CN112182421B - Potential customer mining method and device and electronic equipment - Google Patents
Potential customer mining method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the specification provides a potential customer mining method, a potential customer mining device and electronic equipment. The method comprises the following steps: determining one or more target shops, and acquiring shop information of one or more dimensions corresponding to the target shops; acquiring a user related to a service class of the target shop according to the target shop, and acquiring user data of one or more dimensions corresponding to the user; respectively executing calculation or matching operation on user data of one or more dimensions corresponding to each user and store information of one or more dimensions corresponding to a target store by using a predetermined algorithm model to obtain a matching degree score between the user and the target store; and determining potential clients of the target shops according to the matching degree scores.
Description
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to a method and an apparatus for mining a potential customer, and an electronic device.
Background
With the development of internet technology, a large number of users are accumulated in the internet platform, and a large amount of data is generated when the users use the services in the internet platform. On the other hand, how to develop new clients during the operation of the on-line shops to promote the growth of the business has become one of the major concerns of the shops.
In the prior art, new customers are attracted to in-store consumption by developing marketing activities in ways of reducing prices of products in shops or putting advertisements and the like, but the user range covered by the in-store marketing way is greatly limited, and the advertisement putting way cannot obtain accurate potential customer groups, so that the advertisement putting effect is poor. Based on the prior art, a technical scheme capable of helping a shop to dig out potential customers so as to enable the shop to realize accurate marketing and delivery is needed to be provided.
Disclosure of Invention
The embodiment of the specification provides a potential customer mining method, a potential customer mining device and electronic equipment, which are used for solving the problem that a shop cannot be helped to accurately mine potential customers in the prior art.
In order to solve the above technical problems, the embodiments of the present specification are implemented as follows:
the embodiment of the specification provides a potential customer mining method, which comprises the following steps:
Determining one or more target shops, and acquiring shop information of one or more dimensions corresponding to the target shops;
Acquiring a user related to a service class of the target shop according to the target shop, and acquiring user data of one or more dimensions corresponding to the user;
Respectively executing calculation or matching operation on user data of one or more dimensions corresponding to each user and store information of one or more dimensions corresponding to a target store by using a predetermined algorithm model to obtain a matching degree score between the user and the target store;
and determining potential clients of the target shops according to the matching degree scores.
A potential customer mining apparatus provided by an embodiment of the present specification, the apparatus comprising:
The first acquisition module is used for determining one or more target shops and acquiring shop information of one or more dimensions corresponding to the target shops;
the second acquisition module is used for acquiring a user related to the service class of the target shop according to the target shop and acquiring user data of one or more dimensions corresponding to the user;
The computing and matching module is used for respectively executing computing or matching operation on the user data of one or more dimensions corresponding to each user and the shop information of one or more dimensions corresponding to the target shop by utilizing a preset algorithm model to obtain a matching degree score between the user and the target shop;
And the determining module is used for determining potential clients of the target shops according to the matching degree scores.
An electronic device provided in an embodiment of the present disclosure includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements a potential customer mining method as described above when executing the program.
The above-mentioned at least one technical scheme that this description embodiment adopted can reach following beneficial effect:
Determining one or more target shops and acquiring shop information of one or more dimensions corresponding to the target shops; acquiring users related to service categories of the target shops according to the target shops, and acquiring user data of one or more dimensions corresponding to the users; respectively executing calculation or matching operation on user data of one or more dimensions corresponding to each user and store information of one or more dimensions corresponding to a target store by using a predetermined algorithm model to obtain a matching degree score between the user and the target store; and determining potential customers of the target shop according to the matching degree scores. Based on the scheme, through acquiring information of the shops and known user data resources, the algorithm model is utilized to match the multidimensional information of the shops with the user data, the matching degree score corresponding to the user and the shops is determined, potential clients are dug for the shops according to the matching degree score, and accurate marketing and delivery of the shops are facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a potential customer mining method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a potential customer excavating device according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
As described above, with the rapid development of internet platforms, a large number of users are accumulated in various internet platforms, and the users generate related user data when using the services of the platforms. On the other hand, off-line shops face the problem that new customers are difficult to develop, the coverage of the traditional in-store marketing mode is limited by stores, and the new customers need to be accurately potential customers to be put in by adopting the advertisement putting mode. The following describes the problems in the prior art in detail by taking a vehicle owner as a user, taking a gas station as a shop, taking excavation of potential customers around the gas station as a technical scene, and the specific contents are as follows:
with the rapid development of the China automobile industry, the national automobile conservation amount and the automobile driver number break through the new height, and the automobile conservation amount and the automobile driver number are taken as high-frequency use scenes of automobile owners on the automobiles, and gas stations are indispensable in travel scenes. With policies open, more and more camping and foreign resource enterprises offer gas stations in China, however, unlike large state-owned enterprise gas stations, one of the biggest difficulties encountered in the operation of gas stations is the problem of new customer development for camping and foreign resource oil stations. Because oil stations of civil and foreign brands have disadvantages of scattered brands, low confidence, small number of linked oil stations, insufficient distribution density and the like, marketing activities can be carried out only in ways of price reduction in the oil stations so as to attract owners around the oil stations, and the range of radiation of the marketing ways is limited to off-line stores or spontaneous public praise propagation among the owners. Because the potential customers around the oil outlet station cannot be excavated aiming at a specific oil station, the accurate potential owner customers cannot be acquired by the oil station, accurate marketing and delivery of the oil station are not facilitated, and the development of shops is hindered.
Based on the problems, the scheme is based on accumulated user data resources in an Internet platform, multi-dimensional portraits are built for users and shops by collecting user data and shop information in various different dimensions, matching degree between the users and the shops is scored by utilizing an algorithm model, and the probability that the users are converted into clients of target shops and consumed by shops can be accurately measured by the matching degree scoring.
It should be noted that, in the following embodiments of the present disclosure, the technical solution is developed by taking mining performed by potential owner users around a gas station as an example, so that a shop in the following embodiments may be considered as a gas station, a user may be considered as a vehicle owner, a mined potential client may be considered as a potential owner client of the gas station, the noun transformation is only for convenience of describing a specific embodiment, and the application scenario of the present solution is not limited, and the present solution is not limited to the application scenario of mining the potential client around the gas station, and other technical scenarios related to mining the potential client of the shop may be applicable to the present solution.
Based on the above-described scenario, the following describes the scheme of the present specification in detail.
Fig. 1 is a flow chart of a task processing flow arranging method according to an embodiment of the present disclosure, where the method specifically may include the following steps:
In step S110, one or more target shops are determined, and shop information of one or more dimensions corresponding to the target shops is acquired.
In one or more embodiments of the present description, the target shop may be considered a shop that needs to dig potential customers, and thus, the target shop may be contoured by an operator to a shop that needs to make an accurate marketing impression. The shops can also be called shops, merchants and the like, and in the embodiment of the specification, the shops can comprise off-line entity shops, but also can be on-line shops, the embodiment of the specification does not limit the concrete form of the shops, and all shops needing to be mined by potential customers and marketed on-line are suitable for the scheme.
Further, in the embodiment of the present specification, the shop information may include at least one of the following types of information: geographic location information and service information. In practical applications, the geographical location information may be considered as POIs (information points) of shops in the navigation map data; the service information may be regarded as information related to a shop product, service, or the like.
The following describes in detail how to obtain the information of the target shop according to the specific embodiment, and may specifically include the following:
For geographic position information of a shop, acquiring information corresponding to a position point of a target shop from navigation map data, wherein the information corresponding to the position point comprises the following dimension information: location point identification, shop name, location information, and brand information. Specifically, firstly, acquiring all POI position points with the type of 'gas station' in the navigation map data, and then generating a national gas station geographic position information table according to the acquired POI position points corresponding to all the gas stations, wherein an information field corresponding to one shop (i.e. the gas station) in the geographic position information table can comprise, but is not limited to, the following dimension information: oil station POI ID, oil station longitude, oil station latitude, oil station name, oil station address, oil station brand, oil station province, oil station city, etc. Each of the above information corresponds to one dimension of the shop information, for example, the POI ID of the oil station may be regarded as one dimension of the shop information, the oil station name may be regarded as one dimension of the shop information, the oil station brand may be regarded as one dimension of the shop information, and the like.
For service information of the shops, the service information of the shops can be acquired and acquired through the target shops, and the service information comprises, but is not limited to, the following dimension information: commodity information, price information, offer information, payment method information, user evaluation information, and service providing information. Taking gas stations as an example, the service information of a business may include, but is not limited to, the following dimensions of information: number of oil sold (e.g., 92#, 98#, etc.), price of oil (e.g., 5.92/L), presence of convenience store, presence of toilet, presence of high-speed service area, presence of preferential activity, presence of payment support for cell phone, presence of car washing service, user rating star, etc. Each of the above information corresponds to one dimension of the information of the shop, for example, the number of the oil sold may be regarded as one dimension of the information of the shop, whether the convenience store may be regarded as one dimension of the information of the shop, whether the preferential activity may be regarded as one dimension of the information of the shop, and so on.
In step S120, a user related to a service class of the target shop is acquired according to the target shop, and user data of one or more dimensions corresponding to the user is acquired.
In one or more embodiments of the present disclosure, when acquiring the user and the user data, the data generated by the full-scale user when using the service of the internet platform may be used as the full-scale user data based on the user accumulated in the existing internet platform (such as a third party payment platform, a social platform, a shopping platform, etc.) as the full-scale user. The user in the scheme can be considered as a user account registered in the internet platform or account identification information corresponding to an actual user group. Because the scheme can be suitable for different application scenes, when the user is acquired in actual operation, only the user related to the service class of the target shop can be acquired. For example, continuing with the example in the foregoing embodiment, when the target shop is a gas station, the class of service provided by the gas station is a fueling service, and therefore, when a user object actually used for mining potential customers is acquired from among the full-scale users of the internet platform, a population having vehicles or with vehicles in the platform user may be taken as the user object, for example, a population having vehicles or with vehicles in the payment platform user may be acquired as the user object in actual operation.
Further, in the embodiment of the present specification, for how to acquire the relevant users according to the service class of the target shop, the following manner may be adopted in actual operation:
Taking the crowd with vehicles or vehicles in the payment platform users as user objects as an example, users with the following characteristics, such as users who have generated transactions in service scenes after vehicles such as vehicle insurance, parking, high speed, oiling and the like, users with traveling certificates bound in card packages, users with license plates bound in vehicle owner service channels, users applying for and binding ETC equipment and the like, can be obtained from the payment platform database.
In a specific embodiment of the present disclosure, after obtaining a user related to a service class from an application platform database according to the service class of a target shop, and obtaining user data, the user and the user data may also be formed into a user data set; the composed user data set may be used as a database in which the algorithm model in the following embodiments crawls data when performing computation or matching operations.
In practical applications, the user data includes, but is not limited to, the following types of data: action trace data, service preference data, and transaction behavior data.
The action track data can be generated in the following way, for a platform user who opens a position acquisition authority, when the user uses certain functions (such as wallet payment functions and the like) in the platform, the platform periodically reports the LBS geographic position (such as longitude and latitude information) of the user on the premise of acquiring the user authorization, and the action track of the user can be defined through the reported position data.
The service preference data may be considered fueling preference data of the user, and may include, for example, but not limited to, the following data: basic data such as a user's vehicle brand, model and the like, preferential sensitivity data, high-speed traffic data and the like.
Transaction behavior data may be considered data generated by user behavior, for example, which may include, but is not limited to, the following data: the user browses or clicks on the behavior data of the vehicle owner service application, and the user refuels the data such as the oil station position, the oil number, the oil gun number and the like in the transaction order.
In step S130, using a predetermined algorithm model, computing or matching operation is performed on the user data of one or more dimensions corresponding to each user and the shop information of one or more dimensions corresponding to the target shop, so as to obtain a matching degree score between the user and the target shop.
In one or more embodiments of the present disclosure, the predetermined algorithm model may be modeled based on an owner user of the internet platform, and in the process of algorithm modeling, the following factors may be considered, and the matching degree score corresponding to each factor may be calculated by calculating or matching each factor, where the factors may be considered as numerous factors affecting the score. The matching relation used by each factor matching degree algorithm can be pre-configured in the algorithm model, and the algorithm model can grasp corresponding user data and store information to calculate the matching degree by establishing the matching relation. The following details of the process of calculating the matching degree score corresponding to each factor are described in connection with the specific embodiment, and may specifically include the following:
And the first factor matching degree algorithm is used for executing calculation operation according to the track position points contained in the action track data of the user and the positioning information corresponding to the position points of the target shops, and determining a first matching degree score according to a calculation result and a pre-configured weight.
According to the foregoing embodiments, it is known that, under the condition of obtaining the user authorization, LBS location points of the owner user can be collected, so that a movement track of the owner user is outlined according to the LBS location points. In practical application, the algorithm model can be used for capturing the acquired action track of the user in the user data set and the longitude and latitude information of the acquired POI position point of the gas station, and the captured data is used for calculating a first matching degree score, and the first matching degree score measures the influence of the action track of the user on the matching degree of the target oil station, so that the first matching degree score can be also called a track factor matching degree score. Specifically, calculating the first matching score based on the LBS location point of the owner user and the gas station POI location point may include the operations of:
According to the track position points contained in the action track data of the user and a preset frequency threshold, taking the track position points with the occurrence frequency larger than the frequency threshold as high-frequency track position points; and calculating the distance between the high-frequency track position point and the position point of the target shop by using a Cartesian product, respectively configuring weights for the frequency of occurrence and the distance of the high-frequency track position point, and calculating according to the frequency of occurrence, the distance and the weights to obtain a first matching degree score.
The following describes the process of calculating the first matching score in combination with a specific embodiment, which is specifically as follows:
The calculation of the first degree of matching score (i.e., the trajectory factor degree of matching score) is exemplified, for example, with the frequency and distance between the trajectory line of the owner user and the position point of the gas station shown in table 1 below, and the trajectory factor degree of matching score calculated by the weight as specific values.
First, some temporary and midway points in the trace line of the owner user are filtered, the starting point and the ending point (namely the starting place and the destination) of the high frequency are reserved, and the Cartesian product is used for calculating the distance between the starting point and the longitude and latitude of the POI of the nearby gas station. Generally, the higher the frequency of occurrence, the higher the score corresponding to the user action track closer to the user action track, the frequency of occurrence and the weight closer to the user action track can be adjusted according to the situation, for example, the weight is set to 5:5 when the distance is within 3km range or the frequency is higher than 30 times/month, the distance weight is reduced when the distance exceeds 3km range, and the frequency weight is increased when the frequency of occurrence is higher than 30 times/month.
And the second factor matching degree algorithm is used for respectively matching one or more dimensions of data in the service preference data of the user with one or more dimensions of the service information of the target shop, and determining a second matching degree score according to a matching result.
In this embodiment of the present disclosure, after capturing the above data collected in the user data set by using the algorithm model based on the service information (such as merchandise information, price information, preference information, etc.) of the target shop and the service preference data of the user obtained in the foregoing embodiment, a second matching degree score obtained by matching the service preference of the user with the service provided by the gas station is used, where the second matching degree score can measure the influence of the service preference of the user on the matching degree of the target oil station. The following describes, by some embodiments, a process of performing a matching operation between services of a gas station and service preferences of a user, which may specifically include the following:
And matching the data of the brand, model, preferential sensitivity, high-speed passing and the like of the user with the service provided by the gas station, wherein the score is higher as the matching degree between the user preference and the oil station service is higher.
For example, a high-end brand owner consumes 95# and 98# gasoline, and pays more attention to the brands of oil stations, so the brands of the oil stations are medium petroleum or medium petrochemical, the matching degree of the oil stations selling the high-standard gasoline is higher, and the matching degree of the civil oil stations is relatively lower; the matching degree between the owner user sensitive to the preferential effect and the oil station with preferential effect or oil price direct-descending effect is higher; and the matching degree between the owner user who handles ETC equipment and the oil station in the high-speed service area is higher, and the like.
And the third factor matching degree algorithm is used for respectively matching one or more dimensions of data in the transaction behavior data of the user with one or more dimensions of the service information of the target shop, and determining a third matching degree score according to a matching result.
In this embodiment of the present disclosure, after capturing the data collected in the user data set by using the algorithm model based on the service information (such as merchandise information, price information, and preference information) of the target shop and the transaction behavior data of the user obtained in the foregoing embodiment, a third matching degree score obtained by matching the transaction behavior of the user with the service of the gas station is used, where the third matching degree score can measure the influence of the user behavior on the matching degree of the target oil station. The following describes, by way of some embodiments, a process for performing a matching operation between the service of a gas station and the behavior of a user, which may specifically include the following:
And matching the information in the transaction order of the user vehicle with the service provided by the oil station, wherein the score is higher as the matching degree between the transaction behavior of the user and the service of the oil station is higher. For example, users who frequently browse or click on car wash service have a higher degree of match with oil stations that provide free car wash service; the matching degree of the data such as the position of the oil station, the oil number, the oil gun number and the like in the user oiling transaction order and the oil station which has been transacted is highest; the matching degree of the transaction oil number and the oil station selling oil number is higher, etc.
In practical application, the algorithm model may use one or a combination of several of the above factor matching algorithms to calculate the matching degree score, for example, only the influence of the user action track on the matching degree score may be considered, or the influence of the user action track and the user service preference data on the matching degree score may also be considered.
Further, in the embodiment of the present disclosure, after the score is determined by calculation or matching, the first matching degree score, the second matching degree score, and/or the third matching degree score may be normalized, and then the matching degree score between the vehicle owner user and the target shop may be obtained by using weighted average calculation.
In step S140, potential customers of the target shop are determined according to the matching score.
In one or more embodiments of the present description, the matching score that is ultimately calculated via normalization and weighted averaging may also be referred to as a potential score, which may be used to measure the degree of matching between the owner user and the target oil station, with a higher potential score indicating a greater likelihood that the owner user will be converted to the target oil station customer.
Specifically, in the embodiment of the present specification, the matching degree score may be analyzed in the following manner, so as to mine potential customers for the target shop according to the analysis result, which may specifically include the following:
Sorting the matching degree scores of the users, dividing the matching degree scores into a plurality of score intervals according to a preset dividing mode, and taking the users corresponding to the matching degree scores in the score intervals meeting the conditions as potential clients of the target shop.
The following describes an actual operation of dividing a user interval according to a matching degree score in connection with a specific embodiment, firstly, after the matching degree score between each user and a target shop pair is obtained, all the matching degree scores are ranked according to the score from high to low, the median of the matching degree scores is taken as a reference score (for example, 0 score), and the users higher than the reference score are divided into the potential passenger groups of the target oil station. In addition, a plurality of intervals can be divided according to potential division, so that the high-potential, medium-potential and low-potential fine layering of the potential customers of the target oil station can be realized, for example, potential division of more than 0.8 into high-potential customers and division of between 0.5 and 0.8 into medium-potential customers, and potential division of less than 0.5 into low-potential customers.
Further, in the embodiment of the specification, after the owner user is layered according to the potential score, different layered labels can be set for owner users of different layers, for example, the owner user with the potential score greater than 0.8 can be marked with a label of a high potential client, and the owner user with the label set falls into a data table, so that operators can conveniently and accurately conduct on-line marketing and delivery according to the user population label.
Based on the technical scheme of the specification, based on the existing user data resources in an Internet platform, user crowd data and shop information with multi-source characteristics are obtained, a multi-dimensional image is established for users and shops, three factors including a user action track, service preference, transaction behavior and the like are subjected to matching calculation through a comprehensive and accurate potential score intelligent algorithm, and the possibility of conversion of a vehicle owner user in a target oil station is objectively and accurately measured. The potential division of the users is divided, so that the shops are helped to dig out surrounding high-potential client groups, accurate marketing and delivery of the shops are facilitated, and new clients are brought into the station from online to form consumption for the shops.
Based on the same concept, the embodiment of the present disclosure further provides a potential customer excavating device, as shown in fig. 2, which is a schematic structural diagram of the potential customer excavating device provided in the embodiment of the present disclosure, where the device 200 mainly includes:
a first obtaining module 201, configured to determine one or more target shops, and obtain shop information corresponding to the target shops in one or more dimensions;
a second obtaining module 202, configured to obtain, according to the target shop, a user related to a service class of the target shop, and obtain user data of one or more dimensions corresponding to the user;
The computing and matching module 203 is configured to perform computing or matching operations on user data of one or more dimensions corresponding to each user and store information of one or more dimensions corresponding to a target store, respectively, by using a predetermined algorithm model, so as to obtain a matching degree score between the user and the target store;
a determining module 204, configured to determine potential customers of the target shop according to the matching score.
The embodiments of the present disclosure also provide an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a potential customer mining method as described above when executing the program.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, electronic devices, non-volatile computer storage medium embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to the description of the method embodiments.
The apparatus, the electronic device, the nonvolatile computer storage medium and the method provided in the embodiments of the present disclosure correspond to each other, and therefore, the apparatus, the electronic device, the nonvolatile computer storage medium also have similar beneficial technical effects as those of the corresponding method, and since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, the electronic device, the nonvolatile computer storage medium are not described here again.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing one or more embodiments of the present description.
It will be appreciated by those skilled in the art that the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present description is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments.
The foregoing description is by way of example only and is not intended as limiting the application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (15)
1. A method of potential customer mining, the method comprising:
Determining one or more target shops, and acquiring shop information of one or more dimensions corresponding to the target shops;
Acquiring a user related to a service class of the target shop according to the target shop, and acquiring user data of one or more dimensions corresponding to the user;
Respectively executing calculation or matching operation on user data of one or more dimensions corresponding to each user and store information of one or more dimensions corresponding to a target store by using a predetermined algorithm model to obtain a matching degree score between the user and the target store; the match score includes a first match score; the first matching degree score is calculated according to the distance between the high-frequency track position point and the position point of the target shop, the weight of the distance, the frequency of occurrence of the high-frequency track position point and the weight of the frequency of occurrence; the high-frequency track position points are track position points with the occurrence frequency larger than a preset frequency threshold value in track position points contained in the action track data of the user;
and determining potential clients of the target shops according to the matching degree scores.
2. The method of claim 1, the store information comprising at least one of the following types of information: geographic location information and service information.
3. The method of claim 2, the obtaining the store information for the one or more dimensions corresponding to the target store, comprising:
Collecting information corresponding to a position point of the target shop from navigation map data, wherein the information corresponding to the position point comprises the following dimensions: location point identification, shop name, positioning information and brand information; and/or the number of the groups of groups,
Acquiring and acquiring service information of the shops from the target shops, wherein the service information comprises the following dimension information: commodity information, price information, offer information, payment method information, user evaluation information, and service providing information.
4. The method of claim 1, wherein the obtaining, according to the target shop, the user related to the service class of the target shop, and obtaining the user data of one or more dimensions corresponding to the user, comprises:
Acquiring a user related to the service category from an application platform database according to the service category of the target shop, acquiring user data, and forming a user data set from the user and the user data;
wherein the user data comprises the following types of data: action trace data, service preference data, and transaction behavior data.
5. The method according to claim 1, wherein the predetermined algorithm model comprises one or a combination of several of the following factor matching algorithms, in particular:
the first factor matching degree algorithm is used for executing calculation operation according to the track position points contained in the action track data of the user and the positioning information corresponding to the position points of the target shop, and determining a first matching degree score according to a calculation result and a pre-configured weight;
The second factor matching degree algorithm is used for respectively matching one or more dimensionalities of data in the service preference data of the user with one or more dimensionalities of the service information of the target shop, and determining a second matching degree score according to a matching result;
And the third factor matching degree algorithm is used for respectively matching one or more dimensions of data in the transaction behavior data of the user with one or more dimensions of the service information of the target shop, and determining a third matching degree score according to a matching result.
6. The method of claim 5, wherein the first factor matching algorithm is specifically configured to perform the following operations:
According to the track position points contained in the action track data and a preset frequency threshold, taking the track position points with the occurrence frequency larger than the frequency threshold as high-frequency track position points; calculating the distance between the high-frequency track position point and the position point of the target shop by using a Cartesian product, respectively configuring weights for the frequency of occurrence of the high-frequency track position point and the distance, and calculating according to the frequency of occurrence, the distance and the weights to obtain a first matching degree score.
7. The method of claim 5, further comprising, prior to said deriving a match score between the user and the target store:
And respectively carrying out normalization processing on the first matching degree score, the second matching degree score and/or the third matching degree score, and then obtaining the matching degree score between the user and the target shop by using weighted average calculation.
8. The method of claim 1, the determining potential customers of the target shop based on the matching score comprising:
Sorting the matching degree scores of the users, dividing the matching degree scores into a plurality of score intervals according to a preset dividing mode, and taking the users corresponding to the matching degree scores in the score intervals meeting the conditions as potential clients of the target shop.
9. A potential customer mining apparatus, the apparatus comprising:
The first acquisition module is used for determining one or more target shops and acquiring shop information of one or more dimensions corresponding to the target shops;
the second acquisition module is used for acquiring a user related to the service class of the target shop according to the target shop and acquiring user data of one or more dimensions corresponding to the user;
The computing and matching module is used for respectively executing computing or matching operation on the user data of one or more dimensions corresponding to each user and the shop information of one or more dimensions corresponding to the target shop by utilizing a preset algorithm model to obtain a matching degree score between the user and the target shop; the match score includes a first match score; the first matching degree score is calculated according to the distance between the high-frequency track position point and the position point of the target shop, the weight of the distance, the frequency of occurrence of the high-frequency track position point and the weight of the frequency of occurrence; the high-frequency track position points are track position points with the occurrence frequency larger than a preset frequency threshold value in track position points contained in the action track data of the user;
And the determining module is used for determining potential clients of the target shops according to the matching degree scores.
10. The apparatus of claim 9, the first acquisition module further to:
Collecting information corresponding to a position point of the target shop from navigation map data, wherein the information corresponding to the position point comprises the following dimensions: location point identification, shop name, positioning information and brand information; and/or the number of the groups of groups,
Acquiring and acquiring service information of the shops from the target shops, wherein the service information comprises the following dimension information: commodity information, price information, offer information, payment method information, user evaluation information, and service providing information.
11. The apparatus of claim 9, the second acquisition module further to:
Acquiring a user related to the service category from an application platform database according to the service category of the target shop, acquiring user data, and forming a user data set from the user and the user data;
wherein the user data comprises the following types of data: action trace data, service preference data, and transaction behavior data.
12. The apparatus of claim 9, the computational matching module further configured to perform a computation or matching operation according to one or a combination of several of the following factor matching algorithms contained in the algorithm model, in particular:
the first factor matching degree algorithm is used for executing calculation operation according to the track position points contained in the action track data of the user and the positioning information corresponding to the position points of the target shop, and determining a first matching degree score according to a calculation result and a pre-configured weight;
The second factor matching degree algorithm is used for respectively matching one or more dimensionalities of data in the service preference data of the user with one or more dimensionalities of the service information of the target shop, and determining a second matching degree score according to a matching result;
And the third factor matching degree algorithm is used for respectively matching one or more dimensions of data in the transaction behavior data of the user with one or more dimensions of the service information of the target shop, and determining a third matching degree score according to a matching result.
13. The apparatus of claim 12, the computation matching module further to:
And before the matching degree score between the user and the target shop is obtained, respectively carrying out normalization processing on the first matching degree score, the second matching degree score and/or the third matching degree score, and then obtaining the matching degree score between the user and the target shop by using weighted average calculation.
14. The apparatus of claim 9, the determination module further to:
Sorting the matching degree scores of the users, dividing the matching degree scores into a plurality of score intervals according to a preset dividing mode, and taking the users corresponding to the matching degree scores in the score intervals meeting the conditions as potential clients of the target shop.
15. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 8 when the program is executed.
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| CN113254503B (en) * | 2021-06-08 | 2021-11-02 | 腾讯科技（深圳）有限公司 | Content mining method and device and related products |
| CN113723997A (en) * | 2021-08-23 | 2021-11-30 | 深圳集智数字科技有限公司 | Potential customer pushing method and device and electronic equipment |
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