CN112767029A - Marketing data processing method and system - Google Patents
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- CN112767029A CN112767029ACN202110077835.7ACN202110077835ACN112767029ACN 112767029 ACN112767029 ACN 112767029ACN 202110077835 ACN202110077835 ACN 202110077835ACN 112767029 ACN112767029 ACN 112767029A
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- G06Q30/02—Marketing; Price estimation or determination; Fundraising
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Abstract
Description
Technical Field
The present application relates to the field of electronic technologies, and in particular, to a method and a system for processing marketing data.
Background
The shop, as a form of electronic commerce, can make things convenient for people to purchase when internet network browses, and can pay through various online payment means and accomplish the transaction, greatly made things convenient for people's life. Because a shop merchant can market multiple commodities simultaneously, if the sale condition of each commodity cannot be known timely, the commodity is lost and finally lost, and therefore how to evaluate the marketing condition of each product is a key problem to be solved urgently.
Disclosure of Invention
The embodiment of the application provides a method and a system for processing marketing data, which are beneficial to avoiding commodity lost sales.
In a first aspect, an embodiment of the present application provides a method for processing marketing data, where the method includes:
acquiring first historical marketing data of a target commodity of a target merchant in a first time period and second historical marketing data of the target commodity in a second time period, wherein the ending time of the first time period is the starting time of the second time period;
processing the first historical marketing data and the second historical marketing data to determine the marketing stability of the target commodity;
under the condition that the marketing stability is not qualified, determining a target user with intention on the target commodity and preferential information of the target user;
and sending the preset preferential information of the target commodity to the target user.
In a second aspect, an embodiment of the present application provides a system for processing marketing data, where the system includes a database and an electronic device:
a database for storing the first historical marketing data and the second historical marketing data;
the electronic equipment is used for acquiring first historical marketing data of a target commodity of a target merchant in the database within a first time period and acquiring second historical marketing data of the target commodity in the database within a second time period, wherein the first time period is earlier than the second time period, and the ending moment of the first time period is the starting moment of the second time period;
the electronic equipment is further used for processing the first historical marketing data and the second historical marketing data and determining the marketing stability of the target commodity;
the electronic equipment is also used for determining a target user with intention to the target commodity and preferential information of the target user under the condition that the marketing stability is not qualified;
and the electronic equipment is also used for sending the preset preferential information of the target commodity to the target user.
It can be seen that, in the embodiment of the present application, the electronic device first obtains first historical marketing data of a target commodity of a target merchant in a first period, and obtains second historical marketing data of the target commodity in a second period, where an end time of the first period is a start time of the second period, then processes the first historical marketing data and the second historical marketing data to determine marketing stability of the target commodity, then determines a target user having an intention to the target commodity and preferential information of the target user under the condition that the marketing stability is not qualified, and finally sends preset preferential information of the target commodity to the target user. The electronic equipment determines the target user after determining that the stability of the target commodity is unqualified, and sends the preferential information of the target commodity to the target customer, so that the commodity is prevented from being lost.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a system for processing marketing data according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 3 is a flowchart illustrating a marketing data processing method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The following are detailed below.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, 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.
Hereinafter, some terms in the present application are explained to facilitate understanding by those skilled in the art.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a system for processing marketing data provided by an embodiment of the present application, where the system for processing marketing data includes a database and an electronic device. The data and electronic devices shown in fig. 1 are only examples and do not limit the embodiments of the present application.
The electronic device may be a server, a cloud server, or various other devices having a computing function.
As shown in fig. 2, fig. 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device includes at least one of: processors, Memory, signal processors, transceivers, Random Access Memory (RAM), sensors, and so forth. The memory, the signal processor, the RAM and the sensor are connected with the processor, and the transceiver is connected with the signal processor.
The following describes embodiments of the present application in detail.
As shown in fig. 3, a flowchart of a processing method of marketing data provided in the embodiment of the present application is applied to the electronic device, and specifically includes the following steps:
step 301: the method comprises the steps of obtaining first historical marketing data of a target commodity of a target merchant in a first time period, and obtaining second historical marketing data of the target commodity in a second time period, wherein the ending time of the first time period is the starting time of the second time period.
The duration of the first time interval and the duration of the second time interval are both preset, and the duration of the first time interval and the duration of the second time interval can be the same or different.
And the ending moment of the second time interval is the current moment.
Step 302: and processing the first historical marketing data and the second historical marketing data to determine the marketing stability of the target commodity.
Wherein, marketing stability can be qualified or unqualified.
Step 303: and under the condition that the marketing stability is not qualified, determining target users with intention on the target commodity and preferential information of the target users.
The target user may be a user who purchased a commodity at the target merchant, or a user who did not purchase a commodity at the target merchant.
The preferential information corresponding to different target users may be the same or different.
Step 304: and sending the preset preferential information of the target commodity to the target user.
The preset preferential information may be coupon information, discount information, full discount information, and the like.
It can be seen that, in the embodiment of the present application, the electronic device first obtains first historical marketing data of a target commodity of a target merchant in a first time period, and obtains second historical marketing data of the target commodity in a second time period, where an end time of the first time period is a start time of the second time period, then processes the first historical marketing data and the second historical marketing data to determine marketing stability of the target commodity, then determines a target user having an intention to the target commodity under the condition that the marketing stability is not qualified, and finally sends preset preferential information of the target commodity to the target user. The electronic equipment determines the target user after determining that the stability of the target commodity is unqualified, and sends the preferential information of the target commodity to the target customer, so that the commodity is prevented from being lost.
In an implementation manner of the present application, the processing the first historical marketing data and the second historical marketing data to determine the marketing stability of the target product includes:
determining a first volume of deals, a first past order volume, a first new order volume, a first past goodness rating and a first new goodness rating of the target commodity in the first time period based on the first historical marketing data;
determining a first order satisfaction based on the first old customer order quantity and the first new customer order quantity, and determining a first customer satisfaction based on the first old customer goodness rating and the first new customer goodness rating;
determining a first satisfaction based on the first volume of transactions, the first order satisfaction, and the first customer satisfaction;
determining a second volume of transaction, a second past customer order volume, a second new customer order volume, a second past customer rating and a second new customer rating of the target commodity in the second period of time based on the second historical marketing data;
determining a second order satisfaction based on the second past order quantity and the second new customer order quantity, and determining a second customer satisfaction based on the second past goodness rating and the second new customer goodness rating;
determining a second satisfaction based on the second volume of funds, the second order satisfaction, and the second customer satisfaction;
determining that the marketing stability of the target commodity is not qualified in the case that the difference between the first satisfaction and the second satisfaction is not within a first numerical range;
and determining that the marketing stability of the target commodity is qualified if the difference value of the first satisfaction degree and the second satisfaction degree is within the first difference value range.
The historical marketing data comprises the total volume of deals, the total volume of deals of old customers, the total volume of deals of new customers, the evaluation level of each old customer on the target commodity, the evaluation level of each new customer on the target commodity and the like.
And if the evaluation grade is greater than or equal to the preset grade, the evaluation is favorable.
The order range of the old customer order, the order range of the new customer order and the order satisfaction can be in an association relationship.
Wherein the greater the order satisfaction, the more satisfied the representative is with the order quantity.
For example, the order range 1 of the old customer order, the order range 2 of the new customer order are associated with the order satisfaction 3, the order range 1 and the order range 3 of the new customer order are associated, or the order range 4 of the old customer order and the order range 2 are associated with the order satisfaction 2, and the order range 3 and the order range 4 are associated with the order satisfaction 1.
The favorable rating may be obtained based on a first formula H = a/B, where H is the favorable rating, a is the amount of the favorable-rated orders, and B is the amount of the orders.
For example, if the order quantity of the old visitor is 70 and the order quantity of the old visitor who is good for the old visitor is 50 within the preset time period, the good rating is 5/7.
The customer satisfaction can be obtained based on a second formula C = α 1 × H1+ α 2 × H2, where α 1 and α 2 are both weights, the sum of α 1 and α 2 is 1, C is the customer satisfaction, H1 is the old customer goodness rating, and H2 is the new customer goodness rating.
Where α 1 and α 2 may be predetermined.
Wherein the satisfaction may be determined based on a third formula D = β 1 × Z + β 2 × H + β 3 × C, the D being the satisfaction, the Z being the volume of transactions, the H being the order satisfaction, the C being the customer satisfaction, β 1, β 2, and β 3 being preset, the sum of β 1, β 2, and β 3 being 1.
Wherein the first numerical range may be predetermined.
It can be seen that in the embodiment of the application, the satisfaction is determined through the volume of transaction, the satisfaction of the order and the satisfaction of the customer, the stability of the target commodity is determined through the satisfaction, and the accuracy of determining the stability is improved.
In an implementation manner of the present application, the target user includes N first users, where N is a positive integer;
the determining of the target user having the intention to the target commodity and the discount information of the target user includes:
acquiring T pieces of purchase information of T second users, wherein the T second users correspond to the T pieces of purchase information one by one, and the second users are users who browse commodities of the target merchant;
determining T using discount times based on the T purchasing information, wherein the T purchasing times, the T purchasing amount and the T discount amount are in one-to-one correspondence with the T purchasing information;
determining a usage discount ratio based on each usage discount time and each purchasing time, and obtaining T usage discount ratios;
determining an amount discount proportion based on each purchase amount and each discount amount to obtain T amount discount proportions;
determining a remaining quantity M of the target product, and determining the N first users in the T second users based on a principle that a discount use ratio is from large to small, the T discount use ratios and the M, wherein the M is larger than or equal to the N;
and determining the discount information of each second user based on the discount proportion of the amount corresponding to each second user and the price of the target commodity to obtain N pieces of discount information.
The purchase information is information of a user purchasing a commodity, and the purchased commodity may be of a target merchant or not.
Wherein the use discount rate may be determined based on a fourth formula W1= W2/W3, where W1 is the use discount rate, W2 is the number of use discounts, and W3 is the number of purchases.
Wherein the discount rate of the amount may be based on a fifth formula J1= J2/(J2 + J3), wherein J1 is the discount rate of the amount, J2 is the discount amount, and J3 is the purchase amount.
Optionally, T and M may have an association relationship, or T and M may have an association relationship in the range.
Wherein the association relationship is preset, and the association relationship may be determined based on preset element information influencing the marketing of the target commodity.
The preset element information is different for different target commodities, and for commodities influenced by external factors, the preset element information is the external factors, for example, the target commodities are influenced by clothes first seasons, and stationery is influenced by months. For the commodity which is not affected by the outside, the preset element information is first dimension information of the target commodity, and the first dimension information is determined based on the first historical marketing data and the second historical marketing data, and may be determined based on user evaluation, for example.
Optionally, the determining, based on the discount rate of the amount of money corresponding to each second user and the price of the target product, the offer information of each second user includes:
determining a discount price of the target commodity based on each discount proportion of the amount and the price of the target commodity;
determining the discount information as a first preset discount under the condition that the discount price of the target commodity is larger than or equal to the cost of the target commodity, wherein the first preset discount is larger than the discount rate of the amount;
and under the condition that the discount price of the target commodity is smaller than the cost of the target commodity, determining that the discount information is a second preset discount, wherein the second preset discount is smaller than the discount proportion of the amount, and the second preset discount is larger than the first discount proportion.
Optionally, the determining a target user having an intention on the target commodity and the offer information of the target user includes:
acquiring a first behavior set of the purchased target commodity by a third user, wherein the first behavior set comprises P1 first behaviors of the third user on the target commodity;
acquiring a second action set of a fourth user on the target commodity which is not purchased, and acquiring a third action set of the fourth user on the first commodity which is purchased, wherein the second action set comprises P2 second actions of the second user on the target commodity, and the third action set comprises P3 third actions of the second user on the target commodity;
determining a first behavior similarity of the first behavior set and the second behavior set based on the P1 first behaviors and the P2 second behaviors, and determining a second behavior similarity of the second behavior set and the third behavior set based on the P2 second behaviors and the P3 third behaviors;
determining a first duration for the third user to perform each of the first actions, resulting in P1 first durations, determining a second duration for the fourth user to perform each of the second actions, resulting in P2 second durations, and determining a third duration for the fourth user to perform each of the third actions, resulting in P3 third durations;
determining a first duration similarity of the first behavior set and the second behavior set based on the P1 first durations and the P2 second durations, and determining a second duration similarity of the second behavior set and the third behavior set based on the P2 second durations and the P3 third durations;
determining a first number of times each of the first activities was performed by the third user, resulting in P1 first times, determining a second number of times each of the second activities was performed by the fourth user, resulting in P2 second times, and determining a third number of times each of the third activities was performed by the fourth user, resulting in P3 third times;
determining a first degree of similarity of the first behavior set and the second behavior set based on the P1 first degrees and the P2 second degrees, and determining a second degree of similarity of the second behavior set and the third behavior set based on the P2 second degrees and the P3 third degrees;
determining a first target similarity based on the first behavior similarity, the first duration similarity and the first degree of similarity, and determining a second target similarity based on the second behavior similarity, the second duration similarity and the second degree of similarity;
determining a third target similarity based on the first target similarity and the second target similarity;
determining the fourth user as the target user when the third target similarity is greater than or equal to a preset similarity;
determining the offer information based on the third target similarity.
Wherein the second action may be browsing, collecting, communicating with the target merchant, etc.
The method for determining the first behavior similarity and the method for determining the second behavior similarity are the same, the method for determining the first time length similarity and the second time length similarity are the same, the method for determining the first time number similarity and the second time number similarity are the same, and the method for determining the first target similarity and the second target similarity are the same.
And in the case that the third user is multiple, the first duration of each first behavior is the average duration of each first behavior executed by the third user, and the first times of each first behavior is the average times of each first behavior executed by the third user.
And under the condition that the first commodities are multiple, the third time length of each third action is the ratio of the total time length of the fourth user for executing each first action to the number of the first commodities, and the third frequency of each third action is the ratio of the total frequency of the fourth user for executing each first action to the number of the first commodities.
The determining of the similarity of the first behaviors of the first behavior set and the second behavior set based on the P1 first behaviors and the P2 second behaviors may be to take each first behavior as a vector dimension to obtain a first vector, take each second behavior as a vector dimension to obtain a second vector, and calculate the similarity of the first vector and the second vector by cosine distance to obtain the similarity of the first behaviors of the first behavior set and the second behavior set.
The similarity of the first durations of the first behavior set and the second behavior set is determined based on the P1 first durations and the P2 second durations, where each first behavior may be used as a vector dimension, the first duration corresponding to each first behavior is used as a weight of each first behavior, a third vector is obtained, each second behavior is used as a vector dimension, the second duration corresponding to each second behavior is used as a weight of each second behavior, a fourth vector is obtained, and the similarity of the third vector and the fourth vector is calculated by a cosine distance, so as to obtain the similarity of the first durations of the first behavior set and the second behavior set.
The similarity of the first times of the first behavior set and the second behavior set is determined based on the P1 first times and the P2 second times, where each first behavior may be taken as a vector dimension, the first time corresponding to each first behavior is taken as a weight of each first behavior to obtain a fifth vector, each second behavior is taken as a vector dimension, the second time corresponding to each second behavior is taken as a weight of each second behavior to obtain a sixth vector, and the similarity of the fifth vector and the sixth vector is calculated by a cosine distance to obtain the similarity of the first times of the first behavior set and the second behavior set.
The target similarity may be determined based on a sixth formula X = c1 × 1+ c2 × 2+ c3 × 3, if X is the first target similarity, X1 is the first behavior similarity, X2 is the first behavior duration similarity, X3 is the first behavior order similarity, if X is the second target similarity, X1 is the second behavior similarity, X2 is the second behavior duration similarity, X3 is the second behavior order similarity, c1, c2, and c3 are preset weights, and a sum of c1, c2, and c3 is 1; if X is the third target similarity, X1 is the first target similarity, X2 is the second target similarity, the sum of c1 and c2 is 1, and c3 is zero.
The preference information is determined based on the third target similarity, which may be that the third target similarity and the preference information have an association relationship, and the association relationship is preset.
Because the similarity of the purchased third user and the fourth user to the target commodity on the behavior and the similarity of the fourth user to the target commodity and the first commodity on the behavior are used, whether the fourth user is the target user or not is determined, and the efficiency of determining the target user is improved.
It can be seen that, in the embodiment of the application, the first user is determined by using the discount proportion, which is beneficial to selecting the user interested in the discount, and the discount information is determined by using the discount proportion, which is beneficial to improving the marketing quantity of the target product.
In an implementation manner of the present application, the method further includes:
obtaining friend information of each first user to obtain N pieces of friend information;
determining first friends with affinity greater than or equal to preset affinity with each first user based on the friend information to obtain N first friends;
and sending each piece of the preferential information to each first friend.
The intimacy degree can be determined by the number of times of sharing the commodity or the number of times of exchanging.
It can be seen that in the embodiment of the application, the preference information is sent to the friends of the first user, which is beneficial to promoting the interest of the first user and the friends thereof in the target commodity so as to improve the marketing quantity of the target commodity.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the embodiments of the present application in further detail, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present application, and are not intended to limit the scope of the embodiments of the present application, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of the embodiments of the present application.
Claims (6)
1. A marketing data processing method applied to an electronic device, the method comprising:
acquiring first historical marketing data of a target commodity of a target merchant in a first time period and second historical marketing data of the target commodity in a second time period, wherein the ending time of the first time period is the starting time of the second time period;
processing the first historical marketing data and the second historical marketing data to determine the marketing stability of the target commodity;
under the condition that the marketing stability is not qualified, determining a target user with intention on the target commodity and preferential information of the target user;
and sending the preferential information of the target commodity to the target user.
2. The method of claim 1, wherein said processing the first historical marketing data and the second historical marketing data to determine the marketing stability of the target good comprises:
determining a first volume of deals, a first past order volume, a first new order volume, a first past goodness rating and a first new goodness rating of the target commodity in the first time period based on the first historical marketing data;
determining a first order satisfaction based on the first old customer order quantity and the first new customer order quantity, and determining a first customer satisfaction based on the first old customer goodness rating and the first new customer goodness rating;
determining a first satisfaction based on the first volume of transactions, the first order satisfaction, and the first customer satisfaction;
determining a second volume of transaction, a second past customer order volume, a second new customer order volume, a second past customer rating and a second new customer rating of the target commodity in the second period of time based on the second historical marketing data;
determining a second order satisfaction based on the second past order quantity and the second new customer order quantity, and determining a second customer satisfaction based on the second past goodness rating and the second new customer goodness rating;
determining that the marketing stability of the target commodity is not qualified in the case that the difference between the first satisfaction and the second satisfaction is not within a first numerical range;
and determining that the marketing stability of the target commodity is qualified if the difference value of the first satisfaction degree and the second satisfaction degree is within the first difference value range.
3. The method according to claim 1 or 2, wherein the target users comprise N first users, wherein N is a positive integer;
the determining of the target user having the intention to the target commodity and the discount information of the target user includes:
acquiring T pieces of purchase information of T second users, wherein the T second users correspond to the T pieces of purchase information one by one, and the second users are users who browse commodities of the target merchant;
determining T using discount times based on the T purchasing information, wherein the T purchasing times, the T purchasing amount and the T discount amount are in one-to-one correspondence with the T purchasing information;
determining a usage discount ratio based on each usage discount time and each purchasing time, and obtaining T usage discount ratios;
determining an amount discount proportion based on each purchase amount and each discount amount to obtain T amount discount proportions;
determining a remaining quantity M of the target product, and determining the N first users in the T second users based on a principle that a discount use ratio is from large to small, the T discount use ratios and the M, wherein the M is larger than or equal to the N;
and determining the discount information of each second user based on the discount proportion of the amount corresponding to each second user and the price of the target commodity to obtain N pieces of discount information.
4. The method of claim 3, wherein M is associated with T, and the association relationship between M and N is determined based on preset factor information affecting the targeted merchandise marketing.
5. The method of claim 4, further comprising:
obtaining friend information of each first user to obtain N pieces of friend information;
determining first friends with affinity greater than or equal to preset affinity with each first user based on the friend information to obtain N first friends;
and sending each piece of the preferential information to each first friend.
6. A system for processing marketing data, the system comprising a database and an electronic device:
the database is used for storing first historical marketing data of a target commodity of a target merchant in a first time period and second historical marketing data of the target commodity in a second time period, and the ending moment of the first time period is the starting moment of the second time period;
the electronic equipment is used for acquiring the first historical marketing data and acquiring the second historical marketing data;
the electronic equipment is further used for processing the first historical marketing data and the second historical marketing data and determining the marketing stability of the target commodity;
the electronic equipment is further used for determining a target user with intention to the target commodity and preferential information of the target user under the condition that the marketing stability is not qualified;
and the electronic equipment is also used for sending the preset preferential information of the target commodity to the target user.
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