WO2018223672A1 - 数据处理方法及装置 - Google Patents
数据处理方法及装置 Download PDFInfo
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- WO2018223672A1 WO2018223672A1 PCT/CN2017/119096 CN2017119096W WO2018223672A1 WO 2018223672 A1 WO2018223672 A1 WO 2018223672A1 CN 2017119096 W CN2017119096 W CN 2017119096W WO 2018223672 A1 WO2018223672 A1 WO 2018223672A1
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
Definitions
- the present disclosure relates to the field of Internet technologies, and in particular, to a data processing method and apparatus.
- Internet application is the main means to integrate offline business opportunities with the Internet.
- O2O Internet To Offline
- Internet applications such as online shopping, group purchase, and take-out are also developing. Especially for take-away applications that are essential for everyday life.
- These Internet applications have undergone a period of large-scale expansion, and are now gradually entering the stage of maintaining market stability and pursuing high-quality service quality.
- the inventor of the present disclosure provides a solution after paying a large amount of creative labor, the main principle of which is to provide a data collection script, and determine the data collection to be used based on the data collection task corresponding to the merchant to be processed.
- the script collects the associated data of the to-be-processed merchant, and then runs the data analysis script corresponding to the business requirement associated with the merchant to be processed, and performs grouping and aggregation processing on the associated data collected by the data collection script.
- the solution captures and analyzes merchant data efficiently and accurately, giving business demand data.
- an embodiment of the present disclosure provides a data processing method, including:
- the data collection script is executed to obtain the associated data of the to-be-processed merchant from the data source corresponding to the data collection script according to the data collection rule;
- the method before the data collection task corresponding to the to-be-processed merchant is analyzed to determine the data collection rule and the data collection script, the method further includes:
- a data collection rule is configured to match the data source address, and the data collection rule is configured to generate a data collection task corresponding to the to-be-processed merchant.
- the data collection script is executed to obtain the associated data of the to-be-processed merchant from the data source corresponding to the data collection script, including at least one of the following:
- a data collection script for the delivery agent client of the to-be-processed merchant to obtain feedback data of the delivery staff for the to-be-processed merchant from the delivery staff client;
- a data collection script of a client of the business personnel of the to-be-processed merchant to obtain, from the client of the businessperson, access data of the businessperson for the to-be-processed merchant.
- Business personnel for the pending merchant to obtain, from the client of the businessperson, access data of the businessperson for the to-be-processed merchant.
- the data analysis script corresponding to the service requirement associated with the to-be-processed merchant is executed, and the associated data is grouped and aggregated to obtain at least one dimension of service requirement data, where the method is Also includes:
- the data analysis script corresponding to the service requirement associated with the to-be-processed merchant is executed, and the associated data is grouped and aggregated to obtain service requirement data of at least one dimension, including:
- the at least one dimension of the service requirement data is obtained from the at least one set of aggregated data, including:
- At least one dimension of the investment demand data that can reflect the marketing strategy requirement of the to-be-processed merchant is obtained.
- the at least one dimension of the investment demand data that can reflect the marketing strategy requirement of the to-be-processed merchant is obtained from the at least one set of aggregated data, including:
- the at least one dimension of the investment demand data that can reflect the operating status of the to-be-processed merchant is obtained from the at least one set of aggregated data, including at least one of the following:
- the method further includes:
- the embodiment of the present disclosure further provides a data processing apparatus, including:
- the data parsing module is configured to parse the data collection task corresponding to the to-be-processed merchant to determine a data collection rule and a data collection script;
- the data acquisition module is configured to: run the data collection script to obtain the associated data of the to-be-processed merchant from the data source corresponding to the data collection script according to the data collection rule;
- the data processing module is configured to run a data analysis script corresponding to the service requirement associated with the to-be-processed merchant, and perform grouping and aggregation processing on the associated data to obtain service requirement data of at least one dimension.
- the device further includes:
- a task generation module configured to acquire a data source address associated with the to-be-processed merchant; and select, according to the data label and routing rule used by the data collection script, data from the plurality of data collection scripts to match the data source address
- the data collection script is configured to generate a data collection rule for the data collection script that matches the data source address to generate a data collection task corresponding to the to-be-processed merchant.
- the data acquiring module is specifically configured to perform at least one of the following operations:
- a data collection script for the delivery agent client of the to-be-processed merchant to obtain feedback data of the delivery staff for the to-be-processed merchant from the delivery staff client;
- a data collection script of a client of the business personnel of the to-be-processed merchant to obtain, from the client of the businessperson, access data of the businessperson for the to-be-processed merchant.
- the device further includes:
- a pre-processing module configured to perform de-dirty processing on the associated data according to a similarity between a data tag used by the data collection script data collected by the script name and a core word included in the associated data; and Or, de-duxing the associated data according to the similarity between the core words included in the associated data.
- the data processing module includes:
- a grouping sub-module configured to group the associated data to obtain at least one data packet based on an application platform to which the data belongs and/or a region to which the data belongs;
- An aggregation submodule configured to aggregate the at least one data packet according to the at least one dimension to obtain at least one set of aggregated data
- the obtaining submodule is configured to acquire the service demand data of the at least one dimension from the at least one set of aggregated data.
- the obtaining submodule includes:
- a first obtaining unit configured to acquire, from the at least one set of aggregated data, investment demand data of at least one dimension that can reflect an operation status of the to-be-processed merchant;
- the second obtaining unit is configured to obtain, from the at least one set of aggregated data, at least one dimension of the investment demand data that can reflect the marketing strategy requirement of the to-be-processed merchant.
- the second acquiring unit is specifically configured to:
- the first acquiring unit is specifically configured to perform at least one of the following operations:
- the data processing module is further configured to:
- Embodiments of the present disclosure also provide an electronic device including a memory and a processor; the memory for storing one or more computer instructions, wherein the one or more computer instructions are executable by the processor The steps in the data processing method provided by the method embodiments.
- the embodiment of the present disclosure further provides a computer readable storage medium storing a computer program, where the computer program is executed by a computer to implement the steps in the data processing method provided by the foregoing method embodiment.
- a data collection script is provided, and a data collection script and a data collection rule to be used are determined based on a data collection task corresponding to the merchant to be processed, and the data collection script determined by the data collection rule is used to collect the association of the to-be-processed merchant.
- Data and then run a data analysis script corresponding to the business requirements associated with the merchant to be processed, grouping and aggregating the associated data collected by the data collection script to obtain service demand data that meets the service requirements, and can obtain the data efficiently and accurately. And analyzing the business data, and then giving the business demand data, is conducive to providing data support for various business related businesses.
- FIG. 1 is a schematic diagram of a service logic of a data processing apparatus according to an embodiment of the present disclosure
- FIG. 1b is a schematic flowchart of a data processing method according to an embodiment of the present disclosure
- FIG. 2 is a schematic flowchart diagram of a data processing method according to another embodiment of the present disclosure.
- FIG. 3 is a schematic flowchart of grouping and aggregating processing of associated data according to another embodiment of the present disclosure
- FIG. 4a is a schematic diagram of a style of a visualization chart according to another embodiment of the present disclosure.
- 4b is another schematic diagram of a visual chart provided by another embodiment of the present disclosure.
- FIG. 5 is a schematic structural diagram of a data processing apparatus according to another embodiment of the present disclosure.
- FIG. 6 is a schematic structural diagram of a data processing apparatus according to another embodiment of the present disclosure.
- the embodiments of the present disclosure provide a universal data processing solution, the main principle of which is to provide a data collection script, and determine a data collection script and a data collection rule to be used based on a data collection task corresponding to a merchant to be processed.
- the data collection script determined by the data collection rule is used to collect the associated data of the to-be-processed merchant, and then run the data analysis script corresponding to the business requirement associated with the business to be processed, and group and aggregate the associated data collected by the data collection script. , get business demand data that meets the business needs.
- FIG. 1 is a schematic diagram of a service logic of a data processing apparatus according to an embodiment of the present disclosure.
- the scheduling in Figure 1a is optional business logic.
- FIG. 1 is a schematic flowchart of a data processing method according to an embodiment of the present disclosure. As shown in FIG. 1b, the method includes:
- the pending merchant refers to a merchant associated with a certain business requirement.
- the pending merchant can be either a merchant or multiple merchants.
- the pending merchants can be different depending on the specific application scenario.
- the pending merchant may be a merchant that the Internet application wishes to attract, or a merchant that has an attractive value for Internet applications.
- the Internet application can be an online shopping application, a purchasing application, and/or a takeaway application.
- the person who can attract the merchant for the Internet application may be a salesperson of the Internet application, a BD person, or a marketer, but is not limited thereto.
- the business requirement data required to implement the business requirement is obtained according to the associated data of the business to be processed, that is, the data needs to be provided for the business requirements associated with the business to be processed. stand by.
- a data collection script and a data analysis script are provided, and the data collection script is responsible for collecting data, and the data analysis script is responsible for analyzing data collected by the data collection script to obtain business demand data.
- the application scenarios are different, the merchants to be processed will be different, and the data collection scripts to be used will be different.
- this embodiment allows data collection tasks to be established for the merchants to be processed, and data collection rules and data collection scripts required for collecting data are defined in the data collection tasks.
- the data collection script can be uniquely identified by the script name. Based on this, after determining the merchant to be processed, a data collection task can be established for the merchant to be processed.
- the data collection task may be automatically determined by the device for the to-be-processed merchant, or may be manually set by the service personnel corresponding to the to-be-processed merchant, such as a BD personnel, for the to-be-processed merchant.
- the data processing device can obtain the data collection task of the merchant to be processed.
- the data processing device may receive the data collection task of the to-be-processed merchant manually input by the service personnel; or may receive the data collection task of the to-be-processed merchant transmitted by the device that generates the data collection task by wire or wireless communication.
- the data collection task of the merchant to be processed can be parsed to determine data collection rules and data collection scripts.
- different pending merchants generally have different data collection rules.
- the data collection rules mainly define some control strategies for collecting data, such as the collection period, the collection time, the amount of data to be collected, and the length of time each time it needs to be collected.
- the data collection script can be uniquely identified by the script name.
- the data structure used by the data collection task can be preset. Based on this, the data processing device can parse the data collection task according to a preset data structure.
- the data collection task may use an existing data structure or a customized data structure.
- the existing data structure may be a JSON (JavaScript Object Natation) data structure, but is not limited thereto.
- the JSON-based data collection task can be expressed as ⁇ "script name": xxx, "data collection rule”: xxx ⁇ .
- the JSON data structure includes two name fields, which are a script name field and a data collection rule field; Correspondingly, "xxx" after each name field indicates the value of the corresponding name field.
- a custom data structure can be ⁇ the value of the script name (in m characters): the value of the data collection rule (p characters) ⁇ , and the data structure includes two fields, respectively a script The name field and the data collection rule field. The two fields are distinguished by the number of characters occupied.
- the first m characters in the data structure are the values of the script name field, and the last p characters are the values of the data collection rule field, m, p. Both are natural numbers.
- the data collection rule corresponding to the parsed script name may be run according to the parsed data collection rule, so as to obtain the association of the to-be-processed merchant from the data source corresponding to the data collection script.
- the data collection script has a corresponding relationship with the data source, and the data source can be uniquely identified by the data source address, and the data source address can be an IP address or a URL address.
- the data collection script automatically obtains the associated data of the pending merchant from the data source identified by the data source address.
- the associated data here can be any data related to the merchant to be processed.
- the associated data of the to-be-processed merchant may be the operational data of the business to be processed, the user evaluation, the operational data of the merchant, and the like.
- a merchant refers to a merchant that has a competitive relationship with a merchant to be processed.
- the data collection task of the to-be-processed merchant may include a data collection rule, a script name, and a data source address.
- the correspondence may be one or more groups.
- the data collection rules, script names, and data source addresses in each set of correspondences are not identical.
- the data collection rule corresponding to the script name may be run according to the data collection rule in the corresponding relationship of the group, to obtain the associated data of the to-be-processed merchant from the data source corresponding to the corresponding data source address.
- data obtained from data sources corresponding to different data source addresses is generally not identical.
- the data collection task in this embodiment may also adopt a JSON data structure or a custom data structure.
- a data collection task using JSON as an example can be expressed as ⁇ "script name": xxx, "data source address”: xxx, "data collection rule”: xxx ⁇ , the JSON data structure includes a total of three name fields, respectively It is a script name field, a data source address field, and a data collection rule field; correspondingly, "xxx" after each name field indicates the value of the corresponding name field.
- a custom data structure can be ⁇ the value of the script name (m characters): the value of the data source address (n characters): the value of the data collection rule (p characters) ⁇ , in the data structure
- the field includes three fields, a script name field, a data source address field, and a data collection rule field. The three fields are distinguished by the number of characters occupied.
- the first m characters in the data structure are the values of the script name field.
- the n characters are the values of the data source address field, and the last p characters are the values of the data collection rule field, and m, n, and p are natural numbers.
- the data processing device can parse the data collection task corresponding to the merchant to be processed, thereby determining the data collection rule and the data collection script, and then running the corresponding data collection script according to the data collection rule, and automatically acquiring the pending data from the corresponding data source.
- the associated data of the merchant does not require the service personnel to manually collect the associated data of the merchant to be processed.
- the efficiency of obtaining the associated data of the merchant to be processed can be ensured, and on the other hand, the associated data of the merchant to be processed can be obtained more comprehensively.
- step 103 based on the associated data of the to-be-processed merchant acquired in step 102, the data analysis script corresponding to the business requirement associated with the merchant to be processed may be run, and the associated data collected by the data collection script may be further collected by the data analysis script. Perform grouping and aggregation processing to obtain business demand data of at least one dimension.
- the service requirements may be determined by the merchants to be processed and the application scenarios.
- the data processing cores corresponding to different service requirements are the same, mainly data packets and aggregations, but corresponding to different packet and aggregation details, so different data analysis scripts are corresponding.
- the business requirements associated with the business to be processed may be preset by the business personnel, and the data analysis script may be developed in advance for the business requirements. Among them, the business personnel can carry out the business that meets the business requirements based on the business demand data, which provides data support for the business personnel to carry out the corresponding business.
- the data processing device can parse the data collection task corresponding to the merchant to be processed, thereby determining the data collection rule and the data collection script, and then running the corresponding data collection script according to the data collection rule, and automatically acquiring the pending data from the corresponding data source.
- the associated data of the merchant further runs the data analysis script to group and aggregate the associated data collected by the data collection script, which can efficiently and accurately acquire and analyze the merchant data, thereby giving the business demand data, which is beneficial to various types of Business related services provide data support.
- the data collection script and the data analysis script can be adaptively set according to different service requirements, different merchants, and different application scenarios, and are easily implemented based on mature script technology, which is equivalent to providing a universal Appropriate access to and analysis of merchant data schemes, therefore, should be configured to provide data support for various business-related businesses in various scenarios.
- the method provided in this embodiment may be configured to attract a merchant scenario
- the to-be-processed merchant refers to a merchant having a merchant investment value
- the business requirement associated with the to-be-processed merchant is to attract the demand of the merchant to be processed
- at least The business demand data of one dimension is actually the investment demand data of at least one dimension.
- the investment demand data here refers to the data that the BD personnel need in the process of recruiting the merchants to be processed, and has the value of attracting guidance to the BD personnel.
- the investment demand data may be data indicating the operation status of the merchant to be processed, or the comparison data between the merchant to be processed and the merchant, or the marketing strategy data given to the merchant to be processed.
- the investment demand data may be one dimension or multiple dimensions.
- the investment demand data is multi-dimensional, so that the BD personnel can understand the pending merchants from multiple dimensions, thereby occupying an advantage in the attracting process and improving the success rate of attracting merchants.
- the data processing device not only has the functions of data parsing, data acquisition, and data processing, but also provides an access portal to facilitate the service personnel to access the required service through the access portal.
- Demand data For the business personnel, an access request is provided to the data processing device through the access portal provided by the data processing device to request the required business demand data.
- the data processing device may determine, according to the access request of the businessperson, the business requirement data requested by the businessperson from the business requirement data of the at least one dimension; generate a visualization chart according to the business requirement data requested by the businessperson, and display the visualization chart to Business people.
- the business person can be a BD person; correspondingly, the business demand data can be investment demand data.
- the data processing device may display, according to the access request of the BD personnel, a pie chart corresponding to the 30-day flow of the to-be-processed merchant, a weekly cycle ratio data comparison table, a regional hot spot map, and a flow order amount graph.
- the pending merchants can be recruited accordingly.
- the BD personnel recruiting the pending merchants mainly refers to the process of negotiating with the merchants to be processed according to the investment demand data, analyzing the operation status of the merchants to be processed, and giving targeted marketing strategies and suggestions.
- the data processing device runs the corresponding data collection script to automatically obtain the associated data of the to-be-processed merchant.
- the data analysis script corresponding to the investment demand is used to group and aggregate the associated data to obtain the investment demand data of at least one dimension, which can be efficiently and accurately. Acquire and analyze business data to provide data support for BD personnel to attract merchants.
- the BD personnel need not manually collect the associated data of the to-be-processed merchants, which not only saves time, ensures the efficiency of acquiring the associated data of the merchants to be processed, improves the efficiency of attracting merchants, but also more comprehensively acquires the associated data of the merchants to be processed, so that BD personnel are able to take advantage of the process of negotiating with merchants and increase the success rate of attracting merchants.
- a manner of generating a data collection task corresponding to the to-be-processed merchant includes:
- the data source address may include, but is not limited to, the address of the operating website or client of the to-be-processed merchant, the address of the operating website or client of the business to be processed, the address of the selling outlet of the pending merchant, and the like.
- the data label refers to the core word or keyword or search term used in the process of data capture by the data collection script. For example, it can be “30 days of running water”, “dish name”, “dish price” and the like.
- the routing rule mainly defines the data access path in the process of data capture script data capture. Generally, the routing rules supported by different data sources may be different, and the data provided may be different. Therefore, data matching the data source identified by the data source address associated with the business to be processed may be selected. Capture scripts.
- the data collection rule is configured to generate a data collection task corresponding to the merchant to be processed.
- the data collection rule may be: obtaining data from a data source corresponding to the data source address every day, or acquiring data from a data source corresponding to the data source address every specified duration, or continuously obtaining data from a data source corresponding to the data source address, or according to
- the scheduling instruction acquires data from a data source corresponding to the data source address at the scheduling time, and the like.
- the data collection script may be run according to the data collection rule to obtain the associated data of the to-be-processed merchant from the data source corresponding to the data collection script.
- the data collection scripts that are run may be different, and the associated data of the acquired merchants may be different.
- the business personnel can visit the physical stores of the pending merchants to understand the basic information of the pending merchants, such as preferential activities, logistics configuration, order flow, main business scope, and actual business conditions. After that, the business person can upload the access data of the merchant to be processed to its client.
- the data processing device can provide a data collection script for the client of the business person, and the data label used by the data collection script to collect data and the routing rules are all adapted to the client of the business personnel.
- the data tag can be data related to promotions, logistics configurations, order flow, main business scope, and the like.
- the routing rule may be accessing a data source address through a gateway of Netcom or China Unicom.
- the data collection script of the client facing the business person of the business to be processed may be run according to the data collection rule to The client's client obtains access data of the business personnel for the pending merchant.
- the client for the business person may be a mobile office system that accesses the data processing device, but is not limited thereto.
- the delivery staff is an intermediary between the user and the merchant.
- the distributor can reach both the user and the merchant, so the distributor can get some information from the merchant.
- the delivery staff can feed back information about the merchant's meal speed, service attitude, meal packaging, logistics and solutions.
- the distributor can then upload feedback data for the merchant to their client.
- the data processing apparatus can provide a data collection script for the client of the dispatcher, and the data label used by the data collection script to collect data and the routing rules are all adapted to the client of the dispatcher.
- the data tag may be data related to meal speed, service attitude, meal packaging, logistics laying, and the like.
- the routing rule may be to access the data source address through the gateway of the telecommunications or China Unicom.
- the script name parsed from the data collection task of the pending merchant identifies the data collection script for the client of the dispatcher
- the data collection script of the client of the dispatcher facing the merchant to be processed may be run according to the data collection rule to The delivery staff's client obtains feedback data from the distributor for the pending merchant.
- the client facing the dispatcher may also be a mobile office system that accesses the data processing device, but is not limited thereto.
- the data processing apparatus may provide a data collection script for the website operated by the merchant to be processed, or may provide a data collection script for the business website of the merchant to be processed, and the data collection script collects data labels used by the data and
- the routing rules are respectively adapted to the business site to be processed or to the operating website of the merchant.
- the data tag may be data related to the price of the dish, the offer, the sales volume, the location of the merchant, the business district to which it belongs, and the like.
- the data collection script for the operating website of the to-be-processed merchant may be run according to the data collection rule to The operational data of the pending merchant is obtained on the operation website of the pending merchant.
- the operator may operate the business to be processed according to the data collection rule.
- the data collection script of the website obtains the operation data of the to-be-processed merchant from the operating website of the merchant to be processed.
- the data collection script for the pending merchant or its operating website of the merchant can be implemented as a web crawler, and various crawling algorithms in the prior art can be used to crawl data from the corresponding website. This is not detailed.
- the data processing method provided by another embodiment of the present disclosure further includes:
- the associated data may include any data related to the merchant to be processed, some of which may be worthless, some may be irregular or illegal data, and these data are collectively referred to as dirty data. Because there is dirty data, it may affect the entire data processing process. Therefore, the collected related data may be decontaminated in advance.
- the de-dirty processing here refers to the process of removing dirty data in the associated data.
- the associated data may be de-stained according to the similarity between the data tag used by the data collection script corresponding to the script name and the core word included in the associated data.
- the core word of each associated data may be extracted, and the similarity between the core word and the data tag is calculated, and the associated data whose similarity does not meet the first requirement is removed from the associated data.
- the association data with the similarity between the data tags and less than the set threshold may be removed, or the association data with the similarity between the data tags not within the set similarity range may be removed.
- the de-duplication processing of the associated data is beneficial to save processing. Repeat the resources consumed by the data to improve the efficiency of subsequent processing.
- the de-reprocessing process here mainly refers to the process of ensuring that there is no duplicate data in the associated data through the deletion operation.
- the associated data may be de-duplicated according to the similarity between the core words included in the associated data. For example, a core word of each associated data may be extracted; a similarity between core words of each two associated data is calculated, and one of the two associated data whose similarity meets the second requirement is retained. For example, for two associated data whose similarity is greater than the set threshold, the similarity is high, and one piece of data is retained. For example, you can randomly choose to keep one and discard the other. Alternatively, the latest one may be retained in the order of the acquisition time, that is, one of the later acquisition times, thereby discarding the one with the earlier acquisition time.
- the data analysis script corresponding to the service requirement associated with the merchant to be processed may be run, and the associated data is grouped and aggregated to obtain service demand data of at least one dimension.
- a manner of data packet and aggregation processing includes:
- steps 1051-1053 describe an embodiment in which the data processing device runs a data analysis script to group and aggregate the associated data.
- the data processing device may group the associated data of the processing merchant according to the application requirement, and then aggregate the obtained data packets according to the application requirements, and then acquire the service demand data based on the aggregated data.
- the grouping method will be different depending on the application scenario.
- the associated data may be grouped based on an application platform to which the data belongs to obtain at least one data packet.
- the application platform to which the associated data of the to-be-processed business belongs may be counted, and the data belonging to the same application platform may be divided into one group, thereby obtaining at least one data packet.
- the acquired associated data of the merchant to be processed includes the related business to be processed obtained from the A take-out platform.
- the associated data of the merchant to be processed may be divided into three data packets according to the application platform, and the data obtained from the A take-out platform is classified into one data packet, and The data obtained by the B take-out platform is divided into one data group, and the data obtained from the C take-out platform is classified into one data group.
- the associated data may be grouped based on the region to which the data belongs to obtain at least one data packet. For example, the area to which the associated data of the to-be-processed business belongs may be counted, and the data belonging to the same area may be divided into one group, thereby obtaining at least one data packet.
- the acquired associated data of the merchants to be processed includes the acquired and the merchants to be processed from the regions in the province. related data.
- the associated data of the to-be-processed merchant may be divided into multiple data packets according to the region. For example, data from the same city in the province may be classified into one data packet. Or, data from the same business district in the same city under the province can be classified into one data group.
- the packet granularity may be determined according to a specific application scenario, and is merely an example and is not limited herein.
- the associated data may be grouped based on the application platform to which the data belongs and the region to which the data belongs to obtain at least one data packet. This can be combined with the application platform and the geographic area to provide business demand data.
- the associated data of the processing merchants may be initially grouped based on the application platform to which the data belongs; and the preliminary grouping results are secondarily grouped according to the region to which the data belongs to obtain at least one data packet.
- the associated data of the processing merchants may be initially grouped based on the region to which the data belongs; and the preliminary grouping results may be secondarily grouped according to the application platform to which the data belongs to obtain at least one data packet.
- the aggregation mode between data packets may be specifically determined by application requirements. For example, geographically adjacent data packets can be aggregated together, or data packets from similar application platforms can be aggregated together, and so on.
- the amount of data of the associated data may be large, and may be disordered.
- the associated data is relatively regular, so that the associated data can be extracted more conveniently and efficiently.
- Business demand data may be used to grouping and aggregating the associated data.
- At least one dimension of the business requirement data may be obtained from the at least one set of aggregated data.
- the dimension of the business demand data here has nothing to do with the number of groups of aggregated data.
- the business requirement data may be separately obtained from each group of aggregated data, and the business requirement data obtained from each set of aggregated data may be a certain dimension or a multi-dimensional.
- the plurality of sets of aggregated data may be comprehensively analyzed to obtain business demand data, and the business demand data obtained by comprehensively analyzing the plurality of sets of aggregated data may be a certain dimension or a multi-dimensional.
- the business demand data may be investment demand data.
- the investment demand data can be divided into two categories, one is the investment demand data reflecting the operation status of the business to be processed, and for the BD personnel, the investment demand data is descriptive for the operation status of the processing merchant. It can be referred to as descriptive investment demand data; the other is the investment demand data reflecting the marketing strategy needs of the merchants to be processed. For BD personnel, such investment demand data can give the marketing strategy required by the merchants to be processed. Referred to as strategic investment demand data.
- the manner in which the service requirement data is obtained from the at least one set of aggregated data may include:
- At least one dimension of investment demand data that can reflect the marketing strategy requirements of the merchant to be processed is obtained.
- each of the above types of investment demand data can be of a certain dimension or multiple dimensions.
- the marketing strategy requirements of the pending merchants can be reflected from at least one of the following dimensions: the business dimension, the user group dimension, the geographic area dimension, and the distributor dimension.
- the methods for obtaining strategic investment demand data include:
- a marketing strategy for the dispatcher is generated based on the dispatcher data of the to-be-processed merchant in the at least one set of aggregated data.
- the marketing data of the merchant may include, but is not limited to, the order of the merchant, the delivery method of the merchant, the online duration of the merchant, the processing time of the merchant, and the marketing of the merchant. At least one of a strategy and the like.
- a marketing strategy that is beneficial to the merchant to be processed can be generated. For example, if the marketing strategy for the merchant is 30 minus 5 yuan, the marketing strategy of the pending merchant may be 30 minus 8 yuan.
- the online time of the merchant is 12 hours
- the online duration of the pending merchant may be 24 hours.
- the foregoing user group data may include, but is not limited to, at least one of a surrounding residence of a user group, an office building, a school, a merchant coverage, a population, and the like.
- a marketing strategy that is beneficial to the merchant to be processed can be generated. For example, if there are more office buildings around the user group, indicating that the user group belongs to the office worker and has certain economic ability, it can generate a marketing strategy with relatively small reduction and allow for predetermined sales to increase revenue. For example, if the coverage of the merchants around the user group is large, a marketing strategy such as a full or full reduction or a free delivery fee may be generated to enhance the competitiveness of the merchant to be processed.
- the order distribution data may include, but is not limited to, at least one of an order quantity per day in the order distribution area, an order quantity in a peak period in the order distribution area, and an average monthly order quantity in the order distribution area.
- the geographical area with dense orders and sparse orders can generate a predetermined marketing strategy for the geographically dense orders, in order to spread the order, and the geographically sparse orders. Areas can generate full-decrease, full-fledged marketing strategies to increase the volume of orders in these geographic regions.
- the operation status of the pending merchants can be reflected from at least one of the following dimensions: the order quantity of the flow, the order quantity of the day, the order completion status, the subsidy situation, the order-intensive area, the ring ratio data, and the sales ranking. Wait.
- the manner of obtaining descriptive investment demand data includes at least one of the following:
- the data processing device may obtain the investment demand data from the aggregated data according to the platform identifier of the to-be-processed merchant, the unique code of the to-be-processed merchant, the order ID, and the business circle identifier of the order. For example, according to the platform identifier of the business to be processed, the unique code of the business to be processed, and the order ID, the order and order status of the pending merchant are identified from the aggregated data, and then the flow order quantity and/or order of the pending merchant is counted. Completion.
- the BD personnel may issue an access request to the data processing device when the demand data is required according to actual needs.
- the investment demand data requested by the BD personnel can be provided to the BD personnel in response to the access request of the business personnel, so that the BD personnel can recruit the pending merchant based on the investment demand data.
- the data processing device may generate a visualization chart according to the invitation demand data requested by the BD personnel in response to the access request of the business personnel; and display the visualization chart to the BD personnel for the BD personnel to recruit the pending merchant.
- Such investment demand data is more intuitive, and BD personnel are more convenient to use, which is conducive to improving the success rate of attracting merchants.
- the implementation style of the above visualization chart may be determined by the investment demand data.
- one visualization chart is a pie chart, as shown in Figure 4b, and another visualization chart is a data table.
- the visualization chart can also be a combination of a data map and a data table. It is worth noting that Figures 4a and 4b focus on the pattern of data and data tables, where the data values are not of interest.
- the investment demand data can also be directly provided to the BD personnel.
- Example 1 In the process of recruiting a pending merchant, the BD personnel need to know the comparison of the 30-day running data of all the merchants in the area where the pending merchant and the pending merchant are located, and the comparison of the 30-day running data between the pending merchant and the competing merchant. The situation, as well as the trend of historical data of the pending merchants, so that BD personnel can take advantage of providing marketing strategies to the pending merchants.
- the timed scheduling data collection script obtains the data of the merchants to be processed, all the merchants in the area where the merchants are to be processed, and the merchants to be processed.
- the acquired data is subjected to deduplication and/or decontamination processing.
- the corresponding data analysis script is run to group the data after deduplication and/or decontamination processing.
- the data is grouped according to the merchant, and the data of each merchant is grouped into one group.
- the data of the merchants to be processed can be grouped into one group, and the data of the merchants is divided into one group, and other merchants are The data is also divided into a group.
- the investment demand data is obtained.
- the data of the merchants to be processed, the data of the merchants, and the data of other merchants are separately screened out for 30 days of flow data, and then 30 days of the merchants to be processed, the merchants and other merchants are drawn. Comparison table and/or comparison chart of running water data.
- the historical flow data of the merchant to be processed is counted, the trend of the historical flow data is obtained, and the trend chart is drawn.
- the BD personnel need to use the comparison of the 30-day running water data in the process of recruiting the pending merchants, but it is not limited thereto.
- the merchant's current day sales volume, order completion status, merchant subsidy rate, and week can also be used.
- Data such as ring ratio and regional heat map.
- Example 2 In the process of recruiting the pending merchants, the BD personnel need to know the comparison between the order transaction volume of the merchants and the full-decrease activities of the merchants, the order transaction volume of the holiday activities, and the data of the expired red packets. In this way, BD personnel can take advantage of subsidies for the merchants to be processed.
- the timed scheduling data collection script obtains the data of the merchant to be processed and the merchant to be processed.
- the acquired data is subjected to deduplication and/or decontamination processing.
- the scheduling data analysis script groups the data after deduplication and/or decontamination processing.
- the data after deduplication and/or decontamination is grouped, for example, the data of the merchant to be processed is divided into one group, and the data of the merchant is divided into one.
- the processing of the merchant and the data of the merchant are further grouped, and the data related to the same activity is divided into one group; then, according to the time, the data in each data group is counted, and each is obtained.
- the transaction volume corresponding to the activity type then draw a comparison chart and/or comparison table of the order transaction volume between the pending merchant and the merchant under different activity types.
- the BD personnel collect the above data by themselves, it is necessary to collect the 30-day running data or the order transaction volume of a large number of merchants, and then perform a data comparison table. Faced with large (eg, millions of) merchant data, BD personnel spend a significant amount of time collecting and producing the data they need.
- the BD personnel can obtain the required data from the data processing device only by initiating the access request, and the data in the form of an icon is seen, which is more intuitive and can greatly increase the process of the BD personnel in attracting the merchant. The advantage.
- the execution bodies of the steps of the method provided by the foregoing embodiments may all be the same device, or the method may also be performed by different devices.
- the execution body of steps 101 to 105 may be device A; for example, the execution body of steps 101 and 103 may be device A, the execution body of step 105 may be device B, and the like.
- FIG. 5 is a schematic structural diagram of a data processing apparatus according to still another embodiment of the present disclosure. As shown in FIG. 5, the apparatus includes: a data parsing module 51, a data acquiring module 52, and a data processing module 53.
- the data parsing module 51 is configured to parse the data collection task corresponding to the to-be-processed merchant to determine a data collection rule and a data collection script.
- the data obtaining module 52 is configured to run the data collection script according to the data collection rule to obtain the associated data of the to-be-processed merchant from the data source corresponding to the data collection script.
- the data processing module 53 is configured to run a data analysis script corresponding to the service requirement associated with the merchant to be processed, and group and aggregate the associated data to obtain service demand data of at least one dimension.
- the apparatus further includes: a task generation module 54.
- the task generation module 54 is configured to acquire a data source address associated with the to-be-processed merchant before the data parsing module 51 parses the data collection task, and collect data labels and routing rules used by the data collection script from the plurality of data collection scripts.
- the data collection script matching the data source address is selected; the data collection rule is configured to match the data source address, and the data collection rule is configured to generate a data collection task corresponding to the to-be-processed merchant.
- the data acquisition module 52 is specifically configured to perform at least one of the following operations:
- the data collection script for the merchant to be processed is executed to obtain the operation data of the merchant from the operation website of the merchant;
- the data collection script of the client of the business personnel of the business to be processed is run to obtain the access data of the business personnel for the business to be processed from the client of the business personnel.
- the apparatus further includes: a pre-processing module 55.
- the pre-processing module 55 is configured to: before the data processing module 53 operates the data analysis script to group and aggregate the associated data, the data tag used by the data collection script corresponding to the script name is collected between the data tag and the core word included in the associated data. Similarity, de-graining the associated data; and/or de-duplicating the associated data according to the similarity between the core words included in the associated data.
- an implementation structure of the data processing module 53 includes a packet submodule 531, an aggregation submodule 532, and an acquisition submodule 533.
- the grouping sub-module 531 is configured to group the associated data based on the application platform to which the data belongs and/or the region to which the data belongs to obtain at least one data packet.
- the aggregation sub-module 532 is configured to aggregate at least one data packet according to at least one dimension to obtain at least one set of aggregated data.
- the obtaining submodule 533 is configured to obtain at least one dimension of the business requirement data from the at least one set of aggregated data.
- an implementation structure of the obtaining submodule 533 includes at least one acquiring unit: a first acquiring unit and a second acquiring unit.
- the first obtaining unit is configured to acquire, from the at least one set of aggregated data, at least one dimension of the investment demand data that can reflect the operating status of the to-be-processed merchant.
- the second obtaining unit is configured to acquire, from the at least one set of aggregated data, the investment demand data of the at least one dimension that can reflect the marketing strategy requirement of the to-be-processed merchant.
- the second obtaining unit is specifically configured to:
- a marketing strategy for the dispatcher is generated based on the dispatcher data of the merchant to be processed in the at least one set of aggregated data.
- the first obtaining unit is specifically configured to perform at least one of the following operations:
- the data processing module 53 is further configured to: determine, according to the access request of the service personnel, the service requirement data requested by the service personnel from the service requirement data of the at least one dimension; A visual chart is generated according to the business demand data requested by the business personnel; the visual chart is displayed to the business personnel for display to the business personnel.
- the apparatus further includes: a data storage module 56.
- the data storage module 56 is configured to store the association data acquired by the data acquisition module 52 and the service requirement data obtained by the data processing module 53.
- the data processing apparatus provided in this embodiment may be configured to perform the flow of the data processing method provided by the foregoing method embodiments.
- the specific working principles and implementation details are not described herein. For details, refer to the description in the foregoing method embodiments.
- the data processing device provides a data collection script, and determines a data collection script and a data collection rule to be used according to the data collection task corresponding to the merchant to be processed, and collects the data collection script determined according to the data collection rule to collect the to-be-processed merchant. Correlation data, and then run a data analysis script corresponding to the business requirements associated with the business to be processed, grouping and aggregating the associated data collected by the data collection script to obtain business demand data that meets the business requirements, and is efficient and accurate.
- the ability to capture and analyze business data can provide data support for a variety of business-related businesses.
- the data processing apparatus provided by the embodiment of the present disclosure is applied to the attracting merchant scenario, and the BD personnel can be used to obtain the associated data of the to-be-processed merchant, and then the associated data is grouped and aggregated, thereby obtaining the investment requirement of at least one dimension.
- Data with the effect of efficiently and accurately acquiring and analyzing merchant data, can provide data support for BD personnel when recruiting merchants, which is conducive to improving the success rate and efficiency of attracting merchants.
- embodiments of the present disclosure can be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
- computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
- the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
- the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
- These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
- the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
- a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
- processors CPUs
- input/output interfaces network interfaces
- memory volatile and non-volatile memory
- the memory may include non-persistent memory, random access memory (RAM), and/or non-volatile memory in a computer readable medium, such as read only memory (ROM) or flash memory.
- RAM random access memory
- ROM read only memory
- Memory is an example of a computer readable medium.
- Computer readable media includes both permanent and non-persistent, removable and non-removable media.
- Information storage can be implemented by any method or technology.
- the information can be computer readable instructions, data structures, modules of programs, or other data.
- Examples of computer storage media 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 disk read only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape storage or other magnetic storage devices or any other non-transportable media can be used to store information that can be accessed by a computing device.
- computer readable media does not include temporary storage of computer readable media, such as modulated data signals and carrier waves.
- embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or a combination of software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
- computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
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Abstract
本公开实施例提供一种数据处理方法及装置。数据处理方法包括:解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本;根据数据采集规则,运行数据采集脚本,以从数据采集脚本对应的数据源中获取待处理商户的关联数据;运行与待处理商户关联的业务需求对应的数据分析脚本,对关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。本公开实施例提供一种具有普适性的数据处理方案,可以高效、准确地获取和分析商户数据,为各类与商户相关的业务提供数据支持。
Description
本公开涉及互联网技术领域,尤其涉及一种数据处理方法及装置。
互联网应用是将线下商务机会与互联网进行融合的主要手段,随着互联网线上到线下(Online To Offline,O2O)技术的快速发展,像网购、团购、外卖等互联网应用也在不断发展,特别是对于日常生活必不可少的外卖类应用。这些互联网应用经历了大规模扩张阶段,现在逐渐进入维持市场稳定,追求高品质服务质量的阶段。
对上述互联网应用来说,除了要吸引消费用户之外,还需要招引商户,为消费用户提供商品或服务。目前,商户的招引主要依赖于互联网应用的业务拓展(Business Development,BD)人员,亟需一种高效、精准的商户数据获取和分析方案。
发明内容
针对现有技术存在的问题,本公开发明人在付出大量创造性劳动之后,提供一种解决方案,其主要原理是:提供数据采集脚本,基于待处理商户对应的数据采集任务确定需要使用的数据采集脚本,运行所确定的数据采集脚本采集待处理商户的关联数据,进而运行与待处理商户关联的业务需求对应的数据分析脚本,对数据采集脚本采集到的关联数据进行分组和聚合处理,得到满足所述业务需求的业务需求数据。该解决方案可以高效、精准地获取和分析商户数据,进而给出业务需求数据。
基于上述分析,本公开实施例提供一种数据处理方法,包括:
解析待处理商户对应的数据采集任务,以确定数据采集规则和数据 采集脚本;
根据所述数据采集规则,运行所述数据采集脚本,以从所述数据采集脚本对应的数据源中获取所述待处理商户的关联数据;
运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。
在一可选实施方式中,解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本之前,所述方法还包括:
获取与所述待处理商户关联的数据源地址;
根据数据采集脚本采集数据使用的数据标签和路由规则,从多个数据采集脚本中,选择与所述数据源地址匹配的数据采集脚本;
为与所述数据源地址匹配的数据采集脚本,配置数据采集规则,以生成所述待处理商户对应的数据采集任务。
在一可选实施方式中,根据所述数据采集规则,运行所述数据采集脚本,以从所述数据采集脚本对应的数据源中获取所述待处理商户的关联数据,包括以下至少一种:
根据所述数据采集规则,运行面向所述待处理商户的数据采集脚本,以从所述待处理商户的运营网站上获取所述待处理商户的运营数据;
根据所述数据采集规则,运行面向所述待处理商户的竟对商户的数据采集脚本,以从所述竟对商户的运营网站上获取所述竟对商户的运营数据;
根据所述数据采集规则,运行面向所述待处理商户的配送员客户端的数据采集脚本,以从所述配送员客户端中获取配送员针对所述待处理商户的反馈数据;
根据所述数据采集规则,运行面向所述待处理商户的业务人员的客户端的数据采集脚本,以从所述业务人员的客户端中获取业务人员针对所述待处理商户的访问数据。业务人员针对所述待处理商户
在一可选实施方式中,运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据之前,所述方法还包括:
根据所述脚本名称对应的数据采集脚本采集数据使用的数据标签和所述关联数据包含的核心词之间的相似度,对所述关联数据进行去脏处理;和/或
根据所述关联数据包含的核心词之间的相似度,对所述关联数据进行去重处理。
在一可选实施方式中,运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据,包括:
基于数据所属的应用平台和/或数据所属的地域,对所述关联数据进行分组,以获得至少一个数据分组;
按照所述至少一个维度,对所述至少一个数据分组进行聚合,以获得至少一组聚合数据;
从所述至少一组聚合数据中,获取所述至少一个维度的业务需求数据。
在一可选实施方式中,从所述至少一组聚合数据中,获取所述至少一个维度的业务需求数据,包括:
从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的运营状况的招商需求数据;和/或
从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的营销策略需求的招商需求数据。
在一可选实施方式中,从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的营销策略需求的招商需求数据,包括:
根据所述至少一组聚合数据中竟对商户的营销数据,生成应对竟对商户的营销策略;和/或
根据所述至少一组聚合数据中所述待处理商户的用户群体数据,生成针对用户群体的营销策略;和/或
根据所述至少一组聚合数据中所述待处理商户的订单分布数据,生成针对地理区域的营销策略;和/或
根据所述至少一组聚合数据中所述待处理商户的配送员数据,生成 针对配送员的营销策略。
在一可选实施方式中,从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的运营状况的招商需求数据,包括以下至少一种:
从所述至少一组聚合数据中,获取所述待处理商户的流水订单量;
从所述至少一组聚合数据中,获取所述待处理商户的当日订单量;
从所述至少一组聚合数据中,获取所述待处理商户的订单完成信息;
从所述至少一组聚合数据中,获取所述待处理商户的补贴数据;
从所述至少一组聚合数据中,获取所述待处理商户的订单密集区域数据;
从所述至少一组聚合数据中,获取所述待处理商户的环比数据;
从所述至少一组聚合数据中,获取所述待处理商户的销量排名数据。
在一可选实施方式中,在得到所述至少一个维度的业务需求数据之后,所述方法还包括:
响应于业务人员的访问请求,从所述至少一个维度的业务需求数据中确定所述业务人员请求访问的业务需求数据;
根据所述业务人员请求访问的业务需求数据,生成可视化图表;
将所述可视化图表展示给所述业务人员。
本公开实施例还提供一种数据处理装置,包括:
数据解析模块,被配置为解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本;
数据获取模块,被配置为根据所述数据采集规则,运行所述数据采集脚本,以从所述数据采集脚本对应的数据源中获取所述待处理商户的关联数据;
数据处理模块,被配置为运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。
在一可选实施方式中,所述装置还包括:
任务生成模块,被配置为获取与所述待处理商户关联的数据源地址; 根据数据采集脚本采集数据使用的数据标签和路由规则,从多个数据采集脚本中,选择与所述数据源地址匹配的数据采集脚本;为与所述数据源地址匹配的数据采集脚本,配置数据采集规则,以生成所述待处理商户对应的数据采集任务。
在一可选实施方式中,所述数据获取模块具体被配置为执行以下至少一种操作:
根据所述数据采集规则,运行面向所述待处理商户的数据采集脚本,以从所述待处理商户的运营网站上获取所述待处理商户的运营数据;
根据所述数据采集规则,运行面向所述待处理商户的竟对商户的数据采集脚本,以从所述竟对商户的运营网站上获取所述竟对商户的运营数据;
根据所述数据采集规则,运行面向所述待处理商户的配送员客户端的数据采集脚本,以从所述配送员客户端中获取配送员针对所述待处理商户的反馈数据;
根据所述数据采集规则,运行面向所述待处理商户的业务人员的客户端的数据采集脚本,以从所述业务人员的客户端中获取业务人员针对所述待处理商户的访问数据。
在一可选实施方式中,所述装置还包括:
预处理模块,被配置为根据所述脚本名称对应的数据采集脚本采集数据使用的数据标签和所述关联数据包含的核心词之间的相似度,对所述关联数据进行去脏处理;和/或,根据所述关联数据包含的核心词之间的相似度,对所述关联数据进行去重处理。
在一可选实施方式中,所述数据处理模块包括:
分组子模块,被配置为基于数据所属的应用平台和/或数据所属的地域,对所述关联数据进行分组,以获得至少一个数据分组;
聚合子模块,被配置为按照所述至少一个维度,对所述至少一个数据分组进行聚合,以获得至少一组聚合数据;
获取子模块,被配置为从所述至少一组聚合数据中,获取所述至少一个维度的业务需求数据。
在一可选实施方式中,所述获取子模块包括:
第一获取单元,被配置为从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的运营状况的招商需求数据;和/或
第二获取单元,被配置为从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的营销策略需求的招商需求数据。
在一可选实施方式中,所述第二获取单元具体被配置为:
根据所述至少一组聚合数据中竟对商户的营销数据,生成应对竟对商户的营销策略;和/或
根据所述至少一组聚合数据中所述待处理商户的用户群体数据,生成针对用户群体的营销策略;和/或
根据所述至少一组聚合数据中所述待处理商户的订单分布数据,生成针对地理区域的营销策略;和/或
根据所述至少一组聚合数据中所述待处理商户的配送员数据,生成针对配送员的营销策略。
在一可选实施方式中,所述第一获取单元具体被配置为执行以下至少一种操作:
从所述至少一组聚合数据中,获取所述待处理商户的流水订单量;
从所述至少一组聚合数据中,获取所述待处理商户的当日订单量;
从所述至少一组聚合数据中,获取所述待处理商户的订单完成信息;
从所述至少一组聚合数据中,获取所述待处理商户的补贴数据;
从所述至少一组聚合数据中,获取所述待处理商户的订单密集区域数据;
从所述至少一组聚合数据中,获取所述待处理商户的环比数据;
从所述至少一组聚合数据中,获取所述待处理商户的销量排名数据。
在一可选实施方式中,所述数据处理模块还被配置为:
响应于所述业务人员的访问请求,从所述至少一个维度的业务需求数据中确定所述业务人员请求访问的业务需求数据;
根据所述业务人员请求访问的业务需求数据,生成可视化图表;
将所述可视化图表展示给所述业务人员。业务人员的访问请求展示 给所述业务人员
本公开实施例还提供一种电子设备,包括存储器和处理器;所述存储器用于存储一条或多条计算机指令,其中,所述一条或多条计算机指令被所述处理器执行时能够实现上述方法实施例提供的数据处理方法中的步骤。
本公开实施例还提供一种存储有计算机程序的计算机可读存储介质,所述计算机程序被计算机执行时实现上述方法实施例提供的数据处理方法中的步骤。
在本公开实施例中,提供数据采集脚本,基于待处理商户对应的数据采集任务确定需要使用的数据采集脚本和数据采集规则,按照数据采集规则运行所确定的数据采集脚本采集待处理商户的关联数据,进而运行与待处理商户关联的业务需求对应的数据分析脚本,对数据采集脚本采集到的关联数据进行分组和聚合处理,得到满足所述业务需求的业务需求数据,可以高效、精准地获取和分析商户数据,进而给出业务需求数据,有利于为各类与商户相关的业务提供数据支持。
此处所说明的附图用来提供对本公开的进一步理解,构成本公开的一部分,本公开的示意性实施例及其说明用于解释本公开,并不构成对本公开的不当限定。在附图中:
图1a为本公开一实施例提供的数据处理装置的业务逻辑示意图;
图1b为本公开一实施例提供的数据处理方法的流程示意图;
图2为本公开另一实施例提供的数据处理方法的流程示意图;
图3为本公开又一实施例提供的对关联数据进行分组和聚合处理的流程示意图;
图4a为本公开又一实施例提供的可视化图表的一种样式示意图;
图4b为本公开又一实施例提供的可视化图表的另一种样式示意图;
图5为本公开又一实施例提供的数据处理装置的结构示意图;
图6为本公开又一实施例提供的数据处理装置的结构示意图。
为使本公开的目的、技术方案和优点更加清楚,下面将结合本公开具体实施例及相应的附图对本公开技术方案进行清楚、完整地描述。显然,所描述的实施例仅是本公开一部分实施例,而不是全部的实施例。基于本公开中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本公开保护的范围。
在现有技术中,商户的招引主要依赖于BD人员,亟需一种高效、精准的商户数据获取和分析方案,以为BD人员在招引商户过程中提供数据支持。
针对上述问题,本公开实施例提供一种具有普适性的数据处理方案,其主要原理是:提供数据采集脚本,基于待处理商户对应的数据采集任务确定需要使用的数据采集脚本和数据采集规则,按照数据采集规则运行所确定的数据采集脚本采集待处理商户的关联数据,进而运行于待处理商户关联的业务需求对应的数据分析脚本,对数据采集脚本采集到的关联数据进行分组和聚合处理,得到满足所述业务需求的业务需求数据。
图1a为本公开一实施例提供的数据处理装置的业务逻辑示意图。其中,图1a中的调度为可选的业务逻辑。基于图1a,图1b为本公开一实施例提供的数据处理方法的流程示意图。如图1b所示,所述方法包括:
101、解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本。
103、根据所述数据采集规则,运行所述数据采集脚本,以从所述数据采集脚本对应的数据源中获取所述待处理商户的关联数据。
105、运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。
在本实施例中,待处理商户是指与一定业务需求相关联的商户。待 处理商户可以是一个商户,也可以是多个商户。根据具体应用场景的不同,待处理商户可以不同。例如,在招引商户场景中,待处理商户可以是互联网应用希望招引的商户,或者是对互联网应用来说具有招引价值的商户等。举例说明,互联网应用可以是网购类应用、代购类应用和/或外卖类应用。相应地,在本实施例中,可以为互联网应用招引商户的人员可以是互联网应用的销售人员、BD人员或市场人员,但不限于此。
在本实施例中,为了满足待处理商户关联的业务需求,需要根据待处理商户的关联数据,获得实现所述业务需求所需的业务需求数据,即需要针对待处理商户关联的业务需求提供数据支持。
在本实施例中,提供数据采集脚本和数据分析脚本,数据采集脚本负责采集数据,数据分析脚本负责分析数据采集脚本采集到的数据,以获得业务需求数据。其中,应用场景不同,待处理商户会不同,所要使用的数据采集脚本会有所不同。为了适应不同应用场景,适应不同待处理商户,本实施例允许针对待处理商户制定数据采集任务,在数据采集任务中定义了采集数据所需的数据采集规则和采集数据所需的数据采集脚本。可选地,在数据采集任务中,可以通过脚本名称来唯一标识数据采集脚本。基于此,在确定待处理商户后,可以针对待处理商户制定数据采集任务。可选地,可以由设备自动为待处理商户制定数据采集任务,也可以由待处理商户对应的业务人员,例如BD人员手动为待处理商户制定数据采集任务。
数据处理装置可以获得待处理商户的数据采集任务。例如,数据处理装置可以接收业务人员手动输入的待处理商户的数据采集任务;或者,也可以接收生成数据采集任务的设备以有线或无线通信方式传输过来的待处理商户的数据采集任务。
对数据处理装置来说,可以解析待处理商户的数据采集任务,以确定数据采集规则和数据采集脚本。其中,不同的待处理商户,一般具有不同的数据采集规则。数据采集规则主要定义采集数据的一些控制策略,例如采集周期、采集时刻、需要采集的数据量、每次需要采集的时间长度等。数据采集脚本可由脚本名称唯一标识。
可选地,可以预先设定数据采集任务所采用的数据结构。基于此,数据处理装置可以按照预设的数据结构,对数据采集任务进行解析。可选地,在本实施例中,数据采集任务可以使用现有的数据结构,也可以使用自定义的数据结构。
例如,现有的数据结构可以是JSON(JavaScript Object Natation)数据结构,但不限于此。以JSON为例的数据采集任务可表示为{“脚本名称”:xxx,“数据采集规则”:xxx},该JSON数据结构一共包括两个名称字段,分别是脚本名称字段和数据采集规则字段;相应的,每个名称字段后面的“xxx”表示相应名称字段的取值。
又例如,一种自定义的数据结构可以是{脚本名称的值(占m个字符):数据采集规则的值(占p个字符)},在该数据结构中包括两个字段,分别是脚本名称字段和数据采集规则字段,两个字段通过占用的字符个数来区分,该数据结构中前m个字符是脚本名称字段的值,后p个字符是数据采集规则字段的值,m、p均为自然数。
在成功解析待处理商户对应的数据采集任务之后,可以根据解析出的数据采集规则,运行解析出的脚本名称对应的数据采集脚本,以从数据采集脚本对应的数据源中获取待处理商户的关联数据。其中,数据采集脚本与数据源之间具有对应关系,数据源可以通过数据源地址来唯一标识,数据源地址可以是IP地址或URL地址等。数据采集脚本会自动到数据源地址所标识的数据源获取待处理商户的关联数据。这里的关联数据可以是任何与待处理商户相关的数据。例如,待处理商户的关联数据可以是待处理商户的运营数据、用户评价、竟对商户的运营数据等。竟对商户是指与待处理商户存在竞争关系的商户。
在一可选实施方式中,待处理商户的数据采集任务中可以包括数据采集规则、脚本名称以及数据源地址。数据采集规则、脚本名称以及数据源地址之间具有对应关系,而且这种对应关系可以是一组或多组。每组对应关系中的数据采集规则、脚本名称以及数据源地址不完全相同。对任意一组对应关系来说,都可以根据该组对应关系中的数据采集规则,运行相应脚本名称对应的数据采集脚本,以从相应数据源地址对应的数 据源中获取待处理商户的关联数据。一般来说,从不同数据源地址对应的数据源中获取的数据一般不完全相同。与上述数据采集任务类似,该实施方式中的数据采集任务也可以采用JSON数据结构,或自定义的数据结构。
例如,以JSON为例的数据采集任务可表示为{“脚本名称”:xxx,“数据源地址”:xxx,“数据采集规则”:xxx},该JSON数据结构一共包括三个名称字段,分别是脚本名称字段、数据源地址字段和数据采集规则字段;相应的,每个名称字段后面的“xxx”表示相应名称字段的取值。
又例如,一种自定义的数据结构可以是{脚本名称的值(m个字符):数据源地址的值(n个字符):数据采集规则的值(p个字符)},在该数据结构中包括三个字段,分别是脚本名称字段、数据源地址字段和数据采集规则字段,三个字段通过占用的字符个数来区分,该数据结构中前m个字符是脚本名称字段的值,中间n个字符是数据源地址字段的值,最后p个字符是数据采集规则字段的值,m、n、p均为自然数。
在本实施例中,数据处理装置可以解析待处理商户对应的数据采集任务,从而确定数据采集规则和数据采集脚本,进而按照数据采集规则,运行相应数据采集脚本,从相应数据源自动获取待处理商户的关联数据,无需业务人员手动收集待处理商户的关联数据,一方面可以保证获取待处理商户的关联数据的效率,另一方面可以更加全面地获取待处理商户的关联数据。
在步骤103中,基于步骤102中获取到的待处理商户的关联数据,可以运行与待处理商户关联的业务需求所对应的数据分析脚本,进一步通过数据分析脚本对数据采集脚本采集到的关联数据进行分组和聚合处理,以获得至少一个维度的业务需求数据。其中,业务需求可由待处理商户以及应用场景而定,不同业务需求对应的数据处理核心相同,主要是数据分组和聚合,但对应具体的分组和聚合细节不同,故会对应不同的数据分析脚本。其中,待处理商户关联的业务需求可由业务人员预先设定,并可预先针对业务需求开发数据分析脚本。其中,业务人员可 以基于业务需求数据开展符合所述业务需求的业务,这就为业务人员开展相应业务提供了数据支持。
在本实施例中,数据处理装置可以解析待处理商户对应的数据采集任务,从而确定数据采集规则和数据采集脚本,进而按照数据采集规则,运行相应数据采集脚本,从相应数据源自动获取待处理商户的关联数据,进一步运行数据分析脚本,对数据采集脚本采集到的关联数据进行分组和聚合处理,可以高效、精准地获取和分析商户数据,进而给出业务需求数据,有利于为各类与商户相关的业务提供数据支持。
另外,在本实施例中,数据采集脚本、数据分析脚本可根据不同业务需求、不同商户以及不同应用场景适应性设定,并且基于成熟的脚本技术很容易实现,相当于提供了一种具有普适性的获取和分析商户数据的方案,因此,可以应被配置为各种场景中,为各类与商户相关的业务提供数据支持。
进一步,可将本实施例提供的方法应被配置为招引商户场景中,则待处理商户是指具有招商价值的商户,待处理商户关联的业务需求即招引待处理商户的需求,相应地,至少一个维度的业务需求数据实际上是至少一个维度的招商需求数据。这里的招商需求数据是指BD人员在招引待处理商户过程中所需的、对BD人员具有招商指导价值的数据。招商需求数据可以是能够说明待处理商户运营状况的数据,也可以是待处理商户与其竟对商户的比对数据,还可以是针对待处理商户给出的营销策略数据等。另外,根据应用场景的不同,招商需求数据可以是一个维度的,也可以是多个维度的。优选的,招商需求数据是多维度的,这样BD人员能够从多维度了解待处理商户,进而在招引过程中占据优势,提高招引商户的成功率。
进一步,在上述实施例或下述实施例中,数据处理装置不仅具有数据解析、数据获取和数据处理的功能,还会对外提供访问入口,以便于业务人员能够通过该访问入口访问所需的业务需求数据。对业务人员来说,可以通过数据处理装置对外提供的访问入口,向数据处理装置发出访问请求,以请求所需的业务需求数据。数据处理装置可响应于业务人 员的访问请求,从至少一个维度的业务需求数据中确定业务人员请求访问的业务需求数据;根据业务人员请求访问的业务需求数据,生成可视化图表,将可视化图表展示给业务人员。
以招引商户场景为例,业务人员可以是BD人员;相应地,业务需求数据可以是招商需求数据。数据处理装置可以根据BD人员的访问请求,向BD人员展示待处理商户的30天流水对应的饼状图,周环比数据对比表,区域热点图,流水订单量的曲线图等。对BD人员来说,在获得这些可视化图表之后,可以据此招引待处理商户。其中,BD人员招引待处理商户主要是指根据招商需求数据与待处理商户进行谈判,分析待处理商户的运营状况,并有针对性的给出营销策略和建议的过程。
在招引商户场景中,基于成熟的脚本技术,可以很容易地根据招商需求适应性设定数据采集脚本和数据分析脚本。数据处理装置运行相应数据采集脚本,自动获取待处理商户的关联数据;进而,运行招商需求对应的数据分析脚本对关联数据进行分组和聚合处理得到至少一个维度的招商需求数据,可以高效、精准地获取和分析商户数据,为BD人员招引商户提供数据支持。其中,无需BD人员手动收集待处理商户的关联数据,不仅可以节约时间,保证获取待处理商户的关联数据的效率,提高招引商户的效率,而且可以更加全面地获取待处理商户的关联数据,使得BD人员能够在与商户谈判的过程中处于优势,提高招引商户的成功率。
在上述实施例或下述实施例中,在解析待处理商户对应的数据采集任务之前,需要生成待处理商户对应的数据采集任务。可选地,一种生成待处理商户对应的数据采集任务的方式包括:
首先,获取与待处理商户关联的数据源地址,以明确从哪里获取待处理商户的关联数据。例如,数据源地址可以包括但不限于:待处理商户的运营网站或客户端的地址,待处理商户的竟对商户的运营网站或客户端的地址,待处理商户的待售网点的地址等等。
接着,根据数据采集脚本采集数据使用的数据标签和路由规则,从多个数据采集脚本中,选择与待处理商户关联的数据源地址匹配的数据 采集脚本。在该实施方式中,预先生成多个数据采集脚本,不同数据采集脚本使用的数据标签和路由规则不同。数据标签是指数据采集脚本抓取数据过程中使用的核心词或关键词或搜索词,例如可以是“30天流水”、“菜品名称”、“菜品价格”等。路由规则主要定义数据采集脚本抓取数据过程中的数据访问路径。一般来说,不同数据源支持的路由规则可能不同,所提供的数据也会有所不同,故可以选择路由规则、数据标签均与待处理商户关联的数据源地址所标识的数据源匹配的数据采集脚本。
最后,为所选择的与数据源地址匹配的数据采集脚本,配置数据采集规则,以生成待处理商户对应的数据采集任务。数据采集规则可以是每天定时从数据源地址对应的数据源获取数据,或者是每隔指定时长从数据源地址对应的数据源获取数据,或者持续从数据源地址对应的数据源获取数据,或者根据调度指令,在调度时刻从数据源地址对应的数据源获取数据,等等。
在上述实施例或下述实施例中,可根据数据采集规则,运行数据采集脚本,以从数据采集脚本对应的数据源中获取待处理商户的关联数据。可选地,根据应用场景的不同,所运行的数据采集脚本会有所不同,且所获取的待处理商户的关联数据也会有所不同。
下面以外卖应用场景为例,例如,可以运行以下数据采集脚本获取相应的关联数据:
1)面向业务人员的客户端的数据采集脚本
业务人员可以到待处理商户的实体店中进行拜访,了解待处理商户的基本信息,例如优惠活动、物流配置、订单流水、主要营业范围、以及竟对商户的情况等。之后,业务人员可以将对待处理商户的访问数据上传至其客户端。基于此,数据处理装置可以提供面向业务人员的客户端的数据采集脚本,该数据采集脚本采集数据使用的数据标签以及路由规则均适应于业务人员的客户端。例如,数据标签可以是与优惠活动、物流配置、订单流水、主营业范围等相关的数据。例如,路由规则可以是通过网通或联通的网关接入数据源地址。当从待处理商户的数据采集任务中解析出的脚本名称标识面向业务人员的客户端的数据采集脚本 时,可以根据数据采集规则,运行面向待处理商户的业务人员的客户端的数据采集脚本,以从业务人员的客户端中获取业务人员针对待处理商户的访问数据。可选地,面向业务人员的客户端可以是接入数据处理装置的移动办公系统,但不限于此。
2)面向配送员的客户端的数据采集脚本
在外卖应用场景中,配送员是介于用户与商户之间的中间人。配送员既能接触到用户,又能接触到商户,因此通过配送员可以获得商户的一些信息。例如,配送员可以反馈商户的出餐速度、服务态度、餐品包装情况、物流铺设以及解决方案等信息。之后,配送员可以将对商户的反馈数据上传至其客户端。基于此,数据处理装置可以提供面向配送员的客户端的数据采集脚本,该数据采集脚本采集数据使用的数据标签以及路由规则均适应于配送员的客户端。例如,数据标签可以是与出餐速度、服务态度、餐品包装情况、物流铺设等相关的数据。例如,路由规则可以是通过电信或联通的网关接入数据源地址。当从待处理商户的数据采集任务中解析出的脚本名称标识面向配送员的客户端的数据采集脚本时,可以根据数据采集规则,运行面向待处理商户的配送员的客户端的数据采集脚本,以从配送员的客户端中获取配送员针对待处理商户的反馈数据。可选地,面向配送员的客户端也可以是接入数据处理装置的移动办公系统,但不限于此。
3)面向商户运营网站的数据采集脚本
互联网应用依赖于互联网,因此可以从互联网中收集待处理商户的相关数据。例如,可以从互联网中收集待处理商户的菜品价格、优惠活动、销售量、商户位置、所属商圈以及竟对商户的运营数据等。基于此,数据处理装置可以提供面向待处理商户本身运营网站的数据采集脚本,也可以提供面向待处理商户的竟对商户的运营网站的数据采集脚本,这些数据采集脚本采集数据使用的数据标签以及路由规则分别适应于待处理商户或竟对商户的运营网站。例如,数据标签可以是与菜品价格、优惠活动、销售量、商户位置、所属商圈等相关的数据。
当从待处理商户的数据采集任务中解析出的脚本名称标识面向待 处理商户的运营网站的数据采集脚本时,可以根据数据采集规则,运行面向待处理商户的运营网站的数据采集脚本,以从待处理商户的运营网站上获取待处理商户的运营数据。
当从待处理商户的数据采集任务中解析出的脚本名称标识面向待处理商户的竟对商户的运营网站的数据采集脚本时,可以根据数据采集规则,运行面向待处理商户的竟对商户的运营网站的数据采集脚本,以从待处理商户的竟对商户的运营网站上获取待处理商户的运营数据。
值得说明的是,面向待处理商户或其竟对商户的运营网站的数据采集脚本具体可以实现为网络爬虫,并且可以采用现有技术中的各种爬取算法从相应网站上爬取数据,在此不做详述。
在上述实施例或下实施例中,在数据采集脚本从相应数据源获取待处理商户的关联数据之后,在数据分析脚本对关联数据进行分组和聚合处理之前,可以对关联数据进行预处理。如图2所示,本公开另一实施例提供的数据处理方法在步骤105之前,还包括:
104、对关联数据进行去重和/或去脏处理。
在本实施例中,考虑到关联数据可以包括任何与待处理商户相关的数据,这些数据中有些可能毫无价值,有些可能是不规范或不合法的数据,将这些数据统称为脏数据。因为有脏数据的存在,可能影响整个数据处理过程,故可以预先对收集到的关联数据进行去脏处理,这里的去脏处理是指将关联数据中的脏数据去除的过程。
可选地,可以根据脚本名称对应的数据采集脚本采集数据使用的数据标签和关联数据包含的核心词之间的相似度,对关联数据进行去脏处理。具体的,可以提取每个关联数据的核心词,再计算核心词与数据标签之间的相似度,进而从关联数据中去除相似度不符合第一要求的关联数据。例如,可以将与数据标签之间的相似度小于设定阈值的关联数据去除,或者可以将与数据标签之间的相似度不在设定相似度范围内的关联数据去除。
另外,在本实施例中,考虑到由不同数据采集脚本从不同数据源获取待处理商户的关联数据,关联数据中可能存在重复数据,因此,通过 对关联数据进行去重处理,有利于节约处理重复数据所消耗的资源,提高后续处理效率。这里的去重处理主要是指通过删除操作保证关联数据中不存在重复数据的过程。
例如,可以根据关联数据包含的核心词之间的相似度,对关联数据进行去重处理。例如,可以提取每个关联数据的核心词;计算每两个关联数据的核心词之间的相似度,对于相似度符合第二要求的两个关联数据保留其中之一。例如,对于相似度大于设定阈值的两个关联数据,说明其相似度较高,保留一份数据即可。例如,可以随机选择保留其中一个,丢弃另一个。或者,可以按照获取时间的先后顺序,保留最新的一个,即获取时间较晚的一个,从而丢弃获取时间较早的一个。
在上述实施例或下述实施例中,可以运行与待处理商户关联的业务需求对应的数据分析脚本,对关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。可选地,如图3所示,一种数据分组和聚合处理的方式包括:
1051、基于数据所属的应用平台和/或数据所属的地域,对关联数据进行分组,以获得至少一个数据分组。
1052、按照至少一个维度,对至少一个数据分组进行聚合,以获得至少一组聚合数据。
1053、从至少一组聚合数据中,获取至少一个维度的业务需求数据。
在本实施例中,步骤1051-1053描述了数据处理装置运行数据分析脚本对关联数据进行分组和聚合处理的一种实施方式。在该实施方式中,数据处理装置可以根据应用需求对待处理商户的关联数据进行分组,再根据应用需求对得到的数据分组进行聚合,进而基于聚合数据,获取业务需求数据。
其中,根据应用场景的不同,分组方式也会有所不同。可选地,可基于数据所属的应用平台,对关联数据进行分组,以获得至少一个数据分组。例如,可以统计待处理商户的关联数据所属的应用平台,将属于同一应用平台的数据划分为一组,从而获得至少一个数据分组。
以外卖应用场景为例,假设待处理商户同时加盟A外卖平台、B外 卖平台以及C外卖平台,则所获取到的待处理商户的关联数据包括从A外卖平台获取到的与待处理商户相关的数据,从B外卖平台获取到的与待处理商户相关的数据以及从C外卖平台获取到的与待处理商户相关的数据。为了从不同应用平台的角度给出业务需求数据,则可以按照应用平台,将待处理商户的关联数据划分为三个数据分组,将从A外卖平台获取到的数据划为一个数据分组,将从B外卖平台获取到的数据划为一个数据分组,将从C外卖平台获取到的数据划为一个数据分组。
在另一可选实施方式中,可基于数据所属的地域,对关联数据进行分组,以获得至少一个数据分组。例如,可以统计待处理商户的关联数据所属的地域,将属于同一地域的数据划分为一组,从而获得至少一个数据分组。
仍以外卖应用场景为例,假设待处理商户属于某个省份,并且面向整个省份提供外卖服务,则所获取到的待处理商户的关联数据包括从该省份中各区域内获取到与待处理商户相关的数据。为了从不同服务区域的角度给出业务需求数据,则可以按照地域,将待处理商户的关联数据划分为多个数据分组,例如,可以将来自于该省份下同一城市的数据划为一个数据分组,或者可以将来自于该省份下同一城市的同一商圈的数据划为一个数据分组。分组粒度可视具体应用场景而定,这里仅是举例,不做限定。
在又一可选实施方式中,可基于数据所属的应用平台和数据所属的地域,对关联数据进行分组,以获得至少一个数据分组。这样可以同时结合应用平台以及地域给出业务需求数据。
例如,可以先基于数据所属的应用平台,对待处理商户的关联数据进行初步分组;再根据数据所属的地域对初步分组结果进行二次分组,以获得至少一个数据分组。
又例如,也可以先基于数据所属的地域,对待处理商户的关联数据进行初步分组;再根据数据所属的应用平台对初步分组结果进行二次分组,以获得至少一个数据分组。
其中,数据分组之间的聚合方式具体可视应用需求而定。例如,可 以将地域相邻的数据分组聚合在一起,又或者,可以将来自类似应用平台的数据分组聚合在一起,等等。
在本实施例中,考虑到关联数据的数据量可能会很大,且可能杂乱无章,通过对关联数据进行分组和聚合,使得关联数据比较有规律,这样可以更加方便、高效地从关联数据中提取业务需求数据。
进一步,在获得聚合数据之后,则可以从至少一组聚合数据中,获取至少一个维度的业务需求数据。值得说明的是,这里业务需求数据的维度,与聚合数据的组数没有关系。例如,可以从每一组聚合数据中,分别获取业务需求数据,而从每一组聚合数据中所获取的业务需求数据可以是某一维度的,也可以是多维度的。或者,也可以综合分析多组聚合数据,从中获取业务需求数据,且综合分析多组聚合数据所获取的业务需求数据可以是某一维度的,也可以是多维度的。
在招引商户场景中,则业务需求数据可以为招商需求数据。可选地,可以将招商需求数据分为两类,一类是反映待处理商户的运营状况的招商需求数据,对BD人员来说,这类招商需求数据对待处理商户的运营状况具有说明性,可简称为说明性招商需求数据;另一类是反映待处理商户的营销策略需求的招商需求数据,对BD人员来说,这类招商需求数据可以给出待处理商户所需的营销策略,可简称为策略性招商需求数据。
基于上述,从至少一组聚合数据中,获取业务需求数据的方式可以包括:
从至少一组聚合数据中,获取至少一个维度的可以反应待处理商户的运营状况的招商需求数据;和/或
从至少一组聚合数据中,获取至少一个维度的可以反应待处理商户的营销策略需求的招商需求数据。
值得说明的是,上述每一类招商需求数据可以是某个维度的,也可以是多维度的。
举例说明:对于策略性招商需求数据,可以从以下至少一个维度反应待处理商户的营销策略需求:竟对商户维度、用户群体维度、地理区 域维度以及配送员维度。相应地,获取策略性招商需求数据方式包括:
根据至少一组聚合数据中竟对商户的营销数据,生成应对竟对商户的营销策略;和/或
根据至少一组聚合数据中所述待处理商户的用户群体数据,生成针对用户群体的营销策略;和/或
根据至少一组聚合数据中所述待处理商户的订单分布数据,生成针对地理区域的营销策略;和/或
根据至少一组聚合数据中所述待处理商户的配送员数据,生成针对配送员的营销策略。
可选地,上述竟对商户的营销数据可以包括但不限于:竟对商户的菜品、竟对商户的配送方式、竟对商户的在线时长、竟对商户的订单处理时长以及竟对商户的营销策略等中的至少一种。针对竟对商户的这些数据,可以生成对待处理商户有利的营销策略。例如,竟对商户的营销策略是满30减5元,则待处理商户的营销策略可以是满30减8元。又例如,竟对商户的在线时长是12小时,则待处理商户的在线时长可以是24小时。
可选地,上述用户群体数据可以包括但不限于:用户群体的周围住宅、写字楼、学校、商户覆盖度,人口数量等中的至少一种。针对与用户群体相关的数据,可以生成对待处理商户有利的营销策略。例如,如果用户群体周围的写字楼较多,说明用户群体属于上班族,有一定经济能力,则可以生成满减力度相对较小且允许预定的营销策略,以增加收益。又例如,如果用户群体周围的商户覆盖度较大,则可以生成满赠或满减力度较大或免配送费等营销策略,以提升待处理商户的竞争力。
可选地,上述订单分布数据可以包括但不限于:订单分布区域内每天的订单量、订单分布区域内高峰期的订单量、订单分布区域内的月平均订单量等中的至少一种。例如,根据订单分布区域内每天的订单量,可以看出订单比较密集和稀疏的地理区域,则对于订单比较密集的地理区域可以生成允许预定的营销策略,以分散订单,对于订单比较稀疏的地理区域可以生成满减、满赠力度较大的营销策略,以提升这些地理区 域内的订单量。
举例说明:对于说明性招商需求数据,可以从以下至少一种维度来体现待处理商户的运营状况:流水订单量、当日订单量、订单完成情况、补贴情况、订单密集区域、环比数据以及销量排名等。基于此,获取说明性招商需求数据的方式包括以下至少一种:
从至少一组聚合数据中,获取待处理商户的流水订单量;
从至少一组聚合数据中,获取待处理商户的当日订单量;
从至少一组聚合数据中,获取待处理商户的订单完成信息;
从至少一组聚合数据中,获取待处理商户的补贴数据;
从所述至少一组聚合数据中,获取待处理商户的订单密集区域数据;
从至少一组聚合数据中,获取待处理商户的环比数据;
从至少一组聚合数据中,获取待处理商户的销量排名数据。
可选地,数据处理装置可以根据待处理商户所属平台标识、待处理商户的唯一编码、订单ID、订单所属商圈标识等信息,从上述聚合数据中获取招商需求数据。例如,可以根据待处理商户所属平台标识、待处理商户的唯一编码以及订单ID,从聚合数据中识别出待处理商户的订单及订单状态,进而统计出待处理商户的流水订单量和/或订单完成情况。
在上述实施例或下述实施例中,在获得招商需求数据之后,BD人员可以根据实际需求在需要招商需求数据时,向数据处理装置发出访问请求。对数据处理装置来说,可响应于业务人员的访问请求,将BD人员请求的招商需求数据提供给BD人员,这样BD人员可基于招商需求数据招引待处理商户。
可选地,数据处理装置可响应于业务人员的访问请求,根据BD人员请求的招商需求数据,生成可视化图表;将可视化图表展示给BD人员,以供BD人员招引待处理商户。这样的招商需求数据更加直观,BD人员使用起来更加便捷,有利于提高招引商户的成功率。
可选地,上述可视化图表的实现样式可视招商需求数据而定。如图4a所示,一种可视化图表为饼状图,如图4b所示,另一种可视化图表为数据表。当然,可视化图表还可以是数据图和数据表的结合。值得说 明的是,图4a和图4b重点关注数据图和数据表的样式,其中的数据值不做关注。
值得说明的是,除了通过可视化图表向BD人员展示招商需求数据之外,也可以直接将招商需求数据提供给BD人员。
下面结合外卖场景中招引商户的两个具体实例,对本公开实施例提供的方法做进一步说明。实例1:BD人员在招引待处理商户的过程中,需要了解待处理商户与待处理商户所在区域中所有商户的30日流水数据的对比情况,待处理商户与竞争商户的30日流水数据的对比情况,以及待处理商户的历史数据的变化趋势,这样BD人员可以在给待处理商户提供营销策略方面占据优势。
首先,定时调度数据采集脚本获取待处理商户、待处理商户所在区域中所有商户以及待处理商户的竟对商户的数据。接着,对获取到的数据进行去重和/或去脏处理。然后,根据BD人员或产品经理(PM)提供的数据需求和维度划分,运行相应数据分析脚本对去重和/或去脏处理后的数据进行分组。在该实例1中,按照商户对数据进行分组,将每个商户的数据分为一组,例如可以将待处理商户的数据分为一组,将竟对商户的数据分为一组,其它商户的数据也分别分为一组。之后,从数据分组中,获取招商需求数据。在该实例1中,一方面,从待处理商户的数据、竟对商户的数据以及其它商户的数据中分别筛选出30日流水数据,进而绘制待处理商户、竟对商户以及其它商户的30日流水数据的对比表和/或对比图。另一方面,统计待处理商户的历史流水数据,获取历史流水数据的变化趋势,并绘制变化趋势图。
在该实例1中,BD人员在招引待处理商户的过程中需要利用30日流水数据的对比情况,但并不限于此,例如还可以使用商户的当日销量、订单完成情况、商户补贴率、周环比、区域热点图等数据。
实例2:BD人员在招引待处理商户的过程中,需要了解待处理商户与竟对商户的满减活动的订单交易量、节假日活动的订单交易量以及到期未使用红包等数据的对比情况,这样BD人员可以在给待处理商户提供补贴方面占据优势。
首先,定时调度数据采集脚本获取待处理商户以及待处理商户的竟对商户的数据。接着,对获取到的数据进行去重和/或去脏处理。然后,根据BD人员或产品经理(PM)提供的数据需求和维度划分,调度数据分析脚本对去重和/或去脏处理后的数据进行分组。在该实例2中,一方面,首先,从商户维度,对去重和/或去脏后的数据进行分组,例如将待处理商户的数据分为一组,将竟对商户的数据分为一组,再按照活动类型分别对待处理商户和竟对商户的数据进一步分组,将与同一活动相关的数据分为一组;之后,按照时间,对每个数据分组中的数据进行统计,获得每个活动类型对应的交易量;然后绘制待处理商户与竟对商户在不同活动类型下的订单交易量的对比图和/或对比表。另一方面,可以分别统计待处理商户和竟对商户的到期未使用红包数量,进而绘制待处理商户与竟对商户在到期未使用红包方面的对比图和/或对比表,供BD人员使用。
在上述两个实例中,如果BD人员自己统计上述数据,则需要去采集大量商户的30日流水数据或订单交易量,然后再做数据对比表。面对庞大的(例如几百万)商户数据,BD人员需要花费巨大的时间代价去收集和产出所需的数据。采用本实施例提供的方法,BD人员只需发起访问请求即可从数据处理装置获取所需的数据,而且看到的是图标形式的数据,更加直观,可以大大地增加BD人员在招引商户过程中的优势。
需要说明的是,上述实施例所提供方法的各步骤的执行主体均可以是同一设备,或者,该方法也由不同设备作为执行主体。比如,步骤101至步骤105的执行主体可以为设备A;又比如,步骤101和103的执行主体可以为设备A,步骤105的执行主体可以为设备B;等等。
图5为本公开又一实施例提供的数据处理装置的结构示意图。如图5所示,装置包括:数据解析模块51、数据获取模块52和数据处理模块53。
数据解析模块51,被配置为解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本。
数据获取模块52,被配置为根据数据采集规则,运行数据采集脚本,以从数据采集脚本对应的数据源中获取待处理商户的关联数据。
数据处理模块53,被配置为运行与待处理商户关联的业务需求对应的数据分析脚本,对关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。
在一可选实施方式中,如图6所示,装置还包括:任务生成模块54。
任务生成模块54,被配置为在数据解析模块51解析数据采集任务之前,获取与待处理商户关联的数据源地址;根据数据采集脚本采集数据使用的数据标签和路由规则,从多个数据采集脚本中,选择与数据源地址匹配的数据采集脚本;为与数据源地址匹配的数据采集脚本,配置数据采集规则,以生成待处理商户对应的数据采集任务。
在一可选实施方式中,数据获取模块52具体被配置为执行以下至少一种操作:
根据数据采集规则,运行面向待处理商户的数据采集脚本,以从待处理商户的运营网站上获取待处理商户的运营数据;
根据数据采集规则,运行面向待处理商户的竟对商户的数据采集脚本,以从竟对商户的运营网站上获取竟对商户的运营数据;
根据数据采集规则,运行面向待处理商户的配送员客户端的数据采集脚本,以从配送员客户端中获取配送员针对待处理商户的反馈数据;
根据数据采集规则,运行面向待处理商户的业务人员的客户端的数据采集脚本,以从业务人员的客户端中获取业务人员针对待处理商户的访问数据。
在一可选实施方式中,如图6所示,装置还包括:预处理模块55。
预处理模块55,被配置为在数据处理模块53运营数据分析脚本对关联数据进行分组和聚合处理之前,根据脚本名称对应的数据采集脚本采集数据使用的数据标签和关联数据包含的核心词之间的相似度,对关联数据进行去脏处理;和/或,根据关联数据包含的核心词之间的相似度,对关联数据进行去重处理。
在一可选实施方式中,如图6所示,数据处理模块53的一种实现 结构包括:分组子模块531、聚合子模块532以及获取子模块533。
分组子模块531,被配置为基于数据所属的应用平台和/或数据所属的地域,对关联数据进行分组,以获得至少一个数据分组。
聚合子模块532,被配置为按照至少一个维度,对至少一个数据分组进行聚合,以获得至少一组聚合数据。
获取子模块533,被配置为从至少一组聚合数据中,获取至少一个维度的业务需求数据。
可选地,获取子模块533的一种实现结构包括以下至少一种获取单元:第一获取单元和第二获取单元。
第一获取单元,被配置为从至少一组聚合数据中,获取至少一个维度的可以反应待处理商户的运营状况的招商需求数据。
第二获取单元,被配置为从至少一组聚合数据中,获取至少一个维度的可以反应待处理商户的营销策略需求的招商需求数据。
进一步可选地,第二获取单元具体被配置为:
根据至少一组聚合数据中竟对商户的营销数据,生成应对竟对商户的营销策略;和/或
根据至少一组聚合数据中待处理商户的用户群体数据,生成针对用户群体的营销策略;和/或
根据至少一组聚合数据中待处理商户的订单分布数据,生成针对地理区域的营销策略;和/或
根据至少一组聚合数据中待处理商户的配送员数据,生成针对配送员的营销策略。
进一步可选地,第一获取单元具体被配置为执行以下至少一种操作:
从至少一组聚合数据中,获取待处理商户的流水订单量;
从至少一组聚合数据中,获取待处理商户的当日订单量;
从至少一组聚合数据中,获取待处理商户的订单完成信息;
从至少一组聚合数据中,获取待处理商户的补贴数据;
从至少一组聚合数据中,获取待处理商户的订单密集区域数据;
从至少一组聚合数据中,获取待处理商户的环比数据;
从至少一组聚合数据中,获取待处理商户的销量排名数据。
在一可选实施方式中,如图6所示,数据处理模块53还被配置为:响应于业务人员的访问请求,从至少一个维度的业务需求数据中确定业务人员请求访问的业务需求数据;根据业务人员请求访问的业务需求数据,生成可视化图表;将可视化图表展示给业务人员展示给业务人员。
在一可选实施方式中,如图6所示,装置还包括:数据存储模块56。
数据存储模块56,被配置为存储数据获取模块52获取到的关联数据以及数据处理模块53获得的业务需求数据。
本实施例提供的数据处理装置,可被配置为执行上述方法实施例提供的数据处理方法的流程,其具体工作原理以及实施细节不再赘述,可参见上述方法实施例中的描述。
本实施例提供的数据处理装置,提供数据采集脚本,基于待处理商户对应的数据采集任务确定需要使用的数据采集脚本和数据采集规则,按照数据采集规则运行所确定的数据采集脚本采集待处理商户的关联数据,进而运行与待处理商户关联的业务需求对应的数据分析脚本,对数据采集脚本采集到的关联数据进行分组和聚合处理,得到满足所述业务需求的业务需求数据,具有高效、准确地获取和分析商户数据的效果,可以为各类与商户相关的业务提供数据支持。
进一步,将本公开实施例提供的数据处理装置应用于招引商户场景中,可代替BD人员获取待处理商户的关联数据,然后对关联数据进行分组和聚合处理,进而从中获取至少一个维度的招商需求数据,具有高效、准确地获取和分析商户数据的效果,可以为BD人员在招引商户时提供数据支持有利于提高招引商户的成功率和效率。
本领域内的技术人员应明白,本公开的实施例可提供为方法、系统、或计算机程序产品。因此,本公开可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且,本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本公开是参照根据本公开实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器,使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中,使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品,该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上,使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理,从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中,计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器,随机存取存储器(RAM)和/或非易失性内存等形式,如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括,但不限于相变内存(PRAM)、静态随机存取存储器(SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内 存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带,磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质,可用于存储可以被计算设备访问的信息。按照本文中的界定,计算机可读介质不包括暂存电脑可读媒体(transitory media),如调制的数据信号和载波。
还需要说明的是,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。
本领域技术人员应明白,本公开的实施例可提供为方法、系统或计算机程序产品。因此,本公开可采用完全硬件实施例、完全软件实施例或结合软件和硬件方面的实施例的形式。而且,本公开可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
以上所述仅为本公开的实施例而已,并不用于限制本公开。对于本领域技术人员来说,本公开可以有各种更改和变化。凡在本公开的精神和原理之内所作的任何修改、等同替换、改进等,均应包含在本公开的权利要求范围之内。
Claims (20)
- 一种数据处理方法,包括:解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本;根据所述数据采集规则,运行所述数据采集脚本,以从所述数据采集脚本对应的数据源中获取所述待处理商户的关联数据;运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。
- 根据权利要求1所述的方法,其中,解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本之前,所述方法还包括:获取与所述待处理商户关联的数据源地址;根据数据采集脚本采集数据使用的数据标签和路由规则,从多个数据采集脚本中,选择与所述数据源地址匹配的数据采集脚本;为与所述数据源地址匹配的数据采集脚本,配置数据采集规则,以生成所述待处理商户对应的数据采集任务。
- 根据权利要求1所述的方法,其中,根据所述数据采集规则,运行所数据采集脚本,以从所述数据采集脚本对应的数据源中获取所述待处理商户的关联数据,包括以下至少一种:根据所述数据采集规则,运行面向所述待处理商户的数据采集脚本,以从所述待处理商户的运营网站上获取所述待处理商户的运营数据;根据所述数据采集规则,运行面向所述待处理商户的竟对商户的数据采集脚本,以从所述竟对商户的运营网站上获取所述竟对商户的运营数据;根据所述数据采集规则,运行面向所述待处理商户的配送员客户端的数据采集脚本,以从所述配送员客户端中获取配送员针对所述待处理商户的反馈数据;根据所述数据采集规则,运行面向所述待处理商户的业务人员的客 户端的数据采集脚本,以从所述业务人员的客户端中获取业务人员针对所述待处理商户的访问数据。
- 根据权利要求1所述的方法,其中,运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据之前,所述方法还包括:根据所述数据采集脚本采集数据使用的数据标签和所述关联数据包含的核心词之间的相似度,对所述关联数据进行去脏处理;和/或根据所述关联数据包含的核心词之间的相似度,对所述关联数据进行去重处理。
- 根据权利要求1所述的方法,其中,运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据,包括:基于数据所属的应用平台和/或数据所属的地域,对所述关联数据进行分组,以获得至少一个数据分组;按照所述至少一个维度,对所述至少一个数据分组进行聚合,以获得至少一组聚合数据;从所述至少一组聚合数据中,获取所述至少一个维度的业务需求数据。
- 根据权利要求5所述的方法,其中,从所述至少一组聚合数据中,获取所述至少一个维度的业务需求数据,包括:从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的运营状况的招商需求数据;和/或从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的营销策略需求的招商需求数据。
- 根据权利要求6所述的方法,其中,从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的营销策略需求的招商需求数据,包括:根据所述至少一组聚合数据中竟对商户的营销策略数据,生成应对竟对商户的营销策略;和/或根据所述至少一组聚合数据中所述待处理商户的用户群体数据,生成针对用户群体的营销策略;和/或根据所述至少一组聚合数据中所述待处理商户的订单分布数据,生成针对地理区域的营销策略;和/或根据所述至少一组聚合数据中所述待处理商户的配送员数据,生成针对配送员的营销策略。
- 根据权利要求6所述的方法,其中,从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的运营状况的招商需求数据,包括以下至少一种:从所述至少一组聚合数据中,获取所述待处理商户的流水订单量;从所述至少一组聚合数据中,获取所述待处理商户的当日订单量;从所述至少一组聚合数据中,获取所述待处理商户的订单完成信息;从所述至少一组聚合数据中,获取所述待处理商户的补贴数据;从所述至少一组聚合数据中,获取所述待处理商户的订单密集区域数据;从所述至少一组聚合数据中,获取所述待处理商户的环比数据;从所述至少一组聚合数据中,获取所述待处理商户的销量排名数据。
- 根据权利要求1-8任一项所述的方法,其中,在得到所述至少一个维度的业务需求数据之后,所述方法还包括:响应于业务人员的访问请求,从所述至少一个维度的业务需求数据中确定所述业务人员请求访问的业务需求数据;根据所述业务人员请求访问的业务需求数据,生成可视化图表;将所述可视化图表展示给所述业务人员。
- 一种数据处理装置,包括:数据解析模块,被配置为解析待处理商户对应的数据采集任务,以确定数据采集规则和数据采集脚本;数据获取模块,被配置为根据所述数据采集规则,运行所述数据采集脚本,以从所述数据采集脚本对应的数据源中获取所述待处理商户的关联数据;数据处理模块,被配置为运行与所述待处理商户关联的业务需求对应的数据分析脚本,对所述关联数据进行分组和聚合处理,以得到至少一个维度的业务需求数据。
- 根据权利要求10所述的装置,还包括:任务生成模块,被配置为获取与所述待处理商户关联的数据源地址;根据数据采集脚本采集数据使用的数据标签和路由规则,从多个数据采集脚本中,选择与所述数据源地址匹配的数据采集脚本;为与所述数据源地址匹配的数据采集脚本,配置数据采集规则,以生成所述待处理商户对应的数据采集任务。
- 根据权利要求10所述的装置,其中,所述数据获取模块具体被配置为执行以下至少一种操作:根据所述数据采集规则,运行面向所述待处理商户的数据采集脚本,以从所述待处理商户的运营网站上获取所述待处理商户的运营数据;根据所述数据采集规则,运行面向所述待处理商户的竟对商户的数据采集脚本,以从所述竟对商户的运营网站上获取所述竟对商户的运营数据;根据所述数据采集规则,运行面向所述待处理商户的配送员客户端的数据采集脚本,以从所述配送员客户端中获取配送员针对所述待处理商户的反馈数据;根据所述数据采集规则,运行面向所述待处理商户的业务人员的客户端的数据采集脚本,以从所述业务人员的客户端中获取业务人员针对所述待处理商户的访问数据。
- 根据权利要求10所述的装置,还包括:预处理模块,被配置为根据所述数据采集脚本采集数据使用的数据标签和所述关联数据包含的核心词之间的相似度,对所述关联数据进行去脏处理;和/或,根据所述关联数据包含的核心词之间的相似度,对所述关联数据进行去重处理。
- 根据权利要求10所述的装置,其中,所述数据处理模块包括:分组子模块,被配置为基于数据所属的应用平台和/或数据所属的地 域,对所述关联数据进行分组,以获得至少一个数据分组;聚合子模块,被配置为按照所述至少一个维度,对所述至少一个数据分组进行聚合,以获得至少一组聚合数据;获取子模块,被配置为从所述至少一组聚合数据中,获取所述至少一个维度的业务需求数据。
- 根据权利要求14所述的装置,其中,所述获取子模块包括:第一获取单元,被配置为从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的运营状况的招商需求数据;和/或第二获取单元,被配置为从所述至少一组聚合数据中,获取至少一个维度的可以反应所述待处理商户的营销策略需求的招商需求数据。
- 根据权利要求15所述的装置,其中,所述第二获取单元具体被配置为:根据所述至少一组聚合数据中竟对商户的营销数据,生成应对竟对商户的营销策略;和/或根据所述至少一组聚合数据中所述待处理商户的用户群体数据,生成针对用户群体的营销策略;和/或根据所述至少一组聚合数据中所述待处理商户的订单分布数据,生成针对地理区域的营销策略;和/或根据所述至少一组聚合数据中所述待处理商户的配送员数据,生成针对配送员的营销策略。
- 根据权利要求15所述的装置,其中,所述第一获取单元具体被配置为执行以下至少一种操作:从所述至少一组聚合数据中,获取所述待处理商户的流水订单量;从所述至少一组聚合数据中,获取所述待处理商户的当日订单量;从所述至少一组聚合数据中,获取所述待处理商户的订单完成信息;从所述至少一组聚合数据中,获取所述待处理商户的补贴数据;从所述至少一组聚合数据中,获取所述待处理商户的订单密集区域数据;从所述至少一组聚合数据中,获取所述待处理商户的环比数据;从所述至少一组聚合数据中,获取所述待处理商户的销量排名数据。
- 根据权利要求10-17任一项所述的装置,其中,所述数据处理模块还被配置为:响应于所述业务人员的访问请求,从所述至少一个维度的业务需求数据中确定所述业务人员请求访问的业务需求数据;根据所述业务人员请求访问的业务需求数据,生成可视化图表;将所述可视化图表展示给所述业务人员展示给所述业务人员。
- 一种电子设备,包括存储器和处理器;所述存储器用于存储一条或多条计算机指令,其中,所述一条或多条计算机指令被所述处理器执行时能够实现权利要求1-9任一方法中的步骤。
- 一种存储有计算机程序的计算机可读存储介质,所述计算机程序被计算机执行时实现权利要求1-9任一方法中的步骤。
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