Revenue management strategy for the Spa Department
3 min read

Revenue management strategy for the Spa Department

Revenue management principles, tools, and techniques are widely and successfully used to maximize room revenue. Are they applicable to the other revenue-generating outlets? How to develop a strategy to maximize revenue and profit for the Spa Department?
Revenue management strategy for the Spa Department

Does the Spa Department fulfill the necessary criteria for applying revenue management principles?

The answer is yes, and this is why:

1) The Spa has a fixed capacity. It has a limited number of treatment rooms that can accommodate a limited number of treatments based on hours of operation. The goal would be to maximize revenue per treatment room-hour (RevPATH).

2) Perishable inventory. If there is no treatment booked and the treatment room is available, revenue is lost.

3) Demand varies by day of the week and time of the day. Also, there is a different demand for 30-minute, 60-minute, and 90-minute services. We can set different prices for treatments based on variable demand patterns.

4) Demand-based dynamic pricing can be applied to spa guests. Some guests are price-sensitive, and some are not. Spa guests can be divided into different segments, and effective rate fences can be created.

Developing the strategy.

Step 1 - Collect the data.

For example:

1. The number of treatments sold and revenue generated to identify demand patterns per day of the week and per time of the day. Also, look at this data from a weekly and monthly perspective to determine seasonality.

2. Analyze cancellations and no-shows to assess revenue lost and develop effective overbooking, waitlist policy.

3. Analyze which treatments are most popular.

4. Analyze which duration of treatment is the most popular (30, 60, or 90-minute).

5. The number of services that were turned down and analyze if there is a pattern. It could be a capacity issue and could be a staffing issue.

6. Booking patterns. Advance vs. same day bookings.

Step 2 - Analyze the collected data.

For example, we received the following results:

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