Revenue management is the use of data analytics to optimize sales. The term is associated with dynamic pricing that considers inventory, customer and competitive factors in setting each price. Revenue management can also be used to optimize other marketing factors such as promotion, customer relationship management and sales channels. The following are illustrative examples.
ForecastingForecasting demand to set prices. For example, a hotel chain that forecasts demand by property and room type based on historical patterns to set initial prices for an upcoming season. Forecasting may also be used to plan promotional activities such as advertising.
Price SensitivityDetecting customer price sensitivity to implement price discrimination such as an airline that attempts to detect business travelers by route and dates in order to charge them more. For example, a flight that doesn't include a weekend stay is a common method for detecting business travel.
InventoryAdjusting promotional activity and prices to avoid ending up with unsold inventory. This is particularly important for industries that have inventory that occurs at a point in time such as a seat on a flight.
ChannelsEffective use of sales channels to clear inventory and obtain the best price. For example, a hotel may sell through a discount travel agency to clear inventory that isn't likely to sell through higher price channels.
SegmentationIdentifying segments of customers who have different price sensitivity or who respond to different types of promotion. For example, a bicycle helmet manufacturer may find that customers who are more price sensitive are likely to purchase bright color products whereas customers who willing to pay more tend to prefer conservative colors.
OptimizationFirms use revenue management to optimize for different types of goal. A firm with limited inventory may optimize for average selling price to improve margins. A company that can scale up production may optimize for total sales with a minimum acceptable margin. In some cases, firms may optimize for customer lifetime value. For example, prices that are always jumping up and down due to dynamic pricing can result in loss of loyal customers if a competitor is offering flat prices that customers prefer.
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