Creation method and evaluation method of customer dining experience evaluation model

Abstract

The invention discloses a method for establishing a customer dining experience evaluation model and an evaluation method. The method comprises the following steps: collecting a parameter set corresponding to the customer dining experience evaluation model; Calculating an expected time for the i-th dish to be eaten by n diners; After the elapse of time t, the relationship between the quantity of edible vegetables Q (t) on the table and time t is calculated, and obtaining the dining experience evaluation model The evaluation method includes: the relationship between the quantity of edible vegetables Q (t) and the time t; Calculating the cumulative time tw from the end of the order to the end of the order to the end of the order to the end of the order. Presetting threshold T; calculating Customer satisfaction based on the cumulative time tw and the preset threshold T from the time the order is completed to the time when the table is empty and the customer is dried. The invention modelsand evaluates the dining experience of the customer from the serving time aspect, reduces the time of the customer waiting for the dish and the possibility of urging the dish, and further improves thedining experience of the user.

Description

technical field [0001] The present invention relates to the fields of model construction and algorithms, and more specifically, to a method for creating and evaluating a customer dining experience evaluation model. Background technique [0002] With the accelerated pace of urban life and the pace of modernization, people have more and more opportunities to choose to eat out. Especially for office workers in cities, on weekdays, most office workers eat out to solve the problem of three meals a day. Needless to say, cities and their populations are expanding. There will be more and more consumers dining in restaurants, and besides the aforementioned office workers, consumers also include many business people. Whether it is an office worker or a business person, the requirements for the quality of life are getting higher and higher. Due to the particularity of the catering industry, people spend more time on meals. People living in the city, time is usually very precious. F...

AI Technical Summary

Problems solved by technology

People living in the city, time is usually very precious. Faced with the situation of a large number of customers dining at the same time, the traditional catering industry has become increasingly unable to meet people's fast-paced needs, and will waste people's time

At present, in the existing restaurant management, the waiters are usually responsible for ordering, delivery and settlement, and the kitchen staff manage the raw materials. This traditional restaurant management model is very inflexible. On the one hand, it requires many people to provide services, and it is easy to Cause problems such as serving wrong dishes, unsuitable dishes, and dishes sold out without early detection, which will lead to dissatisfaction among consumers and affect the efficiency of operations. Customer dining experience, and cannot obtain feedback on customer experience information in a timely manner, and cannot enable operators to grasp enough data as a basis to adjust marketing strategies in a timely manner

[0003] In the prior art, it is generally used to evaluate the quality of the customer's dining experience in terms of serving time according to the frequency of the customer's initiative to urge the food; however, the evaluation method based on the frequency of the customer's initiative to urge the food is too passive, because when the customer actively urges the food , has obviously affected their dining experience; there is another method based on the waiter's observation of the table and finding that there is no food to eat, so as to evaluate the customer's dining experience; but the coverage rate of the scheme based on the waiter's observation of the table is low. Because the waiters are busy during peak hours, it is difficult to monitor all tables in real time

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