CN113781098A - Method and device for improving sales efficiency and computer storage medium - Google Patents
Method and device for improving sales efficiency and computer storage medium Download PDFInfo
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- CN113781098A CN113781098A CN202110878399.3A CN202110878399A CN113781098A CN 113781098 A CN113781098 A CN 113781098A CN 202110878399 A CN202110878399 A CN 202110878399A CN 113781098 A CN113781098 A CN 113781098A
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0203—Market surveys; Market polls
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
- G06Q30/0224—Discounts or incentives, e.g. coupons or rebates based on user history
Abstract
The embodiment of the application discloses a method, a device and a computer storage medium for improving sales efficiency, wherein the method comprises the following steps: collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as a standard label; acquiring behavior data or/and personal information of a plurality of second users who potentially purchase the product A, and analyzing the behavior data or/and the personal information of the second users to obtain a feature tag of each second user; and screening out second users with the characteristic labels conforming to the standard labels, and recommending products A to the second users so as to improve the sales efficiency.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for improving sales efficiency, and a computer storage medium.
Background
At present, sales consultants generally recommend commodities to all potential clients, the recommendation mode not only causes disturbance to the potential clients, but also has large workload and low efficiency, partial sales consultants determine the client preference by subjective judgment of themselves, and due to the fact that the subjective judgment accuracy is low, not only the intended clients are easily missed, but also the recommendation success rate is very low.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present application is to provide a method, an apparatus, and a computer storage medium for improving sales efficiency, by screening users meeting a feature standard and recommending products to the users, the sales efficiency is improved.
In a first aspect, an embodiment of the present application provides a method for improving sales efficiency, which is characterized by including the steps of:
collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as a standard label;
acquiring behavior data or/and personal information of a plurality of second users who potentially purchase the product A, and analyzing the behavior data or/and the personal information of the second users to obtain a feature tag of each second user;
and screening out second users with the characteristic labels conforming to the standard labels, and recommending products A to the second users.
In one possible implementation, the behavior data includes at least one of: browsing records of various products on a preset platform and the time for browsing various products on the preset platform; the personal information includes at least one of: the number of family members and the composition structure of the family members;
the analyzing the collected behavior data or/and personal information, with the most number of similar behavior data or/and personal information in the plurality of first users as standard labels, comprises the steps of:
obtaining personal preference of each first user based on browsing records of various products browsed on a preset platform before each first user purchases the product A and the time for browsing various products on the preset platform;
obtaining the personal requirements of the first users based on the number of family members and the family member composition structure before each first user purchases the product A;
and counting the number of the first users with similar personal preferences and similar personal demands, and forming the standard label by using the most number of the similar personal preferences and the most number of the personal demands in the first users.
In one possible implementation, the counting the number of first users having similar personal preferences and similar personal needs includes the steps of:
carrying out classification statistics on a plurality of first users who have purchased products A according to personal preferences and personal requirements;
and screening out the personal preference of the first user with the highest similarity number and the personal demand of the first user with the highest similarity number.
In a possible implementation manner, the analyzing the behavior data or/and the personal information of the second user includes the steps of:
obtaining personal preference of each second user based on browsing records of each product browsed by each second user on a preset platform and the time for browsing each product on the preset platform;
obtaining the personal requirements of the second users based on the number of family members of each second user and the family member composition structure;
and obtaining the feature label of each second user based on the personal preference and the personal requirement of each second user.
In a possible implementation manner, the screening out the second users whose feature tags conform to the standard tags includes the steps of:
comparing the personal requirements and the personal information in the feature tag of the second user with the personal requirements and the personal information in the standard tag, respectively;
and when one item of the personal requirement and the personal information in the characteristic label is similar to one item of the personal requirement and the personal information in the standard label, determining that the characteristic label is consistent with the standard label, and screening out a second user corresponding to the characteristic label.
In a second aspect, an apparatus for improving sales efficiency is provided, which includes at least one processor and a memory, where the memory is configured to store computer instructions, and the processor is configured to execute the computer instructions and implement the following steps:
collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as a standard label;
acquiring behavior data or/and personal information of a plurality of second users who potentially purchase the product A, and analyzing the behavior data or/and the personal information of the second users to obtain a feature tag of each second user;
and screening out second users with the characteristic labels conforming to the standard labels, and recommending products A to the second users.
In one possible implementation, the behavior data includes at least one of: browsing records of various products on a preset platform and the time for browsing various products on the preset platform; the personal information includes at least one of: the number of family members and the composition structure of the family members;
the analyzing the collected behavior data or/and personal information, with the most number of similar behavior data or/and personal information in the plurality of first users as standard labels, the processor being configured to perform the following steps:
obtaining personal preference of each first user based on browsing records of various products browsed on a preset platform before each first user purchases the product A and the time for browsing various products on the preset platform;
obtaining the personal requirements of the first users based on the number of family members and the family member composition structure before each first user purchases the product A;
and counting the number of the first users with similar personal preferences and similar personal demands, and forming the standard label by using the most number of the similar personal preferences and the most number of the personal demands in the first users.
In one possible implementation, the counting the number of first users having similar personal preferences and similar personal needs, the processor being configured to perform the steps of:
carrying out classification statistics on a plurality of first users who have purchased products A according to personal preferences and personal requirements;
and screening out the personal preference of the first user with the highest similarity number and the personal demand of the first user with the highest similarity number.
In one possible implementation, the analyzing the behavior data or/and the personal information of the second user, the processor is configured to perform the steps of:
obtaining personal preference of each second user based on browsing records of each product browsed by each second user on a preset platform and the time for browsing each product on the preset platform;
obtaining the personal requirements of the second users based on the number of family members of each second user and the family member composition structure;
and obtaining the feature label of each second user based on the personal preference and the personal requirement of each second user.
In one possible implementation, when the screening out the second users whose feature tags conform to the standard tags, the processor is configured to execute the steps of:
comparing the personal requirements and the personal information in the feature tag of the second user with the personal requirements and the personal information in the standard tag, respectively;
and when one item of the personal requirement and the personal information in the characteristic label is similar to one item of the personal requirement and the personal information in the standard label, determining that the characteristic label is consistent with the standard label, and screening out a second user corresponding to the characteristic label.
In a third aspect, an embodiment of the present application provides a computer storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method described above.
In the embodiment of the application, the standard labels are obtained by analyzing the behavior data and the personal information of the first user purchasing the product A before purchasing the product A, the behavior data and the personal information of each second user are analyzed to obtain the characteristic label of each second user, the second users with the characteristic labels consistent with the standard labels are screened out, and the product A is recommended to the second users, so that the selling efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
FIG. 1 is a schematic flow chart of a method for improving sales efficiency provided in the practice of the present application;
fig. 2 is a schematic flowchart of obtaining a standard tag in a method for improving sales efficiency according to an embodiment of the present application;
FIG. 3 is a schematic flow chart illustrating a method for improving sales efficiency according to an embodiment of the present application for screening out similar personal preferences and similar personal needs;
FIG. 4 is a schematic flow chart illustrating obtaining a second user feature tag in a method for improving sales efficiency according to the present application;
fig. 5 is a schematic flow chart illustrating a process of screening feature tags similar to standard tags in a method for improving sales efficiency according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an apparatus for improving sales efficiency according to an embodiment of the present disclosure.
110, a device; 110. a processor; 120. a memory; 130. a communication interface; 140. a bus.
Detailed Description
The embodiments of the present application will be described below with reference to the drawings.
The terms "including" and "having," and any variations thereof, in the description and claims of this application and the drawings described above, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
At present, sales consultants generally recommend commodities to all potential clients, the recommendation mode not only causes disturbance to the potential clients, but also has large workload and low efficiency, partial sales consultants determine the client preference by subjective judgment of themselves, and due to the fact that the subjective judgment accuracy is low, not only the intended clients are easily missed, but also the recommendation success rate is very low.
Referring to fig. 1, fig. 1 is a main flow diagram of a method for improving sales efficiency according to an embodiment of the present disclosure, the method including the following steps:
s1, collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as standard labels;
specifically, the first user is a user who has purchased a product a on a preset platform, and before the first user purchases the product a, the first user stores behavior data and personal information in the preset platform, for example, the product a is a house source of a house type a, and before the first user purchases the house source of a house type a, the personal information stored by the first user on the preset platform may include a name, family members, an existing house property, income condition, and the like; the behavior data of the first user on the preset platform can be the behavior of the first user in viewing the house source, the behavior of the first user in collecting the house source, the behavior of the first user in subscribing the house manufacturer, and the behavior of the first user in leaving a message or commenting.
In the embodiment provided by the application, the behavior data and the personal information of the first user on the preset platform before purchasing the product a are analyzed to obtain the feature tag of the first user when purchasing the product a, for example, the first user feature tag may be "like a large house type, and a family needs the large house type".
After the characteristic labels of the first users before purchasing the product A are obtained, the behavior data or/and the personal information of the first users are counted, the similar behavior data with the largest number of people is taken as one factor in the standard labels, or the similar personal information with the largest number of people is taken as one factor in the standard labels, and the similar behavior data with the largest number of people and the similar personal information with the largest number of people can be combined into the standard labels.
For example, 600 persons having the largest number of similar behavior data and 700 persons having similar personal information among 1000 first users who have purchased the a-house type constitute the standard tag with the largest number of similar behavior data and the largest number of personal information.
S2, collecting behavior data or/and personal information of a plurality of second users who potentially purchase the product A, analyzing the behavior data or/and the personal information of the second users to obtain a feature label of each second user;
in the embodiment provided by the application, taking a product a as a house of a house type a as an example, users looking at the house through a preset platform are all second users who potentially purchase the house of the house type a, most of the second users may not want to purchase the house of the house type a, and in order to improve the sales efficiency of the house type a, the behavior data and the personal information of the second users can be collected first, and the behavior data and the personal information of the second users are analyzed to obtain the feature tag of the second users.
And S3, screening out second users with the characteristic labels matched with the standard labels, and recommending products A to the second users.
In the embodiment provided by the application, after the standard tag is determined, whether the feature tag of the second user conforms to the standard tag is judged, when the feature tag conforms to the standard tag, the situation of the second user is similar to the situation of the first user before the product A is purchased, and the product A is recommended to the second user, so that the probability of success is higher than that of the general user for recommending the product A.
Through the steps S1-S3, the second users with the characteristic labels conforming to the standard labels are screened out by analyzing the behavior data and the personal information of the potential second users and generating the characteristic labels, the screened second users have a higher probability of being interested in the product A, and the product A is recommended to the second users with the characteristic labels conforming to the standard labels, so that the selling efficiency is improved.
In a possible implementation manner, referring to fig. 2, fig. 2 is a schematic flow chart of obtaining a standard tag in a method for improving sales efficiency according to an embodiment of the present application.
The behavioral data includes at least one of: browsing records of various products on a preset platform and the time for browsing various products on the preset platform; the personal information includes at least one of: the number of family members and the composition structure of the family members;
the analyzing the collected behavior data or/and personal information, with the most number of similar behavior data or/and personal information in the plurality of first users as standard labels, comprises the steps of:
s11, obtaining personal preference of each first user based on browsing records of various products browsed on a preset platform before each first user purchases the product A and the time for browsing various products on the preset platform;
s12, obtaining the personal demand of the first user based on the number of family members and the composition structure of the family members before each first user buys the product A;
and S13, counting the number of the first users with similar personal preferences and similar personal needs, and forming the standard label by using the most number of the similar personal preferences and the most number of the personal needs of the first users.
In an embodiment provided by the application, the behavior data of the first user may further include a subscription record, a collection record, a comment record, and an inquiry record on a preset platform, and when the behavior data of the first user before purchasing a product a is obtained through specific analysis, the first user preference may be obtained according to at least one of the subscription record, the collection record, the comment record, the inquiry record, the browsing record, and the length of the browsing time of the first user on the preset platform, for example, before purchasing a product a, if the subscription record of the first user is a message about a certain property provider, the first user may be obtained through analysis to trust the property provider in comparison; if the favorite records of the first user are all about the large-dwelling houses, it is described that the first user is interested in the large-dwelling houses, if the browsing records are mainly for the large-dwelling houses, it is described that the first user is relatively interested in the large-dwelling houses, if the first user inquires about a salesperson for the large-dwelling houses, it is described that the first user is relatively interested in the large-dwelling houses, if the first user has a comment record for the large-dwelling houses, it is described that the first user is relatively interested in the large-dwelling houses, the personal preference of the first user may be 'interested in the large-dwelling houses'. The personal needs of the first user can be obtained according to the personal information of the first user before buying the product a, for example, two children, two old people and two couples (6 in total) in the family members of the first user, so that the first user has a need to buy a large house, and the personal needs of the first user is "a need to buy a large house".
In the embodiment provided by the application, when counting the number of first users having similar personal preferences and similar personal needs, where the similar personal preferences refer to that more than two first users are interested in the same type of product, the personal preferences of the two first users are similar, for example, one first user browses more houses of a large dwelling size, and the other first user collects more relevant data of the houses of the large dwelling size, it may be determined that the personal preferences of the two first users are similar; similar personal requirements refer to that more than two first users have requirements for the same type of products, for example, if two children and two old people are in the family members of one first user, and three children and two old people are in the family members of the other first user, and both of them have requirements for purchasing a large house, the personal requirements of the two first users are similar.
In the step of forming the standard tag, the most number of personal preferences of the first users are used as one of the subsets in the standard tag, and the most number of personal needs of the first users are used as one of the subsets in the standard tag.
In the embodiment provided by the present application, referring to fig. 3, fig. 3 is a schematic flow chart illustrating a process of screening out similar personal preferences and similar personal needs in a method for improving sales efficiency according to the embodiment of the present application.
The counting of the number of first users having similar personal preferences and similar personal needs, comprising the steps of:
s14, carrying out classification statistics on a plurality of first users who have purchased the product A according to personal preference and personal requirements;
and S15, screening out the personal preference of the first user with the most similar quantity and the personal requirement of the first user with the most similar quantity.
For example, in 1000 first users, the first users are divided into several categories according to personal preferences, for example, the personal preferences of the first users in the first category prefer houses of a type a, the personal preferences of the first users in the second category prefer houses of a type B, the personal preferences of the first users in the third category prefer houses of a type C, and the personal preferences of the first users in the first category that prefer a type a with the largest number can be screened out as subsets in the standard tags; in 1000 first users, the first users are divided into several categories according to personal requirements, for example, the personal requirements of the first users in the first category are houses requiring a house type a, the personal requirements of the first users in the second category are houses requiring B, the personal requirements of the first users in the third category are houses requiring C, and the personal requirements of the first users in the first category having the greatest number of the personal requirements of a house type a can be screened out to be used as subsets in the standard tags.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating a process of obtaining a second user feature tag in the method for improving sales efficiency provided by the present application, in an embodiment provided by the present application, the analyzing behavior data or/and personal information of the second user includes the steps of:
s21, obtaining personal preference of each second user based on browsing records of each second user browsing various products on a preset platform and time for each second user browsing various products on the preset platform;
s22, obtaining the personal requirements of the second users based on the number of family members and the family member composition structure of each second user;
and S23, obtaining the feature label of each second user based on the personal preference and the personal requirement of each second user.
In the embodiment provided by the application, the behavior data of the second user may further include a subscription record, a collection record, a comment record, and an inquiry record on the preset platform, and when the behavior data of the second user before purchasing the product a is obtained through specific analysis, the preference of the second user may be obtained according to at least one of the subscription record, the collection record, the comment record, the inquiry record, the browsing record, and the length of the browsing time of the second user on the preset platform, for example, if the subscription records of the second user are all messages related to a certain property provider, the second user may be obtained through analysis to trust the property provider in comparison; if the favorite records of the second user are all about the large-dwelling houses, it is described that the second user is interested in the large-dwelling houses, if the browsing records are mainly for the large-dwelling houses, it is described that the second user is relatively interested in the large-dwelling houses, if the second user asks for salesmen for the large-dwelling houses, it is described that the second user is relatively interested in the large-dwelling houses, if the second user has comment records for the large-dwelling houses, it is described that the second user is relatively interested in the large-dwelling houses, then the personal preference of the second user may be "interested in the large-dwelling houses". The personal requirement of the second user can be obtained according to the personal information stored in the preset platform by the second user, for example, two children, two old people and two couples (total 6 persons) in the family members of the second user, so that the second user can have the requirement of purchasing a house of a large house type, and the personal requirement of the second user is "the requirement of purchasing a house of a large house type".
In composing the feature labels of the second users, the personal preferences and personal needs of each second user are combined together to form the feature labels.
In an embodiment provided by the present application, referring to fig. 5, fig. 5 is a schematic flow chart illustrating a process of screening a feature tag similar to a standard tag in a method for improving sales efficiency provided by the embodiment of the present application, where the screening of a second user whose feature tag matches the standard tag includes the steps of:
s31, comparing the personal requirement and the personal information in the characteristic label of the second user with the personal requirement and the personal information in the standard label respectively;
and S32, when one item of the personal requirement and the personal information in the characteristic label is similar to one item of the personal requirement and the personal information in the standard label, determining that the characteristic label is consistent with the standard label, and screening out a second user corresponding to the characteristic label.
Specifically, when the second user whose feature label matches the standard label is screened out, it is first determined whether the feature label of the second user matches the standard label, the manner of determination is as follows, the personal preference of the second user is obtained, whether the feature label of the second user is similar to the personal preference in the standard label, if the feature label of the second user is similar to the standard label, if the personal preference of the second user is not similar to the personal preference in the standard label, the personal requirement of the second user is further obtained, and if the personal requirement of the second user is similar to the personal requirement in the standard label, it is determined that the feature standard of the second user matches the standard label.
And screening out the second users which are consistent, and recommending the product A to the screened second users when recommending the product A.
Specifically, people often can refer to the opinion of other families when buying the house property, and other family members also have to look over the house source on presetting the platform, for further improving sales efficiency, can recommend the house of A house type to each other family member's user simultaneously, make things convenient for family member to refer to, promote family member's purchase opinion unanimity, specific step is as follows:
collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as a standard label;
acquiring behavior data or/and personal information of a plurality of second users who potentially purchase the product A, and analyzing the behavior data or/and the personal information of the second users to obtain a feature tag of each second user;
screening out second users who are family members mutually based on the personal information of each second user, and enabling each second user who is a family member mutually to form a second user set;
and screening out second users with the characteristic labels conforming to the standard labels, recommending products A to the second users, and recommending the products A to other second users who are family members of the second users.
In one possible implementation, the behavior data includes at least one of: browsing records of various products on a preset platform and the time for browsing various products on the preset platform; the personal information includes at least one of: the number of family members and the composition structure of the family members;
the step of analyzing the collected behavior data or/and personal information comprises the steps of:
acquiring browsing records of a second user browsing various products on a preset platform and the time for browsing various products on the preset platform; and obtaining the display style preferred by the second user based on the browsing records of the second user for browsing various products and the time for browsing various products on the preset platform.
In a possible implementation manner, when the second user whose feature label is matched with the standard label is screened out and the product a is recommended to the second user, the product a is recommended in a display style preferred by the second user.
In a possible implementation manner, when the product a is recommended to another second user who is a family member of the second user, the product a is recommended in a preferred display style of the other second user who is a family member of the second user.
An apparatus for improving sales efficiency is provided in an embodiment of the present application, and referring to fig. 6, fig. 6 is a schematic structural diagram of an apparatus for improving sales efficiency provided in the present application; the apparatus 100 includes a processor 110, a memory 120, and a communication interface 130. The processor 110, the memory 120, and the communication interface 130 are coupled by a bus 140, the memory 120 is configured to store instructions, and the processor 110 is configured to execute the instructions stored by the memory 120 to implement the method steps corresponding to fig. 1-5, as described above.
The processor 110 is configured to execute the instructions stored in the memory 120 to control the communication interface 130 to receive and transmit signals, thereby implementing the steps of the above-described method. The memory 120 may be integrated in the processor 110, or may be provided separately from the processor 110.
In one possible implementation, the function of the communication interface 130 may be implemented by a transceiver circuit or a dedicated chip for transceiving. The processor 110 may be considered to be implemented by a dedicated processing chip, processing circuit, processor, or a general-purpose chip.
In another possible implementation manner, the apparatus provided by the embodiment of the present application may be implemented by using a general-purpose computer. Program code that will implement the functions of the processor 110 and the communication interface 130 is stored in the memory 120, and a general-purpose processor implements the functions of the processor 110 and the communication interface 130 by executing the code in the memory 120.
The memory 120 is configured to store computer instructions, and the processor 110 is configured to execute the computer instructions and to implement the steps of:
collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as a standard label;
acquiring behavior data or/and personal information of a plurality of second users who potentially purchase the product A, and analyzing the behavior data or/and the personal information of the second users to obtain a feature tag of each second user;
and screening out second users with the characteristic labels conforming to the standard labels, and recommending products A to the second users.
In one possible implementation, the behavior data includes at least one of: browsing records of various products on a preset platform and the time for browsing various products on the preset platform; the personal information includes at least one of: the number of family members and the composition structure of the family members;
the analyzing the collected behavior data or/and personal information, with the largest number of similar behavior data or/and personal information among the plurality of first users as a standard label, the processor 110 is configured to perform the following steps:
obtaining personal preference of each first user based on browsing records of various products purchased on a preset platform before each first user purchases the product A and the time for browsing various products on the preset platform;
obtaining the personal requirements of the first users based on the number of family members and the family member composition structure before each first user purchases the product A;
and counting the number of the first users with similar personal preferences and similar personal demands, and forming the standard label by using the most number of the similar personal preferences and the most number of the personal demands in the first users.
In one possible implementation, the counting the number of first users having similar personal preferences and similar personal needs, the processor 110 is configured to perform the following steps:
carrying out classification statistics on a plurality of first users who have purchased products A according to personal preferences and personal requirements;
and screening out the personal preference of the first user with the highest similarity number and the personal demand of the first user with the highest similarity number.
In one possible implementation, the analyzing the behavior data or/and the personal information of the second user, the processor 110 is configured to perform the steps of:
obtaining personal preference of each second user based on browsing records of each product browsed by each second user on a preset platform and the time for browsing each product on the preset platform;
obtaining the personal requirements of the second users based on the number of family members of each second user and the family member composition structure;
and obtaining the feature label of each second user based on the personal preference and the personal requirement of each second user.
In one possible implementation, when the screening out the second users whose feature tags conform to the standard tags, the processor 110 is configured to perform the steps of:
comparing the personal requirements and the personal information in the feature tag of the second user with the personal requirements and the personal information in the standard tag, respectively;
and when one item of the personal requirement and the personal information in the characteristic label is similar to one item of the personal requirement and the personal information in the standard label, determining that the characteristic label is consistent with the standard label, and screening out a second user corresponding to the characteristic label.
The present embodiment provides a computer storage medium, which stores a computer program, and when the computer program is executed by the processor 110, the computer program implements the method described above.
In summary, the behavior data and the personal information of the first user purchasing the product a before the first user sufficiently purchases the product a are analyzed to obtain the standard label, the behavior data and the personal information of each second user are analyzed to obtain the feature label of each second user, the second users whose feature labels are consistent with the standard label are screened out, and the product a is recommended to the second users, so that the sales efficiency is improved.
It should also be understood that reference herein to first, second, third, fourth, and various numerical designations is made only for ease of description and should not be used to limit the scope of the present application.
It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 110. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by the hardware processor 110, or may be implemented by a combination of hardware and software modules in the processor 110. The software modules may be located in ram 120, flash memory, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and the processor 110 reads information in the memory and performs the steps of the method in combination with hardware thereof. To avoid repetition, it is not described in detail here.
In the embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various Illustrative Logical Blocks (ILBs) and steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. 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 application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described embodiment of the apparatus 100 is merely illustrative, and for example, the division of the modules is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of modules 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 modules, and may be in an electrical, mechanical or other form.
The modules described as separate components may or may not be physically separate. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), among others.
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 (10)
1. A method for improving sales efficiency, comprising the steps of:
collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as a standard label;
acquiring behavior data or/and personal information of a plurality of second users who potentially purchase the product A, and analyzing the behavior data or/and the personal information of the second users to obtain a feature tag of each second user;
and screening out second users with the characteristic labels conforming to the standard labels, and recommending products A to the second users.
2. The method of claim 1, wherein the behavioral data includes at least one of: browsing records of various products on a preset platform and the time for browsing various products on the preset platform; the personal information includes at least one of: the number of family members and the composition structure of the family members;
the analyzing the collected behavior data or/and personal information, with the most number of similar behavior data or/and personal information in the plurality of first users as standard labels, comprises the steps of:
obtaining personal preference of each first user based on browsing records of various products browsed on a preset platform before each first user purchases the product A and the time for browsing various products on the preset platform;
obtaining the personal requirements of the first users based on the number of family members and the family member composition structure before each first user purchases the product A;
and counting the number of the first users with similar personal preferences and similar personal demands, and forming the standard label by using the most number of the similar personal preferences and the most number of the personal demands in the first users.
3. The method of claim 2, wherein the counting the number of first users with similar personal preferences and similar personal needs, comprises the steps of:
carrying out classification statistics on a plurality of first users who have purchased products A according to personal preferences and personal requirements;
and screening out the personal preference of the first user with the highest similarity number and the personal demand of the first user with the highest similarity number.
4. A method for improving sales efficiency according to claim 3, wherein the analyzing the second user's behavior data or/and personal information comprises the steps of:
obtaining personal preference of each second user based on browsing records of each product browsed by each second user on a preset platform and the time for browsing each product on the preset platform;
obtaining the personal requirements of the second users based on the number of family members of each second user and the family member composition structure;
and obtaining the feature label of each second user based on the personal preference and the personal requirement of each second user.
5. The method of claim 4, wherein the screening out the second users whose feature tags match the standard tags comprises the steps of:
comparing the personal requirements and the personal information in the feature tag of the second user with the personal requirements and the personal information in the standard tag, respectively;
and when one item of the personal requirement and the personal information in the characteristic label is similar to one item of the personal requirement and the personal information in the standard label, determining that the characteristic label is consistent with the standard label, and screening out a second user corresponding to the characteristic label.
6. An apparatus for improving sales efficiency, comprising at least one processor and a memory, the memory configured to store computer instructions, the processor configured to execute the computer instructions and to perform the steps of:
collecting behavior data or/and personal information before purchase of a plurality of first users who have purchased products A, analyzing the collected behavior data or/and personal information, and taking the similar behavior data or/and personal information with the largest quantity in the plurality of first users as a standard label;
acquiring behavior data or/and personal information of a plurality of second users who potentially purchase the product A, and analyzing the behavior data or/and the personal information of the second users to obtain a feature tag of each second user;
and screening out second users with the characteristic labels conforming to the standard labels, and recommending products A to the second users.
7. The apparatus of claim 6, wherein the behavior data comprises at least one of: browsing records of various products on a preset platform and the time for browsing various products on the preset platform; the personal information includes at least one of: the number of family members and the composition structure of the family members;
the analyzing the collected behavior data or/and personal information, with the most number of similar behavior data or/and personal information in the plurality of first users as standard labels, the processor being configured to perform the following steps:
obtaining personal preference of each first user based on browsing records of various products browsed on a preset platform before each first user purchases the product A and the time for browsing various products on the preset platform;
obtaining the personal requirements of the first users based on the number of family members and the family member composition structure before each first user purchases the product A;
and counting the number of the first users with similar personal preferences and similar personal demands, and forming the standard label by using the most number of the similar personal preferences and the most number of the personal demands in the first users.
8. The apparatus for improving sales efficiency of claim 7, wherein the statistics of the number of first users with similar personal preferences and similar personal needs, the processor is configured to perform the steps of:
carrying out classification statistics on a plurality of first users who have purchased products A according to personal preferences and personal requirements;
and screening out the personal preference of the first user with the highest similarity number and the personal demand of the first user with the highest similarity number.
9. The apparatus for improving sales efficiency of claim 8, wherein in the screening out of the second users whose feature tags match the standard tags, the processor is configured to perform the steps of:
comparing the personal requirements and the personal information in the feature tag of the second user with the personal requirements and the personal information in the standard tag, respectively;
and when one item of the personal requirement and the personal information in the characteristic label is similar to one item of the personal requirement and the personal information in the standard label, determining that the characteristic label is consistent with the standard label, and screening out a second user corresponding to the characteristic label.
10. A computer storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1-5.
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