*Case 1: Recalibrating Retail Pricing in Banten*
1. Simple linear formula:
Let’s assume the headline index is the driver for the province-wide pricing baseline. The formula could be:
Price Change = β0 + β1 * Headline Index Change
Where β0 is the intercept and β1 is the slope.
To predict the next 12 months, we would need historical data on the headline index and price changes.
2. Categories to exempt from blunt markup:
Based on the patterns observed, Food and Transport categories might require pricing discretion due to their volatility. Operational considerations:
– Food: promo calendars, supply lead times, festival seasonality
– Transport: fuel policy changes, mobility patterns
3. Updating the formula after a fuel-price adjustment:
We would need to reassess the relationship between the headline index and price changes. If the fuel-price adjustment is significant, we might need to update the β1 coefficient or add a new variable to account for the shock.
*Case 2: Indexing City Minimum Wages*
1. Simple linear formula:
UMK = β0 + β1 * Price Level
Where β0 is the intercept and β1 is the slope.
To forecast UMK for the next 5 years, we would need historical data on UMK and price levels.
2. Considerations for placing UMK above or below the baseline:
– Productivity growth: if productivity growth is high, UMK could be increased above the baseline.
– Employment conditions: if employment rates are high, UMK could be increased above the baseline.
– Sectoral shocks: if certain sectors are experiencing shocks, UMK might need to be adjusted accordingly.
– Regional competition: UMK should be competitive with neighboring regions.
3. Public explanation:
“The city aims to adjust the minimum wage in line with the cost of living, while ensuring the adjustment is sustainable for businesses and promotes employment. Our rule-based approach takes into account the price level and productivity growth, providing a transparent and predictable framework for stakeholders.”
*Case 3: Betting on Mobility*
1. Simple linear formula:
Let’s assume the trend-based forecast is based on historical passenger counts. The formula could be:
Passenger Count = β0 + β1 * Time
Where β0 is the intercept and β1 is the slope.
To project total monthly passengers for the next 24 months, we would need historical data on passenger counts.
2. Operational considerations for launching Route A:
– Vehicle leases: consider the cost and duration of leases.
– Crew shifts: ensure adequate staffing for the new route.
– Seasonality: consider peak travel periods and holidays.
– Holiday spikes: plan for increased demand during holidays.
3. Adapting to international traffic lagging domestic:
– Adjust frequencies: reduce frequencies on international routes and increase frequencies on domestic routes.
– Pricing: consider offering discounts or promotions on domestic routes to attract more passengers.
*Case 4: Betting on a Flagship*
1. Simple linear formula:
Let’s assume the trend-based projection is based on historical hotel occupancy rates or passenger counts. The formula could be:
Demand Proxy = β0 + β1 * Time
Where β0 is the intercept and β1 is the slope.
To project the demand proxy for the next 24 months, we would need historical data on the chosen proxy.
2. Marketing and retail considerations:
– Lease/fit-out timing: consider the cost and timing of fit-out.
– Staffing & training: ensure adequate staffing and training for the new store.
– Media pacing: consider pulsing media spend around seasonal peaks.
– Influencer/event calendar: plan events and influencer partnerships around seasonal peaks.
3. Adapting to tourist/visitor signals lagging:
– Channel mix: shift towards geo-targeted locals and loyalty/CRM pushes.
– Creative: focus on value propositions and promotions that appeal to locals.
– Promotions: offer bundled value and loyalty rewards instead of blanket discounts.
– Event formats: focus on community events and activations that appeal to locals.