In this special guest feature, Anil Kaul, CEO of Absolutdata, discusses how big data, combined with machine learning and artificial intelligence (AI) solutions, allows hotels to personalize campaigns to drive returns from individual customers rather than estimating the effects of an amenity on personas or classes of customers. Anil has over 22 years of experience in advanced analytics, market research, and management consulting. He is very passionate about analytics and leveraging technology to improve business decision-making. Prior to founding Absolutdata, Anil worked at McKinsey & Co. and Personify. Anil holds a Ph.D. and a Master of Marketing degree, both from Cornell University.
People in the hospitality industry generally agree that offering the right amenities can drive sales and increase hotel guest satisfaction. There are ways to project the return on offering free perks, which can help hoteliers make better business decisions, but it’s always been a challenge to accurately quantify the ROI on hotel amenities and use that information to personalize offers.
A combination of market research and big data analytics can change that, with marketing research providing hoteliers with the information they need to choose the right amenities and big data providing the heretofore elusive hard numbers on results. Just as importantly, big data can give hospitality professionals the ability to achieve levels of personalization that can drive ROI even higher.
Hospitality companies can combine market research with long transaction history of customer revenue data to evaluate the potential impact of adding or taking away a perk, such as free bottled water or Wi-Fi in guest rooms. Conjoint analysis, a market research technique, enables hoteliers to estimate the impact of adding a perk on the purchase decision.
With conjoint analysis, hotels give customers a survey that provides a chance to select a brand with or without a perk, then evaluate the change in response as well as the initial sales from the amenity. With this information, hotels can assign a weight that represents the desirability of the perk estimated from the conjoint analysis and project the expected sales using historical data.
But while that information is useful, especially for selecting the right perk, it’s also important to keep in mind that the impact might vary between new and existing customer groups. It’s generally linear response for attracting new customers: however, the reaction to an amenity may not be linear when a repeat customer evaluates a perk. To get insight into that dynamic, hotels must factor in customer expectations as well as their actual use of an amenity.
To understand how hotel amenities affect repeat purchases, hoteliers need data on actual usage. It’s probably safe to assume satisfaction on the part of a guest who chooses a hotel that offers free Wi-Fi and then uses the service. But it’s not safe to assume that guest will return to the same brand in the future. However, a guest who didn’t expect free Wi-Fi but then used it during a stay may be more likely to choose the same brand on a subsequent visit.
These insights can be revealed by nonlinear modeling. To conduct this type of research, hotels should analyze customer sentiment before, during and after a stay to get a read on the effect of expected and actual use of an amenity. This can clarify how perks drive repeat business and provide insight on the impact on revenue. With this expanded information, hotels can predict repeat frequency and revenue.
To compute financial returns, hotels typically take this research and factor in historical average spend and the proportion of new customers. That yields information on the expected sales from an amenity. Then they can gauge any applicable maintenance expenses, customer volume, etc. But thanks to big data, factors like actual usage can easily be determined to improve accuracy.
In addition to providing more accurate metrics, big data can add a new dimension: personalization. Rather than relying solely on surveys that indicate how new and existing customer might respond to a hypothetical choice, big data can drill down to what actual customers did during their visit. Hotels typically have this level of detailed information on hand: who used the gym, which products customer selected from the minibar, etc. Now it’s just a matter of applying that information in personalized campaigns.
Big data, combined with machine learning / artificial intelligence (AI) solutions, allows hotels to personalize campaigns to drive returns from individual customers rather than estimating the effects of an amenity on personas or classes of customers. Marketing research techniques such as conjoint analysis and nonlinear modeling can be an important solution in the hotelier’s tool kit. But big data that allows hotel marketers to drill down to the individual customer’s level opens new possibilities to drive revenue.
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