AI for Venue Analytics — Door Data to Booking Decisions5 prompts that turn your till reports, ticketing exports, and door counts into the numbers that actually decide who gets rebooked, which nights work, and where your programming money goes

What data you actually need

You don’t need a data warehouse. You need a spreadsheet with columns you probably already track:

• Date — when the gig happened
• Act name — who played
• Genre — broad category (rock, acoustic, jazz, DJ, open mic)
• Door count — how many people came through. Doesn’t need to be exact; clicker count or ticket sales work
• Bar take — total bar revenue for that night. Your EPOS system exports this
• Ticket revenue — if you charge a door fee or sell advance tickets
• Artist fee — what you paid the act
• Day of week — auto-derived from the date

If you’ve been running live music for 6+ months, you have enough rows to see patterns. 12 months is better. Under 6 months, the sample is too small — use this time to start tracking consistently and come back when you have 20+ gigs logged. For context: the Music Venue Trust’s 2024 report shows ~810 active grassroots venues in the UK, down 16% since 2022 — the rooms that survive are the ones making data-driven programming decisions, not gut-feel ones. UK Music’s This Is Music 2025 values the live sector at £2.5bn GVA; your venue’s slice of that is in the spreadsheet above.

Prompt 1 — Rebook ranking

Paste your gig history spreadsheet into Claude and run this prompt. It ranks every act by a composite score so rebook decisions are data-backed, not vibes-based.

I run a [capacity]-cap venue in [city]. Here is my gig history for the
last [6/12] months (columns: date, act, genre, door_count, bar_take,
ticket_revenue, artist_fee, day_of_week):

[paste spreadsheet data]

Rank every act by a composite rebook score (0-100) using these weights:
- Door count vs capacity (30%) — higher fill rate = higher score
- Bar take per head (25%) — revenue efficiency, not just volume
- Net profit (20%) — (ticket_revenue + bar_take) minus artist_fee
- Repeat bookings (15%) — acts booked more than once score higher
- Genre consistency (10%) — acts whose genre matches our best nights

Output a table: Act | Rebook Score | Door % | Bar/Head | Net Profit |
Times Played | Recommendation (rebook / maybe / pass). Sort by score
descending. Flag any act scoring above 70 as "strong rebook candidate"
and any below 30 as "review before rebooking".

Prompt 2 — Night-of-week profitability

Which nights make money and which nights are you subsidising? This prompt compares every programming format you run.

Here is my venue's gig data for the last [6/12] months:

[paste spreadsheet data]

Group by day_of_week and genre/format. For each group calculate:
- Average door count and fill rate (capacity: [number])
- Average bar take and bar take per head
- Average ticket revenue
- Average artist fee
- Average net profit per night: (bar_take + ticket_revenue) - artist_fee
- Number of nights in sample

Output a table sorted by net profit descending. Add a column
"Verdict" with one of: profitable (net > £[your threshold]),
break-even (net £0-[threshold]), loss-making (net < £0).
At the end, write 3 sentences: which format is your best performer,
which is underperforming, and one specific change to test next month.

Prompt 3 — Genre-audience fit

Are you booking genres your audience actually turns up for? This prompt cross-references door counts with genre to find mismatches.

Here is my venue's gig data:

[paste spreadsheet data]

Analyse genre performance:
1. Average door count per genre (min 3 gigs in sample to include)
2. Average bar take per head per genre
3. Trend: is each genre's door count rising, stable, or falling over
   the period?
4. Genre pairs: when two genres play the same week/month, does the
   second genre's door count benefit or suffer?

Output: a ranked table of genres by average fill rate, a list of
"growing" vs "declining" genres, and a specific recommendation:
which genre to book MORE of and which to book LESS of based on the
data. Be direct — name the genre, don't hedge.

Prompt 4 — Audience retention

This prompt only works if you have ticketing data with buyer emails or names, or if you run a door list. If your venue is cash-on-the-door with no names, skip to prompt 5.

Here is my venue's ticketing data (columns: event_date, event_name,
buyer_email_or_name, ticket_type, amount_paid):

[paste ticketing export]

Calculate:
1. Total unique buyers in the period
2. Buyers who attended 2+ events (repeat rate)
3. Buyers who attended 3+ events (loyal rate)
4. Average gap between repeat visits (days)
5. Which events had the highest % of repeat buyers?
6. Which events attracted the most first-time buyers?

Output a summary table + 3 actionable insights. If repeat rate is
below 15%, flag it as a retention problem. If above 25%, flag it as
a strength to double down on. Be specific about which event types
drive retention vs which attract one-off visitors.

Prompt 5 — Programming ROI

The big-picture prompt. Paste your full dataset and get a quarterly programming review that answers: is your live music programme making money, breaking even, or losing money — and what specifically to change.

Quarterly programming review for [venue name], [city]. Capacity:
[number]. Here is my complete gig data for [Q1/Q2/Q3/Q4] 2026:

[paste spreadsheet data]

Produce a quarterly report covering:
1. Total nights programmed vs available nights
2. Total door count and average fill rate
3. Total revenue (bar + ticket) vs total artist fees
4. Net profit/loss for the quarter's live music programme
5. Best-performing night (by net profit)
6. Worst-performing night (by net profit)
7. Top 3 acts to rebook (by composite score)
8. Bottom 3 acts to not rebook (by composite score)
9. Genre mix recommendation for next quarter
10. One specific experiment to try next quarter

Format as a 1-page briefing I could hand to a co-owner or investor.
Lead with the headline number (net profit/loss), then the detail.
British English. Use £ for all amounts.

When to run these prompts

• Prompt 1 (rebook ranking): monthly, after each month’s gigs are logged. Takes 10 minutes
• Prompt 2 (night profitability): quarterly. Needs 3 months of data to be meaningful
• Prompt 3 (genre fit): quarterly, alongside prompt 2
• Prompt 4 (retention): quarterly, if you have ticketing data
• Prompt 5 (programming ROI): quarterly. The summary that ties everything together

Total time commitment: ~45 minutes a month (10 min for the monthly rebook ranking, 35 min quarterly for the rest). Compare that to the hours you currently spend debating rebooks over text messages and gut feeling.

What this doesn’t replace

• Your knowledge of the room. The AI can tell you an act filled 80% of capacity. It can’t tell you the crowd was bored by 10pm or that the bar queue killed the atmosphere. Your notes matter — add a “booker notes” column to your spreadsheet.
• Relationship context. An act might score low on bar take because they draw a younger crowd that drinks less. If that crowd is your target demographic for building a scene, the data says “pass” but the strategy says “rebook.” Use the scores as input, not verdicts. (Artists are running a similar data review on their Spotify, socials and door numbers — see AI for music data analysis for the artist-side equivalent.)
• Seasonal patterns. January is always quieter than December. The AI will flag January acts as underperformers unless you tell it to normalise for seasonality. Add a note in your prompt: “January and February are historically 30% lower than average.”

Where this fits in the cluster

This is post 8 of 9 in the AI for UK Music Venues cluster.

• Foundations: 12 ChatGPT prompts, best AI tools for venues, AI to fill your venue.
• Intermediate: AI for programming, AI for compliance, AI for vetting acts.
• Advanced (you are here): inbox automation, venue analytics, festivals & multi-stage.
• The full venue cluster: see AI for UK Music Venues (2026) for all 9 posts.
• The other side: Artists are running a similar data review on their end — see the AI for Musicians cluster for the artist perspective on data-driven booking decisions.

Annual refresh commitment

This guide was published on 15 May 2026 and is refreshed every May. We re-verify every reference, recommendation, and data point once a year. Next scheduled refresh: May 2027. If any claim is outdated before then, email hello@gigxchange.app and we will update it within 24 hours.