Will Raising Prices Increase Revenue? The Revenue Curve Knows
Price and occupancy move in opposite directions. Raise prices and bookings drop; lower them and revenue per night falls. PriceBnb mathematically finds the sweet spot where total revenue peaks.
The Core Formula
Monthly Revenue
Revenue(p)
Nightly Price
p
Occupancy
Occ(p)
Days
days
As price (p) increases, occupancy Occ(p) decreases. The optimal price is where their product peaks. PriceBnb independently finds this peak for each tier.
Revenue Curve Visualization (Weekend Tier Example)
Optimal: $105 · Est. occupancy: ~73% · Weekend revenue: ~$685
Independent 3-Tier Optimization
Weekdays, Fridays, and weekends have completely different demand patterns. The chart below shows all three revenue curves. Each dot (●) marks the peak for that tier.
Weekday (Sun-Thu)
17 days/month
Business travelers, long stays. Less competition but potentially lower occupancy.
Friday
4 days/month
Start of weekend travel. Higher demand than weekdays, needs its own strategy.
Weekend/Holiday (Sat)
9 days/month
Peak demand. Premium pricing possible, but overpricing leads to vacancy.
Optimization Effect — Before / After
Without a revenue curve, prices are set by gut feeling. With it, a small adjustment creates a meaningful difference.
$10 price increase → +12% revenue
Before
$75
Occupancy 62%
Monthly $419
+12% Revenue
$85
Occupancy 55%
Monthly $470 +$51
→ Click the arrow to reveal the optimization result
Confidence Tiers — Accuracy Grows With Data
When data is insufficient, extreme recommendations are withheld. Confidence level is always shown in the report for full transparency.
Confidence tiers based on data accumulation
Analysis Process
STEP 01
Data Collection
Automatically collects pricing and occupancy data weekly from your listing and 5 competitors across 2 weeks.
STEP 02
Similarity-Based Weighting
Not all data points are equal. Competitors similar to your property (guest capacity, location, rating, rooms) receive higher weight.
STEP 03
Weighted Regression Analysis
Estimates the price→occupancy function from weighted data. Recent data receives higher weight to reflect current market conditions.
STEP 04
Optimal Price Discovery
Finds the peak of Revenue(p) = p × Occ(p) × days. Only recommends prices within 70%-150% of competitor median.
STEP 05
Confidence Validation
Validates with R² coefficient. 18+ data points with R²≥0.5 means high confidence; otherwise recommends conservatively.
Automatic Season Adjustment
The same price yields different occupancy in peak vs low season. PriceBnb auto-detects seasons and adjusts the curve.
Low Season
-5%p
Market occupancy < 50%
Normal
0
Base curve applied
Peak · Holiday
+5%p
Market occupancy > 80%
Why Revenue Curve
Escape the Average Price Trap
Following competitor averages doesn't maximize revenue. The curve shows where total earnings peak.
Independent Per-Tier Strategy
Aggressive weekdays + premium weekends. One price doesn't fit all days.
Improves Over Time
New data each week refines the curve. High-confidence recommendations from week 4.
No Extreme Recommendations
Prices outside 70%-150% of competitor median are never suggested.
Find Your Listing's Optimal Price
Experience AI revenue curve analysis with a free plan. See tier-specific optimal prices in your first report.