parity breaks, stale restrictions, and mapping errors scale with the number of connected systems. Modeled at zero for two systems, since that level of simplicity makes this specific failure avoidable in principle. Rising to 0.3 percent of room revenue for three systems, 0.7 percent for four to six, and 1.2 percent for seven or more. This bucket is a modeling judgment built from direct migration and configuration experience, not a published industry figure, and it is deliberately the smallest of the four.

Why this never reaches exactly zero. Even the best inputs available in this calculator leave a small residual, usually under one percent of room revenue. That is deliberate. No property converts every guest who is offered an upsell, and claiming otherwise would make this less honest, not more reassuring. A number that could hit zero under any input would be a worse calculator, not a better one.

Range. The three modeled buckets carry a plus or minus 30 percent band. The labor figure carries plus or minus 10 percent, since it comes from your own input.

Sources. Duplicate profile benchmarks from 2025 to 2026 hotel identity resolution industry reporting (Alliants and Revinate deduplication data). Direct versus OTA booking value differential referenced elsewhere on this site is from SiteMinder's analysis of 125 million reservations across 44,500 properties, 2024 to 2025. Ancillary attach and personalization multiplier ranges are drawn from published hotel upsell and ancillary revenue benchmarking. Distribution drift is Dante's own modeling judgment, not a third party statistic.

This is directional, not an audit. Every figure above is a public benchmark or a disclosed modeling assumption, current as of mid-2026. Industry benchmarks move as software and guest behavior change, so treat this as a case for a conversation, not a certified number. For a figure specific to your actual systems, book the Revenue Diagnostic or request a written quote.