Method for classifying reasons for difference between actual amount and estimated amount of online car-hailing order

Abstract

The invention discloses a method for classifying reasons for differences between actual amounts and estimated amounts of online car-hailing orders, which comprises the following steps of: S1, starting, and judging whether the difference between the actual amount and the estimated amount is too large or not after the orders are completed; if the actual running water / estimated amount is gt; if n, entering the next step, otherwise, ending the process; s2, further judging whether the passenger changes the destination or not, if so, acquiring a new estimated amount, and executing the first step again, and if not, entering the next step; s3, judging whether the actual getting-off or getting-on address deviates from the preset getting-off or getting-on address by more than N kilometers or not; according to the invention, reason positioning can be automatically carried out on the condition that the difference between the estimated amount and the actual amount of the order is large, and an operator does not need to analyze the orders one by one; the method is used for quickly responding to order amount difference reasons during customer complaint, improving customer satisfaction and reducing complaint risk and grade.

Description

Technical Field

[0001] The present invention belongs to the technical field of online car-hailing platforms, and specifically relates to a method for classifying reasons for the difference between an actual amount and an estimated amount of an online car-hailing order. Background Art

[0002] In online ride-hailing orders, there is often a difference between the estimated amount when the customer places the order and the actual amount that needs to be paid when getting off the car. When the difference between the actual amount and the estimated amount is too large, it is often necessary to analyze each order, and there is a lack of mechanisms and methods to quickly locate the cause. This is not conducive to quickly responding to user complaints or analyzing the causes; it will cause complaints and doubts from customers. Therefore, it is necessary to efficiently and quickly attribute and classify the causes of the amount difference. On the one hand, it can quickly discover loopholes in the platform's billing system, and on the other hand, it can quickly explain to users or handle complaints.

[0003] To this end, the present invention designs a method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order. Summary of the invention

[0004] The purpose of the present invention is to provide a method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order, the specific steps comprising: S1: start, after the order is completed, determine whether the difference between the actual flow and the estimated amount is too large; if the actual flow / estimated amount>n, proceed to the next step, otherwise end the process;

[0006] S2: further determine whether the passenger has changed the destination. If yes, obtain a new estimated amount and re-execute the first step. If no, proceed to the next step.

[0007] S3: Determine whether the actual drop-off or pick-up address deviates from the scheduled drop-off or pick-up address by more than N kilometers. If so, the passenger's actual pick-up or drop-off address is inconsistent with the order, which is attributed to: the actual pick-up or drop-off address is inconsistent with the scheduled address; if not, proceed to the next step;

[0008] S4: Determine whether the fit between the estimated and actual driving routes is less than m. If so, it proves that the driver does not drive according to the navigation route; attribution is: the driver does not drive according to the navigation route; if not, proceed to the next step;

[0009] S5: Determine whether the order has a waiting fee or other estimated extra-price fees. If yes, attribute it to: the order has a waiting fee or other fees. If not, proceed to the next step.

[0010] S6: Determine whether the estimated and actual basic package prices of the order are different; if so, it proves that the package prices when placing the order and boarding the vehicle are different, and the order starts at a peak period, which is attributed to: the package prices when placing the order and boarding the vehicle are different, and the order starts at a peak period; if not, proceed to the next step;

[0011] S7: Determine whether the difference between the estimated and actual peak and low-peak mileage charges is greater than n%, if so, attribute it to: the difference between peak and low-peak mileage, if not, proceed to the next step;

[0012] S8: Determine whether the difference between the estimated and actual high and low peak durations is greater than n%. If so, attribute it to: high and low peak duration difference. If not, attribute it to: other.

[0013] S9: The order is marked with the attribution type and stored in the order table for querying the abnormal cause.

[0014] Preferably, in step S1, the value of n is a threshold set by the system and can be set manually.

[0015] Preferably, in step S4, the value m is a threshold set by the system and can be set manually.

[0016] Preferably, in step S7 and step S8, n% is a threshold set by the system, which can be set manually.

[0017] Preferably, in step S9, the information recorded in the order table includes order number, order completion time, order attribution type, passenger personal information, and driver personal information.

[0018] Compared with the prior art, the beneficial effects of the present invention are: the present invention can automatically locate the cause of the large difference between the estimated amount and the actual amount of an order, and the operating personnel no longer need to analyze the orders one by one; when used for customer complaints, it can quickly respond to the reasons for the difference in order amounts, improve customer satisfaction, and reduce the risk and level of complaints. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1, the present invention provides a technical solution: a method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order, the specific steps include: S1: start, after the order is completed, determine whether the difference between the actual flow and the estimated amount is too large; if the actual flow / estimated amount>n, proceed to the next step, otherwise end the process;

[0022] S2: further determine whether the passenger has changed the destination. If yes, obtain a new estimated amount and re-execute the first step. If no, proceed to the next step.

[0023] S3: Determine whether the actual drop-off or pick-up address deviates from the scheduled drop-off or pick-up address by more than N kilometers. If so, the passenger's actual pick-up or drop-off address is inconsistent with the order, which is attributed to: the actual pick-up or drop-off address is inconsistent with the scheduled address; if not, proceed to the next step;

[0024] S4: Determine whether the fit between the estimated and actual driving routes is less than m. If so, it proves that the driver does not drive according to the navigation route; attribution is: the driver does not drive according to the navigation route; if not, proceed to the next step;

[0025] S5: Determine whether the order has a waiting fee or other estimated extra-price fees. If yes, attribute it to: the order has a waiting fee or other fees. If not, proceed to the next step.

[0026] S6: Determine whether the estimated and actual basic package prices of the order are different; if so, it proves that the package prices when placing the order and boarding the vehicle are different, which is generally because the order starts at a peak period. This is attributed to: the package prices when placing the order and boarding the vehicle are different, and the order starts at a peak period; if not, proceed to the next step;

[0027] S7: Determine if the difference between the estimated and actual peak and off-peak mileage charges is greater than n%, if so, attribute it to:

[0028] The difference between high and low peak mileage, if not, go to the next step;

[0029] S8: Determine whether the difference between the estimated and actual high and low peak durations is greater than n%. If so, attribute it to: high and low peak duration difference. If not, attribute it to: other.

[0030] S9: The order is marked with the attribution type and stored in the order table for querying the abnormal cause.

[0031] In this implementation, preferably, in step S9, the information recorded in the order table includes order number, order completion time, order attribution type, passenger personal information, and driver personal information.

[0032] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order, characterized by: The specific steps include: S1: Start, after the order is completed, determine whether the difference between the actual transaction amount and the estimated amount is too large; if the actual transaction amount / estimated amount>n, proceed to the next step, otherwise end the process; S2: further determine whether the passenger has changed the destination. If yes, obtain a new estimated amount and re-execute the first step. If no, proceed to the next step. S3: Determine whether the actual drop-off or pick-up address deviates from the scheduled drop-off or pick-up address by more than N kilometers. If so, the passenger's actual pick-up or drop-off address is inconsistent with the order, which is attributed to: the actual pick-up or drop-off address is inconsistent with the scheduled address; if not, proceed to the next step; S4: Determine whether the fit between the estimated and actual driving routes is less than m. If so, it proves that the driver does not drive according to the navigation route; attributed to: the driver does not drive according to the navigation route; if not, proceed to the next step; S5: Determine whether the order has a waiting fee or other estimated extra-price fees. If yes, attribute it to: the order has a waiting fee or other fees. If not, proceed to the next step. S6: Determine whether the estimated and actual basic package prices of the order are different; if so, it proves that the package prices when placing the order and boarding the vehicle are different, and the order starts at a peak period, which is attributed to: the package prices when placing the order and boarding the vehicle are different, and the order starts at a peak period; if not, proceed to the next step; S7: Determine whether the difference between the estimated and actual peak and low-peak mileage charges is greater than n%, if so, attribute it to: the difference between peak and low-peak mileage, if not, proceed to the next step; S8: Determine whether the difference between the estimated and actual high and low peak durations is greater than n%. If so, attribute it to: high and low peak duration difference. If not, attribute it to: other. S9: The order is marked with the attribution type and stored in the order table for querying the abnormal cause.

2. A method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order according to claim 1, characterized in that: In step S1, the value n is a threshold set by the system and can be set manually.

3. A method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order according to claim 1, characterized in that: In step S4, the value m is a threshold set by the system and can be set manually.

4. A method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order according to claim 1, characterized in that: In step S7 and step S8, n% is a threshold value set by the system and can be set manually.

5. The method for classifying the reasons for the difference between the actual amount and the estimated amount of an online car-hailing order according to claim 1, characterized in that: In step S9, the information recorded in the order table includes order number, order completion time, order attribution type, passenger personal information, and driver personal information.

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