Method for generating subscription parameters and related electronic device

Abstract

A method for generating subscription parameters, performed by an electronic device, is disclosed. The method includes obtaining historical transaction data associated with one or more shipping services offered to multiple users within a time period. The method includes determining, based on the historical transaction data, statistical measures indicating a pattern for each of the one or more shipping services within that time period. The method includes generating subscription parameters for one or more subscription plans associated with the one or more shipping services based on the statistical measures. The method includes providing the subscription parameters for the one or more subscription plans.

Description

[0001] This disclosure relates to the field of transportation and freight. This disclosure relates to a method and related electronic device for generating subscription parameters. Background Technology

[0002] There are many services that can be offered to users who have booked shipments. The diversity and complexity of these services, along with their costs, make it difficult to achieve transparency for users. Summary of the Invention

[0003] There is a need for an electronic device and a method that can improve transparency when selecting services for shipping and / or logistics operations.

[0004] Therefore, there is a need for an electronic device and a method for generating subscription parameters that mitigates, alleviates, or resolves existing shortcomings and allows for the provision of subscription plans with more accurate, robust, and reliable subscription parameters.

[0005] A method for generating subscription parameters, performed by an electronic device, is disclosed. The method includes obtaining historical transaction data associated with one or more shipping services, for example, provided to multiple users within a time period. The method includes determining, based on the historical transaction data, statistical measures indicating a pattern for each of the one or more shipping services within that time period. The method includes generating subscription parameters for one or more subscription plans associated with the one or more shipping services based on the statistical measures. The method includes providing the subscription parameters for the one or more subscription plans.

[0006] An electronic device is disclosed, comprising a memory circuit system, a processor circuit system, and an interface circuit, wherein the electronic device is configured to perform any of the methods disclosed herein.

[0007] A computer-readable storage medium is disclosed for storing one or more programs, the programs including instructions that, when executed by an electronic device (optionally having a display and a touch-sensitive surface), cause the electronic device to perform any of the methods disclosed herein.

[0008] The advantage of this disclosure is that the disclosed electronic devices and methods improve the accuracy of the generated subscription parameters by utilizing the time-varying patterns of the shipping service (e.g., for a given user, a given group of users, and / or a given location), thereby enabling increased and / or optimized possibilities for users, such as the recipient, to select one or more subscription plans. For example, the dynamic nature of the subscription parameters and / or one or more subscription plans, such as that characterized by periodic updates, can improve (e.g., increase) the likelihood of a user selecting a subscription plan among the one or more subscription plans.

[0009] Furthermore, subscription parameters can be generated based on historical data for locations matching a user's location. For example, this allows for the generation and delivery of location-specific (e.g., country-specific) subscription parameters to the user. This can be advantageous because subscription parameters can vary significantly depending on the user's location. This disclosure can account for this variation to optimize the likelihood of a user selecting a subscription plan from one or more subscription plans.

[0010] Furthermore, this disclosure enables the updating of one or more subscription plans, such as subscription parameters, based on user choices or rejections (e.g., periodically). Advantageously, this allows for the "adjustment" of one or more subscription plans based on user choices or rejections. For example, by optimizing subscription parameters based on user choices or rejections, it becomes possible to increase the proportion of user choices.

[0011] The improved accuracy of the subscription parameters generated in this disclosure can advantageously lead to improved profitability for shipping providers (e.g., shippers) offering one or more subscription plans, while saving delivery costs for users (e.g., consignees).

[0012] Advantageously, for example by allowing updates to subscription parameters based on user selections and / or rejections, this disclosure can improve the efficiency of determining accurate subscription parameters for one or more subscription plans, such as time efficiency. Attached Figure Description

[0013] The above and other features and advantages of this disclosure will be readily apparent to those skilled in the art from the following detailed description of exemplary embodiments with reference to the accompanying drawings, in which: Figures 1A to 1B A flowchart illustrating an exemplary method for generating subscription parameters performed by an electronic device according to the present disclosure is shown. Figure 2 This is a block diagram illustrating an exemplary electronic device according to the present disclosure. Figure 3 This is a user interface illustrating example subscription parameters for one or more subscription plans according to this disclosure, and Figure 4 This is a diagram illustrating an example initial user distribution based on this disclosure. Detailed Implementation

[0014] Various exemplary embodiments and details are described below with reference to the accompanying drawings, where applicable. It should be noted that the drawings may be drawn to scale or not, and elements with similar structure or function are indicated by the same reference numerals in all the drawings. It should also be noted that the drawings are intended only to facilitate the description of embodiments. The drawings are not intended as an exhaustive description of this disclosure or a limitation on the scope of this disclosure. Furthermore, the illustrated embodiments need not possess all the aspects or advantages shown. Aspects or advantages described in connection with a particular embodiment are not necessarily limited to that embodiment and can be practiced in any other embodiment, even if not so shown or explicitly described.

[0015] For clarity, the accompanying drawings are schematic and simplified, and only details that aid in understanding this disclosure are shown, while other details are omitted. Throughout, the same reference numerals are used for the same or corresponding parts.

[0016] Figures 1A to 1B A flowchart illustrating an exemplary method 100 performed by an electronic device is shown. Method 100 is, for example, a method for generating subscription parameters according to this disclosure. Method 100 is performed by an electronic device, such as the electronic devices disclosed herein, such as... Figure 2 Electronic device 300.

[0017] Method 100 includes obtaining, in step S102, historical transaction data associated with one or more shipping services provided to multiple users, for example, within a time period.

[0018] In some examples, obtaining S102 historical transaction data includes, for example, retrieving and / or receiving historical transaction data associated with one or more shipping services provided to multiple users (optionally within a time period) from a database and / or server associated with one or more shipping services. In other words, historical transaction data may correspond to transaction data from past time periods.

[0019] In some examples, obtaining S102 historical transaction data includes generating historical transaction data associated with one or more shipping services provided to multiple users within a time period.

[0020] In some examples, such as Figure 2 The electronic device 300 can be configured to obtain historical transaction data from a database and / or server associated with one or more shipping services.

[0021] For example, a user can be associated with a shipment booking. A user can be considered a user who has already booked a shipment and / or is currently booking a shipment. For example, a user may have booked one or more shipments during that period. For example, a user can be a consignee associated with a shipment.

[0022] For example, historical transaction data can be viewed as historical data associated with a transaction. For instance, the transaction could be a booking, such as a booking for one or more shipping services, including booking data and transaction details. Obtaining historical transaction data (S102) includes, for example, obtaining historical transaction data associated with one or more shipping services provided to multiple users within a time period, targeting one or more users such as a user at a given location.

[0023] For example, the one or more shipping services include shipments, such as shipments booked by a user. For example, the one or more shipping services include the shipment of one or more items from their origin location to their destination location. For example, the shipment may be carried out by a ship, aircraft, and / or land vehicle. For example, the one or more shipping services include one or more services associated with the shipping service. In some examples, the one or more shipping services are associated with logistics operations. For example, the one or more shipping services include one or more value-added services (VAS), one or more additional services, and / or one or more surcharges.

[0024] For example, VAS includes customs clearance assistance, document support, reduction of pre-arranged amendment penalties (such as amendment fees), rollaway hooks, refrigerated (such as cooled) containers, etc.

[0025] Additional services and surcharges include, for example, terminal handling fees, storage fees, demurrage and detention (DnD) fees, etc. Surcharges can be viewed as shipment-related costs, such as additional charges. For example, one or more subscription plans may include shipment-related surcharges, such as additional services and surcharges based on their subscription. When a user selects a subscription plan, the user can benefit from reduced and / or waived fees for these shipment services, thereby providing the user with improved transparency and / or cost control. For example, the disclosed methods enable the streamlining of these costs, thereby allowing for improved user satisfaction and operational efficiency.

[0026] In one or more example methods, obtaining historical transaction data S102 includes preprocessing historical transaction data S102A. For example, preprocessing can be viewed as processing the historical transaction data before generating statistical metrics based on it. Preprocessing historical transaction data includes, for example, aggregating historical transaction data for one or more shipping services and / or users. In one or more example methods, preprocessing historical transaction data S102A includes transforming historical transaction data S102AA and / or grouping historical transaction data by user S102AB. For example, transforming historical transaction data S102AA includes converting historical transaction data, such as invoice fees, to a common currency. Grouping historical transaction data S102AB includes, for example, grouping historical transaction data by location and / or by user. In some examples, preprocessing historical transaction data S102A includes extracting historical transaction data associated with one or more shipping services provided to multiple users within a time period. For example, historical transaction data includes one or more fees associated with a user. For example, historical transaction data includes one or more invoice fees associated with a user. For example, historical transaction data may be associated with a given user across one or more locations (such as all locations associated with a user). For example, historical transaction data includes a user's location, such as during the shipment booking period.

[0027] Table 1 Table 1 shows historical transaction data. For a given user (such as a customer) indicated by the user id column (leftmost column), Table 1 shows the value associated with one or more shipping services used by the user. For example, a user id can be considered a user code. For example, a user id includes one or more letters and / or numbers, such as an alphanumeric code.

[0028] The refrigeration fee shown in Table 1 can be used as an example of a VAS. The DnD fee, no-show fee, modification fee, and / or cancellation fee shown in Table 1 can be used as examples of additional services. For example, both the no-show fee and the cancellation fee can be considered as accident fees.

[0029] Method 100 includes determining, based on historical transaction data, a statistical measure S104 indicating the pattern of each of the one or more shipping services within the time period. For example, determining the statistical measure S104 includes determining a statistical measure based on preprocessed historical transaction data, which indicates the pattern of each of the one or more shipping services within the time period for which historical transaction data was obtained.

[0030] For example, determining the statistical measure that indicates the pattern of each of the one or more shipping services in S104 within the time period includes determining the statistical measure that indicates the pattern of each of the one or more shipping services within the time period for one or more users, such as for all users, for example, all users indicated across historical transaction data.

[0031] For example, a statistical measure can be viewed as a measure determined using one or more statistical techniques. In one or more example methods, for each shipping service, the statistical measure includes one or more of the following: mean, median, and distribution.

[0032] Method 100 includes generating, based on statistical measures, subscription parameters for one or more subscription plans associated with the one or more shipping services, S106. For example, subscription parameters can be considered as parameters of the subscription plans. For example, subscription parameters can include the value associated with one or more shipping services. For example, subscription parameters can indicate a reduction in the value of one or more shipping services (e.g., a discount). For example, subscription parameters can vary between one or more subscription plans. For example, one or more subscription plans can be considered as one or more service arrangements between a user (e.g., a consignee) and a shipper. For example, one or more subscription plans can be considered as one or more periodic service arrangements between a user (e.g., a consignee) and a shipper. For example, one or more subscription plans include one or more subscription tiers, such as tiered subscription options. For example, each subscription tier includes one or more different subscription parameters. For example, each subscription tier is associated with a subscription value such as the price of the subscription plan.

[0033] One or more subscription tiers may include lower tiers (such as Bronze), middle tiers (such as Silver), and / or higher tiers (such as Gold), such as... Figure 3 As shown. For example, the subscription value of one or more subscription parameters at a lower tier may be less than the value of one or more subscription parameters at a higher tier. For example, a user can choose the subscription plan that best suits their needs. For example, a user who books a large shipment can choose a higher tier, such as the Gold tier.

[0034] For example, one or more tiers in a subscription tier (such as each tier) will have a subscription value parameter associated with it. One or more subscription plans (such as subscription tiers) can provide certain benefits that customers can customize to some extent.

[0035] For example, one or more subscription plans can be viewed as dynamically priced. In some examples, one or more subscription plans can be viewed as location-centric, such as location-dependent.

[0036] For example, one or more subscription plans include subscription parameters associated with one or more shipping services (e.g., VAS, additional services, and / or surcharges). For instance, subscription parameters can be viewed as being bundled with one or more subscription plans.

[0037] For example, one or more subscription plans can be seen as providing users with improved convenience and value.

[0038] Method 100 includes providing S108 the subscription parameters of the one or more subscription plans. In some examples, the subscription parameters are provided to the user, for example, during the shipment booking period. The one or more subscription plans may offer reduced rates (such as discounted rates) to users based on their subscriptions to one or more shipping services (such as VAS, additional services, and / or surcharges).

[0039] In one or more example methods, providing subscription parameters for one or more subscription plans in S108 includes sending subscription parameters for one or more subscription plans in S108A. Sending subscription parameters for one or more subscription plans in S108 includes, for example, in response to a request, sending subscription parameters for one or more subscription plans to a user electronic device such as a laptop computer. For example, the electronic device is configured to receive (e.g., receive) a request for subscription parameters for one or more subscription plans.

[0040] In one or more example methods, providing subscription parameters for one or more subscription plans in S108 includes causing S108B to display one or more user interface objects representing the corresponding subscription parameters of the one or more subscription plans. For example, this can be done via an electronic device (such as via...). Figure 2 The electronic device 300 (display) displays one or more user interface objects to the user, representing the corresponding subscription parameters of one or more subscription plans. Figure 3 An example user interface is shown, illustrating one or more user interface objects representing the corresponding subscription parameters of one or more subscription plans.

[0041] In one or more example methods, historical transaction data includes one or more of the following: historical service rate data for one or more shipping services, user booking data associated with multiple users associated with shipping bookings, booking data associated with one or more shipping bookings, historical selection rates indicating users' previous choices of subscription plans, and historical shipping data.

[0042] For example, historical service rate data for one or more shipping services indicates the historical service rates for one or more shipping services.

[0043] For example, user booking data associated with multiple users related to shipping bookings indicates that one or more shipping users have booked a shipment.

[0044] For example, booking data associated with one or more shipment bookings indicates a shipment booking.

[0045] For example, historical selection rate indicates a user's previous choice of an offered subscription plan. In other words, historical selection rate can indicate a user's previous choice of an offered subscription plan (such as a subscription plan associated with the generated subscription parameters).

[0046] For example, historical shipment data is associated with one or more historical shipments, such as origin-destination pairs, consignees, ports, and commodities.

[0047] In one or more example methods, the subscription parameters include one or more of the following: a subscription value indicating the value of the corresponding subscription plan, a time extension parameter indicating the time extension for the return and / or storage of containers, one or more incident parameters indicating one or more incident fees, a freight rate parameter indicating the freight rate, a booking modification parameter indicating the modification fee, a demurrage and detention parameter indicating the demurrage and detention fees, and a refrigeration parameter indicating the fees for refrigerated containers.

[0048] Subscription value indicates the value of the corresponding subscription plan. For example, a user can pay subscription value in exchange for subscribing to a subscription plan. Subscription value can be expressed in a given currency.

[0049] For example, the method includes generating subscription value for corresponding subscription plans (such as for each subscription tier) to optimize revenue.

[0050] For example, extended time can be viewed as an extension of the demurrage period and the duration of stay.

[0051] An accident can be considered a possible (such as unforeseen), adverse event and / or adverse circumstance associated with shipment, such as breach of contract and / or cancellation.

[0052] For example, incidental charges include, for instance, cancellation fees and / or cancellation charges. A cancellation charge, for example, is a fee associated with a user (such as a consignee) who cancels a shipment. A cancellation charge, for example, is associated with late cancellation of a shipment. For example, a user can notify the shipper that a cancellation charge can be levied on the consignee for such cancellation.

[0053] For example, a cancellation fee is a charge associated with the cancellation of a shipment without notifying the shipper. In other words, a cancellation fee can be considered a charge associated with the goods to be shipped not being ready to be shipped as previously agreed upon by the consignee and the shipper.

[0054] For example, freight rates can be viewed as rates used for freight transportation. For example, freight rates can be viewed as shipment rates, such as baseline shipment costs. Freight rates, or examples, change dynamically over time. For example, freight rates can vary based on variables such as supply and demand, congestion on a given shipping route, environmental conditions, fuel costs, and seasonal changes in demand. For example, freight rates can be historical ocean freight rates, historical land freight rates, or historical air freight rates corresponding to the mode of transport used to transport goods.

[0055] For example, a modification fee can be considered a booking revision fee. For instance, a modification fee could be a charge associated with modifications and / or revisions to a shipment booking.

[0056] For example, DnD fees can be considered as charges arising from demurrage and / or stay.

[0057] For example, a refrigerated container is a container that is cooled.

[0058] In some examples, subscription parameters include user priority parameters that indicate a user's priority. For example, a user priority parameter might indicate whether a user has the right to priority booking for shipments, where priority booking implies, for example, guaranteeing a shipping time for that user even during peak seasons. For example, a user priority parameter might indicate that a user has the right to expedited booking, where expedited booking includes, for example, faster processing and / or booking confirmation. In some examples, a user priority parameter might indicate that a user has the right to standard booking, where standard booking includes, for example, the right to use regular booking services.

[0059] In one or more example methods, one or more accident parameters indicate a reduction in one or more accident fees. In one or more example methods, a freight rate parameter indicates a reduction in freight rates. In one or more example methods, a booking modification parameter indicates a reduction in modification fees. In one or more example methods, demurrage and detention parameters indicate a reduction in demurrage and detention fees. In one or more example methods, a refrigeration parameter indicates a reduction in costs for refrigerated containers.

[0060] In one or more example methods, generating S106 subscription parameters based on statistical measures includes simulating the corresponding results for each candidate subscription plan of S106A based on statistical measures and one or more of the following: the initial user distribution for each candidate subscription plan and the range of each subscription parameter for each candidate subscription plan.

[0061] In one or more example methods, the simulation is a Monte Carlo simulation. For example, a Monte Carlo simulation takes the following as input: a statistical measure indicating the pattern of each of the one or more shipping services within the time period, the initial user distribution of each candidate subscription plan, and / or the range of each subscription parameter for each candidate subscription plan.

[0062] For example, a candidate subscription plan can be viewed as a simulated subscription plan. For example, an initial user distribution indicates a predicted proportion of users and / or a target proportion of users who can choose, for example, a given subscription plan for a given shipping service. For example, an initial user distribution can be viewed as a target user distribution used in a simulation. In some examples, the initial user distribution can, for example, serve as input to the simulation. For example, the initial user distribution can be associated with one or more shipping services (such as VAS, additional services, and / or surcharges). For example, for one or more (e.g., each) shipping services, there can exist, for example, an initial user distribution across one or more subscription plans. In some examples, the initial user distribution (e.g., box size) is based on a statistical measure of the shipping service, such as the mean. For example, the initial user distribution can be predetermined, such as randomly determined. For example, the initial user distribution in the first iteration of the simulation can be predetermined, randomly determined, and / or uniformly distributed. For example, the initial user distribution can be selected by the users of the electronic device (such as simulation initial users). For example, the user distribution can remain constant across one or more iterations of the simulation. Figure 4 An example initial user distribution is shown.

[0063] For example, the range of subscription parameters for one or more candidate subscription plans can be viewed as the range of value reductions, such as discounts (e.g., discount percentages), to be simulated within one or more subscription plans.

[0064] For example, for Plan A (such as a high-tier subscription plan, e.g., Gold tier), the range of subscription parameters for one or more candidate subscriptions could include a 10-15% reduction in freight rates, a 20-25% reduction in demurrage and holding fees, a 20-40% reduction in default fees, an 8-12% reduction in refrigeration fees, a 7-10% reduction in surcharges, a 7-10% reduction in modification fees, and / or a 7-10% reduction in cancellation fees. In some examples, the range of subscription parameters can be stored as a list and / or a dictionary. For example, the range of subscription parameters might be indicated as follows: Subscription parameter range = {'Plan A'=[10-15, 20-25, 20-40, 8-12, 7-10, 7-10, 7-10], 'Plan B'=[5-10, 10-15, 10-20, 6-8, 5-7, 5-7, 5-7], 'Plan C'=[2-5, 2-5, 2-5, 2-5, 2-5, 2-5, 2-5]} For example, the range of subscription parameters for one or more candidate subscription plans includes the range of subscription values ​​(such as subscription fees) for one or more subscription plans. For example, the range of subscription values ​​can be stored in a list and / or a dictionary. For example, the range from the subscription value can be indicated as shown in the following example: Subscription value range = {'Plan A' = [1000-10000], 'Plan B' = [800-5000], 'Plan C' = [300-2000]} For example, the initial user distribution and / or subscription parameter range of one or more candidate subscription plans can be regarded as constraints and / or boundaries of the simulation (e.g., Monte Carlo simulation) of the corresponding results for each candidate subscription plan.

[0065] In one or more example methods, the corresponding outcome of a candidate subscription plan includes the subscription value and the amount generated by one or more shipping services indicated in historical transaction data adjusted by the subscription parameters of the candidate subscription plan. For example, the corresponding outcome can be viewed as the output of a simulation such as a Monte Carlo simulation. For example, the subscription value can be viewed as the value of the subscription plan. For example, the subscription value can indicate, for example, the cost of the subscription plan to be paid by the user. For example, the amount generated by one or more shipping services can be viewed as the total amount of shipping services generated by one or more shipping services indicated by historical shipping data, but with one or more discounts applied corresponding to the subscription parameters of the candidate subscription plan. In other words, this amount is, for example, the total amount that can be generated for a given candidate subscription plan, taking into account services that may be available to that user based on historical transaction data for the same time period as the historical transaction data.

[0066] In one or more example methods, generating S106 subscription parameters based on statistical metrics includes evaluating S106B candidate subscription plans based on the corresponding results of satisfying criteria. In one or more example methods, the criteria are based on multi-constraint optimization based on subscription parameters. For example, a multi-constraint optimization problem aims to maximize the amount generated by the subscription parameters of each candidate subscription plan. For example, each candidate subscription plan is evaluated to check which combination of subscription parameters (e.g., fees and offered discounts) maximizes the total amount of revenue generated. For example, a subscription plan with higher subscription value is optimized with higher subscription parameters compared to a subscription plan with lower subscription value, while the total revenue should be maximized by 10% to 20%. In some examples, constraints may include constraints on subscription parameters. For example, a first constraint may be that price reductions for subsequent subscription plans should decrease. For example, a second constraint may be that rates for subsequent subscription plans should decrease. For example, a third constraint may be reducing time extensions that can be authorized based on supply and demand in subsequent booking plans.

[0067] In one or more example methods, generating S106 subscription parameters based on statistical metrics includes selecting subscription parameters for S106C associated with one or more subscription plans for one or more shipping services to be provided to users based on evaluation.

[0068] In one or more example methods, the method includes using historical transaction data and a machine learning model to predict user selection parameters for each subscription plan of S110. In one or more example methods, the user selection parameters indicate the probability that a user will choose one of one or more subscription plans. In some examples, the user selection parameters indicate the proportion of users predicted to choose one of one or more subscription plans. After the initial deployment of the subscription plans, a machine learning model can be used to predict which users are likely to choose which plan. In some examples, predicting the user selection parameters for each subscription plan of S110 includes predicting which of the one or more subscription plans a user is most likely to choose.

[0069] In one or more example methods, the machine learning model includes a multi-class classification model. In some examples, the multi-class model is a logistic regression model. For example, a multi-class classification model can be trained on historical data such as historical transaction data, location data, user data, etc.

[0070] In one or more example methods, the method includes updating the S112 subscription parameters based on the predicted user selection parameters. For example, the predicted user selection rate can be used in the next iteration to replace the predetermined percentage for each subscription plan with the predicted percentage for each plan. For example: replacing the initial predetermined numbers (0.2, 0.3, 0.5) with the predicted percentage for each subscription plan. For example, the subscription parameters can be controlled (such as updating) periodically (such as monthly and / or quarterly).

[0071] In one or more example methods, the method includes updating one or more subscription plans in S114 based on a user's choice or rejection. In some examples, updating one or more subscription plans in S114 includes periodically updating the one or more subscription plans based on a user's choice or rejection. For example, the one or more subscription plans may be updated periodically on a monthly and / or quarterly basis. For example, the one or more subscription plans may be considered as adaptive subscription plans, dynamic subscription plans, and / or updatable subscription plans.

[0072] For example, a user's choice or rejection can be considered user feedback. For instance, a user can provide a choice and / or rejection via a user interface object that displays options indicating the selection or rejection of one or more subscription plans.

[0073] Figure 2 A block diagram of an exemplary electronic device 300 according to the present disclosure is shown. The electronic device 300 includes a memory circuitry 301, a processor circuitry 302, and an interface 303. The electronic device 300 is configured to perform... Figure 2 Any method disclosed herein. In other words, electronic device 300 is configured to generate subscription parameters.

[0074] In some examples, electronic device 300 is a subscription parameter generating device. In some examples, electronic device 300 is a subscription parameter providing device. In some examples, electronic device 300 is a server device.

[0075] Electronic device 300 is configured (e.g., via memory circuitry 301 and / or interface 303) to obtain historical transaction data associated with one or more shipping services that may be provided to multiple users within a time period.

[0076] Electronic device 300 is configured (e.g., via processor circuitry 302) to determine, based on historical transaction data, a statistical measure indicating the pattern of each of one or more shipping services within a given time period.

[0077] Electronic device 300 is configured (e.g., via processor circuitry 302) to generate subscription parameters for one or more subscription plans associated with one or more shipping services based on statistical metrics.

[0078] Electronic device 300 is configured (e.g., via processor circuitry 302 and / or interface 303) to provide subscription parameters for one or more subscription plans.

[0079] Processor circuitry 302 is optionally configured to execute Figures 1A to 1B Any operation disclosed in the operation (such as any one or more of the following: S102, S102A, S102AA, S102AB, S104, S106, S106A, S106B, S106C, S108, S108A, S108B, S110, S112, S114). The operation of electronic device 300 may be embodied in the form of executable logic routines (e.g., lines of code, software programs, etc.) stored on a non-transitory computer-readable medium (e.g., memory circuitry 301) and executed by processor circuitry 302.

[0080] Furthermore, the operation of electronic device 300 can be considered as a method configured to be performed by electronic device 300. Additionally, while the described functions and operations can be implemented in software, such functions can also be implemented via dedicated hardware or firmware, or some combination of hardware, firmware, and / or software.

[0081] The memory circuit system 301 may be one or more of a buffer, flash memory, hard disk drive, removable media, volatile memory, non-volatile memory, random access memory (RAM), or other suitable devices. In a typical arrangement, the memory circuit system 301 may include non-volatile memory for long-term data storage and volatile memory used as system memory for the processor circuit system 302. The memory circuit system 301 may exchange data with the processor circuit system 302 via a data bus. Control lines and an address bus may also exist between the memory circuit system 301 and the processor circuit system 302. Figure 2 (Not shown in the image). The memory circuitry 301 is considered a non-transitory computer-readable medium.

[0082] The memory circuit system 301 can be configured to store the following in a portion of the memory: historical transaction data, statistical measures, subscription parameters, one or more subscription plans, corresponding candidate subscription plans, initial user distribution for each candidate subscription plan, range of each subscription parameter for each candidate subscription plan, simulation, criteria, thresholds, user selection parameters, and / or multi-class classification models.

[0083] Figure 3A user interface according to this disclosure is shown, which includes an example user interface object representing example subscription parameters of one or more subscription plans.

[0084] Figure 3 User interface 2 is shown. User interface 2 includes user interface objects representing subscription parameters for subscription plans 10A, 10B, and 10C. Subscription plan 10 (plan A) can be a bronze tier (such as a lower tier) subscription plan. For example, the subscription plan can be viewed as being displayed as a subscription plan card.

[0085] Subscription plans 10A, 10B, and 10C include corresponding subscription parameters 14A, 14B, and 14C. These subscription parameters include a subscription value, represented by user interface objects 16A, 16B, and 16C, which vary between the subscription plans. For example, the subscription value 16 can be viewed as the cost that the user must pay in exchange for subscribing to the subscription plan. In some examples, the subscription value 16 may be displayed as a monetary value, such as US dollars.

[0086] For example, a user can select a subscription plan from one or more subscription plans by choosing a user interface object 12A, 12B, or 12C for each subscription plan. When a user selects user interface object 12A, 12B, or 12C, that user is considered to have selected the subscription plan associated with the selection option. For example, each subscription plan in one or more subscription plans can be associated with a selection switch.

[0087] Figure 4 This is a diagram illustrating an example initial user distribution based on this disclosure. Figure 4 Histogram 50 is shown indicating the initial user distribution for freight rates. The initial user distribution can be viewed as multiple buckets (such as groups), which correspond, for example, to each subscription plan and randomly define the proportions corresponding to them, the sum of which should be 1. This refers to the number of users who might choose a given subscription plan. For example, the initial distribution for a simulation could be: Plan A = 0.2 [200 out of 1000 customers], Plan B = 0.3 [300 out of 1000 customers], Plan C = 0.5 [the remaining 500 customers].

[0088] The following clauses set forth an implementation scheme for the methods and products (electronic devices) according to this disclosure: Clause 1. A method performed by an electronic device, the method comprising: - Obtain historical transaction data associated with one or more shipping services provided to multiple users within a time period; - Based on the historical transaction data, determine statistical measures that indicate the pattern of each of the one or more shipping services within the time period; - Generate subscription parameters for one or more subscription plans associated with the one or more shipping services based on the statistical metrics; and - Provide the subscription parameters for the one or more subscription plans.

[0089] Clause 2. The method described in Clause 1, wherein the historical transaction data includes one or more of the following: historical service rate data for one or more shipping services, user booking data associated with the plurality of users associated with the shipping booking, booking data associated with one or more shipping bookings, historical selection rates indicating a user’s previous selection of a subscription plan, and historical shipping data.

[0090] Clause 3. The method according to any one of the preceding clauses, wherein the subscription parameters include one or more of the following: a subscription value indicating the value of the corresponding subscription plan, a time extension parameter indicating the time extension for the return and / or storage of containers, one or more accident parameters indicating one or more accident fees, a freight rate parameter indicating a freight rate, a booking modification parameter indicating a modification fee, a demurrage and detention parameter indicating demurrage and detention fees, and a refrigeration parameter indicating the fees for refrigerated containers.

[0091] Clause 4. The method according to Clause 3, wherein the one or more accident parameters indicate a reduction in the one or more accident fees, wherein the freight rate parameter indicates a reduction in the freight rate, wherein the booking modification parameter indicates a reduction in the modification fee, wherein the demurrage and detention parameter indicates a reduction in the demurrage and detention fees, and wherein the refrigeration parameter indicates a reduction in the fees for the refrigerated container.

[0092] Clause 5. The method according to any one of the preceding clauses, wherein obtaining the historical transaction data includes preprocessing the historical transaction data.

[0093] Clause 6. The method according to Clause 5, wherein preprocessing the historical transaction data includes transforming the historical transaction data and / or grouping the historical transaction data by user.

[0094] Clause 7. The method according to any one of the preceding clauses, wherein for each shipping service, the statistical measure includes one or more of the following: mean, median, and distribution.

[0095] Clause 8. The method according to any one of the preceding clauses, wherein generating the subscription parameters based on the statistical metric comprises simulating the corresponding results for each candidate subscription plan based on the statistical metric and one or more of the following: - Initial user distribution for each candidate subscription plan; and - The range of each subscription parameter for each candidate subscription plan.

[0096] Clause 9. The method described in Clause 8, wherein the simulation is a Monte Carlo simulation.

[0097] Clause 10. The method according to any one of Clauses 8 to 9, wherein the corresponding result of the candidate subscription plan includes the subscription value and the amount generated by the one or more shipping services indicated in the historical transaction data adjusted by the subscription parameters of the candidate subscription plan.

[0098] Clause 11. The method according to any one of Clauses 8 to 10, wherein generating the subscription parameters based on the statistical metric comprises: - Evaluate the candidate subscription plans based on the corresponding results that meet the criteria; - Select the subscription parameters based on the evaluation for the one or more subscription plans associated with the one or more shipping services provided to the user.

[0099] Clause 12. The method described in Clause 11, wherein the criterion is based on multi-constraint optimization based on subscription parameters.

[0100] Clause 13. The method according to any one of the preceding clauses, the method comprising predicting a user selection parameter for each subscription plan based on the historical transaction data and using a machine learning model, wherein the user selection parameter indicates the probability that a user will select one of the one or more subscription plans.

[0101] Clause 14. The method described in Clause 13, wherein the machine learning model includes a multi-class classification model.

[0102] Clause 15. The method according to any one of Clauses 13 to 14, the method comprising updating the subscription parameters based on the predicted user selection parameters.

[0103] Clause 16. The method according to any one of the preceding clauses, the method comprising updating the one or more subscription plans based on a user's choice or refusal.

[0104] Clause 17. The method according to any one of the preceding clauses, wherein providing the subscription parameters of the one or more subscription plans includes sending the subscription parameters of the one or more subscription plans.

[0105] Clause 18. The method according to any one of the preceding clauses, wherein providing the subscription parameters of the one or more subscription plans includes causing one or more user interface objects representing the respective subscription parameters of the one or more subscription plans to be displayed.

[0106] Clause 19. An electronic device comprising a memory circuitry, a processor circuitry, and an interface, wherein the electronic device is configured to perform any of the methods according to any one of Clauses 1 to 18.

[0107] Clause 20. A computer-readable storage medium storing one or more programs, said one or more programs including instructions that, when executed by an electronic device, cause the electronic device to perform any of the methods described in Clauses 1 to 18.

[0108] The use of terms such as "first," "second," "third," and "fourth," "primary," "secondary," and "auxiliary," etc., does not imply any particular order, but is included to identify individual elements. Furthermore, the use of terms such as "first," "second," "third," and "fourth," "primary," "secondary," and "auxiliary," etc., does not indicate any order or importance, but is used to distinguish one element from another. Note that the use of the terms "first," "second," "third," and "fourth," "primary," "secondary," and "auxiliary," etc., here and elsewhere, is solely for labelling purposes and is not intended to indicate any particular spatial or temporal order. Moreover, the labeling of a first element does not imply the existence of a second element, and vice versa.

[0109] It is understood that the accompanying drawings include some circuit systems or operations shown in solid lines and some circuit systems or operations shown in dashed lines. The circuit systems or operations included in the solid lines are those included in the most broad example embodiments. The circuit systems or operations included in the dashed lines are example embodiments that can be included in or part of the circuit systems or operations of the solid-line example embodiments, or are further circuit systems or operations that can be taken in addition to the circuit systems or operations of the solid-line example embodiments. It should be understood that these operations do not need to be performed in the order presented. Furthermore, it should be understood that not all operations need to be performed. Exemplary operations can be performed in any order and in any combination.

[0110] It should be noted that the word "including" does not necessarily exclude the existence of other elements or steps besides those listed.

[0111] It should be noted that the words "one" or "a kind" preceding an element do not preclude the existence of multiple such elements.

[0112] It should be noted that the term "indication" can be considered as "associated," "related," "description," "characterization," and / or "definition." The terms "indication," "associated," "related," "description," "characterization," and "definition" are used interchangeably. The term "indication" can be considered as indicating a relationship. For example, weight data indicating weight may include one or more weight parameters.

[0113] It should be noted that the word "based on" can be considered as "according to" and / or "derived from". The terms "based on" and "according to" are used interchangeably. For example, a parameter determined "based on" a dataset can be considered as a parameter determined "according to" that dataset. In other words, the parameter can be the output of one or more functions that take that dataset as input.

[0114] Functions can represent the relationship between inputs and outputs, such as mathematical relationships, database relationships, hardware relationships, logical relationships, and / or other suitable relationships.

[0115] It should also be noted that any reference numerals in the drawings do not limit the scope of the claims, exemplary embodiments may be implemented at least in part by both hardware and software, and several “components,” “units,” or “devices” may be represented by the same hardware article.

[0116] The various exemplary methods, apparatuses, nodes, and systems described herein are described in the general context of method steps or processes. In one aspect, these method steps or processes may be implemented by a computer program product embodied in a computer-readable medium, including computer-executable instructions, such as program code, that are executed by a computer in a networked environment. Computer-readable media may include removable and non-removable storage devices, including but not limited to read-only memory (ROM), random access memory (RAM), optical disc (CD), digital versatile disc (DVD), etc. Generally, a program circuit system may include routines, programs, objects, components, data structures, etc., that perform a specified task or implement a particular abstract data type. The computer-executable instructions, associated data structures, and program circuit systems represent examples of program code for performing steps of the methods disclosed herein. Specific sequences of such executable instructions or associated data structures represent examples of corresponding actions for implementing the functionality described in such steps or processes.

[0117] Although features have been shown and described, it should be understood that they are not intended to limit the claimed disclosure, and it will be apparent to those skilled in the art that various changes and modifications can be made without departing from the scope of the claimed disclosure. Accordingly, this specification and drawings are to be considered illustrative rather than restrictive. The claimed disclosure is intended to cover all alternatives, modifications, and equivalents.

Claims

1. A method performed by an electronic device, the method comprising: - Obtain historical transaction data associated with one or more shipping services provided to multiple users within a time period; - Based on the historical transaction data, determine statistical measures that indicate the pattern of each of the one or more shipping services within the time period; - Generate subscription parameters for one or more subscription plans associated with the one or more shipping services based on the statistical metrics; as well as - Provide the subscription parameters for the one or more subscription plans.

2. The method of claim 1, wherein the historical transaction data includes one or more of the following: historical service rate data for one or more shipping services, user booking data associated with the plurality of users associated with the shipping booking, booking data associated with one or more shipping bookings, historical selection rates indicating a user's previous selection of a subscription plan, and historical shipping data.

3. The method according to any one of the preceding claims, wherein the subscription parameters include one or more of the following: a subscription value indicating the value of the corresponding subscription plan, a time extension parameter indicating the time extension for the return and / or storage of containers, one or more accident parameters indicating one or more accident fees, a freight rate parameter indicating a freight rate, a booking modification parameter indicating a modification fee, a demurrage and detention parameter indicating demurrage and detention fees, and a refrigeration parameter indicating the fees for refrigerated containers.

4. The method of claim 3, wherein the one or more accident parameters indicate a reduction in the one or more accident fees, wherein the freight rate parameter indicates a reduction in the freight rate, wherein the booking modification parameter indicates a reduction in the modification fee, wherein the demurrage and detention parameter indicates a reduction in the demurrage and detention fees, and wherein the refrigeration parameter indicates a reduction in the cost for the refrigerated container.

5. The method according to any one of the preceding claims, wherein obtaining the historical transaction data includes preprocessing the historical transaction data.

6. The method of claim 5, wherein preprocessing the historical transaction data includes transforming the historical transaction data and / or grouping the historical transaction data by user.

7. The method according to any one of the preceding claims, wherein for each shipping service, the statistical measure includes one or more of the following: mean, median, and distribution.

8. The method according to any one of the preceding claims, wherein generating the subscription parameters based on the statistical metric comprises simulating the corresponding results for each candidate subscription plan based on the statistical metric and one or more of the following: - Initial user distribution for each candidate subscription plan; and - The range of each subscription parameter for each candidate subscription plan.

9. The method according to any one of the preceding claims, the method comprising predicting a user selection parameter for each subscription plan based on the historical transaction data and using a machine learning model, wherein the user selection parameter indicates the probability that a user will select one of the one or more subscription plans.

10. The method according to any one of the preceding claims, wherein providing the subscription parameters of the one or more subscription plans includes causing one or more user interface objects representing the respective subscription parameters of the one or more subscription plans to be displayed.

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