New product promotion method and new product promotion system based on user behavior analysis

Abstract

The invention relates to the technical field of data analysis and processing, and discloses a new product promotion method and system based on user behavior analysis, and the method comprises the steps: firstly, carrying out the precise analysis of user behaviors in a store based on a computer vision technology, and comprehensively and meticulously mastering the interest degree of a user in a new product; according to the method, the data of all links from new product seeing, new product taking and new product purchasing of the user can be accurately obtained, and compared with a traditional mode depending on sales data or market investigation, the real behaviors and potential demands of the user can be deeply informed. Secondly, a new product promotion strategy is guided and adjusted according to the accurate behavior analysis results, promotion can be more targeted and effective, resource waste caused by blind promotion is avoided, the market acceptance and the sales conversion rate of new products are remarkably improved, new profit growth points are brought to merchants, and the market competitiveness of the merchants is improved. And the market share and the survival and development capability of the plants in fierce competition are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and processing, and in particular to a new product promotion method and a new product promotion system based on user behavior analysis. Background Art

[0002] With the increasing diversification and personalization of user demands and the strengthening of consumer spending power, competition within the retail industry is becoming increasingly fierce. In this increasingly saturated market, standing out from the crowd has become crucial for businesses to survive and thrive. In this process, increasing market share and exploring profit growth opportunities are particularly important. These are not only crucial indicators of a business's competitiveness, but also the cornerstone of its sustainable and healthy development.

[0003] It's worth noting that a merchant's expansion of market share is often closely linked to the successful launch of a new product. The launch of a new product not only injects fresh blood into the market and satisfies user demand for novel and unique products, but also effectively opens up new profit growth points and expands market share. However, current traditional promotional methods for new products often focus on simple analysis of sales data or macro-level market research. These methods often lack in-depth analysis of user behavior during the promotion process, making it difficult to accurately understand user interest in the new product, resulting in poor promotion results and wasted resources.

[0004] Therefore, a new promotion method is needed that can provide more accurate decision-making support for new product promotions based on users' actual behavior data in stores.

[0005] The above information is presented as background information only to assist with an understanding of the present disclosure and is not a determination or admission that any of the above may be applicable as prior art with respect to the present disclosure. Summary of the Invention

[0006] The present invention provides a new product promotion method and a new product promotion system based on user behavior analysis to solve the problems existing in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a new product promotion method based on user behavior analysis, the method comprising:

[0009] S100: Obtaining surveillance video data of the store;

[0010] S200: Conducting a behavior analysis of users in the store based on the surveillance video data to obtain behavior analysis results; the behavior analysis results include the number of users visiting the store, the number of times users saw new products, the number of times users picked up new products, the percentage of users who saw new products among the users who visited the store, the percentage of users who picked up new products among the users who saw new products, the percentage of users who picked up new products among the users who picked up new products, and the percentage of users who purchased new products among the users who picked up new products;

[0011] S300: Generating a new product market report based on the behavior analysis results to guide the adjustment of new product promotion strategies.

[0012] Furthermore, in the new product promotion method based on user behavior analysis, S100 includes:

[0013] S102. Obtain surveillance video data from a surveillance camera installed in the store at a preset time interval; the surveillance range of the surveillance camera covers the new product display area.

[0014] Furthermore, in the new product promotion method based on user behavior analysis, before S102, the method further includes:

[0015] S101. Setting the preset time interval according to the customer flow of the store; the preset time interval is negatively correlated with the customer flow of the store.

[0016] Furthermore, in the new product promotion method based on user behavior analysis, S200 includes:

[0017] S201, preprocessing the surveillance video data;

[0018] S202, detecting users in the surveillance video data, and tracking the motion trajectory of each detected user;

[0019] S203. During the tracking process, identify the behavioral actions of each user in the store, conduct a comprehensive analysis, and obtain behavioral analysis results; the behavioral analysis results include the number of users who visit the store, the number of times users see new products, the number of times users pick up new products, the proportion of users who see new products among the users who visit the store, the proportion of users who pick up new products among the users who see new products, and the proportion of users who buy new products among the users who pick up new products.

[0020] Furthermore, in the new product promotion method based on user behavior analysis, S300 includes:

[0021] S301. Obtain the behavior analysis results corresponding to different stores in the same area;

[0022] S302: Integrate the obtained behavior analysis results to form a new product marketing report to guide the adjustment of the new product promotion strategy.

[0023] Furthermore, in the new product promotion method based on user behavior analysis, in S300, the adjustment of the new product promotion strategy includes at least one of the following:

[0024] Adjust the display location or display method of new products;

[0025] Optimize the appearance, packaging or performance of new products;

[0026] Adjust pricing strategies for new products or launch promotional activities.

[0027] Furthermore, in the new product promotion method based on user behavior analysis, in S200, the behavior analysis result also includes the user's residence time in the new product display area and the user's interaction time with the new product.

[0028] In a second aspect, the present invention provides a new product promotion system based on user behavior analysis, the system comprising:

[0029] Data acquisition module, used to obtain store surveillance video data;

[0030] a behavior analysis module configured to analyze the behavior of users in the store based on the surveillance video data to obtain behavior analysis results; the behavior analysis results including the number of users visiting the store, the number of times users saw new products, the number of times users picked up new products, the percentage of users who saw new products among the users who visited the store, the percentage of users who picked up new products among the users who saw new products, and the percentage of users who purchased new products among the users who picked up new products;

[0031] The adjustment guidance module is used to generate a new product market report based on the behavior analysis results to guide the adjustment of the new product promotion strategy.

[0032] Furthermore, in the new product promotion system based on user behavior analysis, the data acquisition module is specifically used to:

[0033] The surveillance video data of the surveillance camera installed in the store is obtained at a preset time interval; the monitoring range of the surveillance camera covers the new product display area.

[0034] Furthermore, in the new product promotion system based on user behavior analysis, the data acquisition module is further specifically used to:

[0035] The preset time interval is set according to the customer flow of the store; the preset time interval is negatively correlated with the customer flow of the store.

[0036] Furthermore, in the new product promotion system based on user behavior analysis, the behavior analysis module is specifically used to:

[0037] Preprocessing the surveillance video data;

[0038] Detecting users in the surveillance video data and tracking the motion trajectory of each detected user;

[0039] During the tracking process, the behavioral actions of each user in the store are identified and comprehensively analyzed to obtain behavioral analysis results; the behavioral analysis results include the number of users visiting the store, the number of times users see new products, the number of times users pick up new products, the proportion of users who see new products among the users who visit the store, the proportion of users who pick up new products among the users who see new products, the proportion of users who pick up new products among the users who see new products, and the proportion of users who buy new products among the users who pick up new products.

[0040] Furthermore, in the new product promotion system based on user behavior analysis, the adjustment guidance module is specifically used to:

[0041] Obtaining the behavior analysis results corresponding to different stores in the same area;

[0042] The obtained behavioral analysis results are integrated to form a new product market promotion report to guide the adjustment of new product promotion strategies.

[0043] Furthermore, in the new product promotion system based on user behavior analysis, the adjustment of the new product promotion strategy includes at least one of the following:

[0044] Adjust the display location or display method of new products;

[0045] Optimize the appearance, packaging or performance of new products;

[0046] Adjust pricing strategies for new products or launch promotional activities.

[0047] Furthermore, in the new product promotion system based on user behavior analysis, the behavior analysis results also include the user's residence time in the new product display area and the user's interaction time with the new product.

[0048] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the new product promotion method based on user behavior analysis as provided in the first aspect above.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium having computer-executable instructions stored thereon, wherein the computer-executable instructions are executed by a computer processor to implement the new product promotion method based on user behavior analysis as provided in the first aspect above.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a new product promotion method and system based on user behavior analysis. First, through accurate analysis of user behavior in stores based on computer vision technology, it is possible to comprehensively and meticulously grasp the user's interest in new products. Data from each link from the user seeing the new product, picking up the new product to purchasing the new product can be accurately obtained. Compared with the traditional method that relies on sales data or market research, it can provide a deeper insight into the user's real behavior and potential needs. Secondly, according to these accurate behavioral analysis results to guide the adjustment of new product promotion strategies, the promotion can be made more targeted and effective, avoiding the waste of resources caused by blind promotion, thereby significantly improving the market acceptance and sales conversion rate of new products, bringing new profit growth points to merchants, and enhancing their market share and survival and development capabilities in the fierce competition.

[0052] The present invention has other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain certain principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is one of the flow charts of a new product promotion method based on user behavior analysis provided in the first embodiment of the present invention;

[0055] Figure 2 This is a second flow chart of a new product promotion method based on user behavior analysis provided in the first embodiment of the present invention;

[0056] Figure 3 This is a third flow chart of a new product promotion method based on user behavior analysis provided in the first embodiment of the present invention;

[0057] Figure 4 This is a fourth flow chart of a new product promotion method based on user behavior analysis provided in the first embodiment of the present invention;

[0058] Figure 5This is a functional module diagram of a new product promotion system based on user behavior analysis provided by the second embodiment of the present invention;

[0059] Figure 6 This is a structural diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0060] In order to explain in detail the possible application scenarios, technical principles, specific solutions that can be implemented, and the purpose and effects of this application, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of this application and are therefore only examples and are not intended to limit the scope of protection of this application.

[0061] References to "embodiments" herein mean that the specific features, structures, or characteristics described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the word "embodiment" in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or relevance to other embodiments. In principle, in this application, as long as there are no technical contradictions or conflicts, the various technical features mentioned in the embodiments can be combined in any manner to form a corresponding implementable technical solution.

[0062] Unless otherwise defined, the technical terms used herein have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms herein is only for describing specific embodiments and is not intended to limit this application.

[0063] In the description of this application, the term "and / or" is used to describe a logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and both A and B exist. In addition, the character " / " in this document generally indicates that the objects before and after are in a logical "or" relationship.

[0064] In this application, terms such as "first" and "second" are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual quantity, priority or sequence relationship between these entities or operations.

[0065] Without further limitations, in this application, the words "include", "comprise", "have" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those defined elements, but also other elements not explicitly listed, or elements inherent to such process, method or product.

[0066] In this application, expressions such as "greater than," "less than," and "exceed" are understood to exclude the number itself; expressions such as "above," "below," and "within" are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times," unless otherwise specifically limited.

[0067] In the description of the embodiments of the present application, the space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the device or component referred to must have a specific position, a specific orientation, or be constructed or operated in a specific orientation. Therefore, it should not be understood as a limitation on the embodiments of the present application.

[0068] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms "installed", "connected", "connected", "fixed", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For those skilled in the art of the present application, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0069] Example 1

[0070] Please refer to Figure 1 , Figure 1This is a flow chart of a new product promotion method based on user behavior analysis, provided in Example 1 of the present invention. This method is suitable for scenarios where a comprehensive understanding of the market performance of a new product is desired after its launch. The method is executed by a new product promotion system based on user behavior analysis, which can be implemented using software and / or hardware. The method specifically includes the following steps:

[0071] S100: Obtaining surveillance video data of a store.

[0072] It's important to note that surveillance video data is the foundation of user behavior analysis. By acquiring this data, we can monitor user behavior within stores, providing a rich data source for subsequent behavioral analysis. Only with accurate and complete video data can we conduct effective behavioral analysis, thereby understanding user interest in new products, their interests, and their purchase intentions.

[0073] Video data collected by surveillance cameras is transmitted in real time via wired or wireless networks to the store's local server or cloud storage platform. Data integrity and security must be ensured during transmission to prevent data loss or tampering.

[0074] In addition, when surveillance video data is stored on a local server or cloud storage platform, a corresponding database management system will be established to facilitate subsequent retrieval, access, and analysis. It is understood that the storage system should have efficient data compression and backup functions to save storage space and ensure data security.

[0075] Please refer to Figure 2 In one implementation of this embodiment, the S100 may be further refined to include the following steps:

[0076] S102. Obtain surveillance video data from a surveillance camera installed in the store at a preset time interval; the surveillance range of the surveillance camera covers the new product display area.

[0077] It should be noted that the preset time interval can be set to obtain surveillance video data every 5 minutes, 10 minutes, or 30 minutes, for example. The specific setting should be flexibly adjusted and optimized according to actual conditions.

[0078] By capturing surveillance video data at preset intervals, dynamic monitoring of in-store user behavior is possible. This allows for timely identification of changing trends in user behavior, providing real-time insights for adjusting new product promotion strategies. For example, if user interest in a new product decreases significantly over a certain period of time, retailers can take timely action, such as adjusting product display and increasing promotional activities, to enhance its appeal.

[0079] Furthermore, by analyzing and comparing data across different time periods, we can understand the long-term dynamics of user behavior and provide a reference for long-term planning for new product promotions. For example, we can analyze the market performance of new products in different seasons and during different promotional events, draw lessons from these experiences, and optimize future promotion strategies.

[0080] Understandably, ensuring comprehensive and accurate capture of user behavior in new product display areas requires a strategically selected surveillance camera location. Typically, cameras should be installed at a high location that provides full coverage of the entire display area, avoiding blind spots. Furthermore, the camera's angle should be carefully considered to ensure clear capture of user movements and interactions with the new products.

[0081] Surveillance cameras should have high resolution and frame rate to ensure clear and smooth video footage. Furthermore, they should have good low-light performance and be able to operate normally under varying lighting conditions, ensuring that valid video data can be captured in a variety of environments.

[0082] Please refer again Figure 2 In one implementation of this embodiment, before S102, the method further includes the following steps:

[0083] S101. Setting the preset time interval according to the customer flow of the store; the preset time interval is negatively correlated with the customer flow of the store.

[0084] It should be noted that the preset time interval is negatively correlated with the store's customer flow, that is, the greater the customer flow, the shorter the preset time interval; the smaller the customer flow, the longer the preset time interval.

[0085] Specifically, setting the preset time interval requires comprehensive consideration of multiple factors. For one thing, the store's operational characteristics, such as peak and off-peak traffic times, must be taken into account. During peak traffic hours, user behavior fluctuates rapidly, so a shorter time interval may be necessary to capture user behavior dynamics more promptly. During off-peak traffic hours, the interval can be appropriately extended. Furthermore, the system's processing power and storage capacity must also be considered. If the time interval is too short, a large amount of video data will be generated, placing significant pressure on the system's data processing and storage. Conversely, if the time interval is too long, some important user behavior information may be missed.

[0086] Specific setting method: The preset time interval can be determined using empirical formulas or models based on data analysis. For example, a table correlating time intervals with passenger volume can be developed based on historical passenger flow data and actual business needs. Once the current passenger flow is known, the corresponding preset time interval can be obtained by looking up the table. Alternatively, a machine learning algorithm can be used to train a time interval prediction model based on large amounts of passenger flow data and behavioral analysis results. This model can then automatically predict and set reasonable preset time intervals based on real-time passenger flow data.

[0087] S200. Conduct a behavior analysis on users in the store based on the surveillance video data to obtain behavior analysis results; the behavior analysis results include the number of users visiting the store, the number of times users see new products, the number of times users pick up new products, the percentage of users visiting the store who see new products, the percentage of users picking up new products, the percentage of users who see new products who pick up new products, and the percentage of users who buy new products among users who pick up new products.

[0088] It should be noted that the number of users visiting the store is the total number of users who enter the store within a certain period of time.

[0089] Number of times users see new products: Counts the frequency of each user viewing new products, as well as the total number of times all users view new products.

[0090] Number of times users pick up new products: Count the frequency of each user picking up new products, as well as the total number of times all users pick up new products.

[0091] Percentage of in-store users who saw new products: Calculate the ratio of the number of users who saw new products to the total number of in-store users, i.e. (number of users who saw new products / total number of in-store users) × 100%.

[0092] The proportion of users who picked up new products among the users who visited the store: Calculate the ratio of the number of users who picked up new products to the total number of users who visited the store, that is, (number of users who picked up new products / total number of users who visited the store) × 100%.

[0093] Percentage of users who picked up a new product after seeing it: Calculate the ratio of the number of users who picked up a new product after seeing it to the total number of users who saw it, that is, (number of users who picked up a new product after seeing it / total number of users who saw it) × 100%.

[0094] Percentage of users who purchased the new product among those who received it: Calculate the ratio of the number of users who completed the purchase after receiving the new product to the total number of users who received the new product, that is, (number of users who purchased the new product after receiving it / total number of users who received the new product) × 100%.

[0095] Please refer to Figure 3 In one implementation of this embodiment, Figure 2Specifically, the S200 is further refined, which specifically includes the following steps:

[0096] S201: Preprocess the surveillance video data.

[0097] It should be noted that pre-processing may include, for example, denoising, frame rate adjustment, resolution conversion, etc.

[0098] Denoising: Remove noise from the video through filtering algorithms to improve video quality.

[0099] Frame rate adjustment: Adjust the frame rate of the video according to analysis requirements to optimize processing speed and accuracy.

[0100] Resolution conversion: Adjust the video resolution to a size suitable for analysis to reduce the amount of calculation.

[0101] S202: Detect users in the surveillance video data, and track the motion trajectory of each detected user.

[0102] It should be noted that target detection algorithms (such as YOLO and Faster R-CNN) can be used to detect users in the video and obtain the user's location and bounding box information.

[0103] Applying target tracking algorithms (such as ByteTrack) can track detected users, assign a unique ID to each user, and record their movement trajectory in the video.

[0104] S203. During the tracking process, identify the behavioral actions of each user in the store, conduct a comprehensive analysis, and obtain behavioral analysis results; the behavioral analysis results include the number of users who visit the store, the number of times users see new products, the number of times users pick up new products, the proportion of users who see new products among the users who visit the store, the proportion of users who pick up new products among the users who see new products, and the proportion of users who buy new products among the users who pick up new products.

[0105] It should be noted that based on the target detection and tracking results, the user's behavioral characteristics are extracted, such as skeleton key point information (through algorithms such as OpenPose) or optical flow information (through the Farneback optical flow algorithm).

[0106] Use deep learning models (such as 3D convolutional neural networks and LSTM) to analyze the extracted features and identify specific user behaviors, such as entering the store, stopping in the new product display area, viewing new products, picking up new products, putting new products back, and taking new products to the checkout counter.

[0107] In one implementation of this embodiment, the behavior analysis result also includes the user's residence time in the new product display area and the user's interaction time with the new product.

[0108] It should be noted that, based on the original behavioral analysis, the newly added analysis of the time users stay in the new product display area and the length of time users interact with new products can provide a deeper understanding of users' behavioral characteristics and interest in new products, and provide richer data support for the formulation of new product promotion strategies.

[0109] Specific optimization steps:

[0110] (1) Definition and marking of new product display area:

[0111] Based on the store layout and new product display locations, the new product display area can be accurately divided in the surveillance video. This can be done through manual annotation, drawing the bounding box or polygon area of the new product display area in the video frame.

[0112] The marked new product display area information (such as area coordinates, area number, etc.) is stored in the database for subsequent call during the behavior analysis process.

[0113] (2) Calculation of user stay time:

[0114] Based on user motion tracking, determine whether the user enters and leaves the new product display area. This can be determined by determining whether the user's bounding box intersects with the new product display area's bounding box. When a user first enters the new product display area, the entry time is recorded; when the user leaves the area, the exit time is recorded.

[0115] The user's stay time in the new product display area is calculated based on the user's entry and exit times. For users who enter and exit the new product display area multiple times within a period of time, their stay time each time is accumulated to obtain the user's total stay time in the new product display area.

[0116] (3) Calculation of the duration of user interaction with new products:

[0117] Identify user interactions with new products, such as picking up new products, viewing new products, and operating new products. These interactions can be identified using action classification models.

[0118] Based on behavioral action recognition, the system determines when the user's interaction with the new product begins and ends. For example, when the user's hand is detected to come into contact with the new product, the interaction is considered to have begun; when the user's hand separates from the new product, the interaction is considered to have ended.

[0119] The user's interaction duration with the new product is calculated based on the start and end times of each interaction. For interactions involving multiple behaviors, the duration of each interaction is added together to obtain the total duration of the interaction. For users who interact multiple times, the duration of each interaction is added together to obtain the total interaction duration.

[0120] (4) Update of comprehensive analysis results:

[0121] The calculated user residence time in the new product display area and the user's interaction time with the new product are integrated into the original behavior analysis results.

[0122] Based on the integrated data, new proportion indicators are calculated, such as the proportion of in-store users who stay in the new product display area for more than a certain threshold, the proportion of in-store users who interact with new products for more than a certain threshold, etc.

[0123] Understandably, the newly added behavioral analysis results can provide a more comprehensive understanding of user behavior characteristics towards new products, including dwell time and interaction duration, thus more accurately assessing user interest and attention towards new products. This richer behavioral analysis results can provide stronger support for new product promotions in stores.

[0124] S300: Generating a new product market report based on the behavior analysis results to guide the adjustment of new product promotion strategies.

[0125] It should be noted that by collating and summarizing the results of behavioral analysis, a detailed new product market report can be generated.

[0126] New product market reports are essential tools for businesses to understand new product market performance and user behavior. By developing and interpreting these reports, businesses can more clearly understand a new product's market positioning and competitive advantages, as well as existing challenges and problems. Furthermore, the report's analytical findings and recommendations for adjusting new product promotion strategies help businesses more accurately grasp market dynamics and evolving user needs, thereby developing more scientific and rational new product promotion strategies and improving market acceptance and sales conversion rates.

[0127] Next, the present invention will be further described with reference to a specific embodiment:

[0128] Imagine a brand launches a new snack product and promotes it in a convenience store. The convenience store installs multiple high-definition surveillance cameras in the new product display area and other key locations within the store to record customer behavior within the store.

[0129] The results of the statistical behavioral analysis are as follows:

[0130] During the statistical period, the number of users visiting the store was 1,000.

[0131] Users viewed new products a total of 2,000 times, with each user viewing new products an average of 2 times.

[0132] The number of times users picked up new products totaled 300 times.

[0133] Among the users who visited the store, 60%, or 600 people, saw the new products.

[0134] Among the users who came to the store, 15% (150 people) took the new products.

[0135] Among users who saw the new product, 25% took it, which means that 60 out of 150 people chose to take it after seeing the new product.

[0136] Among the users who took the new product, 30% of them purchased it, which means that 45 people eventually bought the new product.

[0137] Analysis revealed that while the new product display area saw significant traffic, only 60% of users actually saw the new products, indicating that some users still failed to notice the new products. Therefore, the company decided to adjust the layout of the new product display area and add more eye-catching signs and guideposts to attract more users.

[0138] The proportion of users who saw the new product who actually took it was 25%, a relatively low percentage. Further analysis of the new product's design and packaging revealed that it lacked novelty and appeal compared to competing products. Therefore, the new product's appearance and packaging were redesigned to be more personalized and appealing.

[0139] 30% of users who picked up new products actually purchased them, indicating room for improvement. Considering that the price of the new product might be influencing users' purchasing decisions, we decided to launch a limited-time promotion offering discounts on the new product and promote it in-store. We also collected user feedback on the performance and quality of the new product and made targeted improvements and optimizations to increase user satisfaction and purchase intention.

[0140] After the above adjustments and optimizations, we conducted another user behavior analysis and statistics, and found that all indicators had improved, the promotion effect of new products had been significantly improved, and market sales had also increased significantly.

[0141] Please refer to Figure 4 In one implementation of this embodiment, Figure 3 Specifically, the S300 is further refined, which specifically includes the following steps:

[0142] S301: Obtain the behavior analysis results corresponding to different stores in the same area.

[0143] It should be noted that the same region refers to a specific area determined based on market segmentation, administrative division or other business needs, such as a commercial district in a city, a city in a province, etc.

[0144] It is understandable that in the process of obtaining corresponding behavioral analysis results from each store, it is necessary to ensure the accuracy and completeness of the data for subsequent analysis and processing.

[0145] S302: Integrate the obtained behavior analysis results to form a new product marketing report to guide the adjustment of the new product promotion strategy.

[0146] It's important to note that data silos refer to data from different stores in the same region being isolated and unable to share or interact. When analyzing behavioral analysis results from different stores in the same region, if this data isn't integrated, each store's data will form independent data silos. For example, store A's data might be stored in its own sales system, while store B's data might be stored in another system. Direct data exchange and analysis between the two systems is impossible. Integration can unify these dispersed data into a single dataset, breaking down data silos and enabling data sharing and collaborative analysis.

[0147] The integrated dataset includes behavioral analysis results from all stores within a region, comprehensively reflecting the overall behavioral characteristics of users within that region during new product promotions. For example, it can analyze user acceptance of new products, purchasing trends, and sales differences between stores across the region, providing strong support for developing unified new product promotion strategies.

[0148] Based on the data integration, data analysis methods and tools are used to conduct in-depth data mining and analysis to produce a new product marketing report. This report typically includes information on the new product's sales in various stores, user behavior analysis, market competition, and promotion effectiveness evaluation. The report can use charts and text to intuitively present the analysis results, providing clear and accurate information to retail decision makers.

[0149] Based on the analysis results in the new product market promotion report, merchants can understand the problems and opportunities in the new product promotion process and adjust the new product promotion strategy accordingly.

[0150] In one implementation of this embodiment, the adjustment of the new product promotion strategy includes at least one of the following:

[0151] Adjust the display location or display method of new products to increase their exposure;

[0152] Optimize the appearance, packaging, or performance of new products to increase users' interest in and willingness to purchase them;

[0153] Adjust the pricing strategy of new products or launch promotional activities to encourage user purchases.

[0154] Although this application frequently uses terms such as surveillance video data, behavioral analysis, and promotion, the use of other terms is not excluded. These terms are used solely to more conveniently describe and explain the essence of the present invention; interpreting them as any additional limitations is contrary to the spirit of the present invention.

[0155] The embodiment of the present invention provides a new product promotion method based on user behavior analysis. First, through accurate analysis of user behavior in stores based on computer vision technology, it is possible to comprehensively and meticulously grasp the user's interest in new products. Data from each link of users seeing new products, picking up new products, to purchasing new products can be accurately obtained. Compared with the traditional method of relying on sales data or market research, it can provide a deeper insight into users' real behavior and potential needs. Secondly, according to these accurate behavioral analysis results to guide the adjustment of new product promotion strategies, the promotion can be made more targeted and effective, avoiding the waste of resources caused by blind promotion, thereby significantly improving the market acceptance and sales conversion rate of new products, bringing new profit growth points to merchants, and enhancing their market share and survival and development capabilities in the fierce competition.

[0156] Example 2

[0157] Please refer to Figure 5 , Figure 5 This is a functional module diagram of a new product promotion system based on user behavior analysis provided in Example 2 of the present invention. The system includes:

[0158] Data acquisition module 501, used to obtain surveillance video data of the store;

[0159] Behavior analysis module 502 is configured to perform behavior analysis on users in the store based on the surveillance video data to obtain behavior analysis results; the behavior analysis results include the number of users visiting the store, the number of times users saw new products, the number of times users picked up new products, the percentage of users who saw new products among the users who visited the store, the percentage of users who picked up new products among the users who saw new products, the percentage of users who picked up new products among the users who picked up new products, and the percentage of users who purchased new products among the users who picked up new products;

[0160] The adjustment guidance module 503 is used to generate a new product market report based on the behavior analysis results to guide the adjustment of the new product promotion strategy.

[0161] Optionally, the data acquisition module 501 is specifically configured to:

[0162] The surveillance video data of the surveillance camera installed in the store is obtained at a preset time interval; the monitoring range of the surveillance camera covers the new product display area.

[0163] Optionally, the data acquisition module 501 is further specifically configured to:

[0164] The preset time interval is set according to the customer flow of the store; the preset time interval is negatively correlated with the customer flow of the store.

[0165] Optionally, the behavior analysis module 502 is specifically configured to:

[0166] Preprocessing the surveillance video data;

[0167] Detecting users in the surveillance video data and tracking the motion trajectory of each detected user;

[0168] During the tracking process, the behavioral actions of each user in the store are identified and comprehensively analyzed to obtain behavioral analysis results; the behavioral analysis results include the number of users visiting the store, the number of times users see new products, the number of times users pick up new products, the proportion of users who see new products among the users who visit the store, the proportion of users who pick up new products among the users who see new products, the proportion of users who pick up new products among the users who see new products, and the proportion of users who buy new products among the users who pick up new products.

[0169] Optionally, the adjustment guidance module 503 is specifically configured to:

[0170] Obtaining the behavior analysis results corresponding to different stores in the same area;

[0171] The obtained behavioral analysis results are integrated to form a new product market promotion report to guide the adjustment of new product promotion strategies.

[0172] Optionally, the adjustment of the new product promotion strategy includes at least one of the following:

[0173] Adjust the display location or display method of new products;

[0174] Optimize the appearance, packaging or performance of new products;

[0175] Adjust pricing strategies for new products or launch promotional activities.

[0176] Optionally, the behavior analysis results also include the user's residence time in the new product display area and the user's interaction time with the new product.

[0177] The above system can execute the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0178] Example 3

[0179] Figure 6 A schematic diagram of the structure of a computer device provided in Example 3 of the present invention. Figure 6 A block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention is shown. Figure 6 The computer device 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0180] like Figure 6 As shown, computer device 12 is implemented as a general-purpose computing device. Components of computer device 12 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and a bus 18 that connects various system components (including system memory 28 and processing unit 16).

[0181] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0182] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0183] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 6 Not shown, usually called a "hard drive"). Although Figure 6 Although not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), as well as an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0184] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally implement the functions and / or methodologies of the embodiments described herein.

[0185] The computer device 12 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. It should be understood that although Figure 6 Not shown, other hardware and / or software modules may be used in conjunction with computer device 12, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0186] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the new product promotion method based on user behavior analysis provided by an embodiment of the present invention.

[0187] Example 4

[0188] A fourth embodiment of the present invention provides a computer-readable storage medium having computer-executable instructions stored thereon. When the instructions are executed by a processor, the new product promotion method based on user behavior analysis as provided in all the embodiments of the invention of this application is implemented.

[0189] Any combination of one or more computer-readable media may be employed. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0190] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0191] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0192] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0193] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concepts of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. A new product promotion method based on user behavior analysis, characterized in that: The method comprises: S100: Obtaining surveillance video data of the store; S200: Conducting a behavior analysis of users in the store based on the surveillance video data to obtain behavior analysis results; the behavior analysis results include the number of users visiting the store, the number of times users saw new products, the number of times users picked up new products, the percentage of users who saw new products among the users who visited the store, the percentage of users who picked up new products among the users who saw new products, the percentage of users who picked up new products among the users who picked up new products, and the percentage of users who purchased new products among the users who picked up new products; S300: Generating a new product market report based on the behavior analysis results to guide the adjustment of new product promotion strategies.

2. The new product promotion method based on user behavior analysis according to claim 1, characterized in that: The S100 includes: S102. Obtain surveillance video data from a surveillance camera installed in the store at a preset time interval; the surveillance range of the surveillance camera covers the new product display area.

3. The new product promotion method based on user behavior analysis according to claim 2, characterized in that: Before S102, the method further includes: S101. Setting the preset time interval according to the customer flow of the store; the preset time interval is negatively correlated with the customer flow of the store.

4. The new product promotion method based on user behavior analysis according to claim 1, characterized in that: The S200 includes: S201, preprocessing the surveillance video data; S202, detecting users in the surveillance video data, and tracking the motion trajectory of each detected user; S203. During the tracking process, identify the behavioral actions of each user in the store, conduct a comprehensive analysis, and obtain behavioral analysis results; the behavioral analysis results include the number of users who visit the store, the number of times users see new products, the number of times users pick up new products, the proportion of users who see new products among the users who visit the store, the proportion of users who pick up new products among the users who see new products, and the proportion of users who buy new products among the users who pick up new products.

5. The new product promotion method based on user behavior analysis according to claim 1, characterized in that: The S300 includes: S301. Obtain the behavior analysis results corresponding to different stores in the same area; S302: Integrate the obtained behavior analysis results to form a new product marketing report to guide the adjustment of the new product promotion strategy.

6. The new product promotion method based on user behavior analysis according to claim 1, characterized in that: In S300, the adjustment of the new product promotion strategy includes at least one of the following: Adjust the display location or display method of new products; Optimize the appearance, packaging or performance of new products; Adjust pricing strategies for new products or launch promotional activities.

7. The new product promotion method based on user behavior analysis according to claim 1, characterized in that: In S200 , the behavior analysis result also includes the user's stay time in the new product display area and the user's interaction time with the new product.

8. A new product promotion system based on user behavior analysis, characterized in that: The system comprises: Data acquisition module, used to obtain store surveillance video data; a behavior analysis module configured to analyze the behavior of users in the store based on the surveillance video data to obtain behavior analysis results; the behavior analysis results including the number of users visiting the store, the number of times users saw new products, the number of times users picked up new products, the percentage of users who saw new products among the users who visited the store, the percentage of users who picked up new products among the users who saw new products, and the percentage of users who purchased new products among the users who picked up new products; The adjustment guidance module is used to generate a new product market report based on the behavior analysis results to guide the adjustment of the new product promotion strategy.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the new product promotion method based on user behavior analysis according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: The computer executable instructions are executed by a computer processor to implement the new product promotion method based on user behavior analysis according to any one of claims 1 to 7.

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