Channel resource allocation method and device for traditional Chinese medicine decoction piece e-commerce platform and storage medium

Abstract

The application discloses a channel resource allocation method and device for a traditional Chinese medicine decoction piece e-commerce platform and a storage medium, and attributes online behavior habits before first login of a user based on push records of a touch channel and operation records of the user, then, based on a touch form of the platform to the user and the online behavior habits before the first login, attribution judgment of a user login response is performed again, so that a login contribution value of each touch channel is obtained; in this way, the application is equivalent to performing multiple login attribution analyses in combination with portrait data of the user, based on which, contribution rates of different touch channels can be more reasonably evaluated, so that reasonable allocation of channel resources is realized, and channel development and maintenance are better performed.

Description

Technical Field

[0001] This invention belongs to the field of e-commerce technology for traditional Chinese medicine decoction pieces, and specifically relates to a method, device and storage medium for allocating channel resources on an e-commerce platform for traditional Chinese medicine decoction pieces. Background Technology

[0002] With the rapid development of e-commerce, e-commerce platforms for traditional Chinese medicine (TCM) decoction pieces have become one of the main procurement channels for grassroots clinics, pharmacies, and health centers. They have enabled the online sales of TCM decoction pieces, greatly improving the convenience of TCM procurement. Currently, e-commerce platforms have set up different channels to push products or messages in order to discover potential users and stimulate consumption, thereby increasing the sales volume of TCM decoction pieces e-commerce platforms. As the number of channels continues to increase, how to allocate channel resources has become an important part of the platform's daily operation.

[0003] In practical applications, channel analysis is mostly conducted using methods such as first-interaction attribution, last-interaction attribution, or linear attribution, and resource allocation is based on the analysis results. Each of these attribution methods is suitable for different use cases. Although they are simple and easy to explain, the algorithms are simplistic and crude, easily attributing results to one or a few touchpoints, amplifying or diminishing the contribution rate of each touchpoint in the actual scenario, thus causing incorrect judgments on channel attribution and wasting channel resources. Therefore, how to provide a more reasonable method for allocating channel resources has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus and storage medium for allocating channel resources on an e-commerce platform for traditional Chinese medicine decoction pieces, in order to solve the problem that the existing technology is prone to misjudging the channel attribution, thereby leading to a waste of channel resources.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, a method for allocating channel resources on an e-commerce platform for prepared Chinese medicine slices is provided, including:

[0007] Obtain user push records for each contact channel of the Chinese herbal medicine e-commerce platform within a historical time period, as well as platform operation records for each user of the Chinese herbal medicine e-commerce platform within the historical time period.

[0008] Based on the user push records of each reach channel and the platform operation records of each user, a reach login behavior sequence log is generated for each user. The reach login behavior sequence log of any user includes the platform operation data of the user before the first login to the Chinese herbal medicine e-commerce platform within the historical time period, and the push data of each reach channel before the user's first login to the Chinese herbal medicine e-commerce platform within the historical time period.

[0009] Based on the login behavior sequence logs of each user, the login contribution value of each contact channel is calculated. The login contribution value of any contact channel is used to characterize the degree of influence of that contact channel on the user's login to the Chinese herbal medicine e-commerce platform.

[0010] The login contribution values ​​of each reach channel are sent to the operations team so that operations personnel can allocate resources to each reach channel based on the login contribution values ​​of each reach channel.

[0011] Based on the aforementioned disclosure, this invention first acquires user push records from each reach channel within a historical time period, as well as platform operation records for each user within the same historical time period. Next, the aforementioned data is processed to obtain a user's reach login behavior sequence log, which includes platform operation data before the user's first login and push data from each reach channel. This is equivalent to using the first-interaction attribution method to analyze the relationship between user login, reach channels, and user online behavior. Then, based on the user's reach login behavior sequence log, the login contribution value of each reach channel is calculated. This step is equivalent to further attributing user login responses based on different reach channels, building upon the first-interaction attribution, to obtain a more reasonable login contribution value. Finally, resources can be allocated based on the login contribution value of each reach channel.

[0012] Through the above design, this invention first attributes the user's online behavior habits before their first login based on the push records of the reach channels and the user's operation records. Then, based on the platform's reach to the user and their online behavior habits before the first login, it performs another attribution judgment on the user's login response, thereby obtaining the login contribution value of each reach channel. In this way, this invention is equivalent to combining user profile data to perform multiple login attribution analyses. Based on this, the contribution rate of different reach channels can be evaluated more reasonably, thereby achieving a reasonable allocation of channel resources and better channel development and maintenance.

[0013] In one possible design, any user's push record includes the channel name, push user ID, and push time, and any user's platform operation record includes the user's purchase data, login data, and user ID;

[0014] Specifically, based on user push records from various reach channels and platform operation records of each user, a sequence log of reach login behavior for each user is generated, including:

[0015] For any user, based on the user ID of the user and the push user ID in each user's push record, the push record corresponding to the user is filtered out from each user's push record;

[0016] The push records corresponding to any user, as well as the purchase data and login data in the platform operation records corresponding to any user, are sorted in chronological order to obtain the original login behavior sequence log of any user.

[0017] Based on the login data in the platform operation records of any user, the original login behavior sequence log is filtered to obtain the login behavior sequence log of any user.

[0018] In one possible design, based on the login data in the platform operation records of any user, the log of the original login behavior sequence is filtered, including:

[0019] Based on the login data in the platform operation records of any user, the first login time of any user is determined;

[0020] Based on the first login time, purchase data prior to the first login time is filtered out from the original login behavior sequence log of any user and used as the platform operation data of any user.

[0021] Based on the initial login time and the push time of each push record in the initial login behavior sequence log, the push records in the initial login behavior sequence log whose push time is before the initial login time are determined as the push data for any user.

[0022] Using the platform operation data and the push data, a sequence log of the login behavior of any user is constructed.

[0023] In one possible design, based on the login behavior sequence logs of each user, the login contribution value of each outreach channel is calculated, including:

[0024] For the contact login behavior sequence log of the i-th user in each user's contact login behavior sequence log, obtain the weight value of each contact channel in the contact login behavior sequence log of the i-th user;

[0025] Based on the weight values ​​and the platform operation data in the login behavior sequence log of the i-th user, the login contribution value of each reach channel to the i-th user is calculated;

[0026] Increment i by 1 and re-obtain the weight values ​​of each reach channel in the reach login behavior sequence log of the i-th user until i equals n, and obtain the login contribution value of each reach channel to each user, where the initial value of i is 1 and n is the total number of users;

[0027] Based on the login contribution value of each reach channel to each user, the login contribution value of each reach channel is calculated.

[0028] In one possible design, the weight values ​​of each reach channel in the reach login behavior sequence log of the i-th user are obtained, including:

[0029] Using the time decay algorithm and the access login behavior sequence log of the i-th user, the weight values ​​of each access channel in the access login behavior sequence log of the i-th user are calculated.

[0030] In one possible design, based on the weight values ​​and the platform operation data in the login behavior sequence log of the i-th user, the login contribution value of each reach channel to the i-th user is calculated, including:

[0031] A contribution value calculation model is obtained, wherein the contribution value calculation model is constructed based on a time decay algorithm;

[0032] The weight values ​​and platform operation data from the login behavior sequence log of the i-th user are input into the contribution value calculation model to obtain the login contribution value of each reach channel to the i-th user.

[0033] In one possible design, based on the login contribution value of each reach channel to each user, the login contribution value of each reach channel is calculated, including:

[0034] For any one of the various outreach channels, sum the login contribution value of that outreach channel to each user, and use the summation result as the login contribution value of that outreach channel.

[0035] Secondly, a channel resource allocation device for an e-commerce platform for traditional Chinese medicine decoction pieces is provided, comprising:

[0036] The data acquisition unit is used to acquire user push records of each contact channel of the Chinese herbal medicine e-commerce platform within a historical time period, as well as platform operation records of each user of the Chinese herbal medicine e-commerce platform within the historical time period.

[0037] The data processing unit is used to generate a user access login behavior sequence log based on the user push records of each access channel and the platform operation records of each user. The access login behavior sequence log of any user includes the platform operation data of the user before the first login to the Chinese herbal medicine e-commerce platform in the historical time period, and the push data of each access channel before the user first login to the Chinese herbal medicine e-commerce platform in the historical time period.

[0038] The login contribution value analysis unit is used to calculate the login contribution value of each reach channel based on the login behavior sequence log of each user. The login contribution value of any reach channel is used to characterize the degree of influence of any reach channel on a user's login to the Chinese herbal medicine e-commerce platform.

[0039] The sending unit is used to send the login contribution value of each reach channel to the operations side, so that the operations staff can allocate resources to each reach channel based on the login contribution value of each reach channel.

[0040] Thirdly, another channel resource allocation device for a traditional Chinese medicine decoction pieces e-commerce platform is provided. Taking the device as an electronic device as an example, it includes a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the channel resource allocation method for the traditional Chinese medicine decoction pieces e-commerce platform as described in the first aspect or any possible design of the first aspect.

[0041] Fourthly, a storage medium is provided, on which instructions are stored, which, when executed on a computer, perform the channel resource allocation method of the e-commerce platform for traditional Chinese medicine decoction pieces as described in the first aspect or any possible design of the first aspect.

[0042] Fifthly, a computer program product containing instructions is provided, which, when executed on a computer, causes the computer to execute the channel resource allocation method of the e-commerce platform for traditional Chinese medicine decoction pieces described in the first aspect or any possible design of the first aspect.

[0043] Beneficial effects:

[0044] (1) This invention first uses push records from the reach channels and user operation records to attribute the user's online behavior habits before the first login. Then, based on the platform's reach to the user and the user's online behavior habits before the first login, it performs another attribution judgment on the user's login response to obtain the login contribution value of each reach channel. Thus, this invention is equivalent to combining user profile data to perform multiple login attribution analyses. Based on this, the contribution rate of different reach channels can be evaluated more reasonably, thereby achieving a reasonable allocation of channel resources and better channel development and maintenance. Attached Figure Description

[0045] Figure 1 A flowchart illustrating the steps of a method for allocating channel resources on an e-commerce platform for traditional Chinese medicine decoction pieces, as provided in an embodiment of the present invention.

[0046] Figure 2 A schematic diagram of the channel resource allocation device for a traditional Chinese medicine decoction pieces e-commerce platform provided in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0049] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0050] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0051] Example:

[0052] See Figure 1 As shown, the channel resource allocation method for the e-commerce platform of traditional Chinese medicine decoction pieces provided in this embodiment can combine user profile data to perform multiple login attribution analyses. Based on this, the login contribution rate of different reach channels can be evaluated more reasonably, thereby achieving reasonable allocation of channel resources. This allows for better channel development and maintenance, and is suitable for large-scale application and promotion in the field of channel maintenance for e-commerce platforms of traditional Chinese medicine decoction pieces. In this embodiment, the method can be run on, but is not limited to, the resource analysis end or the server side of the e-commerce platform of traditional Chinese medicine decoction pieces. The resource analysis end can be, but is not limited to, a personal computer (PC), a tablet computer, or a smartphone. It is understood that the aforementioned execution subject does not constitute a limitation on the embodiment of this application. Accordingly, the operation steps of this method can be, but are not limited to, the steps S1 to S4 below.

[0053] S1. Obtain user push records for each contact channel of the traditional Chinese medicine decoction pieces e-commerce platform within a historical time period, and platform operation records for each user of the traditional Chinese medicine decoction pieces e-commerce platform within the historical time period. In this embodiment, for example, any user push record may include, but is not limited to, channel name, push user ID, push time, push content, etc., while any user's platform operation record includes the user's purchase data, login data, and user ID. The aforementioned contact channels are message push methods, such as telephone, SMS, and APP push, etc. The purchase data includes purchase time and purchased product information, while the login data includes login time and login username, etc. In addition, the historical time period may be, but is not limited to, a historical day, a historical week, or a historical month, etc., which can be specifically set according to actual use and is not specifically limited here.

[0054] After obtaining the user push records and user platform operation records of each reach channel within the historical time period, the first interaction attribution analysis can be performed, which is to analyze the user's behavior before logging into the Chinese herbal medicine e-commerce platform for the first time within the historical time period, as well as the records pushed by each reach channel; the first interaction attribution analysis process is as shown in step S2 below.

[0055] S2. Based on the user push records of each reach channel and the platform operation records of each user, generate a reach login behavior sequence log for each user. The reach login behavior sequence log for any user includes the platform operation data of that user before their first login to the Chinese herbal medicine e-commerce platform within the historical time period, and the push data from each reach channel before that user's first login to the Chinese herbal medicine e-commerce platform within the historical time period. In this embodiment, step S2 is equivalent to attributing and analyzing the messages pushed by different reach channels before the first login, and the user's behavior before the first login, thereby attributing and analyzing the aforementioned profile data. Login profile data; for example, assuming user A's first login time within a historical day is 10:00 AM on February 14, 2023, then, in the aforementioned user push records and platform operation records, analyze user A's operation data on the Chinese herbal medicine e-commerce platform before 10:00 AM on February 14, 2023 (such as purchase data and login data), as well as the message records pushed to user A by various channels before 10:00 AM on February 14, 2023 (such as telephone push, SMS push, and APP push); thus, based on the aforementioned step S2, the data association between the user's first login, the contact channels, and the user's operation behavior can be realized.

[0056] Furthermore, since the attribution analysis principle is the same for each user, the attribution analysis process in step S2 above will be specifically illustrated below using any user as an example. It can be, but is not limited to, the steps S21 to S23 below.

[0057] S21. For any user, based on the user ID of that user and the push user ID in each user's push record, filter out the push record corresponding to that user from the push records of each user; in this embodiment, it is equivalent to using the user ID in the platform operation record of the aforementioned user to filter out the push record of that user from the push records of each reach channel; for example, assuming there are 3 user push records, namely user push record A1 (channel name is telephone channel, push user ID is 01xx, push time is 2023-02-14-02), user push record User A2 (channel name: SMS channel, push user ID: 02xx, push time: 2023-02-14-03), user push record A3 (channel name: APP channel, push user ID: 03xx, push time: 2023-02-14-04), and the user ID of any of these user records is 01xx, then from the aforementioned three user push records, the record with push user ID 01xx is selected, that is, user push record A1 is taken as the push record for any of these user records; of course, the process of finding push records for other different users is the same as the previous example, and will not be repeated here.

[0058] Once the push records of any user are identified, they can be associated with the platform operation records of that user, as shown in step S22 below.

[0059] S22. Sort the push records corresponding to any user, as well as the purchase data and login data in the platform operation records corresponding to any user, in chronological order to obtain the original login behavior sequence log of any user. In this embodiment, as previously explained, both purchase data and login data have their own corresponding times. Therefore, step S22 is equivalent to sorting the data based on the time of the push records, the time of each purchase data item, and the time of each login data item in chronological order. For example, based on the above, assume that the platform operation records of any user include: purchase data B1 ( The corresponding procurement time is 1:00 AM on February 14, 2023. Procurement data B2 (corresponding procurement time is 1:30 AM on February 14, 2023), login data C1 (login time is 7:00 AM on February 14, 2023), and login data C2 (login time is 8:00 AM on February 14, 2023). Therefore, the original login behavior sequence log for any user is as follows (i.e., the aforementioned information is arranged in chronological order): procurement data B1, procurement data B2, user push record A1, login data C1, and login data C2. Of course, the generation principle of the original login behavior sequence logs for other users is the same as the example above, and will not be repeated here.

[0060] After obtaining the original login behavior sequence log of any user, it is necessary to filter the data so as to obtain only the push records and operation data before the first login; the data filtering process is as shown in step S23 below.

[0061] S23. Based on the login data in the platform operation record of any user, perform data filtering processing on the original login behavior sequence log to obtain the login behavior sequence log of any user after data filtering and processing; in this embodiment, the first login time can be determined first based on the login data of any user, and then the original login behavior sequence log of any user can be filtered based on the first login time. The aforementioned filtering process can be, but is not limited to, the steps S23a to S23d below.

[0062] S23a. Based on the login data in the platform operation record of any user, determine the first login time of that user; in this embodiment, the above example is used as a basis for explanation, that is, there are two login records for any user, the earliest login time is 7:00 on February 14, 2023, then the first login time of that user is 7:00 on February 14, 2023; of course, the method for determining the first login time of other users is the same as the above example, and will not be repeated here.

[0063] After obtaining the first login time of any user, the data can be filtered. The filtering process is as shown in steps S23b and S23c below.

[0064] S23b. Based on the first login time, filter out the purchase data prior to the first login time from the original login behavior sequence log of any user, and use it as the platform operation data of any user.

[0065] S23c. Based on the initial login time and the push time of each push record in the initial login behavior sequence log, determine the push records in the initial login behavior sequence log whose push time is before the initial login time, and use them as the push data for any user. In specific applications, the example above will be used as a basis. The initial login time of any user is 7:00 on February 14, 2023. The purchase times of the two purchase data of any user are both before the initial login time. At the same time, the push time of user push record A1 is also before the initial login time. Therefore, the platform operation data of any user is: purchase data B1 and purchase data B2, and the push data of any user is: user push record A1. Finally, using the platform operation data and push data selected above, the initial login behavior sequence log of any user can be formed, as shown in step S23d below.

[0066] S23d. Using the platform operation data and the push data, a login behavior sequence log for any user is formed; in this embodiment, after the aforementioned filtering steps, the login behavior sequence log for any user specifically includes: purchase data B1, purchase data B2, and user push record A1; of course, the generation principle of login behavior sequences for other different users is the same as the aforementioned example, and will not be repeated here.

[0067] Therefore, through the aforementioned steps S21 to S23, and steps S23a to S23d, the online behavior habits of each user before their first login can be analyzed from the user push records of each reach channel and the platform operation records of each user; thus, a data foundation can be provided for the subsequent attribution analysis of the login contribution value of reach channels.

[0068] In this embodiment, the process of performing attribution analysis of login contribution value based on the login behavior sequence logs of each user can be, but is not limited to, the process shown in step S3 below.

[0069] S3. Based on the login behavior sequence logs of each user, calculate the login contribution value of each contact channel. The login contribution value of any contact channel is used to characterize the degree of influence of that contact channel on the user's login to the Chinese herbal medicine e-commerce platform. In this embodiment, calculating the login contribution value of each contact channel is equivalent to obtaining the degree of influence of the messages pushed by each contact channel on the user's login to the platform. That is, the larger the login contribution value of any contact channel, the greater the probability that the user will log in to the Chinese herbal medicine e-commerce platform after receiving the message pushed by that contact channel. Furthermore, the calculation process of the login contribution value corresponding to each contact channel can be, but is not limited to, the steps S31 to S34 below.

[0070] S31. For the login behavior sequence log of the i-th user in each user's login behavior sequence log, obtain the weight value of each contact channel in the login behavior sequence log of the i-th user; in this embodiment, for example, but not limited to, using the time decay algorithm and the login behavior sequence log of the i-th user, the weight value of each contact channel in the login behavior sequence log of the i-th user can be calculated; for example, based on the above example, it is equivalent to obtaining the weight values ​​of telephone channel, SMS channel and APP channel; after obtaining the weight value of each contact channel, the login contribution value of each contact channel to the i-th user can be calculated, as shown in step S32 below.

[0071] S32. Based on the weight values ​​and the platform operation data in the login behavior sequence log of the i-th user, calculate the login contribution value of each reach channel to the i-th user. In specific implementation, for example, but not limited to, first obtain the contribution value calculation model (which is constructed based on the time decay algorithm), and then input the weight values ​​and the platform operation data in the login behavior sequence log of the i-th user into the contribution value calculation model to obtain the login contribution value of each reach channel to the i-th user. In this embodiment, the calculation of the weight values ​​of each reach channel and the calculation of the login contribution value using the time decay algorithm (i.e., the time decay attribution model) are both commonly used algorithms in attribution analysis, and their principles will not be elaborated here.

[0072] After obtaining the login contribution value of each reach channel to the i-th user, the login contribution value of each reach channel to each user can be obtained according to the aforementioned principle and based on the reach login behavior sequence logs of the other users; the loop process can be seen in step S33 below.

[0073] S33. Increment i by 1 and re-obtain the weight values ​​of each reach channel in the reach login behavior sequence log of the i-th user until i equals n, thus obtaining the login contribution value of each reach channel to each user, where the initial value of i is 1 and n is the total number of users; In this embodiment, after obtaining the login contribution value of each reach channel to each user, the login contribution value of each reach channel can be calculated, as shown in step S34 below.

[0074] S34. Based on the login contribution value of each contact channel to each user, calculate the login contribution value of each contact channel. In this embodiment, taking any contact channel as an example, the specific calculation process table for the login contribution value corresponding to any contact channel is explained. That is, for any contact channel, sum the login contribution values ​​of any contact channel to each user to obtain the login contribution value of any contact channel. For example, suppose there are 3 users (A, B, and C), where the login contribution value of the telephone channel to user A is 20, the login contribution value of the SMS channel to user A is 50, and the login contribution value of the APP channel to user A is 30; the login contribution value of the telephone channel to user B is 50. The contribution value is 45. The SMS channel contributes 45 to user B's login, and the APP channel contributes 10. The telephone channel contributes 15 to user C's login, the SMS channel contributes 55, and the APP channel contributes 30. Therefore, the login contribution value of the telephone channel is 20 + 45 + 15 = 80; the login contribution value of the SMS channel is 50 + 45 + 55 = 150; and the login contribution value of the APP channel is 30 + 10 + 30 = 70. Of course, even with the different data mentioned above, the calculation principle of the login contribution value of each reach channel is the same as the previous example, and will not be repeated here.

[0075] After calculating the login contribution value of each reach channel based on the aforementioned step S3, it can be sent to the operations side. Then, the operations personnel can allocate resources for each reach channel based on the aforementioned login contribution value. The resource allocation process is shown in step S4 below.

[0076] S4. Send the login contribution values ​​of each reach channel to the operations team so that the operations team can allocate resources to each reach channel based on the login contribution values. In this embodiment, as explained above, the higher the login contribution value, the greater the probability that a user will log in to the Chinese herbal medicine e-commerce platform after receiving a message pushed by any reach channel. Therefore, resources can be allocated in descending order of login contribution value. Of course, based on the original resource allocation of each reach channel, resources can also be adjusted according to the login contribution value, such as increasing resource investment for those with high login contribution values ​​and reducing resource investment for those with low login contribution values.

[0077] Therefore, through the channel resource allocation device for the e-commerce platform of traditional Chinese medicine decoction pieces described in detail in steps S1 to S4 above, the present invention can combine user profile data to perform multiple login attribution analyses. Based on this, the login contribution rate of different reach channels can be evaluated more reasonably, thereby achieving reasonable allocation of channel resources. As a result, channel development and maintenance can be better carried out, and it is suitable for large-scale application and promotion in the field of channel maintenance of e-commerce platforms of traditional Chinese medicine decoction pieces.

[0078] like Figure 2 As shown, the second aspect of this embodiment provides a hardware device for implementing the channel resource allocation method of the e-commerce platform for traditional Chinese medicine decoction pieces described in the first aspect of the embodiment, comprising:

[0079] The data acquisition unit is used to acquire user push records of each contact channel of the Chinese herbal medicine e-commerce platform within a historical time period, as well as platform operation records of each user of the Chinese herbal medicine e-commerce platform within the historical time period.

[0080] The data processing unit is used to generate a user access login behavior sequence log for each user based on the user push records of each access channel and the platform operation records of each user. The access login behavior sequence log for any user includes the platform operation data of the user before the first login to the Chinese herbal medicine e-commerce platform within the historical time period, as well as the push data of each access channel before the user's first login to the Chinese herbal medicine e-commerce platform within the historical time period.

[0081] The login contribution value analysis unit is used to calculate the login contribution value of each reach channel based on the login behavior sequence log of each user. The login contribution value of any reach channel is used to characterize the degree of influence of any reach channel on a user's login to the Chinese herbal medicine e-commerce platform.

[0082] The sending unit is used to send the login contribution value of each reach channel to the operations side, so that the operations staff can allocate resources to each reach channel based on the login contribution value of each reach channel.

[0083] The working process, working details and technical effects of the device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0084] like Figure 3 As shown, the third aspect of this embodiment provides another channel resource allocation device for a traditional Chinese medicine decoction pieces e-commerce platform. Taking the device as an electronic device as an example, it includes: a memory, a processor, and a transceiver connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the channel resource allocation method for the traditional Chinese medicine decoction pieces e-commerce platform as described in the first aspect of the embodiment.

[0085] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor is used to process data in the wake-up state, also known as the CPU (Central Processing Unit); the coprocessor is a low-power processor used to process data in the standby state.

[0086] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated embedded neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0087] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0088] The fourth aspect of this embodiment provides a storage medium that stores instructions containing the channel resource allocation method of the e-commerce platform for traditional Chinese medicine decoction pieces as described in the first aspect of the embodiment. That is, the storage medium stores instructions, and when the instructions are run on a computer, the channel resource allocation method of the e-commerce platform for traditional Chinese medicine decoction pieces as described in the first aspect is executed.

[0089] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0090] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0091] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the channel resource allocation method of the e-commerce platform for traditional Chinese medicine decoction pieces as described in the first aspect of this embodiment. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0092] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for allocating channel resources on an e-commerce platform for prepared Chinese medicinal herbs, characterized in that, include: Obtain user push records for each contact channel of the Chinese herbal medicine e-commerce platform within a historical time period, as well as platform operation records for each user of the Chinese herbal medicine e-commerce platform within the historical time period. Based on the user push records of each reach channel and the platform operation records of each user, a reach login behavior sequence log is generated for each user. The reach login behavior sequence log of any user includes the platform operation data of the user before the first login to the Chinese herbal medicine e-commerce platform within the historical time period, and the push data of each reach channel before the user's first login to the Chinese herbal medicine e-commerce platform within the historical time period. Based on the login behavior sequence logs of each user, the login contribution value of each contact channel is calculated. The login contribution value of any contact channel is used to characterize the degree of influence of that contact channel on the user's login to the Chinese herbal medicine e-commerce platform. Send the login contribution value of each reach channel to the operations team so that the operations staff can allocate resources to each reach channel based on the login contribution value of each reach channel; Each user's push record includes the channel name, push user ID, and push time; each user's platform operation record includes the user's purchase data, login data, and user ID. Specifically, based on user push records from various reach channels and platform operation records of each user, a sequence log of reach login behavior for each user is generated, including: For any user, based on the user ID of the user and the push user ID in each user's push record, the push record corresponding to the user is filtered out from each user's push record; The push records corresponding to any user, as well as the purchase data and login data in the platform operation records corresponding to any user, are sorted in chronological order to obtain the original login behavior sequence log of any user. Based on the login data in the platform operation record of any user, the original login behavior sequence log is filtered to obtain the login behavior sequence log of any user after data filtering and processing. Based on the login behavior sequence logs of each user, the login contribution value of each outreach channel is calculated, including: For the contact login behavior sequence log of the i-th user in each user's contact login behavior sequence log, obtain the weight value of each contact channel in the contact login behavior sequence log of the i-th user; Based on the weight values ​​and the platform operation data in the login behavior sequence log of the i-th user, the login contribution value of each reach channel to the i-th user is calculated; Increment i by 1 and re-obtain the weight values ​​of each reach channel in the reach login behavior sequence log of the i-th user until i equals n, and obtain the login contribution value of each reach channel to each user, where the initial value of i is 1 and n is the total number of users; Based on the login contribution value of each reach channel to each user, the login contribution value of each reach channel is calculated.

2. The method according to claim 1, characterized in that, Based on the login data in the platform operation records of any user, the original login behavior sequence log is subjected to data filtering processing, including: Based on the login data in the platform operation records of any user, the first login time of any user is determined; Based on the first login time, purchase data prior to the first login time is filtered out from the original login behavior sequence log of any user and used as the platform operation data of any user. Based on the initial login time and the push time of each push record in the initial login behavior sequence log, the push records in the initial login behavior sequence log whose push time is before the initial login time are determined as the push data for any user. Using the platform operation data and the push data, a sequence log of the login behavior of any user is constructed.

3. The method according to claim 1, characterized in that, Obtain the weight values ​​of each reach channel in the reach login behavior sequence log of the i-th user, including: Using the time decay algorithm and the access login behavior sequence log of the i-th user, the weight values ​​of each access channel in the access login behavior sequence log of the i-th user are calculated.

4. The method according to claim 1, characterized in that, Based on the weight values ​​and the platform operation data in the login behavior sequence log of the i-th user, the contribution value of each reach channel to the login of the i-th user is calculated, including: A contribution value calculation model is obtained, wherein the contribution value calculation model is constructed based on a time decay algorithm; The weight values ​​and platform operation data from the login behavior sequence log of the i-th user are input into the contribution value calculation model to obtain the login contribution value of each reach channel to the i-th user.

5. The method according to claim 1, characterized in that, Based on the login contribution value of each reach channel to each user, the login contribution value of each reach channel is calculated, including: For any one of the various outreach channels, sum the login contribution value of that outreach channel to each user, and use the summation result as the login contribution value of that outreach channel.

6. A channel resource allocation device for an e-commerce platform for traditional Chinese medicine decoction pieces, characterized in that, The method for allocating channel resources on an e-commerce platform for prepared Chinese medicine slices as described in any one of claims 1 to 5, wherein the apparatus comprises: The data acquisition unit is used to acquire user push records of each contact channel of the Chinese herbal medicine e-commerce platform within a historical time period, as well as platform operation records of each user of the Chinese herbal medicine e-commerce platform within the historical time period. The data processing unit is used to generate a user access login behavior sequence log based on the user push records of each access channel and the platform operation records of each user. The access login behavior sequence log of any user includes the platform operation data of the user before the first login to the Chinese herbal medicine e-commerce platform in the historical time period, and the push data of each access channel before the user first login to the Chinese herbal medicine e-commerce platform in the historical time period. The login contribution value analysis unit is used to calculate the login contribution value of each reach channel based on the login behavior sequence log of each user. The login contribution value of any reach channel is used to characterize the degree of influence of any reach channel on a user's login to the Chinese herbal medicine e-commerce platform. The sending unit is used to send the login contribution value of each reach channel to the operations side, so that the operations staff can allocate resources to each reach channel based on the login contribution value of each reach channel.

7. A channel resource allocation device for an e-commerce platform for traditional Chinese medicine decoction pieces, characterized in that, include: A memory, a processor, and a transceiver are sequentially connected in communication, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the channel resource allocation method of the e-commerce platform for traditional Chinese medicine decoction pieces as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores instructions that, when executed on a computer, perform the channel resource allocation method for the e-commerce platform of traditional Chinese medicine decoction pieces as described in any one of claims 1 to 5.

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