CN117372065A - Intelligent product pricing method and system based on user information - Google Patents
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Abstract
The invention relates to the technical field of product pricing, in particular to an intelligent product pricing method and system based on user information. The method comprises the steps of obtaining product information characteristics, retrieving products, collecting price information, obtaining monthly delivery data of the products in the last half year, drawing a delivery line diagram, analyzing influences of different variable factors on delivery, predicting the recent delivery of the products according to analysis results.
Description
Technical Field
The invention relates to the technical field of product pricing, in particular to an intelligent product pricing method and system based on user information.
Background
The society today is a technological age surrounding artificial intelligence, cloud computing, big data and the like, intelligent pricing is one of important factors for determining survival of companies at present, when products are sold on an online platform in an up-shelf mode, related pricing of the same-party can be studied when a plurality of existing company products are priced, and due to the fact that the price of the same-party is different, the products are sold on the upper-shelf mode by a plurality of merchants through average prices, in order to improve the product competitiveness, the products are sold through prices lower than average price, however, long-term use is achieved, the profit of the products is slightly thin, healthy development of the companies is not facilitated, in order to evaluate the delivery volume of the products conveniently, the competitiveness and market occupation rate of the products are improved, and meanwhile sufficient profit is obtained to maintain monitoring development of the companies.
Disclosure of Invention
The invention aims to provide an intelligent pricing method and system for products based on user information, which are used for solving the problems in the background technology.
In order to solve the above technical problems, one of the purposes of the present invention is to provide an intelligent pricing method for products based on user information, comprising the following steps:
s1, acquiring product information characteristics, retrieving products and collecting price information;
s2, calculating the production cost of the product, and determining the average price of the product according to the price information in the S1;
s3, acquiring monthly delivery data of the product in the last half year, drawing a delivery line graph, analyzing the influence of different variable factors on the delivery, predicting the recent delivery of the product according to the analysis result, when the predicted delivery is higher than the average delivery, the market demand of the product is proved to be high, the average selling price is kept, when the predicted delivery is lower than the average delivery, the market demand of the product is proved to be low, and the selling price is between the average price and the product cost;
s4, acquiring the recent demand of the product, collecting pricing advice of the user on the product, establishing a pricing decision model, intelligently pricing the product, recording intelligent pricing product shipment data, comparing the recent shipment of the product with the actual shipment of the product according to the predicted product, proving that the variable factors influencing the shipment are correct if the predicted data are matched with the actual shipment data, and updating the variable factors influencing the shipment if the predicted data are not matched with the actual shipment data.
As a further improvement of the technical scheme, in S1, product price information is searched through a search function of a sales platform, and product pricing data is collected by adopting a crawler program to determine a price interval of a product.
As a further improvement of the technical scheme, the production cost of the product is calculated by adopting a weighted average algorithm in the step S2.
As a further improvement of the technical scheme, in the S2, the average price of product selling is determined according to the price interval in the S1, and the method specifically comprises the following steps:
s2.1, obtaining product prices of different merchants in the S1;
s2.2, calculating the average price of the product by adopting an arithmetic average value.
As a further improvement of the technical scheme, the step S3 of obtaining the product output of the latest half-year platform and drawing a product quantity line graph specifically comprises the following steps:
s3.1, acquiring sales volume ranking data of the sales platform products, and calculating the delivery volume data of the products in the last half year according to the ranking data;
s3.2, selecting a Microsoft Excel tool, and importing monthly shipment data of the product in the last half year to form a shipment line diagram;
and S3.3, establishing a statistical model to evaluate the correlation between the monthly delivery volume and the variable factors.
As a further improvement of the present technical solution, the establishing a statistical model in S3.3 includes the following steps:
collecting data: collecting monthly shipment data and other possibly related factor data, ensuring that the data contains sufficient sample size and time span;
model type selection is established: according to the characteristics of the data and the requirements of the research problem, selecting a linear regression model, taking the output as a dependent variable, taking other variable factors as independent variables, and performing model fitting by using a statistical method;
interpretation model: explaining the relation between the coefficients and the variables of the model, wherein the coefficients of the model represent the influence degree and direction of the independent variables on the dependent variables, the positive and negative coefficients represent the influence direction, and the size of the coefficients represent the influence degree;
performing hypothesis testing: performing hypothesis testing to verify the significance of the independent variables to the shipment, checking the p value of each independent variable, and if the p value is smaller than the set significance level, considering the influence of the independent variable to the shipment to be significant;
making predictions and inferences: and predicting the recent shipment according to the established model.
As a further improvement of the technical scheme, in S4, pricing feedback suggestions of the users for the products are collected by sending emails to the users who have purchased the same products.
As a further improvement of the technical scheme, a pricing decision model is established in the step S4, and intelligent pricing is carried out on the product, and the method comprises the following steps:
s4.1, collecting data related to product pricing, including production cost, other merchant pricing, recent inventory predictions, and user pricing suggestions;
s4.2, selecting characteristics related to pricing decisions from the collected data;
s4.3, selecting a price elastic model, training the model by using the collected data, and verifying and evaluating the model after training is finished to obtain the performance of the model;
s4.4, inputting data which are known to be related to the pricing decision into a model, and obtaining the pricing decision result through model analysis.
The second object of the invention is to provide a product intelligent pricing system based on user information, comprising the product intelligent pricing method based on user information as described in any one of the above, comprising a product information collecting unit, a price analyzing unit, a product delivery predicting unit and a pricing decision unit;
the information collection unit is used for obtaining product information characteristics, retrieving products and collecting price information;
the price analysis unit is used for calculating the production cost of the product and determining the average price of the product according to the price information of the searched product;
the product shipment prediction unit is used for acquiring monthly shipment data of the product in the last half year, drawing a shipment line diagram, comprehensively analyzing different factors of the monthly shipment, predicting the recent shipment of the product, when the predicted shipment is higher than the average shipment, the average selling price is kept when the predicted shipment is higher than the average shipment, and when the predicted shipment is lower than the average shipment, the selling price is between the average price and the product cost when the predicted shipment is lower than the average shipment, the market demand of the product is lower;
the pricing decision unit is used for acquiring the recent demand of the product, collecting pricing suggestions of the user on the product, establishing a pricing decision model, intelligently pricing the product, recording intelligent pricing product shipment data, feeding back the shipment data to the shipment prediction unit, comparing the recent shipment data with the actual shipment data of the product according to the predicted product, proving that the variable factors influencing the shipment are correct if the predicted data are consistent with the actual shipment data, and updating the variable factors influencing the shipment if the predicted data are not consistent with the actual shipment data.
Compared with the prior art, the invention has the beneficial effects that:
1. the method comprises the steps of searching product price information of a platform, calculating production cost and average selling price of the product, analyzing output data of each month, drawing a line graph, analyzing influences of different variable factors on the output, predicting the output of the product according to analysis results, judging market demand according to the output, keeping average price of the product when the market demand is high, meeting most of common people, setting the product price between the average price and the product cost price when the market demand is high, stimulating consumption of the common people through price reduction, improving market competitiveness of the product, collecting pricing suggestions of the user on the product, establishing a decision model, inputting collected data information, intelligently pricing the product, improving pricing efficiency and scientificity, intelligently adjusting the product through judging future output of the product and the market demand, guaranteeing profit of the product, and improving the product competitiveness and market occupancy.
2. By recording intelligent pricing product shipment data, comparing the predicted product recent shipment with the product real shipment, if the predicted data are matched with the real shipment, the variable factors influencing the shipment are proved to be correct, and if the predicted data are not matched with the real shipment, the variable factors influencing the shipment are updated, the accuracy of the predicted product shipment is improved, and the pricing decision model decision result is conveniently and rapidly adjusted according to the product shipment.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a block flow diagram of the present invention for calculating an average price for a product;
FIG. 3 is a block flow diagram of the present invention for predicting product output;
FIG. 4 is a flow chart diagram of a pricing decision model built in accordance with the present invention;
fig. 5 is a schematic diagram of the overall module of the present invention.
The meaning of each reference sign in the figure is:
100. an information collection unit; 200. a price analysis unit; 300. a product shipment prediction unit; 400. and a pricing decision unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1-5, one of the objects of the present invention is to provide an intelligent pricing method for products based on user information, comprising the steps of:
s1, acquiring product information characteristics, retrieving products and collecting price information;
because different merchants have different product prices when selling platform merchant products, in order to be convenient for analyzing the difference between merchant price points and reasonably pricing the products, the product price information is searched through the search function of the sales platform in the S1, the product pricing data is collected by adopting a crawler program, and the price interval of the products is determined.
The crawler program can access a search page of the sales platform, input keywords for searching, acquire commodity information of the search result page including prices, and then count and analyze the price data to identify the lowest and highest values of the prices so as to determine the price interval of the product. The method can help you know the price range of the product on the sales platform so as to make better pricing decisions.
S2, calculating the production cost of the product, and determining the average price of the product according to the price information in the S1;
in order to obtain reasonable pricing of the product, the production cost of the product needs to be known, therefore, the production cost of the product is calculated by adopting a weighted average algorithm in the step S2, and a specific algorithm formula is as follows:
weighted average cost= (cost term 1×weight 1) + (cost term 2×weight 2) + … + (cost term n×weight n), wherein:
cost term 1, cost terms 2, …, cost term n represent different cost terms, such as material cost, direct labor cost, etc.;
weight 1, weight 2, …, weight n represent the weight of the corresponding cost item, typically the proportion of the cost item in the total cost;
it should be noted that the weights need to satisfy the following conditions:
the sum of the weights is equal to 1, representing the total cost;
the weights are determined based on the importance of the individual cost terms or the ratio of the total cost.
By using a weighted average method, an enterprise can calculate the cost of a product based on the importance and weight of each cost item, thereby knowing the production cost of the product more accurately.
Considering that most of users are ordinary consumers, most of platforms recommend shops with moderate prices when products are purchased, and in order to quickly acquire average price of the products, the average price of selling the products is determined according to the price interval in S1 in S2, and the method specifically comprises the following steps:
s2.1, obtaining product prices of different merchants in the S1;
s2.2, calculating the average price of the product by adopting an arithmetic average value.
And obtaining pricing of different merchants to products through a crawler program, and obtaining average price of the products by adopting an arithmetic average algorithm, so that a price interval most suitable for common consumers is obtained, and the obtained average price basis of the products is used as an analysis factor of intelligent pricing in the later period, so that the accuracy of intelligent pricing is improved.
S3, acquiring monthly delivery data of the product in the last half year, drawing a delivery line graph, analyzing the influence of different variable factors on the delivery, predicting the recent delivery of the product according to the analysis result, when the predicted delivery is higher than the average delivery, the market demand of the product is proved to be high, the average selling price is kept, when the predicted delivery is lower than the average delivery, the market demand of the product is proved to be low, and the selling price is between the average price and the product cost;
in order to facilitate the observation of the delivery of the product in the last half year, the correlation between the delivery of the product in the last half year and the variable factor is evaluated, so as to analyze the future delivery trend of the product, therefore, the delivery of the product on the last half year platform is obtained in the step S3, and a delivery line diagram is drawn, which specifically comprises the following steps:
s3.1, acquiring sales volume ranking data of the sales platform products, and calculating the delivery volume data of the products in the last half year according to the ranking data;
s3.2, selecting a Microsoft Excel tool, and importing monthly shipment data of the product in the last half year to form a shipment line diagram;
and S3.3, establishing a statistical model to evaluate the correlation between the monthly delivery volume and the variable factors.
Through counting sales data of sales platform merchants, counting total shipment of monthly products in the last half year, importing monthly shipment into Microsoft Excel to form a line graph, visually observing monthly product shipment difference, analyzing reasons for the shipment difference, obtaining variable factors influencing the shipment, determining more accurate variable factors influencing the shipment through real-time updating according to different seasons, accurately analyzing future shipment trend of products, and changing product pricing according to the shipment trend, thereby improving market share of the products.
The step of establishing a statistical model in S3.3 comprises the following steps:
collecting data: collecting monthly shipment data and other possibly related factor data, ensuring that the data contains sufficient sample size and time span;
model type selection is established: according to the characteristics of the data and the requirements of the research problem, selecting a linear regression model, taking the output as a dependent variable, taking other variable factors as independent variables, and performing model fitting by using a statistical method;
interpretation model: explaining the relation between the coefficients and the variables of the model, wherein the coefficients of the model represent the influence degree and direction of the independent variables on the dependent variables, the positive and negative coefficients represent the influence direction, and the size of the coefficients represent the influence degree;
performing hypothesis testing: performing hypothesis testing to verify the significance of the independent variables on the shipment, checking the p value of each independent variable, and if the p value is smaller than a set significance level (usually 0.05), considering that the effect of the independent variable on the shipment is significant;
making predictions and inferences: and predicting the recent shipment according to the established model.
S4, acquiring the recent demand of the product, collecting pricing advice of the user on the product, establishing a pricing decision model, intelligently pricing the product, recording intelligent pricing product shipment data, comparing the recent shipment data with the actual shipment data of the product according to the predicted product, proving that the variable factors influencing the shipment are correct if the predicted data are matched with the actual shipment data, and updating the variable factors influencing the shipment if the predicted data are not matched with the actual shipment data;
in order to improve the scientificity and accuracy of product pricing, in the step S4, an email is sent to a user who has purchased the same product, pricing feedback suggestions of the user for the product are collected, the email is sent to the user who has purchased the same product, the user uses the product to judge according to pricing of the product purchased at the time, a large number of users are analyzed to give pricing suggestions for the product, so that a price interval acceptable by the user is obtained according to user information, the price interval provided by the user is combined with future forecast goods output of the product, data are transmitted to a decision model, and intelligent pricing is accurately carried out on the product.
And in the step S4, a pricing decision model is established to intelligently price the product, and the method comprises the following steps:
s4.1, collecting data related to product pricing, including production cost, other merchant pricing, recent inventory predictions, and user pricing suggestions;
s4.2, selecting characteristics related to pricing decisions from the collected data;
s4.3, selecting a price elastic model, training the model by using the collected data, and verifying and evaluating the model after training is finished to obtain the performance of the model;
s4.4, inputting data which are known to be related to the pricing decision into a model, and obtaining the pricing decision result through model analysis.
When the price information of the product is specifically used, the production cost and the average selling price of the product are calculated through retrieving the price information of the product on a platform, a line graph is drawn through analyzing the data of the output of the product on a monthly basis, the influence of different variable factors on the output of the product is analyzed, the output of the product is predicted according to the analysis result, the market demand degree is judged according to the output of the product, when the market demand degree is high, the average price of the product is kept, most of ordinary people are met, when the market demand degree is high, the price of the product is set between the average price and the cost price of the product, the consumption of the ordinary people is stimulated through reducing the price, so that the market competitiveness of the product is improved, meanwhile, the pricing advice of the user on the product is collected, a decision model is built, the collected data information is input, and the pricing efficiency and the scientificity are improved for intelligent pricing of the product.
The second object of the present invention is to provide a product intelligent pricing system based on user information, including any one of the above product intelligent pricing methods based on user information, including a product information collecting unit 100, a price analyzing unit 200, a product delivery amount predicting unit 300 and a pricing decision unit 400;
the information collection unit 100 is used for acquiring product information characteristics, retrieving products and collecting price information;
the price analysis unit 200 is used for calculating the production cost of the product and determining the average price of the product according to the retrieved product price information;
the product shipment prediction unit 300 is configured to obtain monthly shipment data of a product in the last half year, draw a shipment line graph, comprehensively analyze factors different in monthly shipment, predict a recent shipment of the product, and when the predicted shipment is higher than the average shipment, the product market demand is proved to be high, the average selling price is maintained, and when the predicted shipment is lower than the average shipment, the product market demand is proved to be low, the selling price is between the average price and the product cost;
the pricing decision unit 400 is configured to obtain the recent demand of the product, collect pricing advice of the user on the product, establish a pricing decision model, intelligently price the product, record intelligent pricing product shipment data, feed back the shipment data to the shipment prediction unit 300, compare the recent shipment data with the actual shipment data of the product according to the predicted product, prove that the variable factor affecting the shipment is correct if the predicted data is identical to the actual shipment data, and update the variable factor affecting the shipment if the predicted data is not identical to the actual shipment data.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. An intelligent pricing method for products based on user information is characterized in that: the method comprises the following steps:
s1, acquiring product information characteristics, retrieving products and collecting price information;
s2, calculating the production cost of the product, and determining the average price of the product according to the price information in the S1;
s3, acquiring monthly delivery data of the product in the last half year, drawing a delivery line graph, analyzing the influence of different variable factors on the delivery, predicting the recent delivery of the product according to the analysis result, when the predicted delivery is higher than the average delivery, the market demand of the product is proved to be high, the average selling price is kept, when the predicted delivery is lower than the average delivery, the market demand of the product is proved to be low, and the selling price is between the average price and the product cost;
s4, acquiring the recent demand of the product, collecting pricing advice of the user on the product, establishing a pricing decision model, intelligently pricing the product, recording intelligent pricing product shipment data, comparing the recent shipment of the product with the actual shipment of the product according to the predicted product, proving that the variable factors influencing the shipment are correct if the predicted data are matched with the actual shipment data, and updating the variable factors influencing the shipment if the predicted data are not matched with the actual shipment data.
2. The intelligent pricing method for product based on user information according to claim 1, wherein: in the step S1, product price information is searched through a search function of the sales platform, and the crawler program is adopted to collect the product pricing data so as to determine the price interval of the product.
3. The intelligent pricing method for product based on user information according to claim 1, wherein: and S2, calculating the production cost of the product by adopting a weighted average algorithm.
4. A method for intelligent pricing of products based on user information as recited in claim 3, wherein: in the step S2, the average price of product selling is determined according to the price interval in the step S1, and the method specifically comprises the following steps:
s2.1, obtaining product prices of different merchants in the S1;
s2.2, calculating the average price of the product by adopting an arithmetic average value.
5. The intelligent pricing method for product based on user information according to claim 1, wherein: the step S3 is to obtain the product yield of the last half year platform and draw a product yield line diagram, and the method specifically comprises the following steps:
s3.1, acquiring sales volume ranking data of the sales platform products, and calculating the delivery volume data of the products in the last half year according to the ranking data;
s3.2, selecting a Microsoft Excel tool, and importing monthly shipment data of the product in the last half year to form a shipment line diagram;
and S3.3, establishing a statistical model to evaluate the correlation between the monthly delivery volume and the variable factors.
6. The intelligent pricing method for user information based products of claim 5, wherein: the step of establishing a statistical model in S3.3 comprises the following steps:
collecting data: collecting monthly shipment data and other possibly related factor data, ensuring that the data contains sufficient sample size and time span;
model type selection is established: according to the characteristics of the data and the requirements of the research problem, selecting a linear regression model, taking the output as a dependent variable, taking other variable factors as independent variables, and performing model fitting by using a statistical method;
interpretation model: explaining the relation between the coefficients and the variables of the model, wherein the coefficients of the model represent the influence degree and direction of the independent variables on the dependent variables, the positive and negative coefficients represent the influence direction, and the size of the coefficients represent the influence degree;
performing hypothesis testing: performing hypothesis testing to verify the significance of the independent variables to the shipment, checking the p value of each independent variable, and if the p value is smaller than the set significance level, considering the influence of the independent variable to the shipment to be significant;
making predictions and inferences: and predicting the recent shipment according to the established model.
7. The intelligent pricing method for product based on user information according to claim 1, wherein: and S4, sending an E-mail to the user who has purchased the same product, and collecting pricing feedback suggestions of the user for the product.
8. The intelligent pricing method for user information based products of claim 7, wherein: and in the step S4, a pricing decision model is established to intelligently price the product, and the method comprises the following steps:
s4.1, collecting data related to product pricing, including production cost, other merchant pricing, recent inventory predictions, and user pricing suggestions;
s4.2, selecting characteristics related to pricing decisions from the collected data;
s4.3, selecting a price elastic model, training the model by using the collected data, and verifying and evaluating the model after training is finished to obtain the performance of the model;
s4.4, inputting data which are known to be related to the pricing decision into a model, and obtaining the pricing decision result through model analysis.
9. An intelligent pricing system for products based on user information, comprising the intelligent pricing method for products based on user information according to any of claims 1-8, characterized in that: the system comprises a product information collection unit (100), a price analysis unit (200), a product delivery prediction unit (300) and a pricing decision unit (400);
the information collection unit (100) is used for acquiring product information characteristics, retrieving products and collecting price information;
the price analysis unit (200) is used for calculating the production cost of the product and determining the average price of the product according to the price information of the searched product;
the product shipment prediction unit (300) is used for acquiring monthly shipment data of the product in the last half year, drawing a shipment line diagram, comprehensively analyzing factors with different monthly shipments, predicting the recent shipment of the product, when the predicted shipment is higher than the average shipment, the average selling price is maintained when the predicted shipment is proved to be high in market demand, and when the predicted shipment is lower than the average shipment, the selling price is between the average price and the product cost when the predicted shipment is proved to be low in market demand;
the pricing decision unit (400) is used for acquiring the recent demand of the product, collecting pricing suggestions of the user on the product, establishing a pricing decision model, intelligently pricing the product, recording intelligent pricing product shipment data, feeding back the shipment data to the shipment prediction unit (300), comparing the recent shipment of the product with the actual shipment of the product according to the predicted product, proving that the variable factors influencing the shipment are correct if the predicted data are consistent with the actual shipment data, and updating the variable factors influencing the shipment if the predicted data are not consistent with the actual shipment data.
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