CN111260388A - Method and device for determining and displaying life cycle of commodity - Google Patents
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Abstract
The application discloses a method and a device for determining and displaying a life cycle of a commodity, wherein the method for determining the life cycle of the commodity comprises the following steps: acquiring sales data of a target commodity in a target time period; obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data; and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method and a device for determining and displaying a life cycle of a commodity.
Background
The life cycle of the commodity generally refers to the marketing life cycle of the commodity, and specifically can include an introduction period, a growth period, a maturity period and a decline period. Generally, commodities all have life cycles, and when the commodities are sold, corresponding sale strategies can be made according to the life cycles of the commodities. For example, the amount of the commodity introduced may be increased during the mature period of the commodity and decreased during the decline period of the commodity.
To facilitate the formulation of a sales strategy for an article, it is often necessary to determine which lifecycle the article is currently in. However, the existing method for determining the life cycle of a commodity has low accuracy, and thus an effective commodity sales strategy cannot be made.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining and displaying a life cycle of a commodity, and is used for solving the problem that in the prior art, when the life cycle of the commodity is determined, the accuracy is low, and therefore a commodity sales strategy cannot be effectively formulated.
In order to solve the above technical problem, the embodiment of the present application is implemented as follows:
in a first aspect, a method for determining a life cycle of a commodity is provided, including:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
In a second aspect, an apparatus for determining a life cycle of a product is provided, including:
the first acquisition unit is used for acquiring the sales data of the target commodity in a target time period;
the second acquisition unit is used for acquiring a model used for determining the life cycle of the commodity, and the model is obtained based on sample sales data of the sample commodity and life cycle training corresponding to the sample sales data;
and a determining unit for determining the life cycle of the target commodity in the target time period based on the sales data of the target commodity and the model.
In a third aspect, an electronic device is provided, which includes:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
In a fourth aspect, a computer-readable storage medium is presented, the computer-readable storage medium storing one or more programs that, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
In a fifth aspect, a method for displaying a life cycle of a commodity is provided, including:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
In a sixth aspect, a device for displaying a life cycle of a product is provided, which includes:
the first acquisition unit is used for acquiring the sales data of the target commodity in a target time period;
the second acquisition unit is used for acquiring a model used for determining the life cycle of the commodity, and the model is obtained based on sample sales data of the sample commodity and life cycle training corresponding to the sample sales data;
a determination unit that determines which lifecycle the target item is in at the target time period based on sales data of the target item and the model;
and the display unit is used for displaying the life cycle of the target commodity to a target user.
In a seventh aspect, an electronic device is provided, which includes:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
In an eighth aspect, a computer-readable storage medium is provided that stores one or more programs which, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
according to the technical scheme provided by the embodiment of the application, the model for determining the life cycle of the commodity is obtained in advance according to the sample sales data and the life cycle training of the sample commodity, and when the life cycle of the target commodity is determined, the life cycle of the target commodity in the target time period can be determined according to the sales data of the target commodity in the target time period and the pre-trained model. Because the life cycle of the target commodity is determined based on the model obtained by pre-training, and the model training can take various fluctuation factors related to the life cycle of the commodity into account, the accuracy of the life cycle of the commodity determined based on the model is higher, so that an effective sale strategy can be formulated based on the life cycle of the commodity when the target commodity is sold.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a schematic flow chart diagram illustrating a method for determining a life cycle of a commodity according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram illustrating a method for determining a life cycle of a commodity according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating a method for displaying a life cycle of a product according to an embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 5 is a schematic diagram of the structure of a device for determining the life cycle of an article according to an embodiment of the present application;
FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an apparatus for determining a life cycle of an article according to an embodiment of the present application.
Detailed Description
The life cycle of the commodity can generally comprise an introduction period, a growth period, a maturity period and a decline period, and when the commodity is sold, the life cycle of the commodity can be determined, and then a corresponding sale strategy is formulated.
In the prior art, when determining the life cycle of a commodity, the life cycle is generally determined based on a method of minimizing a loss function. However, this method is not prospective, does not consider a plurality of factors affecting the life cycle, and the data dimension for determining the life cycle of the commodity is relatively single, so the accuracy of determining the life cycle of the commodity is low, and an effective marketing strategy cannot be made.
In order to solve the above problem, an embodiment of the present application provides a method and an apparatus for determining and displaying a life cycle of a commodity, where the method for determining a life cycle of a commodity includes: acquiring sales data of a target commodity in a target time period; obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data; and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
According to the technical scheme provided by the embodiment of the application, the life cycle of the target commodity is determined and obtained based on the model obtained through pre-training, and the model training can take various fluctuation factors related to the life cycle of the commodity into consideration, so that the accuracy of the life cycle of the target commodity determined and obtained based on the model is high, and an effective selling strategy can be formulated based on the life cycle of the commodity when the target commodity is sold.
In order to make those skilled in the art better understand the technical solutions in the present application, 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 a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a method for determining a life cycle of a commodity according to an embodiment of the present disclosure. The method comprises the following steps.
S102: and acquiring the sales data of the target commodity in the target time period.
The target time period may be a time period from a starting time of selling the target product to any time in a process of selling the target product, and may be specifically determined according to an actual demand, which is not specifically limited herein. For example, when it is necessary to determine which lifecycle a target commodity is currently in, the target time period may be a time period from a start time of selling the target commodity to a current time; when it is necessary to determine which life cycle the target commodity is in the last month, the target time period may be a time period from the start time of selling the target commodity to the last month.
In this embodiment, the target time period may include a plurality of sales cycles, and one sales cycle may be one month, two months, one quarter, and the like, which is not specifically limited herein. The present embodiment may be explained in terms of one sale period being one month.
The sales data of the target commodity in the target time period can comprise one or more of search amount, click rate, comment number, collection number, sales amount, growth rate and conversion rate of the target commodity in different sales cycles.
The search amount may be understood as the number of times that the target product is searched in one sales cycle, the click amount may be understood as the number of times that the target product is clicked in one sales cycle, the number of reviews may be understood as the number of times that the target product is reviewed in one sales cycle, the collection number may be understood as the number of times that the target product is collected in one sales cycle, and the sales amount may be understood as the number of times that the target product is sold in one sales cycle.
The increase rate may be understood as an increase rate of at least one of the search volume, the click volume, the number of comments, the number of collections, and the sales volume, and the increase rate is an increase rate of the search volume of one sales cycle compared with the search volume of the previous sales cycle, taking the search volume as an example.
The conversion rate may be understood as a conversion rate of at least one of the search volume, the click volume, the number of comments and the number of collections compared with the sales volume, and taking the search volume as an example, the conversion rate is a conversion rate of the search volume of one sales cycle compared with the sales volume of the sales cycle.
Preferably, the sales data may include all of the search volume, click volume, comment volume, collection volume, sales volume, growth rate, and conversion rate, so that it is possible to accurately obtain which life cycle the target product is in the target time period based on data of multiple dimensions in the subsequent steps.
It should be understood that the sales data described in the present embodiment may be other data related to the target product besides the above-described several data, and are not illustrated here.
In this embodiment, when obtaining the sales data of the target product in the target time period, the method may obtain the sales data of each sales cycle of the target product in a plurality of sales cycles corresponding to the target time period, and specifically includes the following steps:
acquiring a sales record of the target commodity in the target time period;
dividing the sales records according to the sales cycles to obtain the sales records of the target commodities in different sales cycles;
carrying out smoothing treatment on the sales records in the different sales periods to obtain the processed sales records;
and performing summary analysis on the processed sales records to obtain the sales data of the target commodity in the target time period.
The sales record may include at least one of a search record, a click record, a comment record, a collection record, and an order record of the target product in the target time period, and may specifically correspond to the sales data that needs to be obtained. After the sales records are obtained, the sales records can be divided according to the sales cycles, so that the sales records of the target commodities in different sales cycles in the target time period can be obtained.
In this embodiment, when considering that the target product is actually sold, the sales record may be affected by abnormal sales factors (for example, sales promotion activities or preferential activities of the target product), and further affect which life cycle the target product is in, so after obtaining the sales records of the target product in different sales cycles, it is further necessary to perform smoothing processing on the sales records to remove the sales records related to the abnormal factors in the sales records, and obtain the processed sales records.
After the processed sales records are obtained, the sales data of the target commodity in different sales periods within the target time can be obtained by summarizing and analyzing the sales records.
After acquiring the sales data of the target product in the target time period by the above-mentioned method, S104 may be executed.
S104: a model for determining a lifecycle of a good is obtained.
The model can be obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data, and specifically comprises the following steps:
obtaining historical sales data of sample commodities;
determining the introduction period, the growth period, the maturity period and the decline period of the sample commodity according to the historical sales data and preset rules;
selecting sample sales data corresponding to the target time period from the historical sales data;
determining a life cycle corresponding to the sample sales data according to the introduction period, the growth period, the maturity period and the decline period of the sample commodity;
training the sample sales data and the life cycle corresponding to the sample sales data based on a multi-classification machine learning algorithm to obtain the model.
The historical sales data of the sample commodities can be understood as the sales data of the sample commodities in the whole process from the beginning of the sale to the off-shelf, for example, the total time of the sample commodities from the beginning of the sale to the off-shelf is 12 months, and then the historical sales data is the sales data of the sample commodities in the 12 months.
The historical sales data may include at least one of search volume, click volume, comment volume, collection volume, sales volume, growth rate and conversion rate of the sample goods in different sales cycles, and specifically, reference may be made to the above description of the sales data in S102, and a description thereof will not be repeated here. Here, the sales cycle is the same as the sales cycle described in S102, and the specific data type included in the historical sales data may be the same as the data type included in the sales data of the target product described in S102.
In obtaining historical sales data for a sample good, the following steps may be included:
obtaining historical sales records of the sample commodities;
dividing the historical sales records according to the sales cycles to obtain the sales records of the sample commodities in different sales cycles;
carrying out smoothing treatment on the sales records in the different sales periods to obtain the processed sales records;
and performing summary analysis on the processed sales records to determine historical sales data of the sample commodity.
The historical sales record of the sample commodity can be understood as the sales record of the sample commodity in the whole process from the beginning of the sale to the off-shelf sale. After the historical sales records of the sample commodities are obtained, the historical sales records can be divided according to the sales cycles, and the sales records of the sample commodities in each sales cycle in the whole sales process are obtained.
For example, the total time from the beginning of sale to the sale of the sample goods is 12 months, and then the historical sales records of the 12 months can be divided into monthly sales records to obtain the sales records of each month.
After obtaining the sales record of the sample commodity in each sales cycle in the whole sales process, the historical sales data of the sample commodity can be obtained through smoothing processing and summary analysis, and the specific implementation manner can refer to the content of performing smoothing processing and summary analysis on the sales record of the target commodity described in the above S102, and will not be described repeatedly here.
After the historical sales data of the sample commodity is obtained, the life cycle of the sample commodity can be determined according to the historical sales data and preset rules.
In this embodiment, the life cycle of the sample commodity may be determined based on the search volume in the historical sales data. Determining the life cycle of the sample commodity can be understood as determining the introduction period, the growth period, the maturity period and the decline period of the sample commodity, and here, the life cycle of the sample commodity can be determined by determining which sales cycles are in the introduction period, which sales cycles are in the maturity period and which sales cycles are in the decline period.
In determining the life cycle of the sample goods according to the preset rule, one or more sales cycles for initially selling the sample goods may be determined as the introduction period of the sample goods, and the sales cycle having the largest search amount may be determined as the maturity period of the sample goods.
After determining the maturity period of the sample commodity, for at least one sales period of which the sales time is after the maturity period, it may be determined whether the search volume of the consecutive first number of sales periods is smaller than a first ratio of the maximum search volume, if so, the consecutive first number of sales periods is determined as the decline period, and if not, the consecutive first number of sales periods is determined as the maturity period. Wherein the first number may be 3 or more, and the first ratio may be between 10% and 20%.
And judging whether the search quantity of a second number of continuous sale cycles is larger than a second proportion of the maximum search quantity or not aiming at least one sale cycle of which the sale time is before the mature period, if so, determining the second number of continuous sale cycles as the mature period, and if not, determining the second number of continuous sale cycles as the introduction period. Wherein the second number may be 3 or more, and the second ratio may be between 50% and 100%.
For example, the total sale duration of the sample commodity is 12 months, and the search volume of each month is as follows: 5 ten thousand, 10 ten thousand, 30 ten thousand, 60 ten thousand, 80 ten thousand, 90 ten thousand, 100 ten thousand, 95 ten thousand, 85 ten thousand, 70 ten thousand, 40 ten thousand, 10 ten thousand, then the 7 th month (corresponding to a search volume of 100 ten thousand) may be determined as the maturity period, and the first two months (corresponding to a search volume of 5 ten thousand and 10 ten thousand) of initially selling the sample goods may be determined as the induction period.
Assuming that the first number is 3, the first proportion is 10%, the second number is 3, and the second proportion is 50%, for example, the next four months (corresponding to search volumes of 85 ten thousand, 70 ten thousand, 40 ten thousand, and 10 ten thousand) may be determined as a decline period, the 9 th month (corresponding to search volumes of 95 ten thousand) may be determined as a maturity period, the 4 th to 6 th months (corresponding to search volumes of 60 ten thousand, 80 ten thousand, and 90 ten thousand) may be determined as a growth period, and the 3 rd month (corresponding to search volumes of 30 ten thousand) may be determined as an introduction period.
It should be noted that, when determining each life cycle of the sample product, the search amount is used as a judgment basis, in other implementation manners, other sales data may also be used as a judgment basis, and each life cycle of the sample product is determined by using the same method, which is not illustrated here.
After the introduction period, the growth period, the maturity period and the decline period of the sample commodity are determined, a part of sales data in the historical sales data of the sample commodity can be selected as a sample to carry out model training. In this embodiment, since it is necessary to determine which lifecycle the target product is in the target time period, the sales data corresponding to the target time period may be selected from the historical sales data of the sample product as the sample sales data.
For example, when the historical sales data of the sample commodity is the sales data of 12 months, if the target time period is the first 5 months of selling the target commodity, then the sample sales data is the sales data of the sample commodity in the first 5 months; if the target time period is the first 7 months of sale of the target commodity, then the sample sales data is sales data for the sample commodity for the first 7 months.
That is to say, in this embodiment, in order to determine which life cycle the target product is in according to the model obtained by training, it is necessary to ensure that the sales time period corresponding to the sample data during model training is consistent with the sales time period corresponding to the acquired sales data of the target product.
After the sample sales data is obtained, the sales cycle corresponding to the sample sales data can be determined according to the determined life cycles of the sample commodities. Then, the sample sales data of the sample commodity and the sales cycle corresponding to the sample sales data can be used as sample features, and a model for determining the commodity life cycle is obtained through training based on a multi-classification learning algorithm.
In this embodiment, the multi-classification learning algorithm may be a Decision Tree (Decision Tree), a Random forest (Random forest), an xgboost (extreme Gradient boosting), a lightGBM, or the like, which may be determined specifically according to an actual situation, and is not specifically limited herein.
The model obtained by training belongs to a multi-classification model, and the output of the model can be one of an induction period, a growth period, a maturity period and a decline period.
It should be noted that, in practical applications, different commodities may have different life cycle characteristics, some commodities do not include a repetitive life cycle in the whole sales process (i.e., the life cycle of a commodity includes an introduction period, a maturation period, a decay period), and some commodities include a repetitive life cycle in the whole sales process (i.e., between the introduction period and the decay period, multiple maturation periods, and decay periods may be included), so that, when performing model training, two commodities having different life cycle characteristics may be separately trained, and two different models may be obtained, and the different models may determine the life cycles of commodities having different life cycle characteristics. For ease of understanding, the present embodiment is described with reference to a commercial product having an induction period, a growth period, a maturation period, and a decline period.
After training to obtain a model for determining the life cycle of the commodity, when determining the life cycle of the target commodity, the model may be obtained, and S106 is performed.
S106: and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
In this embodiment, which life cycle the target product is in the target time period may be understood as which life cycle the target product is in at the end time of the target time period, for example, if the target time period is 5 months, determining the life cycle of the target product is to determine which life cycle the target product is in the 5 th month.
In S106, the sales data of the target product obtained in S102 may be used as an input of a model, and an output of the model is a life cycle of the target product in the target time period, where the life cycle of the target product may be one of an introduction period, a maturity period, and a decline period.
In this embodiment, during model training, each life cycle of a sample commodity is determined based on historical sales data of the sample commodity in the whole sales process, and the historical sales data of the sample commodity in the whole life cycle is fully considered, so that after the model is trained, the life cycle of a target commodity determined based on the model has a forward-looking property.
In addition, because the expression mode of the model is complex, various fluctuation factors related to the life cycle of the commodity can be taken into consideration, and therefore the life cycle of the obtained target commodity is determined by using the model, and the accuracy is high.
Based on the contents of S102 to S106, the present embodiment can determine which lifecycle the target product is in at different sales stages according to different target time periods, wherein the different target time periods may correspond to different models.
For example, the target commodity has been sold for 8 months, if it is desired to determine which lifecycle the target commodity is in at the 6 th month, then model a may be trained based on sales data of each month and a corresponding lifecycle of each month of the sample commodity within the 6 months of the initial sale, and based on the model a and sales data of each month within the 6 months before the target commodity, which lifecycle the target commodity is in at the 6 th month may be determined; if it is desired to determine which lifecycle the target commodity is currently in, model B may be trained based on sales data of each month and a lifecycle corresponding to each month for the sample commodity within 8 months of the initial sales, and based on sales data of each month within 8 months of model B and the target commodity, which lifecycle the target commodity is currently in may be determined.
In practical application, a plurality of models can be obtained by training according to different target time periods, and the plurality of models can be used for determining the life cycle of the target commodity in different target time periods. The longer the target time period is, the more sales data of the target commodity is acquired, and the higher the accuracy of the life cycle of the target commodity is determined to be.
In one embodiment of the application, after determining the life cycle of the target commodity in the target time period, the determined life cycle may be further displayed to the target user, so that the target user may make a sales strategy for the target commodity.
For the sake of easy understanding of the whole technical solution, reference may be made to fig. 2. Fig. 2 is a flowchart illustrating a method for determining a life cycle of a commodity according to an embodiment of the present application. The present embodiment is described in terms of one sales cycle for one month, and the method includes the following steps.
S201: and acquiring the sales record of the target commodity in the target time period.
The target time period may include N months, where N may be less than or equal to the total number of sales months for the target good. The sales record may be at least one of a search record, a click record, a comment record, a favorite record, and a record of being sold of the target item within the target.
S202: and dividing the sales records according to months to obtain the sales records of the target commodity every month.
S203: and smoothing the sales record of the target commodity every month to obtain the processed sales record.
The smoothing process is performed to remove abnormal factors, such as promotion or benefit, which affect the life cycle of the target product. The smoothing process may be a moving smoothing process.
S204: and summarizing and analyzing the processed sales data to obtain the sales data of the target commodity every month.
The sales data may be at least one of a search volume, a click volume, a number of comments, a number of collections, a sales volume, an increase rate, and a conversion rate, and may specifically correspond to the sales records acquired in S201. Preferably, the sales record may be all of a search record, a click record, a comment record, a favorite record, and a record sold, and the sales data may be all of a search amount, a click amount, a comment number, a favorite number, a sales amount, an increase rate, and a conversion rate.
S205: a model for determining a lifecycle of a good is obtained.
In this embodiment, the model may be trained based on sales data of each month and a life cycle corresponding to each month of the sample goods in the target time period for initial sales, and a specific training method may refer to the contents described in the embodiment shown in fig. 1, which is not described repeatedly here.
S206: and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
The life cycle of the target commodity may be determined to be one of an induction period, a growth period, a maturity period, and a decline period.
Since the model for determining the life cycle of the target commodity is trained based on the sales data of the commodity with the determined life cycle, various fluctuation factors related to the life cycle of the commodity can be taken into consideration, and therefore, the accuracy of determining the life cycle of the target commodity by using the model is high;
when the model is trained, the training can be carried out based on the sales data of the sample commodity on the multiple dimensions, and the life cycle of the target commodity is determined according to the sales data of the target commodity on the multiple dimensions, so that the accuracy of the result can be further improved;
in the process of training the model, the historical sales data of the sample commodity in the whole life cycle are fully considered, so that the life cycle of the target commodity determined by the model obtained by training the sales data of the sample commodity has foresight;
since the accuracy of the commodity life cycle determined by the embodiment is high, based on the commodity life cycle, a user can make a better sales strategy.
According to the technical scheme provided by the embodiment of the application, the model for determining the life cycle of the commodity is obtained in advance according to the sample sales data and the life cycle training of the sample commodity, and when the life cycle of the target commodity is determined, the life cycle of the target commodity in the target time period can be determined according to the sales data of the target commodity in the target time period and the pre-trained model. Because the life cycle of the target commodity is determined based on the model obtained by pre-training, and the model training can take various fluctuation factors related to the life cycle of the commodity into account, the accuracy of the life cycle of the commodity determined based on the model is higher, so that an effective sale strategy can be formulated based on the life cycle of the commodity when the target commodity is sold.
Fig. 3 is a flowchart illustrating a method for displaying a life cycle of a commodity according to an embodiment of the present disclosure. The display method comprises the following steps.
S302: and acquiring the sales data of the target commodity in the target time period.
S304: a model for determining a lifecycle of a good is obtained.
The model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data.
S306: and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
The specific implementation of S302 to S306 can refer to the specific implementation of corresponding steps in the embodiment shown in fig. 1, and a description of one or more embodiments in this specification is not repeated here.
S308: and displaying the life cycle of the target commodity to a target user.
The target user can be understood as a user selling the target commodity, and after the life cycle of the target commodity is displayed to the target user, the target user can make a corresponding selling strategy based on the life cycle of the target commodity.
For example, when the target commodity is currently in the growth stage, the exposure of the target commodity may be increased, such as recommending the target commodity on a commodity sales front page of the internet; when the target commodity is in the mature period, on one hand, the introduction amount of the target commodity can be increased, and on the other hand, if the user searches the target commodity in the internet, the target commodity is displayed to the user, and meanwhile, other commodities, such as the commodity in the introduction period, can be displayed, namely, the target commodity in the mature period drives the other commodities in the introduction period, so that better experience can be brought to merchants; when the target commodity is in the decline period currently, on one hand, the introduction amount of the target commodity can be reduced, the target commodity is prevented from being lost, and on the other hand, the target commodity can be cleared, for example, the target commodity can be sold at a low price.
In this embodiment, since the life cycle of the target commodity is determined based on the model obtained by the pre-training, and the model training can take into account various fluctuation factors related to the life cycle of the commodity, the accuracy of the life cycle of the commodity determined based on the model is high, and after the life cycle of the target commodity is displayed to the target user, the target user can make an effective selling strategy based on the life cycle of the commodity when selling the target commodity.
The foregoing description of specific embodiments of the present application has been presented. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 4, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form the determining device of the commodity life cycle on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
The method performed by the apparatus for determining the life cycle of a product as disclosed in the embodiment of fig. 4 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may further execute the method shown in fig. 1 and fig. 2, and implement the function of the device for determining the life cycle of a commodity in the embodiment shown in fig. 1 and fig. 2, which is not described herein again in this embodiment of the present application.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present application also propose a computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a portable electronic device comprising a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 1 and 2, and in particular to perform the following operations:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
Fig. 5 is a schematic structural diagram of an apparatus 50 for determining a life cycle of an article according to an embodiment of the present application. Referring to fig. 5, in a software implementation, the device 50 for determining the life cycle of the product may include: a first acquisition unit 51, a second acquisition unit 52, and a determination unit 53, wherein:
a first acquisition unit 51 that acquires sales data of a target commodity in a target time period;
the second obtaining unit 52 is configured to obtain a model for determining a life cycle of a commodity, where the model is obtained based on sample sales data of a sample commodity and a life cycle training corresponding to the sample sales data;
the determination unit 53 determines which lifecycle the target product is in at the target time period based on the sales data of the target product and the model.
Optionally, the second obtaining unit 52 trains the model by:
obtaining historical sales data of sample commodities;
determining the introduction period, the growth period, the maturity period and the decline period of the sample commodity according to the historical sales data and preset rules;
selecting sample sales data corresponding to the target time period from the historical sales data;
determining a life cycle corresponding to the sample sales data according to the introduction period, the growth period, the maturity period and the decline period of the sample commodity;
training the sample sales data and the life cycle corresponding to the sample sales data based on a multi-classification machine learning algorithm to obtain the model.
Optionally, the historical sales data comprises at least one of search volume, click volume, comment volume, collection volume, sales volume, growth rate and conversion rate of the sample commodity in different sales periods;
the second obtaining unit 52 obtains historical sales data of sample commodities, and includes:
obtaining historical sales records of the sample commodities;
dividing the historical sales records according to the sales cycles to obtain the sales records of the sample commodities in different sales cycles;
carrying out smoothing treatment on the sales records in the different sales periods to obtain the processed sales records;
and performing summary analysis on the processed sales records to determine historical sales data of the sample commodity.
Optionally, the second obtaining unit 52 determines, according to the historical sales data and according to preset rules, the introduction period, the growth period, the maturity period, and the decline period of the sample commodity, including:
determining the search amount of the sample commodity in each sales period according to the historical sales data;
determining at least one sale period for initially selling the sample goods as a lead-in period;
determining the sales cycle corresponding to the maximum search amount as a maturity period;
aiming at least one sale period of which the sale time is after the maturity period, judging whether the search quantity of a continuous first number of sale periods is smaller than a first proportion of the maximum search quantity, if so, determining the continuous first number of sale periods as a decline period, and if not, determining the continuous first number of sale periods as the maturity period;
and judging whether the search quantity of a second number of continuous sale cycles is larger than a second proportion of the maximum search quantity or not aiming at least one sale cycle of which the sale time is before the mature period, if so, determining the second number of continuous sale cycles as the mature period, and if not, determining the second number of continuous sale cycles as the introduction period.
Optionally, the target time period comprises a plurality of sales cycles;
the sales data of the target commodity in the target time period comprises at least one of search volume, click volume, comment number, collection number, sales volume, growth rate and conversion rate of the target commodity in different sales cycles.
Optionally, the first obtaining unit 51, which obtains the sales data of the target product in the target time period, includes:
acquiring a sales record of the target commodity in the target time period;
dividing the sales records according to the sales cycles to obtain the sales records of the target commodities in different sales cycles;
carrying out smoothing treatment on the sales records in the different sales periods to obtain the processed sales records;
and performing summary analysis on the processed sales records to obtain the sales data of the target commodity in the target time period.
The device 50 for determining a life cycle of a commodity provided in this embodiment of the present application may further perform the method shown in fig. 1 and fig. 2, and implement the functions of the device for determining a life cycle of a commodity in the embodiment shown in fig. 1 and fig. 2, which are not described herein again in this embodiment of the present application.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 6, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (peripheral component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory to the memory and then runs the computer program to form the display device of the commodity life cycle on the logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
The method executed by the display device for the life cycle of the commodity according to the embodiment shown in fig. 6 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may also execute the method of fig. 3, and implement the function of the display apparatus of the life cycle of the product in the embodiment shown in fig. 3, which is not described herein again in this embodiment of the present application.
Of course, besides the software implementation, the electronic device of the present application does not exclude other implementations, such as a logic device or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or a logic device.
Embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by a portable electronic device including a plurality of application programs, enable the portable electronic device to perform the method of the embodiment shown in fig. 3, and are specifically configured to:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
Fig. 7 is a schematic structural diagram of a display device 70 for the life cycle of a commodity according to an embodiment of the present application. Referring to fig. 7, in a software implementation, the display device 70 for the life cycle of the merchandise may include: a first acquisition unit 71, a second acquisition unit 72, a determination unit 73 and a presentation unit 74, wherein:
a first acquisition unit 71 that acquires sales data of a target commodity in a target time period;
the second obtaining unit 72 is configured to obtain a model for determining a life cycle of a commodity, where the model is obtained based on sample sales data of a sample commodity and life cycle training corresponding to the sample sales data;
a determination unit 73 that determines which lifecycle the target item is in at the target time period based on the sales data of the target item and the model;
and the display unit 74 displays the life cycle of the target commodity to the target user.
The display device 70 for the life cycle of the product provided in the embodiment of the present application can also execute the method in fig. 3, and implement the functions of the display device for the life cycle of the product in the embodiment shown in fig. 3, which are not described herein again in the embodiment of the present application.
In short, the above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Claims (13)
1. A method for determining a life cycle of a commodity, comprising:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
2. The method of claim 1, the model being trained by:
obtaining historical sales data of sample commodities;
determining the introduction period, the growth period, the maturity period and the decline period of the sample commodity according to the historical sales data and preset rules;
selecting sample sales data corresponding to the target time period from the historical sales data;
determining a life cycle corresponding to the sample sales data according to the introduction period, the growth period, the maturity period and the decline period of the sample commodity;
training the sample sales data and the life cycle corresponding to the sample sales data based on a multi-classification machine learning algorithm to obtain the model.
3. The method of claim 2, wherein the first and second light sources are selected from the group consisting of a red light source, a green light source, and a blue light source,
the historical sales data comprises at least one of search volume, click volume, comment number, collection number, sales volume, growth rate and conversion rate of the sample commodities in different sales periods;
wherein, obtaining historical sales data for the sample goods comprises:
obtaining historical sales records of the sample commodities;
dividing the historical sales records according to the sales cycles to obtain the sales records of the sample commodities in different sales cycles;
carrying out smoothing treatment on the sales records in the different sales periods to obtain the processed sales records;
and performing summary analysis on the processed sales records to determine historical sales data of the sample commodity.
4. The method of claim 3, wherein determining the introduction period, the growth period, the maturity period and the decline period of the sample commodity according to a preset rule based on the historical sales data comprises:
determining the search amount of the sample commodity in each sales period according to the historical sales data;
determining at least one sale period for initially selling the sample goods as a lead-in period;
determining the sales cycle corresponding to the maximum search amount as a maturity period;
aiming at least one sale period of which the sale time is after the maturity period, judging whether the search quantity of a continuous first number of sale periods is smaller than a first proportion of the maximum search quantity, if so, determining the continuous first number of sale periods as a decline period, and if not, determining the continuous first number of sale periods as the maturity period;
and judging whether the search quantity of a second number of continuous sale cycles is larger than a second proportion of the maximum search quantity or not aiming at least one sale cycle of which the sale time is before the mature period, if so, determining the second number of continuous sale cycles as the mature period, and if not, determining the second number of continuous sale cycles as the introduction period.
5. The method of claim 1, wherein the first and second light sources are selected from the group consisting of a red light source, a green light source, and a blue light source,
the target time period comprises a plurality of sales cycles;
the sales data of the target commodity in the target time period comprises at least one of search volume, click volume, comment number, collection number, sales volume, growth rate and conversion rate of the target commodity in different sales cycles.
6. The method of claim 5, obtaining sales data for the target item over the target time period, comprising:
acquiring a sales record of the target commodity in the target time period;
dividing the sales records according to the sales cycles to obtain the sales records of the target commodities in different sales cycles;
carrying out smoothing treatment on the sales records in the different sales periods to obtain the processed sales records;
and performing summary analysis on the processed sales records to obtain the sales data of the target commodity in the target time period.
7. A method for displaying the life cycle of a commodity comprises the following steps:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
8. An apparatus for determining a life cycle of an article, comprising:
the first acquisition unit is used for acquiring the sales data of the target commodity in a target time period;
the second acquisition unit is used for acquiring a model used for determining the life cycle of the commodity, and the model is obtained based on sample sales data of the sample commodity and life cycle training corresponding to the sample sales data;
and a determining unit for determining the life cycle of the target commodity in the target time period based on the sales data of the target commodity and the model.
9. A merchandise lifecycle display apparatus, comprising:
the first acquisition unit is used for acquiring the sales data of the target commodity in a target time period;
the second acquisition unit is used for acquiring a model used for determining the life cycle of the commodity, and the model is obtained based on sample sales data of the sample commodity and life cycle training corresponding to the sample sales data;
a determination unit that determines which lifecycle the target item is in at the target time period based on sales data of the target item and the model;
and the display unit is used for displaying the life cycle of the target commodity to a target user.
10. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
11. A computer readable storage medium storing one or more programs which, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform a method of:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
and determining which life cycle the target commodity is in the target time period based on the sales data of the target commodity and the model.
12. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
13. A computer readable storage medium storing one or more programs which, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform a method of:
acquiring sales data of a target commodity in a target time period;
obtaining a model for determining the commodity life cycle, wherein the model is obtained based on sample sales data of sample commodities and life cycle training corresponding to the sample sales data;
determining which lifecycle the target good is in at the target time period based on the sales data of the target good and the model;
and displaying the life cycle of the target commodity to a target user.
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| CN111784394A (en) * | 2020-06-30 | 2020-10-16 | 广东奥园奥买家电子商务有限公司 | Commodity life cycle management method and device and computer equipment |
| CN111833110A (en) * | 2020-07-23 | 2020-10-27 | 北京思特奇信息技术股份有限公司 | Customer life cycle positioning method and device, electronic equipment and storage medium |
| CN112989183A (en) * | 2021-02-20 | 2021-06-18 | 湖南视拓信息技术股份有限公司 | Product information recommendation method and device based on life cycle and related equipment |
| CN112989183B (en) * | 2021-02-20 | 2022-03-22 | 湖南视拓信息技术股份有限公司 | Product information recommendation method and device based on life cycle and related equipment |
| CN114444934A (en) * | 2022-01-27 | 2022-05-06 | 南京数族信息科技有限公司 | Enterprise sales periodic evaluation algorithm and tool application thereof |
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