Adding the right free amenity can boost a hotel’s appeal, increase sales, and improve customer satisfaction. But how can you know which amenity will have the best impact on guest behavior?
As we discussed in a recent post, offering free amenities can have a positive effect. To make sure the amenity any hotelier chooses to offer will perform, there has to be some serious analysis first. When you’re calculating the financial return from a free service, you must consider the amenity’s impact on both new and repeat guests.
In this post, we’ll talk about how hospitality companies can leverage historical customer revenue data to assess the impact of adding (or dropping) a complementary service. We’ll also look at why doing this across brands is tricky.
Using Conjoint to Estimate the Impact on Initial Sales
Adding more features or amenities often favorably affects a consumer’s purchasing decision. For example, when customers choose a hotel, their choices may be influenced by factors like free services or megawatt local attractions. This is in addition to core attributes, such as location or price.
So how can you gauge the impact of an additional perk on new sales?
The answer lies in conjoint analysis. A survey conducted on both existing and new customers can compute the effect of each amenity on the customer’s initial choice of brand.
The result will depend on two factors:
- The change in ‘Chance to select a Brand’ after excluding an amenity, and
- The ‘Initial Sales’ from the amenity
The weight for desirability estimated from Conjoint Analysis and expected sales from historical data give the expected impact on initial sales. However, to assess the impact on entire sales picture we need to account for repeat sales too.
The effect of adding an amenity may not be linear when it comes to repeat, even if the perk is evaluated favorably by customers. For that, we need to consider customer expectations and the actual usage of an amenity.
Assessing the Effect of Hotel Amenities on Repeat Purchases
The combined effects of expected and actual use should be considered to account for an amenity’s effect on repurchase. For example, a guest who selects a hotel with a fitness center and then uses the fitness center is likely to leave the hotel feeling satisfied; however, the fitness center may not necessarily drive his return to the same brand. On the other hand, consider a guest who doesn’t expect a free bottled water in the room but enjoys some during her stay. She might be more likely to choose the same hotel brand in the future.
How can we build this into our analysis of a complementary service? Existing and new customers should be analyzed in pre-stay, during stay, and post-stay timeframes to quantify the effect of expected and actual use of an amenity on repeat frequency and revenue per visit. A nonlinear factor model will then be developed with this data. This provides an expansion factor, which is in turn used to predict repeat frequency and revenue.
Computing Financial Returns on an Amenity
The results deduced by the above methods, coupled with customers’ historical average spend and the proportion of new customers, can be used to compute the expected sales from a free service. Related maintenance expenses can also be estimated; they normally directly correlate to per-usage costs and the number of customers using the amenity.
Professors Rebecca Hamilton, Ronald Rust, Michel Wedel and Chekitan Dev have published an interesting paper on this methodology (Return on Service Amenities). Based on their analysis, the following inferences can be drawn:
- Bottled water has a strong effect on repeat purchases: The authors observed that offering bottled water as a perk has only a marginal effect on initial choice. In contrast, customers who use free bottled water are more likely to visit again. (This varies by brand.)
- Free Wi-Fi has a significant impact on initial choice: Free Wi-Fi was shown to have a powerful sway on initial choice, and it has less variation in impact by brand.
Clearly, the type of customer — returning or new — is an important factor in offering an amenity and forecasting its effect on your budget. So, this raises one more question:
Can we use Amenities to Boost Revenue for all Brands?
The profitability of an amenity is brand-specific; several factors drive the ROI, including:
- Customer Lifetime Value (CLTV) – The CLTV differs across brands. Even if the effect of adding an amenity on future visits is the same across brands, its impact on future revenue may differ.
- Mix of new and repeat guests – The relative proportions of new and repeat guests’ changes across brands. This means that the influence an amenity has on revenues will also vary.
- Percentage of guests that use amenities – The percentage of guests using various amenities is also not constant across brands.
As we said at the outset, a one-size-fits-all free amenity across hotel brands can be hard to find. Once again, though, data analysis can help brand managers or individual operators make an informed decision. Initial and repeat purchase factors need to be combined with historical customer data and cost structures. This information is used to evaluate the impact that adding or dropping an amenity will have on expected revenue. Hotel service providers can then choose the best investment option available to them, based on that amenity’s projected ROI.
Successfully balancing attracting new guests and retaining current customers takes hard work, but with the right mix of options, it can be done. By using data analytics and techniques like conjoint analysis and nonlinear factor modeling, insights can be revealed that make the decision process easier.
Authored by Surendra Yadav – Manager at Absolutdata and Rajeev Kumar