CN112396202A - Service target prediction method, device, server and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a method, a device, a server and a storage medium for predicting a business target, wherein the method for predicting the business target comprises the following steps: obtaining historical logistics data of a plurality of sample areas, wherein the plurality of sample areas comprise target areas; predicting future logistics data of a plurality of sample areas in each logistics industry in a future preset time period based on historical logistics data; clustering each logistics industry of the plurality of sample areas based on historical logistics data to obtain area clustering results of the plurality of sample areas on the logistics industry dimension; and determining a business target of the target area based on the future logistics data and the area clustering result. The embodiment of the invention improves the prediction method of the business target, can more accurately obtain the business target of the target area, thereby better providing reference for the operation organization, and leading the operation organization to have clear positioning on the development prospect of the logistics industry of the target area.
Description
Technical Field
The invention relates to the field of logistics, in particular to a method, a device, a server and a storage medium for predicting a business target.
Background
When a logistics company makes a popular logistics operation plan, the logistics company needs to consider the logistics industry structures of all regions, and makes different plans according to the development prospects of the logistics industries of all regions. However, in the prior art, the logistics industries of each sample area are generally divided according to the logistics types by means of experience and a traditional statistical method, the division of the logistics industries by the classification method is not clear enough, and the service targets of each area cannot be accurately predicted, so that an operation organization is difficult to clearly position the development prospect of the logistics industries of each area.
Disclosure of Invention
The embodiment of the invention provides a business target prediction method, a business target prediction device, a server and a storage medium, and aims to improve the business target prediction method in the logistics industry so as to more accurately predict the business target in a target area and enable an operation organization to clearly position the development prospect of each industry.
In order to solve the above problem, in a first aspect, the present invention provides a method for predicting a business objective, including:
obtaining historical logistics data of a plurality of sample areas, wherein the plurality of sample areas comprise target areas;
predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data;
clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on logistics industry dimensions;
and determining the business target of the target region based on the future logistics data and the region clustering result.
In some embodiments of the invention, the historical logistics data comprises historical sales data for each logistics industry in a sample region; predicting future logistics data of the plurality of sample areas in each logistics industry within a future preset time period based on the historical logistics data, wherein the predicting future logistics data comprises the following steps:
forecasting express quantity acceleration of the plurality of sample areas in each logistics industry in a future preset time period based on historical sales data of each logistics industry of the sample areas;
and calculating future sales data of the logistics industries of the plurality of sample areas in a future preset time period based on the express quantity acceleration and historical sales data of the plurality of sample areas in the logistics industries.
In some embodiments of the present invention, the clustering, based on the historical logistics data, the logistics industries of the plurality of sample areas to obtain an area clustering result of the plurality of sample areas in a logistics industry dimension includes:
and inputting historical sales data of each logistics industry of the plurality of sample areas into a preset clustering model to obtain area clustering results of the plurality of sample areas on the logistics industry dimension.
In some embodiments of the present invention, the determining the business objective of the objective region based on the future logistics data and the region clustering result includes:
determining benchmarking areas corresponding to the target areas in each logistics industry in the plurality of sample areas according to the area clustering result;
obtaining benchmarking logistics data of the target area in each logistics industry based on the area clustering result, wherein the benchmarking logistics data are future logistics data of the target area in the benchmarking area corresponding to the current logistics industry and the current logistics industry;
and determining the business target of the target area based on the benchmark logistics data of each logistics industry of the target area.
In some embodiments of the present invention, the region clustering result of the plurality of sample regions in the logistics industry dimension comprises a plurality of categories, each category comprising at least one sample region; determining the benchmarking areas corresponding to the target areas in each logistics industry in the plurality of sample areas according to the area clustering result, wherein the benchmarking areas comprise:
acquiring a target type corresponding to the target area;
respectively taking each logistics industry in the target region as a target logistics industry, and acquiring the gross interest rate and the accumulated income achievement rate of each sample region in the target logistics industry under the target category;
and taking the sample area with the highest gross interest rate and the accumulated income achievement rate larger than or equal to a first threshold value in the target category in the target logistics industry as the target area in the target logistics industry.
In some embodiments of the present invention, the determining the business objective of the target area based on the benchmarking logistics data of each logistics industry of the target area includes:
the benchmarking logistics data of the target area under each logistics industry are used as sub-business targets of the target area in each logistics industry;
and integrating the sub-business targets of the target area in each logistics industry to form the business target of the target area.
In some embodiments of the invention, further comprising:
based on the historical logistics data of the target area, performing industry grouping on the logistics industry of the target area by adopting a Boston matrix to obtain logistics industry grouping information of the target area;
and adding the logistics industry grouping information into a business target of the target area.
In a second aspect, the present invention provides an apparatus for predicting a business objective, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical logistics data of a plurality of sample areas, and the plurality of sample areas comprise target areas;
the prediction module is used for predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data;
the clustering module is used for clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on the logistics industry dimension;
and the determining module is used for determining the business target of the target region based on the future logistics data and the region clustering result.
In some embodiments of the invention, the historical logistics data comprises historical sales data for each logistics industry in a sample region; the prediction module comprises:
the prediction submodule is used for predicting express quantity acceleration of the plurality of sample areas in each logistics industry in a future preset time period based on historical sales data of each logistics industry of the sample areas;
and the calculation module is used for calculating future sales data of each logistics industry of the plurality of sample areas in a future preset time period based on the express quantity acceleration rate and the historical sales data of each logistics industry of the plurality of sample areas.
In some embodiments of the present invention, the clustering module is configured to input historical sales data of each logistics industry in the plurality of sample areas into a preset clustering model, so as to obtain area clustering results of the plurality of sample areas in the logistics industry dimension.
In some embodiments of the invention, the determining module comprises:
the benchmark region determining module is used for determining the benchmark regions corresponding to the target regions in each logistics industry in the plurality of sample regions according to the region clustering result;
the second acquisition module is used for acquiring the benchmark logistics data of the target area in each logistics industry based on future logistics data, wherein the benchmark logistics data are the future logistics data of the target area in the benchmark area corresponding to the current logistics industry and the current logistics industry;
and the business target determining module is used for determining the business target of the target area based on the benchmark logistics data of each logistics industry of the target area.
In some embodiments of the present invention, the region clustering result of the plurality of sample regions in the logistics industry dimension comprises a plurality of categories, each category comprising at least one sample region; the benchmarking area determination module comprises:
the third acquisition module is used for acquiring a target type corresponding to the target area;
the fourth acquisition module is used for respectively taking each logistics industry in the target area as a target logistics industry and acquiring the gross interest rate and the accumulated income achievement rate of each sample area in the target logistics industry under the target category;
and the first sub-determination module is used for taking the sample area which has the highest gross interest rate in the target logistics industry and the accumulated income achievement rate which is greater than or equal to a first threshold value under the target category as the target area in the target logistics industry.
In some embodiments of the present invention, the service targeting module includes:
the second sub-determining module is used for taking the benchmark logistics data of the target area in each logistics industry as sub-business targets of the target area in each logistics industry;
and the third sub-determining module is used for integrating the sub-business targets of the target area in each logistics industry to form the business target of the target area.
In some embodiments of the present invention, the system further includes a clustering module, where the clustering module is configured to perform industry clustering on the logistics industry of the target region by using a boston matrix based on the historical logistics data of the target region to obtain logistics industry clustering information of the target region, and add the logistics industry clustering information to a service target of the target region.
In a third aspect, the present invention provides a server, comprising:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the method of predicting a business objective as described in the first aspect.
In a fourth aspect, the present invention provides a storage medium storing a plurality of instructions, the instructions being suitable for being loaded by a processor to perform the steps of the method for predicting a business objective according to the first aspect.
The business target prediction method of the embodiment of the invention clusters each logistics industry of a plurality of sample areas to obtain the area clustering result of industry dimension, predicts the logistics data of each logistics industry of a plurality of sample areas in a future preset time period, and then determines the business target of the target area by combining the area clustering result of the industry dimension and the predicted logistics data. The method not only greatly reduces manual participation, simplifies data storage, but also can more accurately predict the business target of the target area, thereby better providing reference for the operation organization, and leading the operation organization to have clear positioning on the development prospect of each industry.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow diagram illustrating an embodiment of a method for predicting a business objective provided in an embodiment of the present invention;
FIG. 2 is a flow diagram illustrating one embodiment of determining a business objective for a target region provided in embodiments of the present invention;
FIG. 3 is a schematic structural diagram of one embodiment of a Boston matrix provided in embodiments of the present invention;
fig. 4 is a schematic structural diagram of an embodiment of a business target prediction apparatus provided in the embodiment of the present invention;
fig. 5 is a schematic structural diagram of an embodiment of a server provided in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present disclosure, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the invention. In the following description, details are set forth for the purpose of explanation. It will be apparent to one of ordinary skill in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and processes are not shown in detail to avoid obscuring the description of the invention with unnecessary detail. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
The embodiment of the invention provides a method, a device, a server and a storage medium for predicting a business target. The following are detailed below.
First, an embodiment of the present invention provides a method for predicting a service objective, where the method for predicting a service objective includes: obtaining historical logistics data of a plurality of sample areas, wherein the plurality of sample areas comprise target areas; predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data; clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on logistics industry dimensions; then, based on the future logistics data and the region clustering result, determining the business target of the target region.
As shown in fig. 1, which is a flowchart illustrating an embodiment of a business target prediction method provided in an embodiment of the present invention, an execution subject of the business target prediction method may be a business target prediction apparatus provided in an embodiment of the present invention, or a storage medium, a terminal, a server, and the like, in which the business target prediction method apparatus is integrated.
As shown in fig. 1, the method for predicting a business objective according to an embodiment of the present invention includes steps 101 to 104, which are described in detail as follows:
101. historical logistics data is obtained for a plurality of sample regions, including a target region.
In some embodiments of the present invention, the sample area is an area to be referred to when performing target service prediction on a target area, and the sample area may be some cities or areas in china, or may be cities or areas in other countries. Specific examples thereof include: the sample region may include a Beijing region, a Shenzhen region, a Guangzhou region, and so on, without limitation herein.
In addition, it should be noted that the target area is one of the plurality of sample areas, that is, the target area may be any one of the plurality of sample areas, and may be determined according to actual needs.
In some embodiments of the present invention, the historical logistics data can include historical sales data for various logistics industries of a sample region. The historical sales data may include, among other things, express delivery and/or revenue values over a historical preset time period.
In some embodiments of the present invention, the express delivery amount of each logistics industry in the sample area may include the number of express deliveries of various logistics types transported by the logistics company in the sample area within a historical preset time period. Of course, the express delivery volume of each logistics industry in the sample area may also include the weight or volume of the courier of each logistics type transported by the logistics company in the sample area within a historical preset time period, and may be specifically determined according to the courier type transported by the logistics company.
And the income value of each logistics industry in the sample area can comprise income obtained by couriers of various logistics types transported by the logistics companies in the sample area in historical preset time periods.
In some embodiments of the invention, the historical sales data may also include part amounts to various flow directions, discounts, gross rates, market share, industry acceleration, etc. for various logistics industries of the sample region. The quantity of the express delivery to each flow direction in each logistics industry of the sample area refers to the quantity of the express delivery sent to other areas (which can be other sample areas or other non-sample areas) from the sample area; the discount of each logistics industry of the sample area refers to the discount of the freight note of each logistics industry of the sample area; the industry speed increasing can be the speed increasing of express quantity of each logistics industry of the sample area, and can also be the speed increasing of income value of each logistics industry of the sample area.
102. And predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data.
In some embodiments of the present invention, the specific information included in the future logistics data can be determined according to historical logistics data, such as: when the historical logistics data comprises historical sales data in a sample region within a historical preset time period, the predicted logistics data comprises future sales data in the sample region within a future preset time period.
The method for predicting future logistics data of the plurality of sample areas in each logistics industry within the future preset time period can comprise step 201 and step 202, and the following steps are detailed:
201. and forecasting the express quantity acceleration of the plurality of sample areas in each logistics industry in a future preset time period based on the historical sales data of each logistics industry of the sample areas.
In some embodiments of the invention, historical sales data of each logistics industry of the sample area can be extracted from historical logistics data of the sample area, and then the express quantity acceleration of each logistics industry of the sample area in a future preset time period is predicted by adopting a time series prediction algorithm according to the change rule of the historical sales data of each logistics industry of the sample area.
Wherein the historical sales data may include one or both of express delivery and revenue values over a historical preset time period. It can be understood that the income value of each logistics industry in the sample area is generally in a direct proportion relation with the express delivery amount, the larger the express delivery amount of each logistics industry in the sample area is, the higher the income value of each logistics industry in the sample area is, and conversely, the lower the income value of each logistics industry in the sample area is. Therefore, the change rule of the income value of each logistics industry in the sample area is basically consistent with the change rule of the express delivery quantity of each logistics industry in the sample area. The express quantity acceleration of each logistics industry of the sample area in a future preset time period can be predicted according to the change rule of the historical income value of each logistics industry of the sample area.
Of course, the historical sales data may also include other data capable of predicting the express quantity acceleration of each logistics industry in the sample area in the future preset time period, which is not limited herein.
202. And calculating future sales data of the logistics industries of the plurality of sample areas in a future preset time period based on the express quantity acceleration and historical sales data of the plurality of sample areas in the logistics industries.
In some embodiments of the present invention, a certain business logic relationship exists between the express delivery a of each logistics industry in the sample area in the future preset time period, the express delivery b of each logistics industry in the preset historical time period of the sample area, and the express delivery acceleration c in the sample area in the future preset time period. After the express quantity acceleration rate c of each logistics industry in the sample area in the future preset time period is predicted, the express quantity a of each logistics industry in the future preset time period of a plurality of sample areas can be calculated based on the express quantity acceleration rate c and the service logic of each logistics industry in the future preset time period of the sample area.
In some embodiments of the present invention, the express delivery amount a, the express delivery amount b, and the express delivery amount increase rate c may satisfy: a ═ b × c. Next, the express delivery amount and the express delivery amount increase rate in a certain sample area in 2018 are predicted, and the express delivery amount increase rate in the sample area in 2019 are described as an example.
In 2018, the express quantity of a certain local area is b, and the express quantity is accelerated to be n; the express quantity predicted in the sample area in 2019 is a, the express quantity acceleration is c, based on the express quantity acceleration n of a certain sample local area in 2018, the acceleration correction number of the sample area in 2019 is calculated by adopting a time series prediction algorithm, and then according to a formula: and (c) n (1+ speed increase correction number), and calculating to obtain the express delivery speed increase c of the sample area in 2019. Then according to the formula: and b, calculating to obtain the express delivery a of the sample area in 2019.
Of course, the express delivery of each logistics industry in the sample area in the future preset time period can be predicted in other ways, and details are not repeated here.
In addition, it should be noted that, because the income value of each logistics industry in the sample area is generally in a direct proportion relation with the express delivery, the income value of each logistics industry in a plurality of sample areas in a preset time period in the future can be calculated by combining the express delivery speed increase in each logistics industry in the sample area in a preset time period in the future with the service logic.
103. And clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on the logistics industry dimension.
In some embodiments of the present invention, the region clustering result of the plurality of sample regions in the logistics industry dimension refers to: in the same logistics industry, clustering is carried out on a plurality of sample areas, and the plurality of sample areas are distinguished into different categories to obtain area clustering results. Therefore, the region clustering result of the plurality of sample regions in the logistics industry dimension comprises a plurality of categories, and each category comprises at least one sample region. And the obtained clustering results are different according to different specific information included in the historical logistics data of the sample area.
In some embodiments of the invention, the logistics industries of a plurality of sample areas may be clustered by a clustering model.
Specifically, the historical logistics data of the sample area may include historical sales data of each logistics industry of the sample area, and the historical sales data of each logistics industry of the plurality of sample areas is input into a preset clustering model to obtain an area clustering result of the industry dimension.
The clustering model can cluster each logistics industry of a plurality of sample areas by adopting a k-means clustering algorithm (k-means clustering algorithm). The K-means clustering algorithm is a clustering analysis algorithm for iterative solution, and comprises the steps of randomly selecting K objects as initial clustering centers, then calculating the distance between each object and each seed clustering center, and allocating each object to the nearest clustering center. The cluster centers and the objects assigned to them represent a cluster, and the cluster centers of the clusters are recalculated based on the objects existing in the cluster, for each sample assigned. This process will be repeated until some termination condition is met. The termination condition may be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers are changed again, and the sum of squared errors is locally minimal.
Of course, the Clustering model may also adopt a center-point (K-mediads) Clustering algorithm, a DBSCAN (sensitivity-Based Clustering of Applications with Noise) Clustering algorithm, etc., and since these Clustering algorithms are prior art, they will not be described in detail herein.
In some embodiments of the invention, the historical logistics data for the plurality of sample regions input to the clustering model may also include component amounts, discounts, industry labels, and the like to each flow direction.
The following description will take 3C electronics industry dimension as an example to cluster each sample area.
The clustering model adopts a k-means clustering algorithm, wherein the number of clustering centers is 10. And inputting the express delivery quantity, income value, component quantity to each flow direction, discount, industry label and the like of the sample area in the 3C electronic industry into the clustering model, so that a 3C electronic industry dimension area clustering result can be obtained. The region clustering result may specifically include a first category, a second category, a third category, and so on of at least one sample region, where the first category may include: beijing, Suzhou, Jinan, Qingdao, tobacco station, Hangzhou, Shaoxing, Taizhou, and Xiamen; the second category may include Guangzhou region, Shanghai region, Nanjing region, Jizhou region, Wuxi region, Ningbo region, Jinhua region, Shenzhen region, and Foshan region, which are not listed here.
It should be noted that, the foregoing step 102 and step 103 have no precedence relationship, that is, the step 102 may be performed before the step 103, or after the step 103, or the step 102 and the step 103 are performed simultaneously, which is not limited herein.
104. And determining the business target of the target region based on the future logistics data and the region clustering result.
In the embodiment of the invention, the region clustering result of the industry dimension is obtained by clustering each logistics industry of a plurality of sample regions, the logistics data of each logistics industry of a plurality of sample regions in a future preset time period is predicted, and then the service target of the target region is obtained by combining the region clustering result of the industry dimension and the predicted logistics data. The method not only greatly reduces manual participation, simplifies data storage, but also can more accurately predict the business target of the target area, thereby better providing reference for the operation organization, enabling the operation organization to have clear positioning on the development prospect of each industry, and accurately determining the corresponding KPI aiming at different target areas.
In some embodiments of the present invention, as shown in fig. 2, determining the business target of the target region based on the future logistics data and the region clustering result may include steps 401 to 403, which are described in detail as follows:
401. and determining the benchmarking areas corresponding to the target areas in each logistics industry in the plurality of sample areas according to the area clustering result.
In some embodiments of the invention, the benchmarking areas may be areas where the historical sales data meets a preset condition in the same category of the area clustering results of the logistics industry dimension. Different benchmarking areas will be available for different industries of the target area. In addition, the target area is also a sample area, and thus, when the target area is an area where historical sales data satisfies a preset condition in the same category of the area clustering result of the logistics industry dimension, the target area is its own benchmarking area.
In some embodiments of the present invention, determining the target area in the benchmarking area corresponding to each logistics industry may include steps 4011 to 4013, which are described in detail as follows:
4011. and acquiring a target type corresponding to the target area.
In some embodiments of the present invention, the target category corresponding to the target area refers to a category in which the target area is located in the area clustering result of the logistics industry dimension. Wherein, the corresponding target types of the target areas in different logistics industries are different. Specific examples thereof include: the corresponding target category of the Beijing area in the 3C electronic industry is the first category in the area clustering result of the 3C electronic industry; the target category corresponding to the Guangzhou district in the 3C electronic industry is the second category in the regional clustering results of the 3C electronic industry.
4012. And respectively taking each logistics industry in the target region as a target logistics industry, and acquiring the gross interest rate and the accumulated income achievement rate of each sample region in the target logistics industry under the target category.
In some embodiments of the present invention, since in different logistics industries, the target categories corresponding to the target areas may also be different. For a first logistics industry of a target area, the first logistics industry can be used as the target industry, a first target category corresponding to the target area in the area clustering result of the first logistics industry is obtained, and the gross interest rate and the accumulated income achievement rate of the first logistics industry are obtained from various local areas of the first target category. By adopting the method, the gross interest rate and the accumulated income achievement rate of each sample area in the target logistics industry under the target category can be obtained by respectively taking each logistics industry in the target area as the target logistics industry.
4013. And taking the sample area with the highest gross interest rate and the accumulated income achievement rate larger than or equal to a first threshold value in the target category in the target logistics industry as the target area in the target logistics industry.
In some embodiments of the present invention, there is a greater similarity between the sample region and the target region under the same target category of the same target logistics industry, so that the sample region with the highest gross profit rate of the target logistics industry and the accumulated revenue achievement rate greater than or equal to the first threshold is used as the benchmarking region of the target region under the target category in the target logistics industry. The method can play a good reference role in the sub-target logistics industry of the target area.
Specific examples thereof include: the target type of the Beijing area in the 3C electronic industry is the first type, the sample area with the highest gross profit rate in the 3C electronic industry and the accumulated income achievement rate larger than or equal to the first threshold value is used as the benchmarking area of the Beijing area in the 3C electronic industry.
Similarly, the beijing area needs to be determined in the area clustering results of other logistics industries in the benchmarking areas of other logistics industries, and details are not repeated here.
402. And acquiring post logistics data of each logistics industry of the target area based on the future logistics data, wherein the post logistics data are the future logistics data of the current logistics industry in the post area corresponding to the current logistics industry of the target area.
In some embodiments of the present invention, after determining that the target area is in the target area of the current logistics industry, the future logistics data of the target area in each logistics industry within a future preset time period of the target area may be obtained, and then the future logistics data of the target area in the current logistics industry is obtained from the future logistics data to serve as the target logistics data of the target area in the current logistics industry.
For example, the benchmarking logistics data of the beijing area in the 3C electronic industry needs to determine the benchmarking area of the beijing area in the first category, then obtain future logistics data of the benchmarking area, and finally obtain the logistics data of the benchmarking area in the 3C electronic industry from the future logistics data, as the benchmarking logistics data of the target area in the 3C industry.
403. And determining the business target of the target area based on the benchmark logistics data of each logistics industry of the target area.
In some embodiments of the invention, the business objective for the target region may be sales data. The sales data may include express delivery and/or revenue values, and may also include delivery, gross rate, market share, etc. for each flow.
In some embodiments of the present invention, the method for determining a business target of a target area based on the benchmarking logistics data of each logistics industry of the target area may include step 4031 and step 4032, which is described in detail as follows:
4031. and taking the benchmarking logistics data of the target area in each logistics industry as a sub-business target of the target area in each logistics industry.
In some embodiments of the invention, a greater similarity exists between the sample area and the target area under the same target category of the same target logistics industry, and the benchmarking logistics data under each logistics industry of the target area is used as the sub-business target of the target area in each logistics industry, so that the completion difficulty of the sub-business target of each logistics industry of the target area can be more reasonable.
Of course, the benchmarking logistics data of the logistics industry corresponding to the benchmarking area can be expanded or reduced in a certain proportion according to the actual situation, and then the benchmarking logistics data can be used as the sub-business target of the target area in the logistics industry.
4032. And integrating the sub-business targets of the target area in each logistics industry to form the business target of the target area.
In some embodiments of the present invention, the sub-business targets of each logistics industry in the target area are determined, and then the sub-business targets are integrated together to form the overall business target of the target area, so that the business target of the target area can be more detailed and accurate.
In some embodiments of the present invention, the business target prediction method may further perform industry clustering on the logistics industry of the target region by using a boston matrix based on the historical logistics data of the target region to obtain logistics industry clustering information of the target region, and add the logistics industry clustering information to the business target of the target region.
In some embodiments of the present invention, as shown in fig. 3, the market share and the gross profit rate of each logistics industry in a sample area can be obtained; then, expressing the gross profit rate by a vertical axis, expressing the market share by a horizontal axis, establishing a coordinate system, and adding each logistics industry of the sample area into the coordinate system according to the market share and the gross profit rate; and then, determining standard lines of the ordinate and the abscissa by using the median, the average, the quantile and the like. The determination mode of the standard line can be specifically determined according to the distribution condition of each logistics industry in the coordinate system. For example: when the market share or the gross interest rate difference between the logistics industries is large, the median can be adopted to determine the standard line of the ordinate or the abscissa. And finally, dividing the coordinate system into four quadrants according to the standard lines of the ordinate and the abscissa.
The logistics industry with higher market share and higher profit rate is defined as key industry, which means that the logistics types of the logistics industries are more popular in the area, and the operation strategy of the company is reasonable, the operation is better, and the existing manpower and material resources are required to be fully utilized to keep advantages. The logistics industry with higher gross profit rate and lower market share is a potential industry, which shows that the resource development is properly mobilized in the logistics industry, and more ideal return can be obtained. The logistics industry with higher market share and lower gross interest rate is a maintenance industry, and the logistics industry comprises logistics types generally required by some customers, but the logistics types have lower gross interest rate and can keep stable development. Finally, the logistics industry with lower market share and lower capillary rate is a cautious industry, the emphasis can be slightly reduced, and the logistics industry is gradually improved and perfected.
Therefore, the logistics industry of the target area can be subjected to industry grouping to obtain the logistics industry grouping information of the target area, and the logistics industry of the target area can be determined to belong to any one of key industry, potential industry, maintenance industry and cautious industry.
The embodiment of the invention adds the logistics industry grouping information into the business target of the target area, can improve the business target of the target area, and can accurately position the development prospect of each industry by the operation organization.
In order to better implement the method for predicting a business objective in the embodiment of the present invention, based on the method for predicting a business objective, an embodiment of the present invention further provides a device 500 for predicting a business objective, as shown in fig. 4, the device 500 for predicting a business objective includes a first obtaining module 510, a predicting module 520, a clustering module 530, and a determining module 540, where the first obtaining module 510 is configured to obtain historical logistics data of a plurality of sample regions, where the plurality of sample regions include the target region; the prediction module 520 is configured to predict future logistics data of the plurality of sample areas in each logistics industry within a future preset time period based on the historical logistics data; the clustering module 530 is configured to cluster the logistics industries of the multiple sample areas based on the historical logistics data to obtain area clustering results of the multiple sample areas in the logistics industry dimension; the determining module 540 is configured to determine a business objective of the objective region based on the future logistics data and the region clustering result.
In some embodiments of the invention, the historical logistics data comprises historical sales data for each logistics industry in a sample region; the prediction module 520 includes:
the prediction submodule is used for predicting express quantity acceleration of the plurality of sample areas in each logistics industry in a future preset time period based on historical sales data of each logistics industry of the sample areas;
and the calculation module is used for calculating future sales data of each logistics industry of the plurality of sample areas in a future preset time period based on the express quantity acceleration rate and the historical sales data of each logistics industry of the plurality of sample areas.
In some embodiments of the present invention, the clustering module 530 is configured to input historical sales data of each logistics industry in the plurality of sample areas into a preset clustering model, so as to obtain area clustering results of the plurality of sample areas in the logistics industry dimension.
In some embodiments of the present invention, the determining module 540 comprises:
the benchmark region determining module is used for determining the benchmark regions corresponding to the target regions in each logistics industry in the plurality of sample regions according to the region clustering result;
the second acquisition module is used for acquiring benchmark logistics data of the target area in each logistics industry based on the future logistics data, wherein the benchmark logistics data are future logistics data of the target area in the benchmark area corresponding to the current logistics industry and the current logistics industry;
and the business target determining module is used for determining the business target of the target area based on the benchmark logistics data of each logistics industry of the target area.
In some embodiments of the present invention, the region clustering result of the plurality of sample regions in the logistics industry dimension comprises a plurality of categories, each category comprising at least one sample region; the benchmarking area determination module 540 includes:
the third acquisition module is used for acquiring a target type corresponding to the target area;
the fourth acquisition module is used for respectively taking each logistics industry in the target area as a target logistics industry and acquiring the gross interest rate and the accumulated income achievement rate of each sample area in the target logistics industry under the target category;
and the first sub-determination module is used for taking the sample area which has the highest gross interest rate in the target logistics industry and the accumulated income achievement rate which is greater than or equal to a first threshold value under the target category as the target area in the target logistics industry.
In some embodiments of the present invention, the service targeting module includes:
the second sub-determining module is used for taking the benchmark logistics data of the target area in each logistics industry as sub-business targets of the target area in each logistics industry;
and the third sub-determining module is used for integrating the sub-business targets of the target area in each logistics industry to form the business target of the target area.
In some embodiments of the present invention, the system further includes a clustering module, wherein the clustering module performs industry clustering on the logistics industry of the target region by using a boston matrix based on the historical logistics data of the target region to obtain logistics industry clustering information of the target region; and adding the logistics industry grouping information into a business target of the target area.
The embodiment of the present invention further provides a server, which integrates any one of the prediction apparatuses of the service targets provided by the embodiments of the present invention, where the server includes:
one or more processors;
a memory; and
one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to perform the steps of the method for predicting a business objective described in any of the above embodiments of the method for predicting a business objective.
The embodiment of the invention also provides a server, which integrates the prediction device of any service target provided by the embodiment of the invention. As shown in fig. 5, it shows a schematic structural diagram of a server according to an embodiment of the present invention, specifically:
the server may include components such as a processor 601 of one or more processing cores, memory 602 of one or more computer-readable storage media, a power supply 603, and an input unit 604. Those skilled in the art will appreciate that the server architecture shown in FIG. 5 is not meant to be limiting, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:
the processor 601 is a control center of the server, connects various parts of the entire server using various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 602 and calling data stored in the memory 602, thereby performing overall monitoring of the server. Optionally, processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 601.
The memory 602 may be used to store software programs and modules, and the processor 601 executes various functional applications and data processing by operating the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the server, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.
The server further includes a power supply 603 for supplying power to each component, and preferably, the power supply 603 may be logically connected to the processor 601 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply 603 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The server may also include an input unit 604, which input unit 604 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the server may further include a display module and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 601 in the server loads the executable file corresponding to the process of one or more application programs into the memory 602 according to the following instructions, and the processor 601 runs the application programs stored in the memory 602, thereby implementing various functions as follows:
obtaining historical logistics data of a plurality of sample areas, wherein the plurality of sample areas comprise target areas;
predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data;
clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on logistics industry dimensions;
and determining the business target of the target region based on the future logistics data and the region clustering result.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, an embodiment of the present invention provides a storage medium, which may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like. The storage medium stores a plurality of instructions, which can be loaded by the processor to perform the steps of any of the business target prediction methods provided by the embodiments of the present invention. For example, the instructions may perform the steps of:
obtaining historical logistics data of a plurality of sample areas, wherein the plurality of sample areas comprise target areas;
predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data;
clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on logistics industry dimensions;
and determining the business target of the target region based on the future logistics data and the region clustering result.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed descriptions of other embodiments, and are not described herein again.
In a specific implementation, each of the modules or structures may be implemented as an independent entity, or may be combined arbitrarily to be implemented as one or several entities, and specific implementations of each of the modules or structures may refer to the foregoing method embodiments, which are not described herein again.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
The service target prediction method, device, server and storage medium provided by the embodiments of the present invention are described in detail above, and a specific example is applied in the present disclosure to explain the principle and implementation of the present invention, and the description of the above embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method for predicting a business objective, comprising:
obtaining historical logistics data of a plurality of sample areas, wherein the plurality of sample areas comprise target areas;
predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data;
clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on logistics industry dimensions;
and determining the business target of the target region based on the future logistics data and the region clustering result.
2. The method for predicting business objectives according to claim 1, wherein the historical logistics data includes historical sales data of each logistics industry of a sample region; predicting future logistics data of the plurality of sample areas in each logistics industry within a future preset time period based on the historical logistics data, wherein the predicting future logistics data comprises the following steps:
forecasting express quantity acceleration of the plurality of sample areas in each logistics industry in a future preset time period based on historical sales data of each logistics industry of the sample areas;
and calculating future sales data of the logistics industries of the plurality of sample areas in a future preset time period based on the express quantity acceleration and historical sales data of the plurality of sample areas in the logistics industries.
3. The method for predicting business objective of claim 2, wherein the clustering the logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas in the logistics industry dimension comprises:
and inputting historical sales data of each logistics industry of the plurality of sample areas into a preset clustering model to obtain area clustering results of the plurality of sample areas on the logistics industry dimension.
4. The method for predicting business objectives according to claim 3, wherein said determining business objectives of said target region based on said future logistics data and said region clustering result comprises:
determining benchmarking areas corresponding to the target areas in each logistics industry in the plurality of sample areas according to the area clustering result;
obtaining post logistics data of the target area in each logistics industry based on the future logistics data, wherein the post logistics data are the future logistics data of the target area in the post area corresponding to the current logistics industry and the current logistics industry;
and determining the business target of the target area based on the benchmark logistics data of each logistics industry of the target area.
5. The business objective prediction method of claim 4, wherein the region clustering result of the plurality of sample regions in the logistics industry dimension comprises a plurality of categories, each category comprising at least one sample region; determining the benchmarking areas corresponding to the target areas in each logistics industry in the plurality of sample areas according to the area clustering result, wherein the benchmarking areas comprise:
acquiring a target type corresponding to the target area;
respectively taking each logistics industry in the target region as a target logistics industry, and acquiring the gross interest rate and the accumulated income achievement rate of each sample region in the target logistics industry under the target category;
and taking the sample area with the highest gross interest rate and the accumulated income achievement rate larger than or equal to a first threshold value in the target category in the target logistics industry as the target area in the target logistics industry.
6. The method for forecasting a business objective of claim 4, wherein the determining a business objective of a target region based on benchmarking logistics data of logistics industries of the target region comprises:
the benchmarking logistics data of the target area under each logistics industry are used as sub-business targets of the target area in each logistics industry;
and integrating the sub-business targets of the target area in each logistics industry to form the business target of the target area.
7. The method for predicting a business objective of any one of claims 1 to 6, further comprising:
based on the historical logistics data of the target area, performing industry grouping on the logistics industry of the target area by adopting a Boston matrix to obtain logistics industry grouping information of the target area;
and adding the logistics industry grouping information into a business target of the target area.
8. An apparatus for predicting a business objective, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical logistics data of a plurality of sample areas, and the plurality of sample areas comprise target areas;
the prediction module is used for predicting future logistics data of the plurality of sample areas in each logistics industry in a future preset time period based on the historical logistics data;
the clustering module is used for clustering each logistics industry of the plurality of sample areas based on the historical logistics data to obtain area clustering results of the plurality of sample areas on the logistics industry dimension;
and the determining module is used for determining the business target of the target region based on the future logistics data and the region clustering result.
9. A server, characterized in that the server comprises:
one or more processors;
a memory; and
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the method of predicting a business objective of any of claims 1 to 7.
10. A storage medium storing a plurality of instructions adapted to be loaded by a processor to perform the steps of the method of predicting a business objective of any one of claims 1 to 7.
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