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Group and large-order quote

Is a big booking worth it? Compares a group or bulk order with the regular business it would push out, and suggests the lowest price worth accepting.

Hospitality · BI and dashboardsLive

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What it does

A large booking looks like good news until you count the regular customers it pushes out. This tool puts both side by side, including the extra spend the group brings, so the decision is about net value rather than the headline number.

Who can use it

  • Sales and revenue managers
  • Event and catering coordinators
  • Owners deciding on a large order

Where it fits

  • Hotel group blocks
  • Private dining and catering
  • Venue hire
  • Bulk orders that use limited capacity

How to use it

  1. Enter the dates and size of the request and the price offered
  2. Enter what you'd expect to sell to regular customers on those dates
  3. Add extra spend the group brings, such as meals or rentals
  4. Read the net gain or loss and the lowest price worth accepting

Limitations

  • It's only as good as the forecast of regular business you enter
  • It doesn't price in the long-term value of a repeat client

What's next

  • Pull the regular-demand forecast straight from the demand forecaster
  • A catering edition based on kitchen capacity

Run it on your own data

  1. In the browser (today)Free, no sign-up. What you enter stays on your device. Good for trying it and for small teams.
  2. On your files or databasePoint the same logic at your exports or a database view (PostgreSQL, SQL Server, Azure SQL or DuckDB), on your own machine or network.
  3. As a pipeline and warehouseA scheduled ETL job pulls from your systems (reservations, point of sale, inventory, payroll) into a star-schema warehouse, runs data-quality checks, and the tool or a Power BI report reads the result every morning.

What that adds here

  • Read the forecast and past group results from the warehouse so a quote takes seconds
  • Save every quote and its outcome to learn which groups pay off

How I'd set it up for you

I'd map your exports or database to the tool's model, package the job in a container that runs on your server or cloud account, add the checks your data needs, and hand over the code and a runbook. Your data stays on your systems. Get in touch if you'd like it fitted to your business.

Updates

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