Forecast Demand For The Upcoming Enchanted Event ✓ Solved

Forecast demand for the upcoming Enchanted event and for the

Forecast demand for the upcoming Enchanted event and for the 2018/2019 entertainment season for Levels, a local entertainment events company in Jeddah. Use data from large successful events in the previous year (attendance, ticket price, number of events on the same day, number of events in the week before and after, and entertainment alternatives). Consider changing factors including possible trend decline, introduction of new trends, cinema openings, Saudi innovation initiatives, and increased supply and variety. The season runs November–June; the event launches on 14 November and will kick off the 2018/2019 season. Produce a demand forecast for the upcoming event and for the upcoming season, and describe how post-event data (after the November event) would be used to validate the forecast.

Paper For Above Instructions

Executive summary

This paper produces a pragmatic, data-driven demand forecast for the “Enchanted” event (Levels company) launching 14 November and for the full 2018/2019 Jeddah entertainment season (November–June). Using prior-season event metrics (attendance, ticket price, same-day competing events, and week-adjacent event density) and accounting for market shifts (possible trend decline, new trend introductions, cinema reopenings, Saudi innovation initiatives, and increased supply), I present a scenario-based forecast for the event and season plus a post-event validation plan.

Data inputs and baseline construction

Key inputs are: historical large-event attendance averages, ticket-price tiers and elasticity, counts of simultaneous events, event density in adjacent weeks, and alternative leisure options (notably cinema openings). Where precise Levels data are unavailable here, forecasting uses the prior-year “large successful event” aggregate as a baseline metric and applies adjustments (see methodology). Industry context from GEA and Saudi Vision 2030 indicate rapid growth in organized entertainment demand but also an expanding supply base (GEA, 2018; Vision2030, 2016).

Methodology

The forecasting approach merges a short-term time-series baseline with causal adjustments (a simple blended model):

  • Baseline: mean attendance of comparable large events in the prior season, adjusted for calendar-seasonality (months Nov–Jun with peak Jan–Feb) using seasonal indices (Statista; PwC industry seasonality patterns).
  • Price elasticity adjustment: apply an assumed ticket-price elasticity (ε = -0.8 to -1.0 for live events) to account for pricing changes relative to the prior season (PwC; Deloitte).
  • Competition/cannibalization: subtract estimated attendance share lost to same-day events and week-adjacent event clustering using a proportional reduction factor derived from prior-season overlap analysis.
  • Structural market shifts: additive/subtractive percentage adjustments for cinema reopening, new trend introductions, GEA-driven demand uplift, and supply increase (based on published industry and news reports) (Reuters; NYT; GEA).
  • Scenario analysis: produce conservative, baseline, and optimistic scenarios to reflect uncertainty.

Assumptions (explicit)

1) Baseline comparable-event average attendance = 10,000 (aggregate of prior large events). 2) November is a moderate-demand month (baseline index ≈ 0.75 of peak January rate) due to weather/seasonality. 3) Ticket price for Enchanted is set near prior-year average; no major price shock. 4) Cinema openings reduce average per-event demand by 10–20% in direct substitution among movie-seeking segments (Reuters; NYT). 5) GEA promotions and Saudi innovation/marketing efforts increase overall market attendance by 10–15% relative to organic demand (GEA initiatives; Vision2030). 6) Increased supply (more events) reduces Levels' market share by an estimated 10% unless offset by unique programming or marketing (PwC; Deloitte).

Event-level forecast: “Enchanted” (14 November)

Baseline calculation:

- Baseline comparable attendance: 10,000

- Seasonality adjustment for November: 0.75 → 10,000 × 0.75 = 7,500

- GEA/marketing uplift (net positive): +12% → 7,500 × 1.12 = 8,400 (accounts for improved discoverability and promotion) (GEA, 2018)

- Cinema substitution and supply crowding combined net downward adjustment: -15% → 8,400 × 0.85 = 7,140

- Competition (same-day events): if one or more similar events are scheduled same day, further reduce by 10% → 7,140 × 0.90 = 6,426

Resulting point estimate (baseline scenario): approximately 6,400–7,200 attendees. Scenario ranges:

  • Conservative (higher competition, trend decline): 4,800–6,000 attendees.
  • Baseline (moderate offsets): 6,400–7,200 attendees.
  • Optimistic (strong GEA lift, low competition): 8,000–10,000 attendees.

Season-level forecast: Levels company (Nov–Jun)

Assume Levels schedules 8–12 events across the season with mixed sizes. Using average-event attendances per scenario:

  • Conservative average per event: 4,500; 8 events → season total ≈ 36,000 attendees.
  • Baseline average per event: 7,000; 10 events → season total ≈ 70,000 attendees.
  • Optimistic average per event: 9,000; 12 events → season total ≈ 108,000 attendees.

These totals reflect seasonality weighting (more events concentrated in peak months) and adjustments for market-wide supply increases and cinema competition (PwC; Deloitte).

Risk factors and sensitivity

Primary risks: higher-than-expected supply causing share erosion, rapid trend decline, price sensitivity if Levels chooses premium pricing, or stronger-than-expected substitution by cinemas. Offsetting factors: unique programming, strong marketing partnerships with GEA, and innovation initiatives under Vision 2030 that stimulate broader participation (Vision2030; GEA).

Post-event validation and learning plan

After the 14 November event, collect these data: actual attendance, ticket price distribution, same-day event counts, sales cadence (tickets sold by date), and attendee demographics. Compute forecast accuracy via Mean Absolute Percentage Error (MAPE) and bias statistics. Update model parameters (seasonal index, competition reduction factor, elasticity estimate) using an evidence-weighted learning rule (Bayesian updating or exponential smoothing). If Enchanted attendance deviates >15% from baseline, conduct root-cause analysis (ticketing channels, weather, competing events, pricing, promotion effectiveness) and adjust season forecast accordingly (Oxford Economics approach to iterative forecasting).

Operational recommendations

1) Monitor competing-event calendar weekly and adjust promotional intensity in the 2–3 weeks prior. 2) Use early-bird pricing time windows to test elasticity and accelerate cashflow. 3) Partner with GEA channels to capitalize on cross-promotion. 4) Collect robust post-event data to recalibrate forecasts quickly and communicate revised season projections to stakeholders.

Conclusion

Using prior-event baselines and plausible adjustments for seasonality, cinema openings, GEA-driven demand, and increased supply, the recommended point forecast for Enchanted is ~6,400–7,200 attendees under a baseline scenario, with a season-level expectation of ~70,000 attendees for a 10-event program. These figures should be updated immediately after the November event using the validation approach described. A scenario strategy and active monitoring will allow Levels to manage risk and seize upside if market momentum strengthens (GEA; Vision2030; PwC).

References

  1. General Entertainment Authority (GEA). (2018). GEA official website and market initiatives. https://www.gea.gov.sa
  2. Saudi Vision 2030. (2016). Vision 2030: Entertainment and cultural development. https://vision2030.gov.sa
  3. Reuters. (2017). "Saudi Arabia to allow cinemas — lifting decades-long ban." https://www.reuters.com/article/us-saudi-cinemas-idUSKBN1
  4. The New York Times. (2017). "Saudi Arabia Says It Will Allow Cinemas." https://www.nytimes.com/2017/05/16/world/middleeast/saudi-arabia-cinemas.html
  5. PwC. (2018). Global Entertainment & Media Outlook 2018–2022. https://www.pwc.com/gx/en/industries/tmt/media/outlook.html
  6. Deloitte. (2018). Middle East Entertainment and Media industry insights. https://www2.deloitte.com/middleeast
  7. Statista. (2018). Live entertainment and event attendance statistics (GCC and Saudi Arabia). https://www.statista.com
  8. Arab News. (2018). Coverage of GEA-backed events and Jeddah season developments. https://www.arabnews.com
  9. McKinsey & Company. (2016). "Saudi Arabia beyond oil: Diversification and consumer trends." https://www.mckinsey.com/featured-insights/middle-east-and-africa/saudi-arabia-beyond-oil
  10. Oxford Economics. (2018). Leisure and tourism forecasting methods for emerging markets. https://www.oxfordeconomics.com

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