An Online Marketing Coupon and Promotion Activity Data Tracking and Optimization System

Abstract

The present invention discloses an online marketing coupon and promotion activity data tracking and optimization system, belonging to the technical field of marketing data processing. It includes a data storage module for storing data related to promotion activities; a data collection module for collecting consumption record information of the system; a range setting module for analyzing the data related to promotion activities to obtain a time interval and an activity effect threshold; a promotion analysis module for analyzing newly generated coupon consumption records according to the time interval and the activity effect threshold to obtain an evaluation result of the promotion activity effect; and an optimization warning module for warning users according to the evaluation result to adjust the promotion strategy. By adopting advanced data processing algorithms, the present invention tracks key indicators such as the participation situation and conversion effect of coupons and promotion activities, can accurately evaluate the effects of different promotion strategies, and realizes the real-time collection, efficient storage, intelligent analysis and dynamic optimization of online marketing data.

Description

Technical Field

[0001] The present invention relates to the technical field of marketing data processing, and more particularly to an online marketing coupon and promotion activity data tracking and optimization system. Background Art

[0002] With the continuous emergence of innovations in data processing algorithms, system architectures, and software implementations, new solutions have been provided for the tracking and optimization of online marketing data. However, traditional online marketing data tracking systems often suffer from problems such as low data processing efficiency, insufficient accuracy, and poor real-time performance, making it difficult to meet the needs of enterprises for precise marketing and efficient operation.

[0003] Most of the existing online marketing data tracking systems adopt traditional databases and data processing methods, which are unable to cope when dealing with large-scale and high-concurrency marketing data. They often fail to capture and process data in real time, resulting in the lag and inaccuracy of data analysis. In addition, these systems lack intelligent analysis modules and cannot automatically adjust marketing strategies according to data changes, limiting their effectiveness in practical applications. Summary of the Invention

[0004] To solve the above problems, the present invention provides an online marketing coupon and promotion activity data tracking and optimization system. By adopting advanced data processing algorithms to track key indicators such as the participation status and conversion effect of coupons and promotion activities, it can accurately evaluate the effectiveness of different promotion strategies, and achieve real-time collection, efficient storage, intelligent analysis, and dynamic optimization of online marketing data.

[0005] The above object can be achieved by the following solutions:

[0006] An online marketing coupon and promotion activity data tracking and optimization system, comprising a data storage module, a data collection module, a range setting module, a promotion analysis module, and an optimization warning module; wherein, the data storage module is used to establish and store data related to promotion activities; the data collection module is used to collect consumption record information of the system, classify the consumption record information into coupon consumption records and non-coupon consumption records, and store them in the data storage module; the range setting module is used to analyze the data related to promotion activities in the data storage module to obtain a time interval and an activity effect threshold; the promotion analysis module is used to analyze the newly generated coupon consumption records according to the time interval and the activity effect threshold to obtain an evaluation result of the promotion activity effect; the optimization warning module is used to give a warning to the user according to the evaluation result to adjust the promotion strategy.

[0007] Optionally, the promotion analysis module includes: a promotion monitoring unit, a consumption rate calculation unit, a historical ratio calculation unit, and a threshold judgment unit; wherein, the promotion monitoring unit is configured to identify whether the corresponding product of the newly generated coupon consumption record is a newly listed product, and obtain an identification result; the consumption rate calculation unit is configured to calculate the coupon consumption rate of the corresponding product according to the identification result and the consumption record information of the corresponding product; the historical ratio calculation unit is configured to calculate the coupon consumption rate ratio of the corresponding product according to the coupon consumption rate of the corresponding product and the historical coupon consumption rate of the corresponding product; the threshold judgment unit is configured to evaluate the promotion effect according to the magnitude relationship between the coupon consumption rate ratio of the corresponding product and the preset first threshold and the preset second threshold of the corresponding product, and obtain the evaluation result of the corresponding product.

[0008] Optionally, the step of identifying whether the corresponding product of the newly generated coupon consumption record is a newly listed product and obtaining an identification result includes: obtaining the historical consumption record information of the corresponding product of the newly generated coupon consumption record from the data storage module; judging whether the coupon consumption record of the corresponding product is generated for the first time according to the historical consumption record information; if not, evaluating the promotion effect of the corresponding product; if so, judging whether there is a consumption record for the corresponding product on the previous day according to the historical consumption record information; if there is no consumption record, the corresponding product is a newly listed product and the promotion effect is not evaluated; if there is a consumption record, the promotion effect of the corresponding product is evaluated.

[0009] Optionally, the step of calculating the coupon consumption rate according to the identification result and the consumption record information includes: if the promotion effect of the corresponding product is evaluated, obtaining the consumption record information of the corresponding product; counting the number value of the coupon consumption records of the corresponding product within the corresponding preset time period in the consumption record information; counting the number value of the consumption record information of the corresponding product within the corresponding preset time period in the consumption record information; using the number value of the coupon consumption records of the corresponding product and the number value of the consumption record information, calculating the coupon consumption rate of the corresponding product. For the coupon consumption rate , there is

[0010] ,

[0011] wherein, is the number value of the coupon consumption records of the corresponding product within the corresponding preset time period, is the number value of the consumption record information of the corresponding product within the corresponding preset time period.

[0012] Optionally, calculating the coupon consumption rate ratio of the corresponding product based on the coupon consumption rate of the corresponding product and the historical coupon consumption rate of the corresponding product includes: obtaining the historical coupon consumption rate of the corresponding product from the data storage module to obtain a consumption rate set; sorting the coupon consumption rates in the consumption rate set from largest to smallest, and selecting the coupon consumption rate ranked first to obtain the consumption rate base of the corresponding product; using the coupon consumption rate of the corresponding product and combining with the consumption rate base to calculate the coupon consumption rate ratio of the corresponding product. For the coupon consumption rate ratio , there is

[0013] ,

[0014] In the formula, is the coupon consumption rate of the corresponding product, is the consumption rate base.

[0015] Optionally, evaluating the promotion effect according to the size of the coupon consumption rate ratio of the corresponding product and the preset first threshold and preset second threshold of the corresponding product to obtain the evaluation result of the corresponding product includes: judging whether the coupon consumption rate ratio of the corresponding product is greater than the first threshold; if the coupon consumption rate ratio of the corresponding product is greater than the first threshold of the corresponding product, then evaluate the promotion effect of the corresponding product as excellent; if the coupon consumption rate ratio of the corresponding product is less than or equal to the first threshold of the corresponding product, then judge whether the coupon consumption rate ratio of the corresponding product is greater than the second threshold of the corresponding product; if the coupon consumption rate ratio of the corresponding product is greater than the second threshold of the corresponding product, then evaluate the promotion effect of the corresponding product as average; if the coupon consumption rate ratio of the corresponding product is less than or equal to the second threshold of the corresponding product, then evaluate the promotion effect of the corresponding product as poor.

[0016] Optionally, the optimization warning module is further configured to judge whether the evaluation result matches the actual effect; wherein, if it matches the actual, the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product are not saved; if it does not match the actual, the evaluation result of the corresponding product is updated according to the actual effect, and the absolute value of the difference between the current coupon consumption rate of the corresponding product and the average value of the historical coupon consumption rates of the corresponding product is calculated to obtain the error value of the corresponding product; judge whether the error value of the corresponding product is greater than the preset third threshold; if the error value of the corresponding product is greater than the third threshold, the coupon consumption rate of the corresponding product and the updated evaluation result are not saved; if the error value is less than or equal to the third threshold, the coupon consumption rate of the corresponding product and the updated evaluation result are stored in the data storage module.

[0017] Optionally, the range setting module includes a time configuration unit and an effect threshold configuration unit; wherein, the time configuration unit is used to analyze and calculate the coupon consumption records and non-coupon consumption records of corresponding commodities in different time periods to obtain the preset time period of the corresponding commodity; the effect threshold configuration unit is used to analyze and calculate the evaluation result of the corresponding commodity and the coupon consumption rate of the corresponding commodity to obtain the first threshold of the corresponding commodity and the second threshold of the corresponding commodity.

[0018] Optionally, the analyzing and calculating the coupon consumption records and non-coupon consumption records of corresponding commodities in different time periods to obtain the preset time period of the corresponding commodity includes: collecting historical factor data affecting the coupon consumption records of the corresponding commodity and the coupon consumption records of the historical corresponding commodity, and constructing a training set; using the historical factor data affecting the coupon consumption records of the corresponding commodity as input and the coupon consumption records of the corresponding commodity as output, establishing and training a regression model using the training set to obtain a coupon consumption record prediction model; using the coupon consumption record prediction model to predict the coupon consumption records of the corresponding commodity; dividing the coupon consumption records of the corresponding commodity by different time periods, calculating the coupon consumption rates of the corresponding commodity in each time period, and arranging them from large to small; selecting the time period corresponding to the highest ranked coupon consumption rate to obtain the preset time period of the corresponding commodity.

[0019] Optionally, the analyzing and calculating the evaluation result of the corresponding commodity and the coupon consumption rate of the corresponding commodity to obtain the first threshold of the corresponding commodity and the second threshold of the corresponding commodity includes: obtaining in real time the evaluation result of the corresponding commodity and the coupon consumption rate of the corresponding commodity in the data storage module, and constructing a real-time data set; using the real-time data set to calculate the average value of the coupon consumption rates of the corresponding commodity with excellent promotion effects to obtain the first average value; using the real-time data set to calculate the average value of the coupon consumption rates of the corresponding commodity with average promotion effects to obtain the second average value; using the real-time data set to calculate the average value of the coupon consumption rates of the corresponding commodity with poor promotion effects to obtain the third average value; using the first average value, the second average value, the third average value, the consumption rate base of the corresponding commodity, and the newly saved coupon consumption rate of the corresponding commodity to calculate the new first threshold of the corresponding commodity and the new second threshold of the corresponding commodity. For the new first threshold of the corresponding commodity , there is

[0020] ,

[0021] wherein, is the first average value of the corresponding commodity, is the second average value of the corresponding commodity, is the current first threshold of the corresponding commodity, is the change rate coefficient of the first threshold, is the coupon consumption rate of the corresponding product, is the consumption rate base; for the current second threshold of the corresponding product , there is

[0022] ,

[0023] In the formula, is the third mean of the corresponding product, is the current second threshold of the corresponding product, is the change rate coefficient of the second threshold; among them, when there is no first threshold and second threshold for the corresponding product currently, the preset initial value is used; when the newly saved coupon consumption rate corresponds to an excellent updated evaluation result, only the new first threshold is calculated; when the newly saved coupon consumption rate corresponds to a general updated evaluation result, both the new first threshold and the new second threshold are calculated; when the updated evaluation result corresponding to the newly saved coupon consumption rate is poor, only the new second threshold is calculated.

[0024] Compared with the prior art, the present invention has the following advantages:

[0025] 1. The system of the present invention adopts advanced data processing algorithms to track key indicators such as the participation status and conversion effect of coupons and promotional activities, can accurately evaluate the effects of different promotional strategies, and realizes remarkable advantages such as real-time collection, efficient storage, intelligent analysis and dynamic optimization of online marketing data, eliminating interference to improve evaluation accuracy, and dynamically adjusting thresholds to scientifically evaluate effects;

[0026] 2. The system of the present invention can deeply analyze newly generated coupon consumption records according to time intervals and activity effect thresholds, so as to accurately evaluate the effects of promotional activities; by adopting advanced data processing algorithms and intelligent analysis technologies, the system improves the efficiency and accuracy of data processing and realizes the intelligent analysis of online marketing data; in addition, the optimized warning module can warn users according to the evaluation results to help enterprises adjust promotional strategies in a timely manner, realizing dynamic optimization;

[0027] 3. The system of the present invention can identify whether the corresponding product of the newly generated coupon consumption record is a newly listed product, and decide whether to evaluate the effect of the promotional activity accordingly; this function effectively eliminates the interference that may be brought by newly listed products due to limited sales record information and unstable sales trends, and improves the accuracy and reliability of the evaluation results;

[0028] 4. The system of the present invention also has the ability to dynamically adjust thresholds. It can analyze and calculate the first threshold and the second threshold for the corresponding product based on the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product. These thresholds will be used to evaluate the effectiveness of promotional activities and will be continuously updated as new data is added. By dynamically adjusting the thresholds, the system can more accurately evaluate the effectiveness of promotional activities, help enterprises formulate and adjust marketing strategies, reduce operational risks, and enhance market competitiveness.

[0029] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1 is a framework diagram of an online marketing coupon and promotional activity data tracking and optimization system according to an embodiment of the present invention.

[0032] Figure 2 is a schematic structural diagram of an online marketing coupon and promotional activity data tracking and optimization system according to an embodiment of the present invention.

[0033] Figure 3 is an execution flowchart of an online marketing coupon and promotional activity data tracking and optimization system according to an embodiment of the present invention.

[0034] Figure 4 is an execution flowchart for optimizing the first threshold and the second threshold according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0036] Refer to Figure 1, an embodiment of the present invention proposes an online marketing coupon and promotion activity data tracking and optimization system. By adopting advanced data processing algorithms to track key indicators such as the participation status and conversion effects of coupons and promotion activities, it can accurately evaluate the effects of different promotion strategies, achieving real-time collection, efficient storage, intelligent analysis, and dynamic optimization of online marketing data.

[0037] The system of this embodiment specifically includes: a data storage module, a data collection module, a range setting module, a promotion analysis module, and an optimization warning module; among them,

[0038] The data storage module is used to establish and store data related to promotion activities;

[0039] Specifically, the data storage module is responsible for establishing and storing all data related to promotion activities; these data include but are not limited to the basic information of promotion activities (such as activity name, activity time, participating products, etc.), consumption record information (including coupon consumption records and non-coupon consumption records), coupon consumption rate, preset time period, first threshold, second threshold, and analysis results, etc.; by constructing an efficient and scalable data storage structure, this module can ensure the integrity and security of data, providing a solid foundation for subsequent data analysis and optimization.

[0040] The data collection module is used to collect the consumption record information of the system, classify the consumption record information into coupon consumption records and non-coupon consumption records, and store them in the data storage module;

[0041] Specifically, the data collection module is responsible for real-time collecting the consumption record information of the system and classifying this information into two categories: coupon consumption records and non-coupon consumption records; the collected data includes but is not limited to key information such as the purchase time, purchased products, payment amount, and whether to use coupons of users; through an efficient data collection mechanism, this module can ensure the real-time and accuracy of data, providing a reliable data source for subsequent data analysis.

[0042] The range setting module is used to analyze the data related to promotion activities in the data storage module to obtain a time interval and an activity effect threshold;

[0043] Specifically, the range setting module is responsible for analyzing the data related to promotion activities in the data storage module to determine the time interval for data analysis and the activity effect threshold; the time interval is usually set according to the actual time period of the promotion activity, while the activity effect threshold may be set based on historical data, industry standards, or enterprise goals; through reasonable range setting, this module can ensure the pertinence and effectiveness of data analysis.

[0044] The promotion analysis module is used to analyze the newly generated coupon consumption records according to the time interval and the activity effect threshold, and obtain the evaluation result of the promotion activity effect;

[0045] Specifically, the promotion analysis module deeply analyzes the newly generated coupon consumption records according to the time interval and the activity effect threshold determined by the scope setting module to evaluate the effect of the promotion activity; The analysis content may include indicators such as the usage rate and conversion rate of the coupon. By comparing and analyzing these indicators with the preset threshold, the evaluation result of the promotion activity effect can be obtained.

[0046] The optimization warning module is used to warn users according to the evaluation result to adjust the promotion strategy.

[0047] Specifically, the optimization warning module gives a warning prompt to users according to the evaluation result of the promotion analysis module to help the enterprise adjust the promotion strategy in a timely manner; If the evaluation result shows that the promotion activity effect of a certain product is not good, the optimization warning module will send a warning signal, indicating that the enterprise may need to adjust the promotion strategy of this product, such as increasing the coupon issuance volume, extending the activity time or changing the promotion method, etc.; At the same time, by confirming the evaluation result, the optimization of the preset time period, the first threshold and the second threshold by the scope setting module is realized, improving the accuracy of the system.

[0048] Specifically, the system realizes the comprehensive tracking and in-depth analysis of online marketing data by integrating multiple functional modules such as the data storage module, the data collection module, the scope setting module, the promotion analysis module and the optimization warning module; At the same time, the system also adopts advanced data processing algorithms and intelligent analysis technologies to improve the efficiency and accuracy of data processing; Through the application of this system, the enterprise can understand the effect of the marketing activity in real time, adjust the promotion strategy in a timely manner, reduce the operation risk and enhance the market competitiveness.

[0049] Optionally, as Figure 2 shown, the promotion analysis module includes: a promotion monitoring unit, a consumption rate calculation unit, a historical ratio calculation unit and a threshold judgment unit; Among them,

[0050] The promotion monitoring unit is used to identify whether the corresponding product of the newly generated coupon consumption record is a newly listed product, and obtain the identification result;

[0051] Specifically, the main task of the promotion monitoring unit is to identify whether the corresponding product of the newly generated coupon consumption record is a newly listed product; Since the consumption record information quantity of the newly listed product is small and it is difficult to accurately evaluate the promotion activity effect, and its relevant data will affect the accuracy of the system database, it needs to be excluded.

[0052] The consumption rate calculation unit is used to calculate the coupon consumption rate of the corresponding commodity according to the recognition result and the consumption record information of the corresponding commodity.

[0053] Specifically, the consumption rate calculation unit is responsible for calculating the coupon consumption rate of the corresponding commodity according to the recognition result of the promotion monitoring unit and the consumption record information of the corresponding commodity; the coupon consumption rate refers to the proportion of consumers purchasing a certain commodity when using coupons.

[0054] The historical ratio calculation unit is used to calculate the ratio of the coupon consumption rate of the corresponding commodity according to the coupon consumption rate of the corresponding commodity and the historical coupon consumption rate of the corresponding commodity.

[0055] Specifically, the task of the historical ratio calculation unit is to calculate the ratio of the coupon consumption rate of the corresponding commodity according to the coupon consumption rate of the corresponding commodity and the historical coupon consumption rate; this ratio reflects how the current promotion activity of the commodity compares to the historical level.

[0056] The threshold judgment unit is used to evaluate the promotion activity effect according to the size of the ratio of the coupon consumption rate of the corresponding commodity to the preset first threshold and the preset second threshold of the corresponding commodity, and obtain the evaluation result of the corresponding commodity.

[0057] Specifically, the threshold judgment unit is the last link of the promotion analysis module. It is responsible for comparing the ratio of the coupon consumption rate of the corresponding commodity with the preset first threshold and second threshold to evaluate the effect of the promotion activity and generate the evaluation result of the corresponding commodity.

[0058] Optionally, identifying whether the corresponding commodity of the newly generated coupon consumption record is a newly listed commodity, and the obtained recognition result includes:

[0059] Obtain the historical consumption record information of the corresponding commodity of the newly generated coupon consumption record from the data storage module;

[0060] Judge whether the coupon consumption record of the corresponding commodity is generated for the first time according to the historical consumption record information; if not, evaluate the promotion activity effect of the corresponding commodity;

[0061] If so, judge whether there is a consumption record for the corresponding commodity the previous day according to the historical consumption record information;

[0062] If there is no consumption record, the corresponding commodity is a newly listed commodity and the promotion activity effect is not evaluated;

[0063] If there is a consumption record, evaluate the promotion activity effect of the corresponding commodity.

[0064] Exemplarily, such as Figure 3As shown, the system detected a new coupon consumption record corresponding to product A. The promotion monitoring unit retrieved the historical consumption record information of product A from the data storage module and found that this was the first time product A had a coupon consumption record. Further inspection revealed that there were no consumption records for product A the previous day. According to the recognition logic, the promotion monitoring unit determined that product A was a newly stocked product and thus did not conduct an evaluation of the promotion activity effect. As a newly stocked product, product A had very limited sales record information and an unstable sales trend. According to the exclusion logic, when evaluating the promotion activity effect, the promotion analysis module would exclude product A to avoid interference from its inaccurate data on the evaluation results. The system also detected a new coupon consumption record corresponding to product B. After the promotion monitoring unit retrieved the historical consumption record information of product B, it found that this was not the first time product B had a coupon consumption record. Further inspection revealed that there were multiple consumption records for product B the previous day. Therefore, the promotion monitoring unit considered product B not to be a newly stocked product and would conduct an evaluation of the promotion activity effect for the corresponding product. As an established product, product B had sufficient sales data and a stable sales trend. Therefore, the promotion analysis module would include it in the evaluation scope and accurately evaluate the promotion activity effect by deeply analyzing the sales data and coupon usage of product B. By excluding newly stocked products from the evaluation of the promotion activity effect, the promotion analysis module can ensure the accuracy and reliability of the evaluation results and provide strong support for subsequent market strategies and decision-making.

[0065] Optionally, the calculation of the coupon consumption rate based on the recognition result and the consumption record information includes:

[0066] If an evaluation of the promotion activity effect for the corresponding product is to be conducted, then retrieve the consumption record information of the corresponding product;

[0067] Count the number value of the coupon consumption records of the corresponding product within the corresponding preset time period in the consumption record information;

[0068] Count the number value of the consumption record information of the corresponding product within the corresponding preset time period in the consumption record information;

[0069] Using the number value of the coupon consumption records and the number value of the consumption record information of the corresponding product, calculate the coupon consumption rate of the corresponding product. For the coupon consumption rate , there is

[0070] ,

[0071] In the formula, is the number value of the coupon consumption records of the corresponding product within the corresponding preset time period, is the number value of the consumption record information of the corresponding product within the corresponding preset time period.

[0072] Specifically, the preset time period used for statistics is the most recent preset time period. For example, through statistical analysis, it is found that the consumption data on Saturdays of each month is the most representative, so Saturday is set as the preset time period; if the current collected coupon consumption record is for Friday, then the quantity value of the coupon consumption record being statistically counted at this time is for last Saturday, and correspondingly, the quantity value of the consumption record information being statistically counted at this time should also be for last Saturday; if the current collected coupon consumption record is for Saturday, then the quantity value of the coupon consumption record being statistically counted at this time is for last Saturday, because today (Saturday) has not passed and the recorded data is not complete; similarly, if the current collected coupon consumption record is for Sunday, then the quantity value of the coupon consumption record being statistically counted at this time is for this Saturday.

[0073] Exemplarily, assume that the preset time period is Saturday; from the data storage module, all consumption records of product E on the most recent Saturday are obtained, totaling 1000 records; among these consumption records, it is found that 200 records show that consumers used coupons for purchase, so = 200; the total number of consumption records is 1000, that is = 1000; the calculated coupon consumption rate of product E is = 0.2; this means that at the most recent Saturday, 20% of consumers used coupons when purchasing product E, thus enjoying the discounts brought by the promotional activities; through this data processing, the promotion analysis module can accurately quantify the effect of the promotional activities on increasing the coupon consumption rate, providing strong support for subsequent market strategies and decision-making.

[0074] Optionally, calculating the ratio of the coupon consumption rate of the corresponding product to the historical coupon consumption rate of the corresponding product includes:

[0075] Obtain the historical coupon consumption rate of the corresponding product from the data storage module to obtain a consumption rate set;

[0076] Sort the coupon consumption rates in the consumption rate set from largest to smallest, and select the coupon consumption rate ranked first to obtain the consumption rate base of the corresponding product;

[0077] Use the coupon consumption rate of the corresponding product and combine it with the consumption rate base to calculate the ratio of the coupon consumption rate of the corresponding product. For the ratio of the coupon consumption rate , there is

[0078] ,

[0079] In the formula, is the coupon consumption rate of the corresponding product, is the consumption rate base.

[0080] Exemplarily, from the data storage module, the coupon consumption rate data of product F from 12:00 to 21:00 every day in the past 12 days is obtained, forming a consumption rate set containing 12 values; after sorting these values, it is found that the highest coupon consumption rate is 30%; therefore, 30% is selected as the consumption rate base ( = 0.3); assuming that according to the previous calculation, the coupon consumption rate of product F in the most recent 12:00 - 21:00 period is 25% ( = 0.25), the coupon consumption rate ratio of product F is calculated as = 0.83; this means that the effect of the current promotion is approximately 83% of the historical best performance, slightly lower than the historical best level. Through this data processing, the promotion analysis module can quantitatively evaluate the effect of the current promotion relative to the historical best performance and provide data support for further optimizing the promotion strategy.

[0081] Optionally, evaluating the promotion effect according to the size of the coupon consumption rate ratio of the corresponding product and the preset first threshold and preset second threshold of the corresponding product, and obtaining the evaluation result of the corresponding product includes:

[0082] Judge whether the coupon consumption rate ratio of the corresponding product is greater than the first threshold;

[0083] If the coupon consumption rate ratio of the corresponding product is greater than the first threshold of the corresponding product, evaluate the promotion effect of the corresponding product as excellent;

[0084] If the coupon consumption rate ratio of the corresponding product is less than or equal to the first threshold of the corresponding product, then judge whether the coupon consumption rate ratio of the corresponding product is greater than the second threshold of the corresponding product;

[0085] If the coupon consumption rate ratio of the corresponding product is greater than the second threshold of the corresponding product, evaluate the promotion effect of the corresponding product as average;

[0086] If the coupon consumption rate ratio of the corresponding product is less than or equal to the second threshold of the corresponding product, evaluate the promotion effect of the corresponding product as poor.

[0087] Exemplarily, assume there is an e-commerce company that is evaluating the effectiveness of a promotion campaign for a certain product A on its platform. Two preset thresholds are set for product A: the first threshold is 0.8, and the second threshold is 0.5. These two thresholds represent the expected standards for the effectiveness of the promotion campaign. First, the coupon consumption rate ratio of product A is calculated according to the previous steps. Assume this ratio is 0.75. Next, this ratio is compared with the first threshold of 0.8. Since 0.75 is less than 0.8, it is judged that the promotion effect of product A does not reach the "excellent" standard. Since the promotion effect of product A does not reach "excellent", then its ratio is compared with the second threshold of 0.5. Since 0.75 is greater than 0.5, it is judged that the promotion effect of product A at least reaches the "general" standard. Based on the above analysis, the promotion effect of product A is evaluated as "general". This means that although the promotion campaign of product A does not reach the set highest standard, it does not perform too poorly either and still has a certain effect. Setting different thresholds can help evaluate the effectiveness of the promotion campaign more meticulously and provide targeted suggestions for subsequent optimization. If the promotion effect of product A is evaluated as "general", it may be necessary to consider adjusting the promotion strategy, such as increasing the preferential intensity, improving the publicity method, etc., to enhance the promotion effect. And if the evaluation result is "poor", then it may be necessary to analyze the reasons more deeply and take more proactive measures to improve the promotion effect.

[0088] Optionally, the optimization warning module is further configured to determine whether the evaluation result matches the actual effect.

[0089] Specifically, the function of the optimization warning module is to ensure the accuracy and practicality of the evaluation result. It first compares the evaluation result with the actual effect of the actual promotion campaign, and then decides whether to update the evaluation result according to the comparison result and further determines whether to save these data.

[0090] Among them, as Figure 4 shown, if it matches the actual situation, then do not save the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product.

[0091] Specifically, this step is to verify the accuracy of the evaluation model. If the evaluation result matches the actually observed promotion effect, it means that the evaluation model is reliable and no additional adjustment is required.

[0092] If it does not match the actual situation, then update the evaluation result of the corresponding product according to the actual effect, and calculate the absolute value of the difference between the current coupon consumption rate of the corresponding product and the average value of the historical coupon consumption rates of the corresponding product to obtain the error value of the corresponding product.

[0093] Specifically, if there is a deviation between the evaluation result and the actual effect, the optimization warning module will update the evaluation result according to the actual effect; at the same time, it will calculate the difference (taking the absolute value) between the current coupon consumption rate and the average value of the historical coupon consumption rate, and obtain the error value; this error value reflects the degree of difference between the evaluation result and the actual effect.

[0094] Judge whether the error value of the corresponding commodity is greater than the preset third threshold;

[0095] Specifically, the preset third threshold is a standard for judging whether the error is acceptable; if the error value is greater than this threshold, it means that the deviation of the evaluation result is relatively large, which may be due to defects in the evaluation model or the influence of other unknown factors. Therefore, this inaccurate data should not be saved.

[0096] If the error value of the corresponding commodity is greater than the third threshold, the coupon consumption rate and the updated evaluation result of the corresponding commodity will not be saved;

[0097] If the error value is less than or equal to the third threshold, the coupon consumption rate and the updated evaluation result of the corresponding commodity will be stored in the data storage module.

[0098] Specifically, if the error value is less than or equal to the third threshold, it means that although there is a deviation between the evaluation result and the actual effect, this deviation is within an acceptable range. At this time, the optimization warning module will save the updated evaluation result and the coupon consumption rate to the data storage module for subsequent analysis and reference.

[0099] Optionally, the range setting module includes a time configuration unit and an effect threshold configuration unit; among them,

[0100] The time configuration unit is used to analyze and calculate the coupon consumption records and non-coupon consumption records of the corresponding commodity in different time periods, and obtain the preset time period of the corresponding commodity;

[0101] Specifically, the main task of the time configuration unit is to analyze and calculate the coupon consumption records and non-coupon consumption records of the corresponding commodity in different time periods, so as to obtain the preset time period of each commodity. This time period usually represents the period when the commodity sales are the most active and the promotion effect is the most significant; this preset time period will be used as the time benchmark for subsequent data analysis, which helps to capture the sales trend and promotion effect of the commodity in different time periods.

[0102] The effect threshold configuration unit is used to analyze and calculate the evaluation result of the corresponding commodity and the coupon consumption rate of the corresponding commodity, and obtain the first threshold of the corresponding commodity and the second threshold of the corresponding commodity.

[0103] Specifically, the main task of the effect threshold configuration unit is to analyze and calculate the evaluation results of corresponding products and the coupon consumption rate, so as to obtain the first threshold and the second threshold for each product; these two thresholds will be used to evaluate the effect of promotional activities and help enterprises formulate and adjust marketing strategies.

[0104] Optionally, the analysis and calculation of the coupon consumption records and non-coupon consumption records of corresponding products in different time periods to obtain the preset time period of corresponding products includes:

[0105] Collect historical factor data affecting the coupon consumption records of corresponding products and the historical coupon consumption records of corresponding products to construct a training set;

[0106] Specifically, collect historical factor data affecting the coupon consumption records of corresponding products. These historical factors may include, but are not limited to, promotional activity time, product discount intensity, market competition situation, holiday influence, etc.; at the same time, collect the historical coupon consumption records of corresponding products, which include information such as consumption time, consumption amount, and coupon types used.

[0107] Using the historical factor data affecting the coupon consumption records of corresponding products as input and the coupon consumption records of corresponding products as output, establish and train a regression model using the training set to obtain a coupon consumption record prediction model;

[0108] Use the coupon consumption record prediction model to predict the coupon consumption records of corresponding products;

[0109] Specifically, use the constructed training set to establish and train a regression model. The goal of this model is to predict the coupon consumption records of corresponding products based on the input historical factor data; the choice of regression model can be based on the actual situation. For example, linear regression, decision tree regression, random forest regression, etc. can be selected; during the model training process, the model parameters will be continuously adjusted to minimize the prediction error.

[0110] Divide the coupon consumption records of corresponding products into different time periods, calculate the coupon consumption rate of corresponding products within each time period, and arrange them from large to small;

[0111] Specifically, divide the predicted coupon consumption records into different time periods, such as by hour, by day, or by week; calculate the coupon consumption records within each time period to obtain the corresponding coupon consumption rate. The coupon consumption rate is usually defined as the proportion of the number of consumption records using coupons in a certain time period to the total number of consumption records.

[0112] Select the time period corresponding to the coupon consumption rate ranked first to obtain the preset time period of corresponding products.

[0113] Specifically, arrange the coupon consumption rates within each time period from largest to smallest, and select the time period corresponding to the coupon consumption rate ranked first as the preset time period for the product; this time period usually represents the most active and effective period of the product during the coupon promotion activity; enabling the comparison of the effects of coupon promotion activities for the product in multiple identical time periods.

[0114] Exemplarily, assume that the coupon consumption records of a product named "smart bracelet" are analyzed to determine its preset time period; collect historical factor data affecting the coupon consumption records of the smart bracelet in the past year, such as the time of each promotion activity, discount intensity, activities of competitors during the same period, etc.; at the same time, collect the coupon consumption records of the smart bracelet in the past year, including the time, amount, and type of coupon used for each consumption; integrate the historical factor data and the coupon consumption records to form a training set containing multiple samples; each sample includes the historical factors of a promotion activity and the corresponding coupon consumption record; select a linear regression model for training; by continuously adjusting the model parameters and minimizing the prediction error, obtain a regression model that can accurately predict the coupon consumption records of the smart bracelet; use the trained regression model to predict the coupon consumption records of the smart bracelet in the next month; divide the predicted coupon consumption records by day and calculate the daily coupon consumption rate; arrange the daily coupon consumption rates from largest to smallest, and find that the coupon consumption rate on a certain weekend is the highest; therefore, select this weekend as the preset time period for the smart bracelet; this means that in subsequent data analysis, the sales situation of the smart bracelet on this weekend of each month will be focused on to better evaluate the effect of the promotion activity.

[0115] Optionally, the analysis and calculation of the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product to obtain the first threshold and the second threshold of the corresponding product include:

[0116] Obtain the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product in the data storage module in real time, and construct a real-time data set;

[0117] Use the real-time data set to calculate the average value of the coupon consumption rate with excellent promotion effect for the corresponding product to obtain the first mean value;

[0118] Use the real-time data set to calculate the average value of the coupon consumption rate with general promotion effect for the corresponding product to obtain the second mean value;

[0119] Use the real-time data set to calculate the average value of the coupon consumption rate with poor promotion effect for the corresponding product to obtain the third mean value;

[0120] Using the first mean, the second mean, the third mean, the consumption rate base of the corresponding commodity, and the newly saved coupon consumption rate of the corresponding commodity, calculate the new first threshold and the new second threshold of the corresponding commodity. For the new first threshold of the corresponding commodity , there is

[0121] ,

[0122] In the formula, is the first mean of the corresponding commodity, is the second mean of the corresponding commodity, is the current first threshold of the corresponding commodity, is the change rate coefficient of the first threshold, is the coupon consumption rate of the corresponding commodity, is the consumption rate base;

[0123] For the current second threshold of the corresponding commodity , there is

[0124] ,

[0125] In the formula, is the third mean of the corresponding commodity, is the current second threshold of the corresponding commodity, is the change rate coefficient of the second threshold;

[0126] Among them, when there is no current first threshold and second threshold for the corresponding commodity, use the preset initial value; when the newly saved coupon consumption rate corresponds to an excellent updated evaluation result, only calculate the new first threshold; when the newly saved coupon consumption rate corresponds to a general updated evaluation result, calculate both the new first threshold and the new second threshold; when the newly saved coupon consumption rate corresponds to a poor updated evaluation result, only calculate the new second threshold.

[0127] Specifically, the calculation of the threshold is a dynamic process. By confirming the promotion effect and the actual effect, the database is updated, which enables the system to continuously update as new data is added. This dynamic adjustment mechanism ensures the accuracy and timeliness of the threshold, making the evaluation of the promotion activity effect more scientific and reasonable.

[0128] Exemplarily, as Figure 4 shown, assume that the first threshold and the second threshold of a commodity named "Health Smart Watch" are being calculated; assume that the preset time is Saturday of each week, and obtain the evaluation results and coupon consumption rate data of the Health Smart Watch for the past twenty Saturdays from the data storage module; after statistics, the current first threshold of the Health Smart Watch is 0.6, and the current second threshold is 0.4; Assume the consumption rate base of the healthy smart watch is 0.5. At this time, the coupon consumption rate calculated until the current nearest Saturday is 0.29. Then the ratio of the coupon consumption rate of this product , since 0.58 is less than the current first threshold of 0.6, it is determined that the promotion effect is average; After comparison, it is found that the promotion effect should be excellent at this time (assuming that the error value is less than the third threshold). Therefore, mark the coupon consumption rate of 0.29 as the promotion effect should be excellent and save it. Then, the average coupon consumption rate of the healthy smart watch in the case of excellent promotion effect needs to include the newly saved 0.29. Assume that the newly calculated first mean is 0.34 ( ); At this time, the average coupon consumption rate of the healthy smart watch in the case of average promotion effect is 0.25 ( ), and the average coupon consumption rate in the case of poor promotion effect is 0.15 ( ), and the change rate coefficient of the first threshold = 1, and the change rate coefficient of the second threshold = 1; Since the evaluation result corresponding to the newly saved coupon consumption rate of 0.29 is excellent promotion effect, only the first threshold needs to be adjusted. Then calculate the new first threshold ; At this time, the coupon consumption rate ratio of this product, 0.58, is greater than 0.55. Therefore, the new first threshold is available; The new first threshold is 0.55 at this time, and the second threshold remains unchanged (0.4); In this way, the system can dynamically adjust the threshold according to the actual data, so as to more accurately evaluate the effect of the promotion activity.

[0129] It should be noted that the electrical connections between the above-mentioned various units do not necessarily mean direct connection of the circuits. Indirect connection methods, as long as the purpose of the present invention is achieved, are applicable to the embodiments of the present invention. The above are only exemplary embodiments of the present invention and should not be used to limit the scope of the present invention.

[0130] That is, all equivalent changes and modifications made according to the teachings of the present invention still fall within the scope covered by the present invention. Those skilled in the art will easily think of other implementation schemes of the present invention after considering the specification and the disclosure of the practical truth. This application aims to cover any variations, uses or adaptive changes of the present invention, and these variations, uses or adaptive changes follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not recorded in the present invention.

Claims

1. An online marketing coupon and promotion activity data tracking and optimization system, characterized in that, The system includes: a data storage module, a data acquisition module, a range setting module, a promotion analysis module, and an optimization warning module. The promotion analysis module includes a promotion monitoring unit, a consumption rate calculation unit, a historical ratio calculation unit, and a threshold judgment unit. The range setting module includes a time configuration unit and an effect threshold configuration unit; wherein, The data storage module is used to establish and store data related to promotion activities; The data acquisition module is used to collect consumption record information of the system, classify the consumption record information into coupon consumption records and non-coupon consumption records, and store them in the data storage module; The promotion monitoring unit is used to identify whether the corresponding commodity of the newly generated coupon consumption record is a newly listed commodity, and obtain an identification result; The consumption rate calculation unit is used to calculate the coupon consumption rate of the corresponding commodity according to the identification result and the consumption record information of the corresponding commodity; The historical ratio calculation unit is used to calculate the coupon consumption rate ratio of the corresponding commodity according to the coupon consumption rate of the corresponding commodity and the historical coupon consumption rate of the corresponding commodity; The threshold judgment unit is used to evaluate the promotion activity effect according to the size of the coupon consumption rate ratio of the corresponding commodity and the preset first threshold and preset second threshold of the corresponding commodity, and obtain the evaluation result of the corresponding commodity; The optimization warning module is used to give a warning to the user according to the evaluation result to adjust the promotion strategy; The time configuration unit is used to collect historical factor data affecting the coupon consumption record of the corresponding commodity and the historical coupon consumption record of the corresponding commodity, and construct a training set; and use the historical factor data affecting the coupon consumption record of the corresponding commodity as input and the coupon consumption record of the corresponding commodity as output, and use the training set to establish and train a regression model to obtain a coupon consumption record prediction model; and use the coupon consumption record prediction model to predict the coupon consumption record of the corresponding commodity; and divide the coupon consumption record of the corresponding commodity by different time periods, calculate the coupon consumption rate of the corresponding commodity within each time period, and arrange them from large to small; and select the time period corresponding to the coupon consumption rate ranked first to obtain the preset time period of the corresponding commodity; The effect threshold configuration unit is used to analyze and calculate the evaluation result of the corresponding commodity and the coupon consumption rate of the corresponding commodity to obtain the first threshold of the corresponding commodity and the second threshold of the corresponding commodity.

2. The online marketing coupon and promotion activity data tracking and optimization system according to claim 1, wherein, Identifying whether the corresponding commodity of the newly generated coupon consumption record is a newly listed commodity and obtaining the identification result includes: Obtaining the historical consumption record information of the corresponding commodity of the newly generated coupon consumption record from the data storage module; Judging whether the coupon consumption record of the corresponding commodity is generated for the first time according to the historical consumption record information; if not, evaluating the promotion activity effect of the corresponding commodity; If so, judging whether there is a consumption record for the corresponding commodity the previous day according to the historical consumption record information; If there is no consumption record, the corresponding commodity is a newly listed commodity and the promotion activity effect is not evaluated; If there is a consumption record, the promotion activity effect of the corresponding commodity is evaluated.

3. An online marketing coupon and promotion activity data tracking and optimization system according to claim 1, characterized in that Calculating the coupon consumption rate based on the recognition result and the consumption record information includes: If evaluating the promotion effect of a corresponding product, obtain the consumption record information of the corresponding product; Count the number value of coupon consumption records of the corresponding product within the corresponding preset time period in the consumption record information; Count the number value of consumption record information of the corresponding product within the corresponding preset time period in the consumption record information; Using the quantity value of the consumption records with coupons and the quantity value of the consumption record information for the corresponding product, calculate the coupon consumption rate for the corresponding product. For the coupon consumption rate , there is ， In the formula, is the quantity value of the consumption records with coupons for the corresponding product within the corresponding preset time period, is the quantity value of the consumption record information for the corresponding product within the corresponding preset time period.

4. An online marketing coupon and promotion activity data tracking and optimization system according to claim 1, characterized in that, Calculating the ratio of the coupon consumption rate of a corresponding product based on the coupon consumption rate of the corresponding product and the historical coupon consumption rate of the corresponding product includes: Obtain the historical coupon consumption rate of the corresponding product from the data storage module to obtain a consumption rate set; Sort the coupon consumption rates in the consumption rate set from largest to smallest, and select the coupon consumption rate ranked first to obtain the consumption rate base of the corresponding product; Using the consumption rate of coupon-bearing vouchers for the corresponding product and combining it with the consumption rate base, calculate the ratio of the consumption rate of coupon-bearing vouchers for the corresponding product. For the ratio of the consumption rate of coupon-bearing vouchers , there is ， In the formula, is the coupon consumption rate of the corresponding product, is the consumption rate base.

5. An online marketing coupon and promotion activity data tracking and optimization system according to claim 1, characterized in that, Evaluating the promotion effect based on the magnitude relationship between the coupon consumption rate ratio of the corresponding product and the preset first threshold and preset second threshold of the corresponding product to obtain the evaluation result of the corresponding product includes: Judge whether the coupon consumption rate ratio of the corresponding product is greater than the first threshold; If the coupon consumption rate ratio of the corresponding product is greater than the first threshold of the corresponding product, evaluate the promotion effect of the corresponding product as excellent; If the coupon consumption rate ratio of the corresponding product is less than or equal to the first threshold of the corresponding product, judge whether the coupon consumption rate ratio of the corresponding product is greater than the second threshold of the corresponding product; If the coupon consumption rate ratio of the corresponding product is greater than the second threshold of the corresponding product, evaluate the promotion effect of the corresponding product as average; If the coupon consumption rate ratio of the corresponding product is less than or equal to the second threshold of the corresponding product, evaluate the promotion effect of the corresponding product as poor.

6. An online marketing coupon and promotion activity data tracking and optimization system according to claim 1, characterized in that, The optimization warning module is also used to judge whether the evaluation result is consistent with the actual effect; among them, If it is consistent with the actual situation, do not save the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product; If it is not consistent with the actual situation, update the evaluation result of the corresponding product according to the actual effect, and calculate the absolute value of the difference between the current coupon consumption rate of the corresponding product and the average value of the historical coupon consumption rates of the corresponding product to obtain the error value of the corresponding product; Judge whether the error value of the corresponding product is greater than the preset third threshold; If the error value of the corresponding product is greater than the third threshold, do not save the coupon consumption rate and the updated evaluation result of the corresponding product; If the error value is less than or equal to the third threshold, store the coupon consumption rate and the updated evaluation result of the corresponding product in the data storage module.

7. An online marketing coupon and promotion activity data tracking and optimization system according to claim 1, characterized in that Analyzing and calculating the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product to obtain the first threshold of the corresponding product and the second threshold of the corresponding product includes: Obtain the evaluation result of the corresponding product and the coupon consumption rate of the corresponding product in the data storage module in real time to construct a real-time data set; Use the real-time data set to calculate the average value of the coupon consumption rates with excellent promotion effects of the corresponding product to obtain the first average value; Use the real-time data set to calculate the average value of the coupon consumption rates with average promotion effects of the corresponding product to obtain the second average value; Calculate the average value of the coupon consumption rate corresponding to the poor promotion effect of the corresponding product using the real-time data set to obtain the third average value; Calculate the new first threshold and the new second threshold for the corresponding product by using the first mean, the second mean, the third mean, the consumption rate base of the corresponding product, and the newly saved coupon consumption rate of the corresponding product. For the new first threshold of the corresponding product , there is ， Wherein, is the first mean value of the corresponding commodity, is the second mean value of the corresponding commodity, is the current first threshold value of the corresponding commodity, is the change rate coefficient of the first threshold value, is the consumption rate with coupons of the corresponding commodity, is the consumption rate base; For the current second threshold value of the corresponding product , there is ， In the formula, is the third mean value of the corresponding commodity, is the current second threshold value of the corresponding commodity, is the change rate coefficient of the second threshold value; Among them, when there is no first threshold and second threshold for the corresponding product currently, the preset initial value is used; when the newly saved coupon consumption rate corresponds to an updated evaluation result of excellent, only the new first threshold is calculated; when the newly saved coupon consumption rate corresponds to an updated evaluation result of average, both the new first threshold and the new second threshold are calculated; when the updated evaluation result corresponding to the newly saved coupon consumption rate is poor, only the new second threshold is calculated.

Images