Kipling Flight Pricing Helper

How it works:

Simply the lowest price found across all departure dates in your search range.

Real-life application:

Best for: Price-conscious travelers with flexible dates who can book the absolute cheapest day.

Risk: This might be an outlier. Most customers won't find this price, so using it as a baseline could lead to unhappy customers or lost revenue.

How it works:

Sum all prices and divide by the number of dates. Classic arithmetic mean.

Real-life application:

Best for: Getting a general sense of typical pricing when data is relatively consistent.

Risk: Very sensitive to outliers. One extremely expensive day can skew the average upward significantly.

Example: If most days are 5,000 DKK but one day is 15,000 DKK, the average will be misleadingly high.

How it works:

Remove the lowest 10% and highest 10% of prices, then calculate the average of what remains.

Real-life application:

Best for: Stable pricing that represents typical market conditions without extreme values.

Benefits: More robust than simple average. Removes both unusually cheap anomalies and price spikes.

Use case: When you want an average but suspect some prices might be outliers (technical errors, flash sales, or temporary spikes).

How it works:

The middle value when all prices are sorted. Half the prices are below this, half are above.

Real-life application:

Best for: Understanding the "typical" price that most customers will encounter.

Benefits: Not affected by outliers at all. One extreme price won't change the median.

Use case: Fair baseline pricing. Half your customers will find better deals, half will pay more.

Example: Prices of 5K, 5K, 5.5K, 6K, 20K → Median is 5.5K (much better than average of 8.3K)

How it works:

75% of all prices are at or below this value. Only 25% are higher.

Real-life application:

Best for: Conservative pricing that covers most scenarios while maintaining profitability.

Your research shows: Kipling's current pricing roughly matches the 75th percentile of API data.

Benefits: Provides a buffer against price volatility. Customers are more likely to find prices at or below your estimate.

Use case: Standard pricing strategy for travel companies. Balances competitiveness with safety margin.

How it works:

90% of all prices are at or below this value. Only 10% are higher.

Real-life application:

Best for: Very conservative pricing for high-risk scenarios.

Use cases:

• Peak travel periods (Christmas, New Year, major holidays)
• Routes with high price volatility
• When you need strong guarantees to customers
• Pricing for packages where flight costs must be locked in early

Trade-off: May be less competitive (higher than necessary) but provides maximum safety.

How it works:

The highest price found across all departure dates in your search range.

Real-life application:

Best for: Understanding worst-case scenario and price volatility range.

Risk: Using this as a baseline would make you very uncompetitive. Most customers won't encounter this price.

Use case: Risk analysis and understanding the price spread. Good for identifying if certain dates are anomalously expensive.