In order to explain what is quantitative hospitality and foodservice, let me start with three anecdotes.
I remember the feeling of entering the bar right across the street from where I lived for many years in Italy: they would greet me with my nickname and ask me if I’d like to have my usual breakfast. The barista knew me well and was doing his best to solicit a smile from me. It didn’t matter that the amount I paid for my small breakfast, as always, was no more than 2.5 euros. Often, I would stop there to grab a panino for lunch; without fail he would recall my aversion to brie cheese and would only offer me his daily specials without it. I always wondered, what if I could have the same personalized welcome in a place I’ve never been, maybe across the globe.
When my brother and his partner opened a restaurant just inside the peripherique de Paris, they didn’t know how much perishable food they would be wasting in the following years. Just like the owners of many restaurants around the world, they did not have an accurate idea of the number of people coming to eat in the next few days and the temptation to go shopping with the hope of a large turn out usually ended up with large quantities of food wastage.
A friend of mine, the owner of a small hotel in Myanmar, has been asking me for a while to come up with a way to predict the occupancy of his hotel on certain future dates based on the current bookings. He was venting that if he had a better prediction model, he could adjust accordingly the room prices on the web- booking system and optimize his profits.
These stories/problems are part of the everyday life in the hospitality industry and foodservice industry: know your customer; know how much food to buy for a restaurant; set the right price for a room in a hotel. Only the large restaurant or hotel chains have some of the tools needed to handle these problems analytically; however, most smaller hospitality enterprises are usually relying only on the manager’s intuition and experience(which may or may not be good).
It turns out that many of the tools developed in quantitative finance can be used to solve some, or even most, of these problems.
Ex-post, ex-ante analytics, and optimization tools
There are at least three different types of analytics that can be useful for the hospitality and foodservice industry: optimization tools, ex-post and ex-ante analytics.
The ex-post analytics help us understand better what went well or wrong in a given past period. They answer to questions like:
- What was my most popular dish last month?
- Who is my best waiter in terms of revenues?
- And is the above correlated with repeat visits by customers?
- How much do my clients spend on pizza?
- What is my most profitable dish?
- What is the minimum and maximum occupancy of my hotel in the last quarter
- Which room type is the most popular in my hotel?
- When am I most profitable during the year?
- How many families, singles, or young couples did I serve last week?
Ex-post analytics allow the business to understand more about the customers, the employees, where the money went and what was most profitable, etc. They are important in understanding why we are doing well or not.
The category of ex-ante analytics helps the business to plan the hospitality and the foodservice of his guests for the next few days, week, month, or even quarter. They answer to questions like:
- How many tables should I expect to serve tomorrow?
- How many kilos of flour should I purchase for next week?
- What is the minimum/maximum number of clients that will order my special dish two days from now?
- Given that the weather forecast for tomorrow is sunny, how many ice-cream cones am I expected to sell?
- Given the current status of online reservations, what is the expected occupancy of my hotel in each day of the next week?
- What is the expected cash revenue for a certain client in the next month?
- What is the returning probability of this specific client?
- What is the credit score of a client?
As the perfect forecast of the future is impossible, the ex-ante analytics can only provide answers probabilistically with a certain confidence level. However they are very valuable in navigating the business through the different seasons in good and in bad times. Unfortunately the few current tools available only provide the expected value of the unknown variables. In my view one should be able to provide more descriptors of the whole distribution (minimum, maximum, standard deviation).
While the ex-post and ex-ante analytics provide a measure of past performance and the expected one, the optimization tools help us in making the daily decisions. As such the optimization tools can help us in deciding
- Given the amount of cash available for buying my restaurant grocery, should I concentrate on buying more steaks or flour for pizza?
- Should I send my best waiter to the terrace or to the basement?
- How much should I ask for the rooms with a view?
- How much credit can I give to this client?
- How many waitresses should I allocate to each restaurant room?
- What is the best discount to offer to my guests for tonight specials?
Of course the business manager should make the ultimate call on each decision, however, she can improve the process by using the optimization tools.
Quantitative hospitality and foodservice: research state of the art
The current academic research on the subject of quantitative hospitality and foodserice is poor or non-existent. There are a number of qualitative journals, like e.g. the Journal of Hospitality & Tourism Research) which provide little quantitative details. Then there are the management journals (e.g. the International Journal of Hospitality Management) that offer insights on the best practices for management techniques. Finally there is a restricted number of academic publications, such as the Cornell Hospitality Quarterly, where occasionally you might find some statistical paper with some sort of numerical results.
Connection with quantitative finance for asset managers
Remarkably there is an almost one-to-one link between the tools of quantitative finance and those needed in hospitality and foodservice analytics. For example, the ex-post analytics are very similar to the performance measurement of asset managers. The ex-ante analytics are not too different from the risk measures needed in computing portfolio risk, and similarly for the optimization tools. In finance this job is usually performed by quantitative analysts, also known as quants.
A new type of quantitative analysts: the hospitality and foodservice quant
More research is needed in quantitative hospitality and foodservice. Many industries have been changed completely by extensively using quantitative methods. Even sports, such as baseball, have dramatically changed because of mathematical and statistical tools. I believe that the time has come to create a new professional figure: the hospitality and foodservice quant.
An hospitality and foodservice quant has experience in derivative pricing, portfolio optimization, performance measurements, market risk, and credit risk. These professionals can concentrate on how to transform the traditional quantitative finance methodologies into hospitality and foodservice analytics. A lot of research on the quantitative side is still needs to be performed. Also computational tools needs to be developed so that the hospitality and foodservice industry can take advantage of new opportunities and better manage their current business.
Many of the problems faced by restaurants and hotels can be addressed using the tools of quantitative finance. A new discipline:, quantitative hospitality and foodservice, is emerging to address these problems. The professional figure of the quant develops mathematical models, similarly to those used in quantitative finance, to address the issues faced by the hospitality and foodservice businesses. The new discipline of quantitative hospitality and foodservice is going to bring a disruptive innovation to these industries by providing analytical tools to the food and accommodation business.