CN111369121A - Client data processing method and device, computer equipment and storage medium - Google Patents
Client data processing method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a client data processing method, a client data processing device, computer equipment and a storage medium, wherein the method comprises the following steps: segmenting time, and acquiring index data of a client in each time period; making a target time sequence of the index data changing along with time; presetting a standard time sequence, wherein the standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence; comparing the target time sequence with the standard time sequence, and calculating a slope distance; classifying the customer as a developing customer, a saving customer, or a keeping customer according to the slope distance; and saving the type of the client and displaying. According to the invention, the clients are effectively classified according to the client historical data, the classification result is closer to the real application scene, and the classification result is more accurate and reliable.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a client data processing method and device, computer equipment and a storage medium.
Background
Scientific customer relationship management is an important guarantee for aggregating customers and promoting enterprise business development. Customer information is the source of all transactions. Due to the characteristics of the customer information, scientific customer relationship management is required for information processing, information mining, information extraction and reutilization. By customer relationship management, maximization and optimization of customer information utilization can be achieved. The coming of the information age also enables the marketing focus of enterprises to be changed from a product center to a customer center, and the customer relationship management becomes a core problem of the enterprises. The key problem of customer relationship management is customer classification, and non-value customers and high-value customers are distinguished through customer classification. After the client type is determined, the enterprise can make an optimized personalized client service scheme aiming at clients with different values, and adopt different marketing strategies to concentrate effective marketing resources on the high-value clients, so that the profit maximization target of the enterprise is realized. Accurate customer value classification results are important bases for optimizing marketing resource allocation of enterprises, and customer value classification increasingly becomes one of key problems to be solved urgently in customer relationship management. However, the prior art cannot realize the function of effectively classifying the clients according to the historical data of the clients.
Disclosure of Invention
The embodiment of the invention provides a client data processing method, a client data processing device, computer equipment and a storage medium, and aims to solve the problem that clients cannot be effectively classified according to client historical data in the prior art.
The embodiment of the invention provides a client data processing method, which comprises the following steps:
segmenting time, and acquiring index data of a client in each time period;
making a target time sequence of the index data changing along with time;
presetting a standard time sequence, wherein the standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence;
comparing the target time sequence with the standard time sequence, and calculating a slope distance;
classifying the customer as a developing customer, a saving customer, or a keeping customer according to the slope distance;
and saving the type of the client and displaying.
Preferably, the classifying the customer into a developing customer, a saving customer or a keeping customer according to the slope distance comprises:
judging whether the slope distance between the target time sequence and a standard ascending sequence is smaller than m1, if so, classifying the client as a development client; if not, judging whether the slope distance between the target time sequence and a standard descending sequence is less than m2, if so, classifying the client as a retained client, if not, judging whether the slope distance between the target time sequence and a standard maintaining sequence is less than m3, and if so, classifying the client as a retained client.
Preferably, the comparing the target time series with the standard time series and calculating the slope distance includes:
definitions S' and S "represent the target time series and standard time series, respectively, expressed in equal length and in slope sets:
S'＝{(k'1,t'2),…,(k'i-1,t'i),…,(k'n-1,t'n)}，
S”＝{(k”1,t”2),…,(k”i-1,t”i),…,(k”n-1,t”n)}；
the slope distance of S' and S "is calculated as follows:
and is
Wherein, t'iRepresenting the end time, k ', of the i-1 th time segment of the target time series'i-1The slope of the i-1 time segment of the target time sequence is shown; t'iDenotes the end time, k, of the i-1 th time segment of the standard time series "i-1Is the slope of the i-1 th time segment of the standard time series, ti-ti-1The time length of the i-1 th time period is indicated.
Preferably, the method further comprises the following steps:
and if the index data of a plurality of continuous time periods is lower than a preset value, classifying the client as a low-activity client.
Preferably, the saving and displaying the type of the client includes:
and calculating the proportion of each type of client to all the clients, and displaying the calculated proportion and the details of the clients.
Preferably, the calculating the proportion of each type of customer to all customers and displaying the calculated proportion and the customer details includes:
the proportion of each type of customer to all customers is shown in a graph.
Preferably, the standard ascending sequence, the standard descending sequence and the standard maintaining sequence are all standard time sequences with the same slope of each time segment.
An embodiment of the present invention further provides a device for processing client data, where the device includes:
the client data acquisition unit is used for segmenting time and acquiring index data of a client in each time period;
the time sequence making unit is used for making a target time sequence of the index data changing along with time;
the time sequence presetting unit is used for presetting a standard time sequence, and the standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence;
the slope distance calculation unit is used for comparing the target time sequence with the standard time sequence and calculating the slope distance;
a classification unit for classifying the customer into a developing customer, a saving customer or a keeping customer according to the slope distance;
and the display unit is used for storing the type of the client and displaying the type.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the processing method of the client data as described above when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the processing method of client data as described above.
The embodiment of the invention provides a client data processing method, a client data processing device, computer equipment and a storage medium, wherein the method comprises the following steps: segmenting time, and acquiring index data of a client in each time period; making a target time sequence of the index data changing along with time; presetting a standard time sequence, wherein the standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence; comparing the target time sequence with the standard time sequence, and calculating a slope distance; classifying the customer as a developing customer, a saving customer, or a keeping customer according to the slope distance; and saving the type of the client and displaying. According to the embodiment of the invention, the clients are effectively classified according to the historical data of the clients, the classification result is closer to the real application scene, and the classification result is more accurate and reliable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are 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 schematic flowchart of a method for processing client data according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a standard ascending sequence according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a standard hold sequence provided by an embodiment of the present invention;
FIG. 4 is a diagram of a standard descent sequence provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a sequence comparison of a developing client provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of a sequence alignment of a keep client according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a sequence comparison of a retained client according to an embodiment of the present invention;
FIG. 8 is a schematic time-series diagram of a low-activity client according to an embodiment of the present invention;
fig. 9 is a schematic block diagram of a client data processing apparatus according to an 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 some, not all, embodiments of the present invention. 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.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic flow chart of a client data processing method according to an embodiment of the present invention, including steps S101 to S106:
s101, segmenting time, and acquiring index data of a client in each time period;
s102, making a target time sequence of the index data changing along with time periods;
s103, presetting a standard time sequence, wherein the standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence;
s104, comparing the target time sequence with the standard time sequence, and calculating a slope distance;
s105, classifying the client into a development client, a saving client or a maintenance client according to the slope distance;
and S106, saving the type of the client and displaying.
In the embodiment of the invention, the standard time sequence is preset, the index data of the client in each time period is obtained, and the target time sequence of the index data changing along with the time period is manufactured according to the standard time sequence, so that the target time sequence and the standard time sequence can be compared, the slope distance is calculated, and the client is classified and displayed according to the slope distance. By the embodiment of the invention, the customers can be accurately classified and displayed.
In step S101, the time is segmented, and index data of the client at each time period is acquired.
The embodiment of the invention acquires the historical index data of the client in advance, namely the index data in each past time period.
Each time segment is preferably a time segment with equal length, that is, the length of each time segment is the same, so that statistics and calculation can be conveniently performed.
In the embodiment of the present invention, the index data may adopt different indexes according to different application scenarios, for example, the index data may be a transaction amount, a transaction number, or an online time length. In the embodiment of the present invention, the index data may be data of a single index, or may be index data obtained by integrating a plurality of index data, and may be specifically adjusted according to the needs of an application scenario.
For example, assuming that the index data is the transaction amount, the transaction amount data of all the transaction amounts of the customer in each time period in the past period can be acquired, such as acquiring the transaction amount data of each day of the customer in the past month, wherein the period is one month and the time period is one day. Of course, the data of the transaction amount of the client in the past year can also be obtained, wherein the period is one year and the time period is one week.
In the step S102, a target time series of the index data changing over a period of time needs to be made. The target time series refers to a series in which the abscissa is time and the ordinate is index data.
In the step S103, a standard time series is set in advance. The standard time series is used as a standard series for measuring the type of the target time series.
The standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence.
In one specific application scenario, the standard ascending sequence is shown in FIG. 2, the standard maintaining sequence is shown in FIG. 3, and the standard descending sequence is shown in FIG. 4.
In step S104, the target time series is compared with the standard time series, and a slope distance is calculated.
Specifically, the comparison between the target time series and the standard time series is shown in fig. 5 to 7.
In a specific application scenario, as shown in fig. 5, it is a graph of the target time series of client 1, client 2 and client 3 compared to the standard ascending series; as shown in fig. 6, which is a graph of the target time series of client 1, client 2 and client 3 compared to the standard hold series; as shown in fig. 7, which is a graph of the target time series of client 1, client 2 and client 3 compared to the standard descent series.
In one embodiment, the step S104 includes:
definitions S' and S "represent the target time series and standard time series, respectively, expressed in equal length and in slope sets:
S'＝{(k'1,t'2),…,(k'i-1,t'i),…,(k'n-1,t'n)}，
S”＝{(k”1,t”2),…,(k”i-1,t”i),…,(k”n-1,t”n)}；
the slope distance of S' and S "is calculated as follows:
and is
Wherein, t'iRepresenting the end time, k ', of the i-1 th time segment of the target time series'i-1The slope of the i-1 time segment of the target time sequence is shown; t'iDenotes the end time, k, of the i-1 th time segment of the standard time series "i-1Is the slope of the i-1 th time segment of the standard time series, ti-ti-1The time length of the i-1 th time period is indicated.
ti-ti-1The effect of (1) is weighting, the longer the time, the more weight it takes. DkThe more S ', S' tend to be 0, the more similar the two sequences, defining D when the two sequences are similarkThe value range of S 'and S' can be determined only by carrying out a large number of experiments on practical application occasions.
In the embodiment of the invention, the target time sequence and the standard time sequence are two sequences with equal length, and the meaning of equal length means that the time periods corresponding to the two sequences are the same, namely t'iAnd t'iSame, that is to say, t'2And t'2Same, t'3And t'3Same, t'4And t'4The same, and so on. Thus, the two sequencesThe time lengths of the line i-1 time periods are the same, namely t'i-t'i-1＝t”i-t”i-1In this embodiment, t isi-ti-1And (4) performing representation.
In the target time series, the slope of the 1 st time segment is k'1The slope of the 2 nd period is k'2The slope of the 3 rd time segment is k'3The slope of the 4 th time segment is k'4By analogy, the slope of the n-1 time segment is k'n-1. In the standard time series, the slope of the 1 st time segment is k "1The slope of the 2 nd time segment is k "2The slope of the 3 rd time segment is k "3The slope of the 4 th time segment is k "4By analogy, the slope of the (n-1) th time segment is k "n-1。
In the embodiment of the invention, the slope of the corresponding time period can be calculated by only acquiring the index data of the beginning and the end of two time points of each time period. I.e. the sequence of time segments may form a straight line, thereby obtaining a corresponding slope. Of course, in some application scenarios, other index data of the time period except for the first time point and the last time point may also be obtained, and the average slope of the time period (which may be an arithmetic average, a geometric average, a root-mean-square average, or the like) is calculated through some mathematical algorithms, which is more accurate for some scenarios that do not change according to a straight line rule.
In step S105, the customer needs to be categorized according to the slope distance, that is, the type of the customer is determined, and specifically, the customer may be a developing customer, a staying customer, or a keeping customer.
In one embodiment, the step S105 includes:
judging whether the slope distance between the target time sequence and a standard ascending sequence is smaller than m1, if so, classifying the client as a development client; if not, judging whether the slope distance between the target time sequence and a standard descending sequence is less than m2, if so, classifying the client as a retained client, if not, judging whether the slope distance between the target time sequence and a standard maintaining sequence is less than m3, and if so, classifying the client as a retained client.
In the embodiment of the present invention, it may be determined whether the slope distance between the target time sequence and the standard ascending sequence is smaller than a preset m1, and if so, the customer may be directly classified as a development customer. If not, the determination may be continued as to whether the slope of the standard descending sequence is less than m2, and if so, the customer may be directly classified as a retained customer. If not, a determination may continue as to whether the slope distance from the standard retention sequence is less than m3, and if so, the customer may be classified as a retention customer.
Of course, in other embodiments, it may also be determined whether the slope distance between the target time series and the standard descending series is smaller than m2, if so, the customer is classified as a saving customer; if not, judging whether the slope distance between the target time sequence and a standard ascending sequence is less than m1, if so, classifying the client as a development client, if not, judging whether the slope distance between the target time sequence and a standard maintaining sequence is less than m3, and if so, classifying the client as a maintenance client.
In addition, the judgment sequence may be adjusted in other embodiments, and the clients may be classified finally.
In S106, the types of the clients may be saved and displayed, so that the data user may conveniently view the types of the clients.
In an embodiment, the method for processing customer data further includes:
and if the index data of a plurality of continuous time periods is lower than a preset value, classifying the client as a low-activity client. For example, as shown in FIG. 8, since client 1 has a transaction amount of 0, the client can be directly classified as a low activity client without comparing with the standard time series.
In addition, in the embodiment of the present invention, since the specific values set by m1, m2, and m3 can be adjusted, it is possible that a client is not classified into any of the above types as a result of classification, that is, a developing client, a saving client, a keeping client, and a low-activity client, and for such a client, it can be classified into other clients.
Of course, m1, m2 and m3 may be adjusted more loosely to try to divide the client into the above known types.
In the embodiment of the present invention, each type may be further subdivided, that is, each type of client may be further subdivided into a plurality of sub-types, so that the development rule of the client may be better embodied, and a more accurate service is provided for the client, and when further subdivision is performed, m1, m2, and m3 may also be correspondingly subdivided to determine the type of the client. For example, developing customers are divided into two stages: the first-level development client and the second-level development client, and the remaining clients are divided into two levels: the first level retains customers and the second level retains customers, and the maintenance customers are divided into two levels, a first level maintenance customer and a second level maintenance customer. And m1 is set to two: m11 and m12, and m11 is less than m 12; m2 is set to two: m21 and m22, and m21 is less than m 22; m3 is set to two: m31 and m32, and m31 is less than m 32. Thus, when the slope distance between the target time sequence of a client and the standard ascending sequence is less than m11, the client can be classified as a first-stage development client, and when the slope distance between the target time sequence of a client and the standard ascending sequence is more than m11 and less than m12, the client can be classified as a second-stage development client; when the slope distance between the target time sequence of a client and the standard descending sequence is less than m21, the client can be classified as a first-stage saving client, and when the slope distance between the target time sequence of a client and the standard descending sequence is more than m21 and less than m22, the client can be classified as a second-stage saving client; customers may be classified as first-level retention customers when their target time-series slope distance from the standard retention series is less than m31, and may be classified as second-level retention customers when their target time-series slope distance from the standard retention series is greater than m31 and less than m 32.
Obviously, the number of classifications of the ascenders m1, m2 and m3 is the same as the number of subtypes into which each type of customer is further divided, so according to this case, it is possible to further adjust depending on the actual application scenario.
For low activity customers, the number of consecutive time periods may also be adjusted according to the actual application scenario. For example, if the index data of 10 consecutive time periods is lower than a preset value, the client is classified as a low activity client. In addition, the preset value can be adjusted according to the actual application scene, so that some customers with similar characteristics can be classified into low-activity customers.
In one embodiment, the step S106 includes:
and calculating the proportion of each type of client to all the clients, and displaying the calculated proportion and the details of the clients.
In the embodiment, the proportion of each type of client to all clients can be counted, so that a data user can intuitively know the current service development condition and the client proportion, and conveniently and pertinently adjust the service direction and the like. Customer details may include the type of customer, which may include developing customers, saving customers, keeping customers, and low activity customers.
In addition, in addition to the presentation proportion data, the total amount data of each type of customer and the total amount data of all types of customers may be presented at the same time, for example, the total amount of developing customers is a, the total amount of retained customers is B, the total amount of retained customers is C, the total amount of low-activity customers is D, and the total amount of all types of customers is S, so that the presented contents may be the developing customers a/S and the calculated proportion value, the retained customers B/S and the calculated proportion value, the retained customers C/S and the calculated proportion value, and the low-activity customers D/S and the calculated proportion value.
In an embodiment, the calculating the proportion of each type of customer to all customers and displaying the calculated proportion and the customer details includes:
the proportion of each type of customer to all customers is shown in a graph.
In this embodiment, the scale may be displayed in the form of a graph, for example, in the form of a pie chart, or in the form of a bar chart.
In addition, in the embodiment of the present invention, all information of the client type may be displayed in a single module unit. That is, the information related to the types of customers is all put into the fixed area of the interface for display, and the displayed content may report the total amount data of each type of customer and the total amount data of all types of customers, and the proportion of each type of customer to all customers. Meanwhile, a search bar can be arranged on the module unit, a specific type of a client can be searched through the search bar, all clients cannot be displayed completely due to the fact that the total number of the clients is large, the embodiment of the invention can realize the search of the specific type of the client through the search bar, the search can be specifically carried out through the keywords, the keywords are matched, all clients matched with the keywords are inquired, then the information (including the types) of the clients is displayed, and in addition, the displayed content can also comprise a target time sequence and a comparison graph with a standard time sequence.
In addition, in the embodiment of the present invention, each type of customer may be identified in different colors, for example, developing customers are represented by green, maintaining customers are represented by yellow, and saving customers are represented by red.
In one embodiment, the standard ascending sequence, the standard descending sequence and the standard maintaining sequence are all standard time sequences with the same slope of each time segment.
For the standard time sequence, the slope of each time segment of the standard ascending sequence is the same, i.e. a ascending straight line is formed, the slope of each time segment of the standard descending sequence is the same, i.e. a descending straight line is formed, and the slope of each time segment of the standard maintaining sequence is the same, i.e. a horizontal straight line.
An embodiment of the present invention further provides a client data processing apparatus, as shown in fig. 9, where the client data processing apparatus 200 includes:
a client data obtaining unit 201, configured to segment time and obtain index data of a client in each time period;
a time-series creating unit 202 configured to create a target time series in which the index data changes over a period of time;
a time sequence presetting unit 203, configured to preset a standard time sequence, where the standard time sequence includes a standard ascending sequence, a standard descending sequence, and a standard maintaining sequence;
a slope distance calculating unit 204, configured to compare the target time sequence with the standard time sequence, and calculate a slope distance;
a classifying unit 205 for classifying the customer into a developing customer, a saving customer or a keeping customer according to the slope distance;
and the display unit 206 is used for saving the types of the clients and displaying the types.
In one embodiment, the classifying unit 205 is configured to determine whether a slope distance between the target time series and a standard ascending series is less than m1, and if so, classify the customer as a developing customer; if not, judging whether the slope distance between the target time sequence and a standard descending sequence is less than m2, if so, classifying the client as a retained client, if not, judging whether the slope distance between the target time sequence and a standard maintaining sequence is less than m3, and if so, classifying the client as a retained client.
In one embodiment, the slope distance calculation unit 204 includes:
a definition unit, for defining that S 'and S' respectively represent a target time series and a standard time series with equal length and represented by a slope set:
S'＝{(k'1,t'2),…,(k'i-1,t'i),…,(k'n-1,t'n)}，
S”＝{(k”1,t”2),…,(k”i-1,t”i),…,(k”n-1,t”n)}；
the slope distance of S' and S "is calculated as follows:
and is
Wherein, t'iRepresenting the end time, k ', of the i-1 th time segment of the target time series'i-1The slope of the i-1 time segment of the target time sequence is shown; t'iDenotes the end time, k, of the i-1 th time segment of the standard time series "i-1Is the slope of the i-1 th time segment of the standard time series, ti-ti-1The time length of the i-1 th time period is indicated.
In an embodiment, the apparatus 200 for processing client data further includes:
and the low-activity client classification unit is used for classifying the client as a low-activity client if the index data is 0.
In one embodiment, the presentation unit 206 comprises:
and the proportion calculation unit is used for calculating the proportion of each type of client to all the clients and displaying the proportion.
In one embodiment, the ratio calculation unit includes:
and the chart display unit is used for displaying the proportion of each type of customer to all customers in a chart form.
In one embodiment, the standard ascending sequence, the standard descending sequence and the standard maintaining sequence are all standard time sequences with the same slope of each time segment.
An embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the processing method of the client data as described above when executing the computer program.
An embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program causes the processor to execute the processing method of client data as described above.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-only memory (ROM), a mechanical hard disk, a solid state disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for processing customer data, comprising:
segmenting time, and acquiring index data of a client in each time period;
making a target time sequence of the index data changing along with time;
presetting a standard time sequence, wherein the standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence;
comparing the target time sequence with the standard time sequence, and calculating a slope distance;
classifying the customer as a developing customer, a saving customer, or a keeping customer according to the slope distance;
and saving the type of the client and displaying.
2. The method for processing customer data according to claim 1, wherein the classifying the customer into a developing customer, a saving customer or a keeping customer according to the slope distance comprises:
judging whether the slope distance between the target time sequence and a standard ascending sequence is smaller than m1, if so, classifying the client as a development client; if not, judging whether the slope distance between the target time sequence and a standard descending sequence is less than m2, if so, classifying the client as a retained client, if not, judging whether the slope distance between the target time sequence and a standard maintaining sequence is less than m3, and if so, classifying the client as a retained client.
3. The method for processing customer data according to claim 1, wherein comparing the target time series with the standard time series and calculating the slope distance comprises:
definitions S' and S "represent the target time series and standard time series, respectively, expressed in equal length and in slope sets:
S′＝{(k′1，t′2)，…，(k′i-1，t′i)，…，(k′n-1，t′n)}，
S″＝{(k″1，t″2)，…，(k″i-1，t″i)，…，(k″n-1，t″n)}；
the slope distance of S 'and S' is calculated as follows:
and is
Wherein, t'iRepresenting the end time, k ', of the i-1 th time segment of the target time series'i-1The slope of the i-1 time segment of the target time sequence is shown; t'iDenotes the end time, k, of the i-1 th time segment of the standard time series "i-1Is the slope of the i-1 th time segment of the standard time series, ti-ti-1The time length of the i-1 th time period is indicated.
4. The method for processing customer data according to claim 1, further comprising:
and if the index data of a plurality of continuous time periods is lower than a preset value, classifying the client as a low-activity client.
5. The method for processing customer data according to claim 1, wherein the saving and displaying the type of the customer comprises:
and calculating the proportion of each type of client to all the clients, and displaying the calculated proportion and the details of the clients.
6. The method for processing customer data according to claim 5, wherein the calculating the proportion of each type of customer to all customers and displaying the calculated proportion and the customer details comprises:
the proportion of each type of customer to all customers is shown in a graph.
7. The client data processing method according to claim 1, wherein the standard ascending sequence, the standard descending sequence and the standard maintaining sequence are all standard time sequences having the same slope for each time segment.
8. An apparatus for processing customer data, comprising:
the client data acquisition unit is used for segmenting time and acquiring index data of a client in each time period;
the time sequence making unit is used for making a target time sequence of the index data changing along with time;
the time sequence presetting unit is used for presetting a standard time sequence, and the standard time sequence comprises a standard ascending sequence, a standard descending sequence and a standard maintaining sequence;
the slope distance calculation unit is used for comparing the target time sequence with the standard time sequence and calculating the slope distance;
a classification unit for classifying the customer into a developing customer, a saving customer or a keeping customer according to the slope distance;
and the display unit is used for storing the type of the client and displaying the type.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of processing client data according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to execute the processing method of customer data according to any one of claims 1 to 7.
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