AI for insights and analytics
An account with a 22 percent discount for 1,200 room nights has produced 410, all at one property, while four other hotels on its travel pattern get nothing. AI analytics reads the production, booking and rate files, and answers the commercial director: which accounts are earning their rate, and where is pace behind.
These are the questions hotel groups ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Which corporate accounts are behind their commitment? | Corporate production against commitment | Room nights produced against the pro rata commitment by account, with the discount cost. |
| Where is pace behind the same date last year? | Pace against same date last year | On-the-books by property, segment and arrival month against the same date last year. |
| Which accounts use one property where they could use five? | Property penetration per account | Properties producing per account against the properties on its travel pattern. |
| How much group business washes? | Group conversion and wash | Blocks converted and rooms picked up against blocks held, by property and segment. |
| What does each channel cost us per booking? | Channel cost per booking | Commission and fees per booking by channel, against the rate achieved. |
| Are we seeing our key accounts at cadence? | Account coverage at cadence | Accounts met within their tier cadence over accounts assigned, by sales manager. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
What are under-producing corporate accounts costing us?
Discount given to accounts below 70 percent of their pro rata commitment is worth $410k this year across 3 regions, against rack-adjusted rates. City hotels accounts for $220k, 54% of it.
| City hotels | $220k | 54% of the total |
| Airport hotels | $130k | 32% of the total |
| Resort hotels | $60k | 15% of the total |
| The 3 lines sum to | $410k | 0 unexplained |
Which city accounts?
3 accounts account for $88k of the $220k at the City hotels, 40% of it. The largest has produced at one property only, with four others on its travel pattern producing nothing.
| Halvorsen Consulting | $38k | 410 of 800 pro rata nights; one property of five |
| Brightwater Pharma | $29k | 52% of commitment; rate review in 10 weeks |
| Northline Bank | $21k | 61% of commitment; no contact logged |
| These 3 are | $88k | 40% of City hotels |
What goes into the rate review?
Production against commitment by property, the four hotels producing nothing, and a rate tied to volume. The sales manager owns the conversation; the commercial director signs the rate.
| Sales manager, Halvorsen | Take production by property to the rate review | Before the review |
| Directors of sales | Review production against commitment monthly | Monthly |
| Commercial director | Tie corporate discounts to produced volume at renewal | This cycle |
Each measure has one formula, one source and one meaning. They are computed per property and sales manager and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Pace against same date last year | Rooms and revenue on the books for a future month ÷ same at the equivalent date last year, by segment | Reservations data with booking dates | Which months and segments are behind |
| Corporate production against commitment | Room nights produced ÷ room nights committed, per account, year to date | Reservations by rate code; rate agreements | Discounts given for volume that did not come |
| Property penetration per account | Properties where the account stays ÷ group properties in the account's travel cities | Reservations; account travel pattern | Accounts staying with competitors in your cities |
| Segment mix per property | Room nights and revenue by corporate, group, leisure, contract; against plan and last year | Reservations data | Properties drifting to the wrong mix |
| Group conversion and wash | Group enquiries converted ÷ decided; rooms picked up ÷ rooms blocked | Sales and catering system | Lost group business; blocks that do not fill |
| Meeting space utilisation | Sold hours ÷ available hours, by room and day of week | Sales and catering system | Days and rooms nobody sells |
| Account coverage at cadence | Production of accounts contacted within cadence ÷ total managed production | CRM; reservations | Top accounts nobody has spoken to |
| Channel cost per booking | Commission and fees ÷ bookings and revenue, by channel | Reservations; commission statements | What each channel really costs |
| Displacement on group business | Transient revenue displaced on group dates against group revenue | Reservations; demand history | Groups that cost more than they bring |
| Corporate account concentration | Top ten accounts' share of corporate room nights | Reservations | Dependence on a few travel programmes |
Each measure is worked through, with the export it comes from and what to drop, in Sales KPIs for hotel groups.
From your question and the measures declared for hospitality, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the property or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Segments | Segment room nights sum to the property total; property totals to the group |
| Groups | Rooms blocked = picked up + washed + released |
| Accounts | Every negotiated rate code maps to one account |
| Space | Available hours = sold + unsold + out of service |
The exports hotel groups already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Pace by segment | Revenue management with directors of sales | Weekly |
| Production against commitment; coverage | Sales managers; commercial director | Monthly |
| Group conversion and wash; meeting space | Directors of sales; events | Monthly |
| Penetration; channel cost; displacement; concentration | Commercial director | Quarterly |
Room nights from the production file against the commitment in the contract, pro rated for the months elapsed, by account and property. The discount cost is the difference between the contracted rate and the rack-adjusted rate on the nights produced. Every figure is a line from a file; nothing is estimated.
Yes, from the on-the-books export: rooms and revenue by property, segment and arrival month against the same date last year. Pace is a comparison of two snapshots, and the tool keeps both so the revenue manager can see the movement rather than a single number.
Production by account and property, contracts with commitments and rates, the on-the-books snapshot, group blocks and pick-up, and channel commissions. The CRM contact log adds coverage. Each is a PMS or sales-system export.
For commitment and penetration, yes: the account is one contract and its properties are its footprint. For pace and production it is split by property, because the decision to shift travel to another hotel is made trip by trip.
Pace · Hospitality yield · Penetration · Coverage · Concentration · Realised rate
Asset managers · Commercial banking · Compliance · Construction and building materials · Consulting and advisory · Customer service · Distributors · Education · Finance and FP&A teams · Financial services · FMCG and CPG brands · Foodservice distributors · Freight brokers and 3PLs · Healthcare and med-tech · Industrial distributors · Industrial manufacturers · Insurance brokers · Investment banking · Law firms · Oil and gas services · Pharma · Procurement · Retail banking · SaaS · Sales teams · Shipping and logistics · Sports · Supply chain · Tax and accounting · Telecoms and connectivity · Trading · Wealth managers
Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.