In Part 1 of our series on Big Data in Revenue Management we discussed that, when it comes to incorporating big data into Revenue Management Systems (RMS), relevance is key and that folding big data into RMS decision algorithms is what makes the difference, rather than displaying the data for mere decision support.
We also discussed how online reputation can now be integrated into pricing algorithms and that the use of regrets and denials for revenue management are simply unreliable.
In Part 2 of our series, we’ll evaluate the use of other types of big data in RMS, namely integrating non-room, weather, and airline data into revenue management decision systems.
Maximizing Profits vs. Maximizing Revenue Using Big Data
Another data source that is triggering an evolution in RMS analytics is profitability information. This can be tackled by obtaining ancillary revenue and cost data to generate profitability information contributed by various customer segments.
Ancillary revenues range the gamut from additional food and beverage, golf or spa revenues to " in the casino business "player theoretical loss. Cost or margin data is required across each customer segment when the RMS maximizes total profitability, because certain customer segments, while contributing extra revenue, can also incur additional variable costs.
We have seen profitability maximization prove especially valuable within the function space and group pricing domain, but it is emerging to become as applicable in the transient sector as well.
Incorporating Weather and Airline Data in Demand Forecasting
The weather and airline data is measured frequently and widely, and these are very broad measurements. It is yet to be seen how including weather and airline data can reliably improve forecast performance.
As an input to an RMS demand model, weather and airline data may improve the short term demand fit if, and only if, the data’s immediate impact can be assigned to a particular market or property. That is to say, weather and airline data trends may be impactful to travel patterns at large, but their relationship to business or leisure bookings in a particular location is loosely coupled.
Further, in the case of weather data, much of this data itself is forecast, thereby introducing yet another possible source of error into the demand forecast model. To put it simply, RMS providers have yet to prove how such data types drive better revenue.
Statistical Relevance Is Only One Key to Big Data Inclusion in RMS
All of the examples cited in our two part series are relevant, not just because the data is available, but because we have seen that they are statistically significant in the RMS process, whether by improving price-elasticity estimations, or changing the objective (profitability vs. revenue) used by optimization algorithms, or adding information that guests actually use in selecting hotels.
In many cases, much of the “big data” begging to be incorporated in RMS is demand-related data; that is, data that is assumed to improve forecast accuracy. Our experience has shown that predictive data elements enable an RMS to improve forecast accuracy, basically ensuring that the historical data and the predicted actuals are getting closer together from a statistical perspective.
When it comes to the use of Big Data in revenue management decision systems, it is very important to treat each new data source carefully and ask the following three questions:
- Does it contribute to new information that has not been provided in the data currently used?
- Does it change the nature of the decisions that you are making by offering a new way of thinking about the problem?
- Does it meet any performance standards you have set such as reducing forecast variance or having a reactive pricing strategy?
How you answer these questions will be as unique as your business needs. And finding the right revenue management system will have to accommodate these unique business needs.
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