In this special guest function, Anil Kaul, CEO of Absolutdata, explains how big data, combined with machine learning and artificial intelligence (AI) solutions, enables hotels to personalize campaigns to generate individual guest feedback rather than estimating the effects of a device on personalities or categories of clients. Anil has over 22 years of experience in advanced analysis, market research and management consulting. He is very passionate about analyzing and using technology to improve business decision making. Prior to founding Absolutdata, Anil worked at McKinsey & Co. and Personify. Anil holds a doctorate. and a master’s degree in marketing, both from Cornell University.
Hospitality professionals generally agree that providing the right amenities can drive sales and increase hotel guest satisfaction. There are ways to project ROI of free benefits, which can help hoteliers make better business decisions, but it has always been difficult to accurately quantify ROI for hotel amenities and use. this information to personalize the 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 precise numbers that have been elusive until now. results. Equally important, big data can empower hospitality professionals to achieve levels of personalization that can further increase return on investment.
Hospitality companies can combine market research with a long transaction history of guest revenue data to assess the potential impact of adding or removing a benefit, such as a free bottle of water or a bottle of water. Wi-Fi connection in the rooms. Conjoint analysis, a market research technique, allows hoteliers to estimate the impact of adding a benefit on the purchasing decision.
Using the conjoint analysis, hotels provide guests with a survey that allows them to select a brand with or without a benefit, and then assess the change in response as well as the initial sales of the equipment. With this information, hotels can assign a weight that represents the opportunity benefit estimated from the conjoint analysis and project expected sales using historical data.
But while this information is useful, especially in selecting the right benefit, it’s also important to keep in mind that the impact can vary between new and existing customer groups. This is usually a linear response to attracting new customers; however, the reaction to a convenience may not be linear when a repeat customer evaluates a benefit. To gain insight into this dynamic, hotels must take into account guest expectations as well as their actual use of a facility.
To understand how hotel amenities affect repeat purchases, hoteliers need data on actual usage. It’s probably safe to assume the satisfaction of a customer who chooses a hotel that offers free Wi-Fi and then uses the service. But it is not safe to assume that the customer will return to the same brand in the future. However, a customer 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.
This information can be revealed by nonlinear modeling. To conduct this type of research, hotels need to analyze guest sentiment before, during and after a stay to get a reading on the effect of expected and actual use of a facility. This can clarify how perks drive customer retention and provide insight into the impact on revenue. With this extensive information, hotels can predict repeat frequency and revenue.
To calculate financial returns, hotels typically use this research and take into account historical average spend and the proportion of new guests. This gives information on the expected sales of an equipment. Then they can assess applicable maintenance expenses, customer volume, etc. But with big data, factors like actual usage can easily be determined to improve accuracy.
In addition to providing more precise metrics, big data can add a new dimension: personalization. Rather than relying solely on surveys that show how new and existing customers could be able answering a hypothetical choice, Big Data can analyze what actual customers did during their visit. Hotels usually have this level of detailed information: who used the gym, what products the customer selected from the minibar, etc. All that remains is to apply this information in personalized campaigns.
Big data, combined with machine learning / artificial intelligence (AI) solutions, allows hotels to personalize campaigns to generate individual guest feedback rather than estimating the effects of a device on personalities or categories of guests. clients. Marketing research techniques such as conjoint analysis and nonlinear modeling can be an important solution in the hotelier’s toolbox. But big data that takes hotel marketers to the level of the individual guest opens up new possibilities for generating revenue.
Register for free at insideBIGDATA bulletin.