Programming Model

Current approach to event programming decisions and proposed shift to data-driven model.

Current Programming Approach: “Luck, Speculation and Hope”

Characterisation: Programming decisions are made intuitively, without quantitative analysis or historical precedent.

Decision framework:

• Artist availability and cost negotiation (within budget constraints)
• Thematic diversity (intuitive assessment of audience interest)
• Calendar balance (avoiding consecutive DJ nights)
• Venue availability and venue manager (Monique) capacity
• Vague sense of “what the community wants”

Decision-maker: Emily Rose (Head of Programming) with Mat final approval on artist contracts.

Data Blindness

Emily and Mat cannot distinguish profitable events from loss-making ones because:

1. No event-level P&L: Square data not queried by event; Xero expenses not allocated by event
2. No attendance analysis: TryBooking attendance lists printed and discarded (critical data loss)
3. No customer analysis: Repeat attendance rate by event type not tracked; customer lifetime value not calculated
4. No bar revenue correlation: Unknown if certain event types (e.g., DJ nights vs drag) correlate with higher bar spend
5. No cost tracking: Performer fees and security costs recorded in Xero but not tied to specific events for profitability calculation

Consequence

Programming optimisation is impossible. Emily cannot answer:

• Which events consistently make money vs which are subsidised losses?
• Do customers who attend Drag Bingo also attend Sapphic Nights?
• What is the repeat attendance rate for each event type?
• Do certain promotion timing or messaging strategies drive higher ticket sales?
• Is Thursday underperforming due to poor promotion or inherent low demand?

Proposed Data-Driven Model

Humphrey Intelligence App will deliver:

1. Event-level P&L: Query Square transaction data by event code (TryBooking field) and allocate Xero costs (performer, security) to calculate actual profitability per event.

2. Attendance automation: Daily export of TryBooking attendance data to structured log, enabling:

   • Event attendance trend analysis
   • Customer repeat attendance rate by event type
   • Customer lifetime value calculation

3. Bar revenue analysis: Correlation of bar revenue with event type, attendance, customer segments.

4. Promotion analytics: Meta analytics (impressions, engagement) correlated with ticket sales and attendance (gap: currently unknown).

5. Customer segmentation: Email and attendance data enabling targeted promotion to high-value customers and re-engagement of lapsed attendees.

Shift Timeline

Near term (0–3 months): Continue current intuitive model; focus on cash flow survival via Saturday Anchor Event Strategy.

Medium term (3–6 months): Humphrey Intelligence App delivery (Workstream 3, sprints 3–4); begin collecting structured event data.

Longer term (6+ months): Shift programming decisions to data-informed approach; optimise event mix and pricing based on P&L and customer data.

Emily’s Role Evolution

Currently: Artist booking and promotion (based on intuition and availability)

Future: Data-informed programming strategy including:

• Performance analysis of past events
• Customer segmentation and targeted promotion
• Pricing optimisation by event type and demand
• Artist fee negotiation informed by event profitability
• Promotion timing and content optimisation

This requires Emily’s role to expand from logistics to strategic analysis—but only possible with Automation Opportunities delivering data and reducing manual work.

Show Frequency and Audience Fatigue (April 2026)

Per Venue Revenue Optimisation Research.

Pride’s Position in the Market

Pride at 199+ shows/year is in the top tier nationally — only 12.4% of Victorian venues host 2+ events per week. Edinburgh Fringe benchmark: 33% average fill rate across 53,942 performances — high frequency does not require high fill rates to be viable.

Three Types of Audience Fatigue

1. Format fatigue: Same show weekly — regulars become bored. Mitigation: theme rotation (25% participation lift), seasonal refresh every 3–4 months.
2. Financial fatigue: Cost of attending weekly erodes willingness. Mitigation: loyalty stamp cards (6th free), season passes (15–20% discount), free-entry nights funded by bar spend.
3. Promotional fatigue: Overcommunication desensitises audience. Mitigation: email list of 200 engaged people delivers 3–5× the conversion of social media; reduce social media volume, increase quality.

Recommended Programming Mix

60–70% recurring (predictable, habit-forming): Drag Bingo, weekly drag show, trivia — builds community and baseline revenue.

30–40% destination (one-off, buzz-generating): touring acts, special theme nights, festival programming — drives new customer acquisition and PR.

60-Minute Double-Header Format

Edinburgh Fringe demonstrates that 60-minute shows with 30-minute turnarounds enable two shows per evening (e.g., 7pm and 9pm) — doubling ticket revenue from the same room. Pleasance and Gilded Balloon run 15-minute turnarounds at festival intensity; 30 minutes is comfortable for a 200-capacity venue. This format is the single highest-leverage scheduling change for Friday and Saturday revenue.

Recommended 7-Day Programming Schedule

Day	Format	Revenue Driver
Sunday	Market + Drag Brunch (phased)	Food + tickets
Monday	Dark / private hire	Venue hire
Tuesday	Open mic / emerging talent	Community + low cost
Wednesday	Recurring (e.g., trivia)	Bar spend + loyalty
Thursday	Big event (monthly → weekly)	Tickets + bar
Friday	Double-header (7pm + 9pm)	2× ticket yield
Saturday	Theatre restaurant / anchor event	Premium tickets + food + bar

See Recurring Event Retention for lifecycle phases and retention strategies.

Risk and Limitations

Data quality risk: Event-level P&L requires careful data entry discipline (linking Square transactions to TryBooking event codes, allocating Xero expenses). Initial data may be incomplete or inconsistent.

Attribution challenge: Some costs (rent, general labour, utilities) are difficult to allocate to specific events. Event P&L will focus on direct costs (performer, security) and event-attributed revenue.

Behavior change lag: Even with good data, customer and market behaviour shifts may make historical patterns unreliable for future prediction.

Related Pages

• Events and Programming — programming workflow and context
• Emily Rose — head of programming
• Humphrey Intelligence App — enabling analytics platform
• Drag Bingo — flagship event for performance benchmarking
• Automation Opportunities — data collection and reporting prerequisites
• Recurring Event Retention — lifecycle phases, retention strategies, host dependency
• Venue Revenue Optimisation Research — source: show frequency, fatigue types, 60-min format (April 2026)
• Trading Pattern — overall revenue model context