Revenue Generated Index is the key performance measure used by hotel companies to track changes in market share relative to competitors. But equally important is to understand the key drivers of RGI, those underlying metrics that can be used by functions such as marketing, sales, and revenue management to measure their impact on RGI.
For customer marketing, one such metric is share-of-wallet, the percentage of a customer’s spend at one company’s hotels relative to all spend at all major hotel brands. Every marketing group would like to know which of their customers are spending a significant share of their nights with competitors because that represents opportunity for growth. In an ideal state, a company could know the share of each customer, show how changes in share at the customer level factor into changes in RGI at the hotel or brand level, and identify which are low-share customers it needs to win vs. high-share customers it needs to retain.
Unfortunately, it is problematic to measure and track share-of-wallet at the customer level, and even harder to link it to RGI performance. Companies typically try to estimate customer share-of-wallet through surveys or third-party data partners. Both of these methods can offer a degree of insight, but both also give biased and incomplete views of share-of-wallet at the customer level. And they are even less accurate when aggregated to the hotel or brand level.
First, in the case of surveys and focus groups, researchers can calculate share-of-wallet at an individual level for those customers who are willing to complete a survey, but customers who choose to respond to surveys are rarely representative of the broader customer base. So this approach can give some indication whether share is changing for some types of customers, but is not helpful when trying to determine which individuals are likely to be loyal to you vs. your competitors.
Second, in the case of third-party data, there are growing opportunities to work with partners and data consolidators who have visibility into certain individuals’ stay transactions across all hotel brands, but this approach also has its limits. First, the data partner might see only a subset of your customers, meaning share-of-wallet estimates aren’t available or representative for your full customer base. Second, the data partner might not see all transactions for a given individual, meaning share-of-wallet estimates would be based on incomplete data and inaccurate calculations.
Third, some data partners will only provide share-of-wallet figures at a summarized or aggregated level instead of an individual customer level, making them less useful for marketing purposes.
However, with the right data and some clever econometric methods, it is possible to estimate share-of-wallet across one’s entire active customer base, compare share-of-wallet across various groups of customers and different groups of hotels. The three ingredients are (1) historical market share data available through STR Global, (2) internal customer data that groups customers into relevant segments (e.g., business vs. leisure, elite vs. non-elite, direct book vs. OTA), and (3) econometrics, which is a statistical discipline that studies market/economic data to draw conclusions about economic relationships… in our case, to infer how much business, on average, various customer groups give to competitors.
When combined, these ingredients allow us to study the variation in market share across hotels over time and compare that to variations in the types of customers traveling to each competitive set. From this, we can draw correlations between customer segments and market share, and from those correlations we can proxy the relative share-of-wallet to the company from different segments.
Share-of-wallet estimates derived from this approach are still noisy at the individual level, but it is the most robust, and least biased, method we’ve found for comparing share-of-wallet across customer segments and brands. And, because the estimates are derived from RGI itself, we can forecast the impact on RGI and RevPAR that should result from growing certain customer segments, which ultimately was our goal: to understand the impact of customer performance on brand performance.
About the Authors
Matt Lindsay, Ph.D., is president of Mather Economics. Jim Sprigg serves as director of database marketing for IHG.
