Dynamic pricing adjustment method for user behavior analysis

Abstract

A user behavior analysis-oriented dynamic pricing adjustment method comprises the following steps: data acquisition and preprocessing: collecting user behavior data in real time, including but not limited to commodity browsing history, click data, search keywords, shopping cart operation, purchase records, return records and the like of a user; collecting related data of the commodity, including price, category, brand, inventory, sales volume, user evaluation and the like of the commodity; commodity prices, market demands, promotion activity data and the like of competitors are collected; through a data cleaning algorithm, abnormal data is removed, missing values are filled, data standardization processing is carried out, the accuracy of subsequent analysis is ensured, and through comprehensive analysis of multi-dimensional user behaviors, market environments and competitor dynamic states, more accurate price prediction and adjustment can be realized, the sales conversion rate is improved, and the profit margin is optimized. Especially in the peak period of demand or intense competition, the merchant can avoid sales chance loss or underestimated profit caused by unreasonable price through intelligent pricing optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet e-commerce, specifically a dynamic pricing adjustment method for user behavior analysis. BACKGROUND

[0002] The dynamic pricing adjustment method for user behavior analysis dynamically adjusts the pricing of goods by real-time monitoring and analyzing user behaviors such as browsing, clicking, searching, and purchasing on e-commerce platforms, combined with user purchase intentions, price sensitivity, purchase history, and other data. This method uses machine learning and data mining techniques to predict user reactions to different prices, thereby intelligently adjusting product prices during periods of user activity, demand peaks, or promotional activities to maximize sales and profits. For example, when the system detects that a certain product has a high purchase intention among a specific user group, it can automatically increase the price; conversely, when it detects that demand for a certain product is low, it can stimulate purchasing behavior by lowering prices. Through this personalized and real-time pricing adjustment strategy, merchants can improve conversion rates and enhance user satisfaction.

[0003] Although the dynamic pricing adjustment method based on user behavior analysis has significant advantages in improving sales and profits, there are still some shortcomings and challenges in the existing technology: excessive price fluctuations, leading to user dissatisfaction, dynamic pricing adjustment may cause frequent fluctuations in product prices, especially during periods of high demand. While this method can maximize short-term profits, if the price changes too drastically or is not transparent, it may lead to user distrust and dissatisfaction with the price, affecting the user's shopping experience and platform loyalty. Inaccurate algorithms may result in unreasonable pricing, dynamic pricing relies on complex machine learning algorithms and big data analysis, and if the model is not well trained or the data is not accurate, it may lead to price setting deviating from market expectations. For example, the system may overestimate or underestimate the user's willingness to pay, resulting in overpricing or underpricing of goods, which may affect sales and profits. Ignoring the competitive environment, existing dynamic pricing methods often focus on analyzing the behavior of individual users, while ignoring the competitive situation of the market. If competitors' pricing strategies are not taken into account, it may lead to overpricing, thereby losing price-sensitive users, or underpricing, resulting in reduced profits. Data privacy and security issues, dynamic pricing often requires the analysis of a large amount of user behavior data, which may involve users' personal information and consumption records. How to effectively collect and analyze data while ensuring privacy and data security is an important issue. If user privacy issues are not properly handled, legal risks and user trust crises may arise. Cold start problem, for new users or users without sufficient historical behavior data, the dynamic pricing system may have difficulty accurately determining their needs and willingness to pay, resulting in unreasonable pricing and affecting new users' purchase decisions. This cold start problem is particularly evident in the absence of user data. Over-reliance on user behavior, ignoring other factors, dynamic pricing algorithms rely too much on historical behavior data of users, which may ignore other external factors such as seasonal changes, market hotspots, and product life cycles. These factors also have an important impact on price fluctuations, but if the model only relies on user behavior data, it may miss the best opportunity for price optimization. High demand for computing resources, dynamic pricing based on user behavior analysis requires processing large amounts of data and real-time computing to quickly make pricing adjustments. This puts high demands on the computing power and storage capacity of the system, especially on e-commerce platforms with a large number of users and a wide variety of goods, which may result in high consumption of computing resources and high costs.

[0004] Therefore, we propose a dynamic pricing adjustment method based on user behavior analysis. SUMMARY

[0005] To achieve the above purpose, the present application provides the following technical solutions: a dynamic pricing adjustment method based on user behavior analysis, comprising the following steps:

[0006] Data collection and preprocessing:

[0007] Real-time collection of user behavior data, including but not limited to user's product browsing history, click data, search keywords, shopping cart operations, purchase records, return records, etc.

[0008] Collecting product-related data, including product price, category, brand, inventory, sales, user reviews, etc.

[0009] Collecting competitor product prices, market demand, promotional activity data, seasonal fluctuation information, etc.

[0010] Through data cleaning algorithms to remove abnormal data, fill in missing values, and perform data standardization processing to ensure the accuracy of subsequent analysis.

[0011] User portrait and payment willingness prediction:

[0012] Using user behavior data to build user portraits, including but not limited to user's interest preferences, purchase frequency, brand loyalty, price sensitivity, active time period, etc.

[0013] Using machine learning algorithms (such as random forest, gradient boosting tree, etc.) or deep learning models (such as convolutional neural network CNN, recurrent neural network LSTM) to predict user payment willingness, output user response prediction to different prices.

[0014] Dynamic pricing adjustment:

[0015] Based on user portrait and payment willingness prediction, combined with market environment, product life cycle and other factors, real-time adjustment of product price;

[0016] During the demand peak period (such as holidays, promotional seasons, etc.), increase the price of the product through the automatic price optimization system; in the demand downturn, automatically adjust the price to promote to stimulate user purchase.

[0017] Price fluctuation limit and transparent mechanism:

[0018] Set the price fluctuation range to avoid excessive frequent price adjustment, for example, the price increase cannot exceed a certain percentage or the price decrease cannot be lower than a certain benchmark price;

[0019] Through the price transparency mechanism, show the user the reasons for price adjustment (such as market demand changes, inventory status, competitor pricing, etc.), improve user acceptance of price changes.

[0020] Cold start problem processing:

[0021] For new users, through their social network data, basic portrait information and historical user data, combined with social network recommendation algorithm for preliminary pricing;

[0022] For new products, initial pricing is determined by content-based recommendation algorithms (e.g., analyzing product descriptions, keywords, images, reviews, etc.), combined with market trends and user interests.

[0023] Competitive analysis and pricing optimization:

[0024] Real-time crawling of competitors' product prices, promotion information, inventory data, etc., combined with self-product price and sales data, dynamically adjusts self-product prices;

[0025] Using a price sensitivity analysis model, adjust the pricing strategy according to the competitor's pricing and market feedback to ensure the competitiveness of the product in the market.

[0026] Preferably, the construction of the user portrait is through a joint deep learning model, which uses convolutional neural networks to process user image data and product attributes, and combines recurrent neural networks to model user behavior sequences, generating multi-dimensional, high-precision user interest vectors.

[0027] Preferably, the cold start problem is handled through social network analysis technology, combining user interaction behavior on social platforms to infer potential interests and willingness to pay, and combining basic information of the product for preliminary pricing.

[0028] Preferably, the price fluctuation range is automatically generated by a rule engine, which dynamically adjusts the price fluctuation range and adjustment frequency based on market demand, product life cycle, and historical price fluctuations.

[0029] Preferably, the competitive analysis is through real-time price crawler technology and market trend analysis model to monitor competitors' pricing changes in real time, and through collaborative filtering algorithm combined with market demand data to optimize self-product pricing.

[0030] Compared with the prior art, the present application provides a dynamic pricing adjustment method based on user behavior analysis, which has the following advantages:

[0031] 1. The dynamic pricing adjustment method based on user behavior analysis can achieve more accurate price prediction and adjustment through comprehensive analysis of multi-dimensional user behavior, market environment, and competitors, improve sales conversion rate and optimize profit space. Especially during peak demand or intense competition, through intelligent pricing optimization, merchants can avoid sales opportunities lost or profit underestimated due to unreasonable prices.

[0032] 2、The user behavior analysis-oriented dynamic pricing adjustment method, through a price fluctuation transparency mechanism and an explanation mechanism, allows users to clearly understand the background of price changes (such as market demand changes, inventory fluctuations, etc.), enhancing the users' trust in the platform. The transparent pricing process reduces user dissatisfaction caused by frequent price adjustments, improving the shopping experience and user stickiness.

[0033] 3、The user behavior analysis-oriented dynamic pricing adjustment method effectively solves the cold start problem through social network analysis and content-based recommendation algorithms, ensuring accurate pricing even in the absence of historical data. This not only reduces the risk of new user loss but also helps new products gain initial market share.

[0034] 4、The user behavior analysis-oriented dynamic pricing adjustment method, the competition analysis module can monitor the price dynamics of competitors in real time, automatically adjust prices based on market demand and user behavior, and make the platform's goods always maintain market competitiveness. By comparing and optimizing pricing strategies with competitors, businesses can gain a price advantage in a competitive market.

[0035] 5、The user behavior analysis-oriented dynamic pricing adjustment method is highly flexible and suitable for various types of goods and market environments, allowing it to quickly respond to changes in market demand and competitive situations. At the same time, modular design can be extended and adjusted according to the needs of different e-commerce platforms, reducing the need for manual intervention and improving operational efficiency. Businesses do not need to manually adjust prices or monitor market changes, as the system will automatically adjust prices based on real-time data, reducing management costs and human resource investment. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be described below in a clear and complete manner. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0037] EMBODIMENT

[0038] Embodiment of the user behavior analysis-oriented dynamic pricing adjustment method

[0039] The user behavior analysis-oriented dynamic pricing adjustment method includes the following steps:

[0040] Data collection and preprocessing:

[0041] Real-time collection of user behavior data, including but not limited to user's product browsing history, click data, search keywords, shopping cart operations, purchase records, return records, etc.

[0042] Collecting product-related data, including product price, category, brand, inventory, sales, user reviews, etc.

[0043] Collecting competitor product prices, market demand, promotional activity data, seasonal fluctuation information, etc.

[0044] Through data cleaning algorithms to remove abnormal data, fill in missing values, and perform data standardization processing to ensure the accuracy of subsequent analysis.

[0045] User portrait and payment willingness prediction:

[0046] Using user behavior data to build user portraits, including but not limited to user's interest preferences, purchase frequency, brand loyalty, price sensitivity, active time period, etc.

[0047] Using machine learning algorithms (such as random forest, gradient boosting tree, etc.) or deep learning models (such as convolutional neural network CNN, recurrent neural network LSTM) to predict user payment willingness, output user response prediction to different prices.

[0048] Dynamic pricing adjustment:

[0049] Based on user portraits and payment willingness prediction, combined with market environment, product life cycle and other factors, real-time adjustment of product prices;

[0050] During the demand peak period (such as holidays, promotional seasons, etc.), increase the price of the product through the automatic price optimization system; in the demand downturn, automatically adjust the price to promote to stimulate user purchase.

[0051] Price fluctuation limit and transparent mechanism:

[0052] Set the price fluctuation range to avoid excessive frequent price adjustment, for example, the price increase cannot exceed a certain percentage or the price decrease cannot be lower than a certain benchmark price;

[0053] Through the price transparency mechanism, show the user the reasons for price adjustment (such as market demand changes, inventory status, competitor pricing, etc.), improve user acceptance of price changes.

[0054] Cold start problem processing:

[0055] For new users, through their social network data, basic portrait information and historical user data, combined with social network recommendation algorithm for preliminary pricing;

[0056] For new products, initial pricing is determined by content-based recommendation algorithms (e.g., analyzing product descriptions, keywords, images, reviews, etc.) combined with market trends and user interests.

[0057] Competitive analysis and pricing optimization:

[0058] Real-time crawling of competitors' product prices, promotion information, inventory data, etc., combined with self-product price and sales data, dynamically adjusts self-product prices;

[0059] Using a price sensitivity analysis model, adjust pricing strategies based on competitor pricing and market feedback to ensure product competitiveness in the market.

[0060] Specifically, the construction of user portrait is through the joint deep learning model, using convolutional neural network to process the image data of users and the attributes of goods, and combining recurrent neural network to model the user behavior sequence, generating multi-dimensional and high-precision user interest vector.

[0061] Specifically, the handling of the cold start problem is through social network analysis technology, combined with the interaction behavior of users on social platforms, to infer their potential interests and payment willingness, and combined with the basic information of the goods to make preliminary pricing.

[0062] Specifically, the price fluctuation range is automatically generated by the rule engine, which dynamically adjusts the price fluctuation range and adjustment frequency based on market demand, product life cycle and historical price fluctuation.

[0063] Specifically, competitive analysis uses real-time price crawler technology and market trend analysis model to monitor competitors' pricing changes in real time, and combines with market demand data through collaborative filtering algorithm to optimize self-product pricing.

[0064] Through the technical solution, in the application, through comprehensive analysis of multi-dimensional user behavior, market environment and competitor dynamics, the application can realize more accurate price prediction and adjustment, improve sales conversion rate and optimize profit space. Especially in the demand peak period or in the fierce competition, through intelligent pricing optimization, the merchant can avoid the loss of sales opportunities or the underestimation of profits caused by unreasonable prices, through the price fluctuation transparency mechanism and the explanation mechanism, the user can clearly understand the background of price change (such as market demand change, inventory fluctuation, etc.), and the trust of the user to the platform is enhanced. The transparent pricing process reduces the dissatisfaction of users caused by frequent price adjustment, improves the shopping experience and user stickiness, solves the cold start problem through social network analysis and content-based recommendation algorithm, and ensures accurate pricing in the absence of historical data. This not only reduces the risk of new user loss, but also can win the initial market for new goods. The competition analysis module can monitor the price dynamics of competitors in real time, automatically adjust the price in combination with market demand and user behavior, so that the goods of the platform always maintain market competitiveness. By comparing and optimizing the pricing strategy with competitors, the merchant can gain a price advantage in the competitive market. This method has high flexibility and is suitable for various types of goods and market environments, and can quickly respond to changes in market demand and competition situation. At the same time, modular design can be extended and adjusted according to the needs of different e-commerce platforms, and automatic pricing adjustment and optimization reduce the need for manual intervention and improve operational efficiency. The merchant does not need to manually adjust the price or monitor market changes, and the system will automatically adjust the price according to real-time data, reducing management costs and human resource investment.

[0065] User portrait construction and payment willingness prediction

[0066] Data collection:

[0067] The e-commerce platform collects user browsing data (browsing goods, clicking goods), search data (search keywords), purchase records (purchase frequency, purchase amount), and social media behavior data (likes, comments, shares, etc.) through API interface.

[0068] At the same time, the platform will collect the prices, promotion information and market trend data of competitor goods through crawler technology.

[0069] User portrait generation:

[0070] Use deep learning algorithms (such as LSTM, CNN) to analyze user behavior data and generate user portraits. User portraits include but are not limited to: interest preferences, brand loyalty, price sensitivity, active time period, etc.

[0071] Based on user profiling, use machine learning algorithms (such as random forest, XGBoost) to predict users' willingness to pay, and output users' response to different prices.

[0072] Willingness to pay prediction and price adjustment:

[0073] When the system detects that a certain product has a high willingness to pay for a certain type of user, it automatically raises the price of the product; conversely, when the user's willingness to pay for a certain product is low, the system will automatically lower the price of the product.

[0074] Competitor analysis and real-time price adjustment

[0075] Price monitoring and competition analysis:

[0076] Using real-time price crawler technology, regularly crawl competitors' product prices, promotion information, inventory status, etc.

[0077] By analyzing the price change trend of competitors and market demand fluctuations, combined with the market demand, inventory and sales data of the product itself, calculate the optimized product price.

[0078] Collaborative filtering and optimized pricing:

[0079] Using collaborative filtering algorithm, combined with market trend data and user behavior data, automatically adjust the price of the product. This method ensures that pricing not only responds to user demand, but also adapts to market changes and competitors' strategies.

[0080] Price fluctuation control and transparent mechanism

[0081] Fluctuation range control:

[0082] Through the rule engine, set the maximum increase and minimum decrease of price adjustment. The specific increase and decrease are dynamically calculated based on the market demand, sales and user willingness to pay prediction results of the product.

[0083] For example, the maximum increase of a certain product is limited to 10%, and the price reduction is limited to 5%. If the user's willingness to pay is predicted to decrease, the price will automatically decrease but will not be lower than the set minimum price.

[0084] Transparent mechanism display:

[0085] When the price changes, the system will show the background and reason of price adjustment (such as "due to holiday promotion, price adjustment", or "according to market demand changes, price optimization") to the user through a pop-up window or prompt information on the product page. This transparent mechanism can enhance users' understanding and acceptance of the platform's pricing.

[0086] Cold start problem handling

[0087] New user pricing strategy:

[0088] For new users, the platform will first analyze their social media information, such as the purchasing behavior and interests of their social circle, to make an initial prediction of their price sensitivity.

[0089] Through social network analysis (SNA) technology, combined with social platform data, the platform predicts the user's possible purchasing behavior, and then recommends the user with price-optimized goods.

[0090] New product pricing strategy:

[0091] For new products, by analyzing product description, pictures, tags, keywords, user reviews, etc., combined with market trend data, the initial pricing is determined.

[0092] After the initial pricing, the system adjusts the product price based on user purchasing behavior and market feedback, optimizing the pricing strategy.

[0093] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic pricing adjustment method based on user behavior analysis, characterized by: The following steps are involved: Data collection and preprocessing: Real-time collection of user behavior data, including but not limited to user product browsing history, click data, search keywords, shopping cart operations, purchase records, return records, etc.; Collect product-related data, including price, category, brand, inventory, sales volume, user reviews, etc. Collect competitor product prices, market demand, promotional activity data, seasonal fluctuation information, etc.; Data cleaning algorithms are used to remove abnormal data, fill missing values, and perform data standardization to ensure the accuracy of subsequent analysis. User portrait and willingness to pay prediction: Utilize user behavior data to build user profiles, including but not limited to user interests, purchase frequency, brand loyalty, price sensitivity, active time periods, etc. Use machine learning algorithms (such as random forests, gradient boosting trees, etc.) or deep learning models (such as convolutional neural networks (CNN) and recurrent neural networks (LSTM)) to predict users' willingness to pay and output predictions of users' reactions to different prices. Dynamic pricing adjustments: Adjust product prices in real time based on user profiles and willingness to pay predictions, combined with market environment, product life cycle and other factors; During peak demand periods (such as holidays and promotional seasons), product prices are raised through an automatic price optimization system; when demand is low, prices are automatically adjusted for promotions to stimulate user purchases. Price fluctuation limits and transparency mechanisms: Set a price fluctuation range to avoid excessive price adjustments, such as price increases not exceeding a certain percentage or price reductions not falling below a certain base price; Through the price transparency mechanism, the reasons for price adjustments (such as changes in market demand, inventory status, competitor pricing, etc.) are shown to users, thereby improving user acceptance of price changes. Cold start problem handling: For new users, preliminary pricing is performed based on their social network data, basic profile information, and historical user data, combined with social network recommendation algorithms; For new products, initial pricing is determined through content-based recommendation algorithms (such as analyzing product descriptions, keywords, images, reviews, etc.) combined with market trends and user interests. Competition analysis and pricing optimization: Crawling competitors' product prices, promotion information, inventory data, etc. in real time, and dynamically adjusting the prices of their own products based on their own product prices and sales data; Use price sensitivity analysis models to adjust pricing strategies based on competitor pricing and market feedback to ensure product competitiveness in the market.

2. The method for dynamic pricing adjustment based on user behavior analysis according to claim 1, characterized in that: The user portrait is constructed by combining a deep learning model, using a convolutional neural network to process user image data and product attributes, and combining a recurrent neural network to model user behavior sequences to generate a multi-dimensional, high-precision user interest vector.

3. The method for dynamic pricing adjustment based on user behavior analysis according to claim 1, characterized in that: The cold start problem is addressed by using social network analysis technology, combining users' interactive behaviors on social platforms to infer their potential interests and willingness to pay, and performing preliminary pricing based on basic product information.

4. The method for dynamic pricing adjustment based on user behavior analysis according to claim 1, characterized in that: The price fluctuation range is automatically calculated and generated by the rule engine, which dynamically adjusts the price fluctuation range and adjustment frequency based on market demand, product life cycle and historical price fluctuations.

5. The method for dynamic pricing adjustment based on user behavior analysis according to claim 1, characterized in that: The competition analysis uses real-time price crawling technology and market trend analysis models to monitor competitors' pricing changes in real time, and optimizes the pricing of its own products by combining collaborative filtering algorithms with market demand data.