The creation method and evaluation method of customer dining experience evaluation model

Abstract

The invention discloses a method for creating a customer dining experience evaluation model and an evaluation method. The creating method includes collecting a corresponding parameter set of the customer dining experience evaluation model; calculating the expected time for the i-th dish to be eaten by n dining customers; and the elapsed time After t, the relationship between the amount of edible dishes Q(t) on the table and time t is calculated to obtain a dining experience evaluation model. The evaluation method includes that the restaurant obtains the relationship between the amount of edible dishes Q(t) on the table and time t according to the evaluation model; calculates the cumulative time t from when the order is completed to when there are no edible dishes on the table and the customer waits w ;Preset threshold T; According to the cumulative time t from when the order is completed to when there is no food available at the table, the customer waits w and the preset threshold T to calculate customer satisfaction. The present invention models and evaluates the customer's dining experience from the perspective of serving time, reduces the time for customers to wait for the food and the possibility of urging the food, and further improves the user's dining experience.

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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