CN114331525A - Multi-dimensional customer type analysis method and system - Google Patents
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Abstract
The invention provides a multi-dimensional customer type analysis method and system. The method is suitable for grouping different types of client objects through a plurality of models by a client type analysis system and comprises the following steps: configuring a plurality of operation modules of a customer analysis model on a front-end user interface of a customer type analysis system; importing data of a plurality of client objects in a client type analysis system; the type groupings of the plurality of customer objects are analyzed and determined from the data by at least a portion of a plurality of customer analytical models, wherein each customer analytical model corresponds to a type grouping result. The customer type analysis method and the customer type analysis system can group and arrange customers through different multidimensional models, so that the customers can be accurately positioned through related indexes in scenes with different requirements, and accurate marketing is facilitated.
Description
Technical Field
The invention mainly relates to the field of data analysis, in particular to a multi-dimensional customer type analysis method and system.
Background
At present, CRM (Customer Relationship Management) has a single marketing means for customers, is simple in Customer positioning and cannot globally see the conditions of all customers. Meanwhile, a general CRM platform is heavy, so that the processing of customer information data is too simple, and the customer data cannot be analyzed in complex dimensions and scenes. On some customer management front-end interfaces, only simple data statistics functions are provided. As customer relationship management becomes more important and needs to be emphasized, there is a lack in the art of a tool that can analyze, process and count customer data from different dimensions.
Disclosure of Invention
The invention aims to provide a multi-dimensional customer type analysis method and a multi-dimensional customer type analysis system which can group and arrange customers through different models. The method and the system can accurately group and position the customers through the related indexes under different requirements, thereby being beneficial to realizing accurate marketing.
In order to solve the above technical problem, the present invention provides a multidimensional client type analysis method, which is suitable for grouping different types of client objects through a plurality of models by a client type analysis system, and comprises the following steps: configuring a plurality of operation modules of a customer analysis model on a front-end user interface of the customer type analysis system; importing data of a plurality of customer objects in the customer type analysis system; and analyzing and determining the type grouping of the plurality of customer objects through at least one part of the plurality of customer analysis models according to the data, wherein each customer analysis model corresponds to one type grouping result.
In an embodiment of the present invention, the client analysis model includes an RFM model, and the step of configuring the operation module of the RFM model on the front-end user interface includes configuring one or more parameter groups in the RFM model on the front-end user interface, and configuring a custom input area for an R value, an F value, and an M value for each parameter group, where the R value is a time of last consumption, the F value is a value of frequency of consumption, the M value is a value of amount of consumption, and in data of one or more user objects in a type group corresponding to the RFM model, the R value, the F value, and the M value are each within a preset value range.
In an embodiment of the present invention, the customer type analysis method further includes configuring a custom input area of an expansion filter for each parameter set.
In an embodiment of the invention, the customer analysis model further comprises a customer lifecycle CLV model and/or a customer liveness model.
In an embodiment of the present invention, when the customer analysis model further includes the CLV model, the step of configuring, at the front-end user interface, the operation module of the CLV model includes configuring, at the front-end user interface, a custom input area for one or more adjustable parameters of the CLV model, and in the data of one or more user objects in the type group corresponding to the CLV model, the one or more adjustable parameters are each located within a preset numerical range.
In an embodiment of the present invention, when the customer analysis model further includes the customer activity model, the step of configuring, at the front-end user interface, the operation module of the customer activity model includes configuring, at the front-end user interface, one or more filtering conditions of the activity model and a custom input area corresponding to a value range of the filtering conditions, where, in data of one or more user objects in a type group corresponding to the activity model, the value range of the one or more filtering conditions is respectively located in a preset value range.
In an embodiment of the present invention, in the data of the plurality of client objects, the same client object has a plurality of pieces of data information, and the method further includes adding a unique identifier to each client object after importing the data of the plurality of client objects in the client type analysis system, and associating the plurality of pieces of data information corresponding to each client object to the unique identifier.
The present invention also provides a multidimensional client type analysis system, which is suitable for grouping client objects of different types through a plurality of models, and comprises: a front-end configuration module configured to configure a plurality of operation modules of the customer analysis model at a front-end user interface of the customer type analysis system; a data import module configured to import data of a plurality of customer objects in the customer type analysis system; and a type analysis module configured to analyze and determine type groupings of the plurality of customer objects through at least a portion of the plurality of customer analysis models based on the data, wherein each customer analysis model corresponds to a type grouping result.
The invention also provides a multidimensional client type analysis system, which comprises: a memory for storing instructions executable by the processor; and a processor for executing the instructions to implement the customer type analysis method as described above.
The invention also provides a computer readable medium having stored thereon computer program code which, when executed by a processor, implements a method of client type analysis as described hereinbefore.
The customer type analysis method and the customer type analysis system can group and arrange the customers through different models, so that the customers can be accurately positioned through related indexes under different requirements, and accurate marketing is facilitated. The invention can more intuitively and simply see which group different customer types belong to in the front-end user interface, thereby providing a solution for realizing accurate marketing to customers. Meanwhile, the analysis method and the analysis system can also self-define parameter groups, can add or delete a plurality of parameter groups at any time, are more flexible to use, can enable users to conveniently modify at any time and check at any time, have strong universality and can be widely applied.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the principle of the invention. In the drawings:
FIG. 1 is an exemplary flow diagram of a multidimensional client type analysis methodology in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a portion of a front-end user in a multidimensional client type analysis method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a portion of a front-end user in a multidimensional client type analysis method according to another embodiment of the present invention;
FIG. 4 is an exemplary system framework diagram of a multidimensional client type analysis system in accordance with one embodiment of the present invention;
FIG. 5 is an exemplary system framework diagram of a multidimensional client type analysis system in accordance with another embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
As used in this application and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In the description of the present application, it is to be understood that the orientation or positional relationship indicated by the directional terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc., are generally based on the orientation or positional relationship shown in the drawings, and are used for convenience of description and simplicity of description only, and in the case of not making a reverse description, these directional terms do not indicate and imply that the device or element being referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore, should not be considered as limiting the scope of the present application; the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
It should be noted that the terms "first", "second", and the like are used to define the components, and are only used for convenience of distinguishing the corresponding components, and the terms have no special meanings unless otherwise stated, and therefore, the scope of protection of the present application is not to be construed as being limited. Further, although the terms used in the present application are selected from publicly known and used terms, some of the terms mentioned in the specification of the present application may be selected by the applicant at his or her discretion, the detailed meanings of which are described in relevant parts of the description herein. Further, it is required that the present application is understood not only by the actual terms used but also by the meaning of each term lying within.
It will be understood that when an element is referred to as being "on," "connected to," "coupled to" or "contacting" another element, it can be directly on, connected or coupled to, or contacting the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly on," "directly connected to," "directly coupled to" or "directly contacting" another element, there are no intervening elements present. Similarly, when a first component is said to be "in electrical contact with" or "electrically coupled to" a second component, there is an electrical path between the first component and the second component that allows current to flow. The electrical path may include capacitors, coupled inductors, and/or other components that allow current to flow even without direct contact between the conductive components.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, various steps may be processed in reverse order or simultaneously. Meanwhile, other operations are added to or removed from these processes.
The invention will now be illustrated by means of specific examples.
One embodiment of the present invention proposes a multidimensional client type analysis method 100 (hereinafter referred to as "analysis method 100") with reference to fig. 1. The analysis method 100 can group and arrange the customers through different models, which is beneficial to the accurate marketing of the customers.
Referring to fig. 1, a customer type analysis method 100 in this embodiment includes the following steps.
Step S110 configures a plurality of operation modules of the customer analysis model for a front-end user interface of the customer type analysis system.
Step S120 is to import data of a plurality of customer objects in the customer type analysis system.
Step S130 is analyzing and determining a type grouping of the plurality of customer objects by at least a portion of the plurality of customer analysis models based on the data.
The above steps will now be described in detail.
The customer type analysis method 100 in the embodiment of FIG. 1 may group different types of customer objects through multiple models by a customer type analysis system.
In step S110, the front-end user interface of the customer type analysis system is configured with operation modules of a plurality of customer analysis models. The operation module can be used for analyzing the client type.
In step S120, before analyzing the client type, data of the client object to be analyzed is imported into the client type analysis system.
In step S130, the type groupings of the client objects are analyzed and determined by at least a portion of the plurality of client models based on the data of the client objects imported in step S120. Wherein each customer analysis model corresponds to one type grouping result. This means that, in the analysis method 100 of the present invention, a plurality of different customer type analysis models are configured at the same time, and different customer type analysis models can be selected and used through the front-end user interface, and corresponding statistical results are obtained respectively.
In one embodiment of the invention, the customer analysis model is the RFM model (Recency last consumed, Frequency of consumption by Frequency, money consumed). The step of configuring the operation module of the RFM model at the front-end user interface of the customer type analysis system includes: configuring one or more parameter groups in the RFM model in a front-end user interface, and configuring custom input areas of R value, F value and M value for each parameter group. Wherein, the R value is the time of the last consumption, the F value is the value of the consumption frequency, and the M value is the value of the consumption amount. Meanwhile, in the data of one or more user objects in the type group corresponding to the RFM model, the R value, the F value, and the M value are each within a preset numerical range. In other embodiments of the present invention, a custom input field for the expansion screen option may also be configured for each parameter set.
To further illustrate the RFM model of the present invention, a specific example is given herein.
Referring to the RFM model 200 shown in fig. 2, in this embodiment, the "parameter set names" may be named according to requirements. For example, in the present embodiment, the "parameter group name" is named "a parameter group". It is understood that the RFM model 200 may also include other parameter sets, and is not limited to the A parameter set in this embodiment.
Further, a client object to which the parameter group applies may be selected. For example, in the present embodiment, all the client objects to which the parameter group named "a parameter group" is applied are clients. It will be appreciated that the client type analysis method of the present invention allows for customization of client objects for which a particular set of parameters is applicable. Further, the customer objects in the present invention include online customer objects and offline customer objects, such as consumers and agents.
The "parameter set A" parameter set in the RFM model 200 is then configured with custom input fields for the R, F, and M values. Therefore, a user of the customer type analysis method can customize specific numerical values in the input areas of the R value, the F value and the M value according to requirements. For example, in the embodiment of FIG. 2, the value of the custom input field for the R value is 5, the value of the custom input field for the F value is 10, and the value of the custom input field for the M value is 100.
The customer type analysis method of the invention allows a user to set the types of specific models included in the customer analysis model, such as RFM model, according to the requirements; the RFM model may also be configured to include parameter sets and corresponding specific values of R, F, and M values. Thereby achieving accurate classification of the customer objects.
In an embodiment of the invention, the customer analysis model further comprises a customer life cycle CLV (customer life cycle value) model and/or a customer activity model. Specifically, when the customer analysis model includes a CLV model, the step of configuring an operation module of the CLV model at the front-end user interface includes: configuring a custom input area for one or more adjustable parameters of the CLV model at the front-end user interface. Meanwhile, in the data of one or more user objects in the type group corresponding to the CLV model, one or more adjustable parameters are respectively located in a preset numerical range.
To further illustrate the CLV model of the present invention, a specific example is given here.
Referring to fig. 3, in this embodiment, three adjustable parameters are configured for the CLV model in the front-end user interface configuration, and the custom belonging areas are respectively set corresponding to the three adjustable parameters. The three adjustable parameters are the predicted average future purchase rate of the customer, the predicted life cycle of the customer and the predicted average future customer price of the customer respectively. Wherein, the average future purchase rate of the customer is estimated total amount of orders/estimated total number of customers; the customer life cycle represents the length of time that the customer is permanently not associated with the customer before shopping; the average future customer unit price of the customer is estimated total income/estimated total amount of orders. It can be understood that the number of adjustable parameters in the CLV model can be customized according to the requirement, for example, the number can be increased to 5 adjustable parameters or decreased to 2 adjustable parameters based on the embodiment.
Further, in the embodiment of FIG. 3, each adjustable parameter has a custom input field associated with it, allowing a CLV model user to customize the corresponding parameter. For example, in the present embodiment, "average future purchase rate of the predicted customer" is set equal to 10%, "average future price of the predicted customer" is set equal to 300 yuan, and "life cycle of the predicted customer" is set equal to 5 years. It is understood that the setting of the adjustable parameters in this embodiment is only exemplary and not limiting to the invention.
When the customer analysis model includes a customer activity model, configuring an operational module of the customer activity model at the front-end user interface includes: and configuring one or more screening conditions of the activity model and a user-defined input area corresponding to the value range of the screening conditions on the front-end user interface. Meanwhile, in the data of one or more user objects in the type group corresponding to the activity model, the value ranges of one or more screening conditions are respectively located in a preset numerical range.
For details of the customer activity model, reference may be made to the description of the CLV model, which is not repeated herein.
Preferably, in an embodiment of the present invention, among data of a plurality of client objects, the same client object has a plurality of pieces of data information. At this time, after the data of the client object having the pieces of data information is imported into the client type analysis system, the system adds a unique identifier to the client object and associates the pieces of data information corresponding to the client object with the unique identifier. By adding the unique identifier to the client object, the data information of different sources of the client object can be arranged and unified. On one hand, data information of client objects is prevented from being omitted; on the other hand, the generation of a plurality of client objects which are actually the same client object but have different names in the system is avoided.
The CLV model and the customer activity model in the customer type analysis method allow a user to set the number of adjustable parameters in the model and the values corresponding to the parameters according to requirements. Thereby achieving accurate classification of the customer objects.
The embodiment can understand that the client type analysis method can enable a user to group and arrange client objects from three dimensions of an RFM model, a CLV model and a client activity model, so that the client can be accurately positioned to the client through related indexes in different requirements. However, the present invention is not limited thereto, and in some other embodiments of the present invention, by configuring different models, statistics of the customer data analysis can be obtained from more angles, so as to provide a technical solution for the customer data analysis more carefully and comprehensively.
The invention also provides a multi-dimensional customer type analysis system which can be used for grouping different types of customer objects through a plurality of models. Referring to fig. 4, the customer type analysis system 400 includes a front-end configuration module 410, a data import module 420, and a type analysis module 430. In particular, front-end configuration module 410 may be used to configure the operational modules of a plurality of customer analysis models at a front-end user interface of a customer type analysis system. The data import module 420 may be used to import data for a plurality of customer objects in a customer type analysis system. The type analysis module 430 is operable to analyze and determine a type grouping of the plurality of customer objects from the data via at least a portion of the plurality of customer analysis models. Wherein each customer analysis model corresponds to one type grouping result.
By the client type analysis system 400, the client objects can be analyzed and counted from multiple dimensions on the basis of the imported data information of the client objects, which is beneficial to the accurate classification of the client objects. For example, the customer type analysis system 400 shown in fig. 4 may apply the customer type analysis method described above with reference to fig. 1 to 3, and other details about the customer type analysis system 400 may refer to the description above with reference to fig. 1 to 3, and are not repeated herein.
An embodiment of the present invention further provides a customer type analysis system 500 as shown in fig. 5. According to fig. 5, the client type analyzing system 500 may include an internal communication bus 501, a Processor (Processor)502, a Read Only Memory (ROM)503, a Random Access Memory (RAM)504, and a communication port 505. When implemented on a personal computer, customer type analysis system 500 may also include a hard disk 506.
Client-type analysis system 500 may also include various forms of program storage units and data storage units, such as a hard disk 506, Read Only Memory (ROM)053, and Random Access Memory (RAM)504, capable of storing various data files used in computer processing and/or communications, as well as possibly program instructions executed by processor 502. The processor executes these instructions to implement the main parts of the method. The results processed by the processor are communicated to the user device through the communication port and displayed on the user interface.
In addition, another aspect of the present invention provides a computer readable medium storing computer program code, which when executed by a processor implements the above-mentioned voice interaction method.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing disclosure is by way of example only, and is not intended to limit the present application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. The processor may be one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), digital signal processing devices (DAPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or a combination thereof. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips … …), optical disks (e.g., Compact Disk (CD), Digital Versatile Disk (DVD) … …), smart cards, and flash memory devices (e.g., card, stick, key drive … …).
The computer readable medium may comprise a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. The computer readable medium can be any computer readable medium that can communicate, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, radio frequency signals, or the like, or any combination of the preceding.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
Although the present application has been described with reference to the present specific embodiments, it will be recognized by those skilled in the art that the foregoing embodiments are merely illustrative of the present application and that various changes and substitutions of equivalents may be made without departing from the spirit of the application, and therefore, it is intended that all changes and modifications to the above-described embodiments that come within the spirit of the application fall within the scope of the claims of the application.
Claims (10)
1. A multi-dimensional customer type analysis method suitable for grouping customer objects of different types through a plurality of models by a customer type analysis system, characterized by comprising the steps of:
configuring a plurality of operation modules of a customer analysis model on a front-end user interface of the customer type analysis system;
importing data of a plurality of customer objects in the customer type analysis system;
and analyzing and determining the type grouping of the plurality of customer objects through at least one part of the plurality of customer analysis models according to the data, wherein each customer analysis model corresponds to one type grouping result.
2. The method of claim 1, wherein the customer analysis model comprises an RFM model, and wherein configuring the operational modules of the RFM model at the front-end user interface comprises configuring one or more parameter sets in the RFM model at the front-end user interface, and configuring custom input fields for R, F, and M values for each parameter set, wherein the R value is a time of last consumption, the F value is a frequency of consumption, the M value is a monetary value, and the R, F, and M values each lie within a predetermined range of values in data for one or more user objects in a type group to which the RFM model corresponds.
3. The method of claim 2, further comprising configuring a custom input area of an expansion screen for each of the sets of parameters.
4. The method of claim 1, wherein the customer analysis model further comprises a customer lifecycle CLV model and/or a customer liveness model.
5. The method of claim 4, wherein when the customer analysis model further comprises the CLV model, configuring an operational module of the CLV model at the front-end user interface comprises configuring custom input fields for one or more adjustable parameters of the CLV model at the front-end user interface, the one or more adjustable parameters each being within a preset range of values in data for one or more user objects in a type grouping to which the CLV model corresponds.
6. The method of claim 4 or 5, wherein when the customer analysis model further includes the customer activity model, configuring, at the front-end user interface, the operational module of the customer activity model includes configuring, at the front-end user interface, one or more screening conditions of the activity model and custom input regions corresponding to value ranges of the screening conditions, and the value ranges of the one or more screening conditions each lie within a preset range of values in data of one or more user objects in a type grouping corresponding to the activity model.
7. The method of claim 1, wherein the same client object has a plurality of pieces of data information in the data of the plurality of client objects, the method further comprising adding a unique identifier to each client object after importing the data of the plurality of client objects in the client type analysis system, and associating the plurality of pieces of data information corresponding to each client object to the unique identifier.
8. A multidimensional client type analysis system adapted to group client objects of different types through a plurality of models, comprising:
a front-end configuration module configured to configure a plurality of operation modules of the customer analysis model at a front-end user interface of the customer type analysis system;
a data import module configured to import data of a plurality of customer objects in the customer type analysis system; and
a type analysis module configured to analyze and determine type groupings of the plurality of customer objects through at least a portion of the plurality of customer analysis models based on the data, wherein each customer analysis model corresponds to a type grouping result.
9. A multidimensional client type analysis system, comprising:
a memory for storing instructions executable by the processor; and a processor for executing the instructions to implement the method of any one of claims 1-7.
10. A computer-readable medium having stored thereon computer program code which, when executed by a processor, implements the method of any of claims 1-7.
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