CN111160967A

CN111160967A - Data processing method, device and storage medium

Info

Publication number: CN111160967A
Application number: CN201911379987.1A
Authority: CN
Inventors: 周雪琪
Original assignee: Beijing Sensetime Technology Development Co Ltd
Current assignee: Beijing Sensetime Technology Development Co Ltd
Priority date: 2019-12-27
Filing date: 2019-12-27
Publication date: 2020-05-15
Also published as: JP2023507043A; WO2021129342A1

Abstract

The application discloses a data processing method, a device and a storage medium, wherein the data processing method comprises the following steps: acquiring client data of a target person; determining a customer rating of the target person based on the customer data; and determining the data to be pushed corresponding to the customer grade, and pushing the data to be pushed according to the data pushing mode corresponding to the customer grade.

Description

Data processing method, device and storage medium

Technical Field

The present application relates to the field of computer vision, and in particular, to a data processing method, apparatus, and storage medium.

Background

In actual use, the salesperson can receive the customer in a differentiated mode according to the characteristics of the customer, so that the sales conversion rate is improved, and a higher benefit value is obtained. At present, data arrangement and business follow-up work are generally carried out by a salesperson who is responsible for reception and is used for providing services for customers according to data arrangement results. However, data careless frequently occurs when data is manually collated, and the result obtained by different salespeople collation is uneven, thereby affecting the service follow-up effect. Therefore, a data processing method is needed.

Disclosure of Invention

In view of the above, the present application provides a data processing scheme.

In a first aspect, an embodiment of the present application provides a data processing method, where the method includes:

acquiring client data of a target person;

determining a customer rating of the target person based on the customer data;

and determining the data to be pushed corresponding to the customer grade, and pushing the data to be pushed according to the data pushing mode corresponding to the customer grade.

In one possible implementation, the customer level comprises a first customer level for reflecting the character visiting situation and/or a second customer level for reflecting the purchasing intention;

the customer data includes visit data and/or profile data.

In one possible implementation, in a case that the customer data includes visit data and the customer rating includes the first customer rating, the determining the customer rating of the target person based on the customer data includes:

determining the number of visits and/or the average length of a single visit of the target person within a preset time period according to the visit data;

in response to the number of visits being above a first threshold, and/or the single average visit duration being above a first threshold, determining that a first customer rating of the target person indicates that the target person is a high frequency customer.

In one possible implementation, the customer data further includes an identity type, and the determining the customer rating of the target person based on the customer data includes:

according to the visiting data, determining the time interval between the last visiting time of the target person and the current moment, and/or determining the single average visiting duration of the target person in the preset time period;

in response to the time interval exceeding a second threshold and/or the single average visit duration being below a second threshold, determining that the first client level of the target person indicated as a regular customer by the identity type indicates that the target person is an attrition client or determining that the first client level of the target person indicated as an important customer by the identity type indicates that the target person is a sleeping client.

In one possible implementation, the determining the customer rating of the target person based on the customer data in a case where the customer data includes the profile data and the customer rating includes the second customer rating includes:

determining the selling price of the concerned vehicle type according to the concerned vehicle type;

determining a consumption interval corresponding to the target person according to the economic data;

determining a second customer rating for the target person in response to whether the selling price belongs to the consumption zone.

In a possible implementation manner, in a case that the selling price does not belong to the consumption interval, before the pushing the data to be pushed, the method further includes:

and determining data of other vehicle types with the same type as the concerned vehicle type as the data to be pushed.

In a possible implementation manner, the data push manners corresponding to different customer grades are at least partially different; the data pushing mode at least comprises one of pushing time, time interval between two adjacent pushing and pushing approaches.

In one possible implementation, after the determining the client level of the target person, the method further comprises:

and sending the client level of the target person to a terminal so that the terminal displays the client level of the target person in a manner of displaying a person list.

In a second aspect, an embodiment of the present application provides a data processing apparatus, where the apparatus includes:

the acquisition module is used for acquiring client data of a target person;

a determining module for determining a client level of the target person based on the client data;

and the processing module is used for determining the data to be pushed corresponding to the client grade and pushing the data to be pushed according to the data pushing mode corresponding to the client grade.

the customer data includes visit data and/or profile data.

In a possible implementation manner, in a case that the customer data includes visit data and the customer level includes the first customer level, the determining module is configured to:

In one possible implementation, the client data further includes an identity type, and the determining module is configured to:

In a possible implementation manner, the profile data includes economic data of the target person and a vehicle type of interest, and in a case that the customer data includes profile data and the customer rating includes the second customer rating, the determining module is configured to:

In one possible implementation manner, the processing module is further configured to:

and under the condition that the selling price does not belong to the consumption interval, determining data of other vehicle types with the same type as the concerned vehicle type as the data to be pushed before pushing the data to be pushed.

after the determining module determines the client level of the target person, the client level of the target person is sent to a terminal, so that the terminal displays the client level of the target person in a person list displaying mode.

In a third aspect, an embodiment of the present application provides a data processing apparatus, where the apparatus includes: the data processing system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the data processing method of the embodiment.

In a fourth aspect, the present application provides a storage medium storing a computer program, and when the computer program is executed by a processor, the processor is caused to execute the steps of the data processing method according to the present application.

According to the technical scheme provided by the embodiment of the application, the client data of the target person is obtained; determining a customer rating of the target person based on the customer data; and determining the data to be pushed corresponding to the customer grade, and pushing the data to be pushed according to the data pushing mode corresponding to the customer grade. Therefore, the time consumed by data arrangement can be saved, and the problems of time consumption and labor consumption caused by manual data arrangement are solved. Moreover, the client grade of the target character is determined based on the client data, and the client grade of each character can be effectively divided by utilizing the client data so as to play a role in distinguishing or marking different characters; and the data to be pushed is pushed according to the data pushing mode corresponding to the client grade, so that more targeted data pushing can be provided for clients of different client grades, and the customer experience and the sales conversion rate are further improved.

Drawings

Fig. 1 is a schematic flowchart of a data processing method according to an embodiment of the present application;

fig. 2 is a schematic flow chart of another data processing method according to an embodiment of the present disclosure;

fig. 3(a) is a schematic diagram of a system for automatically identifying today's visiting customer information in a terminal side display according to an embodiment of the present application;

fig. 3(b) is a schematic diagram of client information of a user displayed on a terminal side according to an embodiment of the present application;

fig. 3(c) is a schematic diagram of single client information displayed on the terminal side according to an embodiment of the present application;

fig. 3(d) is a schematic view of a client editing interface displayed on a terminal side according to an embodiment of the present application;

FIG. 4 is a schematic flow chart of an analyst tag based on visit frequency according to an embodiment of the present application;

fig. 5(a) is a schematic diagram of a terminal receiving a push access message according to an embodiment of the present application;

fig. 5(b) is a schematic diagram of querying a history visit message of a client according to an embodiment of the present application;

fig. 5(c) is a schematic diagram of a system for automatically identifying a multi-dimensional identity tag displayed on a terminal side according to an embodiment of the present application;

FIG. 5(d) is a schematic diagram of data analysis based on visitor availability within a certain time period according to an embodiment of the present disclosure;

fig. 6 is a schematic diagram of a personnel tag editing interface provided in an embodiment of the present application;

fig. 7 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of another data processing apparatus according to an embodiment of the present application.

Detailed Description

In order to make the embodiments of the present application better understood, the technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments.

The terms "first," "second," and "third," etc. in the description and claims of the present application and the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprises" and "comprising," as well as any variations thereof, are intended to cover a non-exclusive inclusion, such as a list of steps or elements. A method, system, article, or apparatus is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, system, article, or apparatus.

The embodiment of the present disclosure provides a data processing method, and the embodiment of the present disclosure may be applied to various electronic devices including, but not limited to, a fixed device and/or a mobile device, for example, the fixed device includes, but is not limited to: personal Computers (PCs), or servers, which may be cloud servers or ordinary servers, etc. The mobile devices include, but are not limited to: one or more of a cell phone, a tablet computer, or a wearable device. As shown in fig. 1, the method mainly comprises the following steps:

step S11, obtaining the client data of the target person;

step S12, determining a client rank of the target person based on the client data;

and step S13, determining the data to be pushed corresponding to the customer grade, and pushing the data to be pushed according to the data pushing mode corresponding to the customer grade.

In the disclosed embodiments, the target person generally refers to a person being served, such as a customer, a visitor who is most likely to be a customer, and the like. The target character may refer to one or more of the above-mentioned characters to be served. In one implementation, the target person may be any of all people to the target location except a whitelist. It is understood that the target person does not include people that are white listed.

Wherein the white list includes at least one of: staff in a target place, cleaning staff, a maintenance master, express delivery staff and take-out staff. It should be noted that the white list may be set or adjusted according to the user's needs. In the disclosed embodiments, the target location may generally refer to a place where goods can be displayed or sold, including but not limited to 4S stores, shops, malls, supermarkets, and the like.

In the disclosed embodiment, the client data may include visiting data, and the visiting data is data of a target person visiting a target place. For example, the visit data includes the number of visits within a preset time period. For another example, the visit data includes a single visit time. For another example, the visit data includes an average visit duration of a single visit within a preset time period. The preset time period may include a period of time beginning with a start time and ending with an end time, such as a day, a week, a month, a quarter, a half year, a year, and the like, and may be determined based on actual requirements. Note that the termination time is a time before the current time.

In embodiments of the present disclosure, the customer data may include profile data including parameters reflecting customer economic levels, such as occupation, monthly income, annual income, residence, and the like. The profile data also includes data reflecting the customer's preferred products, such as preferred vehicle models.

In the embodiment of the present disclosure, the customer level may include at least one of a first customer level for reflecting the person's visit situation and a second customer level for reflecting the purchasing intention, and of course, may further include a level for reflecting other attributes. In the embodiment of the present application, the categories included in the customer level, the parallel items included under each category, and the like are not limited.

In one implementation, the first client class includes tags of high-frequency clients, attrition clients, sleeping clients, and the like, and the visiting frequency is different for different tags. The second customer grade comprises an S grade, an A grade, a B grade, a C grade and a D grade; or the second customer level comprises a level A, a level B, a level C, a level D and a level E; or the second customer level comprises an H level, an A level, a B level, a C level, a D level and the like. It should be noted that the range of the purchase possibility size values corresponding to different second customer grades is different.

In the embodiment of the present disclosure, the data to be pushed is data corresponding to a client level, which is to be pushed to a client. For example, the data to be pushed includes vehicle type content. For a client with high purchase intention, vehicle type contents preferred by the client can be directly and pertinently pushed; for customers with low purchase intention, the contents of the models with the same type and/or the larger contrast of the preferred models of the customers can be pushed.

In the embodiment of the present disclosure, the data push modes corresponding to different customer grades are at least partially different; the data pushing mode at least comprises one of pushing time, time interval between two adjacent pushing and pushing approaches. For example, the push path includes mail, short message, telephone, WeChat, circle of friends, public number, and the like.

To determine a customer rating that reflects a target person based on customer data, therefore, in some implementations, where the customer data includes visit data, the determining a customer rating for the target person based on the customer data includes:

It should be noted that the first threshold and the first threshold are both preset thresholds. The first threshold and the first threshold may be preset according to the time, labor cost, and/or the like, and/or the demand, and the setting manner and the like are not limited herein.

In this way, the first customer grade of the visiting person is determined according to the number of visits of the visiting person in a preset time period or the average visit duration of a single visit.

Illustratively, in the event that the number of visits by the customer within a preset time period is above a first threshold, indicating a first customer level of the customer as a high frequency customer; under the condition that the single average visit time length of the customer in a preset time period is higher than a first threshold value, indicating the first customer grade of the customer as a high-frequency customer; and under the condition that the visit times of the customers in the preset time period are higher than a first threshold value and the single average visit duration in the preset time period is higher than the first threshold value, indicating the first customer grade of the customers as a high-frequency customer.

Considering that the identity types of the visiting clients may be different, some visiting clients are common clients, some visiting clients are important clients, and the client grades corresponding to the clients with different identity types may be different. Thus, in one possible implementation, the customer data further includes an identity type, and the determining the customer rating of the target person based on the customer data includes:

The second threshold and the second threshold are both preset thresholds. The second threshold value and the second threshold value may be set in advance according to the time required for input, the labor cost, and/or the like, and the setting method is not limited herein.

In the embodiment of the present disclosure, the distinguishing criterion between the common customer and the important customer may be whether the common customer is a member, and if the common customer is a member, the common customer is determined as an important customer; if not, the system is judged as a common customer.

Exemplarily, if the identity type of the visiting person is the customer and the time interval between the last visit and the current time exceeds a second threshold value, determining that the first customer grade of the visiting person indicates the attrition customer; if the identity type of the visiting person is the customer and the single average visiting time of the visiting person is lower than a second threshold value, determining that the first customer grade of the visiting person indicates the lost customer; if the identity type of the visiting person is a customer, the time interval between the last visit time and the current time exceeds a second threshold value, and the average visit time length of each time is lower than a second threshold value, determining that the first customer grade of the visiting person indicates a lost customer; if the identity type of the visiting person is an important customer such as a member, and the time interval between the last visit and the current time exceeds a second threshold value, determining that the first client level of the visiting person indicates a sleeping client; if the identity type of the visiting person is an important customer such as a member, and the average visiting time of the visiting person in a single time is lower than a second threshold value, determining that the first client level of the visiting person indicates a sleeping client; and if the identity type of the visiting person is an important customer such as a member, the time interval between the last visit time and the current time exceeds a second threshold value, and the average visiting time of a single visit is lower than a second threshold value, determining that the first client grade of the visiting person indicates a sleeping client.

It should be noted that the labels of the high-frequency customers, the attrition customers, the sleeping customers, and the like can be set or adjusted according to the needs of the users.

To determine a customer rating that reflects a target person based on customer data, therefore, in some implementations, the profile data includes economic data of the target person and a vehicle type of interest, where the customer data includes profile data and the customer rating includes the second customer rating, the determining a customer rating of the target person based on the customer data includes:

Therefore, the second customer grade capable of representing the purchasing ability is evaluated by analyzing the concerned vehicle type and the economic data of the customer, differential reception is facilitated according to the determined second customer grade label, more customer reception efforts and customer relationship maintenance efforts are selectively paid to the high-value customer, and the sales conversion rate is favorably improved.

In the following, the second customer level is classified into S level, a level, B level, C level, and D level. The second customer ranks indicate that the customers have the highest purchasing intention, and the second customer ranks indicate that the customers have the lowest purchasing intention. It should be noted that, in practical applications, the second customer level may be further arranged as an S level, an a level, a B level, a C level, and a D level according to the purchase intention from low to high. In the embodiment of the present application, the corresponding relationship between each item (for example, one of the S level, the a level, the B level, the C level, and the D level) in the second customer level and the indicated purchasing intention is not limited, and how many items the second customer level includes is not limited, and may be artificially defined and/or adjusted according to the user' S needs.

In one implementation, when the consumption interval is [ Y1, Y2] and the selling price of the vehicle type of interest is greater than Y5, the second client level of the target person is indicated as a D level, that is, when the consumption interval of the client (where the consumption interval of the client may reflect the economic condition of the client) is far lower than the selling price of the vehicle type of interest of the client, the purchase intention of the client may be considered to be extremely low (here, the client does not have the ability to purchase the vehicle type of interest, or the purchase probability is low).

When the consumption section is (Y2, Y3) and the selling price of the vehicle type of interest (Y4, Y5), the second customer level of the target person is indicated as the C level, that is, when the consumption section of the customer is different from the selling price of the vehicle type of interest of the customer and the consumption section of the customer is lower than the selling price of the vehicle type of interest, it can be considered that the purchase intention of the customer is low.

When the consumption section is (Y3, Y4) and the selling price of the vehicle type of interest (Y3, Y4), the second customer level of the target person is indicated as a B level, that is, when the consumption section of the customer and the selling price of the vehicle type of interest are within the same section range, it can be considered that the purchase intention of the customer is moderate (here, the customer has the ability to purchase the vehicle type of interest, and the selling price of the vehicle type of interest is within the selling price range expected by the customer).

When the consumption section is (Y4, Y5) and the selling price of the vehicle type of interest (Y2, Y3), the second customer level of the target person is indicated as a level a, that is, when the consumption section of the customer is different from the selling price of the vehicle type of interest of the customer and the consumption section of the customer is higher than the selling price of the vehicle type of interest, it can be regarded that the purchase intention of the customer is high.

When the consumption zone is (Y5, ∞) and the selling price of the vehicle type of interest (Y1, Y2), the second client level of the target person is indicated as the S level, that is, when the consumption zone of the client is much higher than the selling price of the vehicle type of interest of the client, it can be considered that the purchase intention of the client is extremely high (here, the client has the ability to purchase the vehicle type of interest, and the economic condition of the client is more than sufficient for paying the selling price of the vehicle type of interest).

Wherein Y1 is more than or equal to 0 and more than Y2 and more than Y3 and more than Y4 and more than Y5.

It is understood that the above-mentioned division of the second customer level and the division of the consumption interval and the selling price are only illustrative, and the present application does not limit the present invention.

Illustratively, if the customer has a consumption interval of 30-50 ten thousand, and if the selling price of the vehicle type concerned is 100 ten thousand, the level indicated by the second customer level can be considered to represent that the purchase intention of the customer is very low, such as level D; if the selling price of the concerned vehicle type is 60 ten thousand, the level indicated by the second customer level can be considered to represent that the purchasing intention of the customer is lower, such as C level; if the selling price of the concerned vehicle type is less than or equal to 50 ten thousand, the level indicated by the level of the second customer can be considered to represent that the customer has higher purchasing intention, and only when the level is divided, the purchasing intention is possibly lower due to the fact that the price is close to the upper limit of the consumption interval, such as level B; if the selling price of the concerned vehicle type is less than or equal to 40 ten thousand and is completely within the consumption interval range, the grade indicated by the second customer grade can be considered to represent that the purchasing intention of the customer is higher, such as grade A; if the selling price of the concerned vehicle type is less than or equal to 30 ten thousand, the concerned vehicle type is completely within the consumption interval range, or is lower than the lowest price of the inner core, the grade indicated by the grade of the second customer can be considered to be very high for representing the purchase intention of the customer, such as the S grade.

Wherein the D level represents that the value of the purchasing possibility size of the target person is less than or equal to P1;

the C grade represents that the value of the purchasing possibility size of the target person is more than P1 and less than or equal to P2;

the B level represents that the value of the purchasing possibility size of the target person is more than P2 and less than or equal to P3;

the value of the purchase possibility size of the A-level representation target character is greater than P3 and less than or equal to P4;

the S level represents that the value of the purchasing possibility size of the target person is larger than P4; wherein, P1 is more than or equal to 0 and more than P2 and more than P3 and more than P4 and more than P5.

As can be seen, a higher rating indicates a greater likelihood of purchase for the customer in the short term; the lower the rating, the lower the likelihood of purchase over the long term by the customer. In some embodiments, determining a second customer rating for the target person in response to whether the selling price falls within the consumption zone comprises:

if the selling price does not belong to the consumption interval, determining that the target person is difficult to purchase the expected vehicle type, and determining that the second customer grade of the target person is a grade representing no purchase intention or the purchase intention is the lowest;

and the selling price belongs to the consumption interval, the target person is determined to have purchase intention, the second customer grade of the target person is determined to be higher than the grade of the selling price which does not belong to the consumption interval, and the grade is divided according to the size of the purchase intention if the selling price has the purchase intention or the purchase intention.

Here, the selling price does not belong to the consumption interval, and it can be understood that the selling price is not in the consumption interval; the selling price is not in the consumption interval, and includes that the selling price is above the upper limit of the consumption interval and below the lower limit of the consumption interval. Illustratively, the consumption interval is 30-50 ten thousand, and if the selling price of the vehicle type concerned by the customer is 60 ten thousand, the selling price is not in the consumption interval; if the selling price of the vehicle type concerned by the customer is 20 ten thousand, the selling price is not in the consumption interval. In practical application, the selling price does not belong to the consumption interval, and can be only understood as the selling price is above the upper limit of the consumption interval; alternatively, the level of the second customer level when the selling price is above the upper limit of the consumption interval is considered to be lower than the level of the second customer level when the selling price is below the lower limit of the consumption interval.

In some embodiments, in the case that the selling price belongs to the consumption interval, a difference between the selling price and an upper limit of the consumption interval may affect the second customer level; the larger the difference, the higher the possibility of purchase indicated by the customer, and the corresponding second customer ranking indicates that the customer is highly likely to purchase a car of that model.

In some embodiments, the determining the second customer rating of the target person may be based on customer data such as a vehicle type of interest and economic data, and thus, in some implementations, the profile data includes economic data of the target person and a vehicle type of interest, and in the case that the customer data includes profile data, the customer rating includes the second customer rating, and the second customer rating includes a plurality of levels, the determining the customer rating of the target person based on the customer data includes: determining that the second customer level of the target person indicates that the target person is a customer in one of the plurality of levels in response to the section to which the disposable income reflected by the economic data belongs being the first disposable section and/or the section to which the pre-allocated income reflected by the vehicle type of interest belongs being the first pre-allocated section. Wherein, prior to determining that the second customer rating of the target persona indicates that the target persona is a customer of one of the plurality of ratings, the method further comprises: dividing the disposable revenue reflected by the economic data into a plurality of disposable intervals, the plurality of disposable intervals corresponding to the plurality of levels; and/or dividing the pre-payment allocation income reflected by the vehicle type of interest into a plurality of pre-domination intervals, wherein the pre-domination intervals correspond to the plurality of levels.

In this manner, the plurality of levels included in the second customer tier are matched to the plurality of disposable intervals, and the plurality of levels included in the second customer tier are matched to the plurality of pre-allocated intervals, thereby making the determined second customer tier more accurate.

To conserve computing resources, therefore, in one implementation, the level of purchasing intent is only divided if a first customer level of the target person indicates that the target person is a high frequency customer, in which case a second customer level of the target person is determined. That is, where the first customer level for the target person indicates that the target person is a churning customer or a sleeping customer, the second customer level for the target person will not be determined.

In order to improve the sales conversion rate, in a possible implementation manner, in a case that the selling price does not belong to the consumption interval, before the pushing the data to be pushed, the method further includes:

Thus, the vehicle type which is consistent with the economic capability of the client is pushed to the client terminal, and the sales conversion rate is improved.

In order to improve the sales conversion rate, in a possible implementation manner, in a case that the selling price belongs to the consumption interval but the selling price is closer to the upper limit of the consumption interval, before the pushing the data to be pushed, the method further includes:

Therefore, the vehicle type matching with the purchase intention of the customer is pushed to the customer terminal, the customer can be provided with a plurality of vehicle types of the same type, and the sales conversion rate is improved.

To facilitate efficient management of personnel responsible for customer follow-up within a store, in some embodiments, after the determining the customer rating of the target person, the method further comprises:

Therefore, the client grade of the client can be conveniently displayed by the terminal, the terminal user can conveniently and visually know the character visiting condition and/or purchasing intention through the client grade, the high-value client can be detected by an auxiliary store, the terminal user can conveniently receive differentiated services according to the determined client grade label if the sales are carried out, more client receiving efforts and client relation maintenance efforts are selectively paid to the high-value client, and the sales conversion rate is favorably improved.

In some embodiments, in the case that the customer data includes visiting data, step S11 includes:

acquiring historical visiting information corresponding to the target person; and determining the visiting data of the target person according to the historical visiting information.

Here, the historical visiting information includes at least a visiting time.

It should be noted that the historical visiting information may be all visiting information before the visiting. Of course, the historical visiting information may also be visiting information within a certain time period from the current visiting, and the certain time period may be set or adjusted according to design requirements.

Optionally, the historical visiting information may further include at least one of the following: visited place information or visited storefront, checkout purchase information, stay time, consultation time, purchase intention, and the like. The stay time as used herein refers to the length of stay a customer has in a store.

It should be noted that the present application does not limit the refinement degree of the historical visiting information. The more detailed the historical visit record, the more beneficial the subsequent exploration of high-value customers.

Therefore, the visiting data of the visiting personnel is determined by analyzing the historical visiting records, the first client level is determined for the visiting personnel according to the visiting data, differential reception is conveniently carried out according to the determined first client level, more client reception efforts and client relationship maintenance efforts are selectively paid to high-value clients, and therefore the sales conversion rate is favorably improved.

In some embodiments, in the case that the customer data includes profile data, step S11 includes:

obtaining questionnaire data provided by the target person during visiting; and determining the profile data of the target person based on the questionnaire data.

In practical applications, when a customer visits, the customer may be provided with a questionnaire, the questionnaire includes a plurality of options such as a desired product to be purchased, an ideal price, an age, a study, a position, a work unit, and the like, each option may correspond to a plurality of answers, and the customer checks the answers.

Therefore, the data of the client can be conveniently determined according to the questionnaire data, and the high-value client can be analyzed according to the data.

To facilitate customer rating by the staff responsible for customer follow-up, in some embodiments, the method further comprises:

obtaining evaluation data given by a user based on the visit record of the target person, wherein the evaluation data at least comprises one of purchasing ability data and purchasing intention data; and determining the client grade corresponding to the target person based on the evaluation data.

Here, the user may be understood as a person who can give evaluation data based on the visit record of the person, such as a salesperson.

Illustratively, in the process of taking up a customer, a salesperson judges the purchasing ability and the purchasing intention of the customer according to own experience and scores or grades the customer on a terminal for one or more evaluation items according to known evaluation criteria, wherein the evaluation items comprise a purchasing ability evaluation item and a purchasing intention evaluation item, and the evaluation data comprises the scoring or grading result; the server or other device determines a purchase likelihood size value for the customer based on the ratings data. It should be noted that the evaluation criteria corresponding to different evaluation items may be the same or different.

Therefore, in the process of receiving customers, the salesperson judges the purchasing ability and purchasing willingness of the customers according to own experience and inputs evaluation data of the customers on the terminal, so that the server or other equipment can determine the client grade of the customers based on the evaluation data conveniently, the salesperson is convenient and quick to operate, and the server or other equipment can provide evaluation information for different customers according to unified standards conveniently.

Based on the above data processing method, an embodiment of the present application further provides a data processing method applied to a terminal, as shown in fig. 2, where the method includes the following steps.

Step S21: and receiving the client level information of the visiting person.

Step S22: and displaying the client grade information of the visiting person.

Therefore, the terminal user can conveniently and quickly identify whether the visiting person is a new client or not according to the client grade information; in addition, the quick allocation of the reception tasks can be realized according to the client level of the target person, the specific work and service of the staff can be facilitated according to the allocation information, and the work efficiency is improved; furthermore, differentiated reception is facilitated according to the determined customer grades, and more customer reception efforts and customer relationship maintenance efforts are selectively paid to high-value customers, so that the sales conversion rate is improved.

In some embodiments, the method further comprises:

receiving an analysis result of the client level information corresponding to the target person;

and displaying the analysis result.

Therefore, the terminal user can know the analysis result of the client grade information of the visitor in time, and the user can perform targeted work and service according to the analysis result, so that the customer experience and the sales conversion rate are improved.

Illustratively, the salesperson may take different marketing strategies to the customer in conjunction with the two types of labels, the first customer rating and the second customer rating. For example, when the second customer level indicates a higher level but the first customer level indicates a loss, based on the tag information, the salesperson needs to pull back customers using a more efficient follow-up and marketing strategy.

Illustratively, when the second customer level indication is low but the first customer level indication is high, based on the above tag information, the salesperson can re-evaluate the true buying intention of this customer, hold key customers for sales, and dislike himself/herself whether the method of determining the high-value customer is wrong during the customer follow-up.

Fig. 3(a) shows a schematic diagram of the system displayed on the terminal side for automatically identifying the information of the clients visiting today, and as can be seen from fig. 3(a), the information of all the clients visiting today is displayed on the interface, and specifically includes information of whether each client is the first visit, the number of times of visits, the time of visits, whether to follow up, and the like, so that detailed data support is provided for the user, the user can know the information of the clients according to the data displayed on the interface, and whether to follow up the clients is determined. Fig. 3(b) shows a schematic diagram of the client information of the user displayed on the terminal side, and as can be seen from fig. 3(b), the client information of the user is displayed on the interface, which specifically includes whether each client is a first visit, the number of visits, and the visit time. Fig. 3(c) shows a schematic diagram of single client information displayed on the terminal side, which specifically includes information such as the number of cumulative visits, the latest visit time, and the fellow staff who visited each time. Fig. 3(d) shows a schematic view of a client editing interface displayed on the terminal side, and as shown in fig. 3(d), a latent level item is displayed on the interface, the latent level represents a second client level, and the user can edit the latent level of the client, that is, the user can select a specific level in the second client level, for example, determine whether to change the level evaluated last time according to the communication situation with the client this time. Therefore, the user can conveniently know the information of the store client, and can receive the information in a differentiated way according to the label information of the store client; it also facilitates the user to rate the customer to the store based on sales experience.

Fig. 4 is a schematic flow chart of the staff tag analysis based on the visit frequency, and as shown in fig. 4, the camera of the end processing layer is responsible for acquiring the face image and the body image in the environment; the collected original images are transmitted into a picture processing and forwarding service through a unified access service, the features of human faces and human bodies are extracted and indexed through the picture processing and forwarding service, and the obtained data are transmitted into a Kafka message queue through a data standardization service for waiting for consumption; carrying out dirtying and duplicate removal processing on the data to be consumed in the Kafka message queue by calling retrieval service, taking the face and/or body characteristic data obtained after the dirtying and duplicate removal processing as a retrieval object, and retrieving whether the data of the person exists in the existing library to judge whether the customer consuming the time is an old customer; if the client is judged to be a new client, a client label is given to the new client, if the client is judged to be an old client, different label analysis is carried out on whether the client is a member according to static library matching, for example, the method sets that the client appears more than or equal to 3 times in the last 15 days as a high-frequency client, the member which has the last store arrival time exceeding 30 days is a sleeping member, and the store client which has the last store arrival time exceeding 30 days is a lost client. Specifically, if the client is judged to be a member, the last visit time is inquired, whether the last visit time exceeds 30 days is judged, when the last visit time is determined to exceed 30 days, the client is marked as a sleeping member, and otherwise, the client is not marked. If the client is a non-member, namely a common client, inquiring the visit times of the client within 15 days, adding 1 to the visit times, then judging whether the visit times within 15 days are more than or equal to 3, and if so, marking the client as a high-frequency client; and if the number of visits is less than 3 within 15 days, inquiring the last visit time, judging whether the last visit time is checked for 30 days, and marking the last visit time as a lost customer when the last visit time is determined to be more than 30 days.

It should be noted that, it is understood that the flow shown in fig. 4 can be set or adjusted according to the user requirement or the design requirement.

Fig. 5(a) shows a schematic diagram of a terminal receiving a visit message push, and a store sale can inquire the identity and the label of the client by receiving a member visit message push in real time, and perform high-quality client reception for the first time. Moreover, the security personnel of the store can determine the blacklist personnel and the position of the blacklist personnel at the first time by receiving the pushing of the blacklist warning message in real time, and the risk can be eliminated efficiently. Fig. 5(b) shows a schematic diagram of inquiring the visiting information of a certain client in the past, which greatly facilitates the sales differentiation and recalls the key information in the past reception and sales process by displaying the historical visiting record of a certain client on the terminal side, and can help to judge the purchasing intention and value of the client through the system accumulation and the latest visiting. Therefore, the sales skill of the selling customer is improved, and the sales conversion rate is further improved. Fig. 5(c) shows a schematic diagram of the system automatically identifying the multi-dimensional identity tag displayed on the terminal side, and since the system supports the client classification based on the tag, the store owner and the sales can perform targeted client marketing and client operation for the client under a certain tag. Fig. 5(d) is a schematic diagram of data analysis based on visitors within a certain time period, after the store manager visits the system, the store manager can master store customer group analysis data and customer flow trend data, such as a comparison graph of total customer flow and visit amount of visitors and a comparison graph of new and old customers, and if the store manager combines with the transaction data, the store manager can help the store manager to analyze and locate current sales and operation problems, and further, the store manager can be used for carrying out sales post promotion and marketing campaign design in the next stage.

FIG. 6 illustrates a personnel tag editing interface that a user may edit. As shown in fig. 6, the interface is provided for the user to manage the visiting person, taking the identity type of the visiting person as an example of a member, items such as an avatar, a person ID, a name, a tag, and an operation of each member are displayed on the interface, and the user can perform an editing operation on each member by clicking an editing button corresponding to the operation item, for example, selecting a tag deemed to be suitable for the member from an optional tag library, or customizing a tag for a certain member, and the like; and deleting the members which are considered to have transacted the quit member procedure or have low value by operating the deletion buttons corresponding to the operation items.

Illustratively, the terminal displays a personnel page, and when receiving the operation of searching for a designated personnel input by a user, displays a brief introduction interface of the designated personnel, wherein a label of the designated personnel is displayed on the interface; when receiving an operation of entering the personnel detail page input by a user, displaying a detailed introduction interface of the designated personnel, wherein the history visiting record of the designated personnel is displayed on the interface; and when the operation of editing the self-defined label of the designated person is received, the label of the designated person is edited based on the editing operation of the user.

Illustratively, the terminal displays a personnel page, the personnel interface displays label information of a plurality of personnel, when receiving the operation of sliding to the left or the right of one personnel in the interface by a user, the label of the personnel is in an editable state, and when receiving the editing information input by the user, the label of the personnel is edited based on the editing information input by the user.

Illustratively, the terminal displays a personnel interface, and when the operation of pulling up a scroll bar on the interface, which is input by a user, is received, the terminal updates a list of personnel displayed on the current interface; when the user finds a customer at a specified store on a specified date from the current interface, the user enters the personnel detail page through clicking operation, and receives the operation of converting the customer into a member input by the user, the identity type of the personnel is changed from the customer to the member based on the operation.

It should be noted that, it is understood that the above-mentioned flow is only an exemplary flow, and in practical applications, differentiated setting operations can be provided for a user to implement different functions.

The camera (camera) is responsible for acquiring images, transmitting the acquired images to the server, so that the server identifies the images, the face and/or the body characteristics of people contained in the images are obtained, and the person identification information of the people contained in the images is obtained based on the face and/or the body characteristics; acquiring evaluation information and historical visiting information corresponding to the person identification information; and determining the client level information of the person according to the evaluation information and the historical visiting information corresponding to the personnel identification information. And the server transmits the determined tag information of the person to a user terminal provided with an APP (application), so that the user terminal displays the client level information of the person. Therefore, the terminal user can conveniently receive differentiated services according to the determined personnel labels, and further more customer service efforts and customer relationship maintenance efforts are selectively paid to high-value customers, so that the sales conversion rate is favorably improved.

An embodiment of the present application further provides a data processing apparatus, as shown in fig. 7, the apparatus includes an obtaining module 10, a determining module 20, and a processing module 30, where:

the acquiring module 10 is configured to acquire client data of a target person;

the determining module 20 is configured to determine a client level of the target person based on the client data;

the processing module 30 is configured to determine data to be pushed corresponding to the customer level, and push the data to be pushed according to a data pushing manner corresponding to the customer level.

In some embodiments, the customer level comprises a first customer level reflecting the visit of the person and/or a second customer level reflecting the purchase intention;

the customer data includes visit data and/or profile data.

In some embodiments, in the case that the customer data includes visit data and the customer rating includes the first customer rating, the determining module 20 is configured to:

In some embodiments, the customer data further comprises an identity type, the determining module 20 is configured to:

In some embodiments, the profile data includes economic data of the target person and a vehicle type of interest, and in the case that the customer data includes profile data and the customer rating includes the second customer rating, the determining module 20 is configured to:

In some embodiments, the processing module 30 is further configured to:

In some embodiments, the data pushing modes corresponding to different customer grades are at least partially different; the data pushing mode at least comprises one of pushing time, time interval between two adjacent pushing and pushing approaches.

In some embodiments, the processing module 30 is further configured to:

It will be appreciated by those skilled in the art that in some alternative embodiments, the functionality of the implementation of the processing modules in the data processing apparatus shown in figure 7 may be understood with reference to the foregoing description of the data processing method.

Those skilled in the art will appreciate that in some alternative embodiments, the functions of the processing units in the data processing apparatus shown in fig. 7 may be implemented by a program running on a processor, or may be implemented by specific logic circuits.

In practical applications, the specific structures of the obtaining module 10, the determining module 20 and the processing module 30 may correspond to a processor. The specific structure of the processor may be a Central Processing Unit (CPU), a Micro Controller Unit (MCU), a Digital Signal Processor (DSP), a Programmable Logic Controller (PLC), or other electronic components or a collection of electronic components having Processing functions. The processor includes executable codes, the executable codes are stored in a storage medium, the processor can be connected with the storage medium through a communication interface such as a bus, and when the corresponding functions of specific units are executed, the executable codes are read from the storage medium and executed. The portion of the storage medium used to store the executable code is preferably a non-transitory storage medium.

The data processing device provided by the embodiment of the application can effectively utilize the client data to divide the client grade of each character so as to play a role in distinguishing or marking different characters; and the data to be pushed is pushed according to the data pushing mode corresponding to the client grade, so that more targeted data pushing can be provided for clients of different client grades, and the customer experience and the sales conversion rate are further improved.

An embodiment of the present application further provides a data processing apparatus, which is applied to a terminal, and as shown in fig. 8, the apparatus includes:

a communication module 40 for receiving the client level information of the visiting person;

and the display processing module 50 is used for displaying the client grade information of the visiting person.

Wherein the customer rating statistics comprise a first customer rating and/or a second customer rating.

In a possible implementation manner, the display processing module 50 is configured to:

and displaying the client level information of the person in a visiting notification interface of the visiting person.

It will be appreciated by those skilled in the art that in some alternative embodiments, the functionality of the various processing modules in the data processing apparatus shown in figure 8 may be understood with reference to the foregoing description of the data processing method.

Those skilled in the art will appreciate that in some alternative embodiments, the functions of the processing units in the data processing apparatus shown in fig. 8 may be implemented by a program running on a processor, or may be implemented by specific logic circuits.

In practical applications, the specific structures of the communication module 40 and the display processing module 50 may correspond to a processor. The specific structure of the processor can be an electronic component or a collection of electronic components with processing functions, such as a CPU, an MCU, a DSP or a PLC. The processor includes executable codes, the executable codes are stored in a storage medium, the processor can be connected with the storage medium through a communication interface such as a bus, and when the corresponding functions of specific units are executed, the executable codes are read from the storage medium and executed. The portion of the storage medium used to store the executable code is preferably a non-transitory storage medium.

The data processing device is convenient for the terminal user to timely know the client grade of the current visiting person, and is convenient for differentiated reception according to the determined client grade label, so that more client reception efforts and client relationship maintenance efforts are selectively paid to high-value clients, and the sales conversion rate is favorably improved.

An embodiment of the present application provides a data processing apparatus, the apparatus includes: the data processing system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the data processing method of the embodiment.

The embodiment of the present application provides a storage medium, where a computer program is stored, and when the computer program is executed by a processor, the processor is caused to execute the steps of the data processing method according to the embodiment of the present application.

It should be understood by those skilled in the art that the functions of the programs in the computer storage medium of the present embodiment can be understood by referring to the related description of the data processing method described in the foregoing embodiments.

It should also be understood that the various alternative embodiments described herein are merely exemplary for helping those skilled in the art better understand the technical solutions of the embodiments of the present application, and should not be construed as limiting the embodiments of the present application, and that those skilled in the art can make various changes and substitutions on the various alternative embodiments described herein, which should also be understood as a part of the embodiments of the present application.

In addition, the description of the technical solutions herein focuses on emphasizing the differences among the various embodiments, and the same or similar parts may be referred to one another, and are not repeated for brevity.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

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; can be located in one place or 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.

In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several 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 methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of data processing, the method comprising:

acquiring client data of a target person;

determining a customer rating of the target person based on the customer data;

2. The method according to claim 1, wherein the customer level includes a first customer level for reflecting a person's visit situation and/or a second customer level for reflecting a purchase intention;

the customer data includes visit data and/or profile data.

3. The method of claim 2, wherein in the case that the customer data includes visit data and the customer rating includes the first customer rating, the determining the customer rating of the target person based on the customer data comprises:

4. The method of claim 3, wherein the customer data further includes an identity type, and wherein determining the customer rating for the target person based on the customer data comprises:

5. The method of claim 2, wherein the profile data includes economic data of the target person and a vehicle type of interest, and the determining the customer rating of the target person based on the customer data in a case where the customer data includes profile data and the customer rating includes the second customer rating comprises:

6. The method according to claim 5, wherein in case the selling price does not belong to the consumption interval, before the pushing the data to be pushed, the method further comprises:

7. The method according to any one of claims 1 to 6, wherein the data pushing manners corresponding to different customer grades are at least partially different; the data pushing mode at least comprises one of pushing time, time interval between two adjacent pushing and pushing approaches.

8. The method of any of claims 1-7, wherein after the determining the client rating of the target person, the method further comprises:

9. A data processing apparatus, characterized in that the apparatus comprises:

the acquisition module is used for acquiring client data of a target person;

10. The apparatus of claim 9, wherein the customer level comprises a first customer level for reflecting the visit of the character and/or a second customer level for reflecting the purchase intention;

the customer data includes visit data and/or profile data.

11. The apparatus of claim 10, wherein if the customer data comprises visit data and the customer rating comprises the first customer rating, the determining module is configured to:

12. The apparatus of claim 11, wherein the customer data further comprises an identity type, and wherein the determining module is configured to:

13. The apparatus of claim 10, wherein the profile data comprises economic data of the target person and a vehicle type of interest, and wherein the determining module is configured to, in the case that the customer data comprises profile data and the customer rating comprises the second customer rating:

14. The apparatus of claim 13, wherein the processing module is further configured to:

15. The apparatus according to any one of claims 9 to 14, wherein the data pushing manners corresponding to different customer classes are at least partially different; the data pushing mode at least comprises one of pushing time, time interval between two adjacent pushing and pushing approaches.

16. The apparatus of any of claims 9 to 15, wherein the processing module is further configured to:

17. A data processing apparatus, the apparatus comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the data processing method of any of claims 1 to 8 when executing the program.

18. A storage medium storing a computer program which, when executed by a processor, causes the processor to perform the data processing method of any one of claims 1 to 8.