Store product conversion method based on live streaming

Abstract

The invention discloses a store commodity conversion method based on network live broadcast. The invention performs a shelf analysis before the commodity is put on the shelf, generates a shelf signal according to the shelf analysis result, and reminds the user to put the product on the shelf; the host can be reminded to put the product on the shelf at an appropriate time, which is convenient for targeted analysis of the product; then, the weight ranking of target commodities, attention objects and ordinary objects is determined by performing product analysis; then, the real-time number of habitual customers, potential objects and regular objects o, m and l is determined; and suitable recommended commodities are determined by performing ratio analysis and real-time number of people, so as to meet the user's needs as much as possible. The invention is simple, effective, and easy to be used.

Description

Technical Field

[0001] The present invention relates to the technical field of online live broadcast commodity conversion, and in particular to a store commodity conversion method based on online live broadcast. Background Art

[0002] The patent with publication number CN111598662A discloses a commodity loading method, server and system based on network live broadcast, wherein the method includes: obtaining a push request for the live broadcast commodity, the push request is: when the identification module at the anchor recognizes the label module on the live broadcast commodity, it is sent by the identification module, wherein the push request includes: the label ID of the label module; according to the label ID carried in the push request, based on the correspondence between the label ID and the commodity information, obtaining the commodity information corresponding to the label ID, wherein the configuration process of the correspondence is: after the label module is installed on the live broadcast commodity, it is established according to the label ID of the label module and the commodity information of the live broadcast commodity; sending the commodity information to the current live broadcast interface corresponding to the anchor, so that the commodity information is dynamically loaded in the dynamic loading area of ​​the current live broadcast interface, which solves the technical problems of cumbersome operation and poor interactivity of the commodity loading method in the existing network live broadcast shopping.

[0003] Although this patent provides a problem of loading products on the shelves, it does not address the issue of when users put products on the shelves, and when some anchors have a rich product library and a large amount of goods, how to improve the targeting of the products to better meet the needs of users in the live broadcast room, especially for live broadcasts in some special occasions, in order to achieve certain promotional activities and get as close to users as possible, it is impossible to select product varieties, and there is a lack of a reasonable method to implement this content. Based on this, a solution is now provided. Summary of the invention

[0004] The purpose of the present invention is to provide a store product conversion method based on network live broadcast;

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A store product conversion method based on online live broadcasting, the method specifically comprises the following steps:

[0007] Step 1: Before the product is put on the shelf, a shelf analysis is performed, and a shelf signal is generated based on the shelf analysis results to remind the user to put the product on the shelf;

[0008] Step 2: Conduct product analysis, specifically:

[0009] S01: First, all target object groups Bi are obtained, and preliminary personnel division is performed to obtain potential objects Qi, i=1...o, habitual customers Gi, i=1...m and regular objects Ci, i=1...l;

[0010] S02: After that, all the habitual customers Gi are obtained, and their inertia analysis is performed to obtain the check values ​​of all the target products, and the target products are sorted in descending order according to the check values;

[0011] S03: Afterwards, all potential objects Qi are obtained, all speeches of all potential objects Qi in the live broadcast room are obtained, all related words containing the product name are automatically identified, the words are marked as attention objects, the desire values ​​of all attention objects are obtained simultaneously, and the attention objects are reordered in descending order according to the desire values;

[0012] S04: After that, all the regular objects Ci are obtained, and the concerned objects of all the regular objects Ci are obtained in the same manner as in step S03, and the concerned objects are marked as ordinary objects, and the demand values ​​of the ordinary objects are obtained, and the ordinary objects are sorted in descending order according to the order of the demand values;

[0013] Step 3: Get the target products, attention objects and common objects in order; get the real-time number of habitual customers, potential objects and regular objects o, m and l; perform proportion analysis based on this. The specific method of proportion analysis is as follows:

[0014] SS1: Get the number of products on the shelves in this session. The number of products on the shelves refers to the total number of products on the shelves at the same time in this session;

[0015] SS2: After that, the target products are obtained, and the number of objects is obtained according to the formula: number of objects = number of products on the shelves * 0.5 * o / n;

[0016] Get the follow object again and get the number of followers according to the formula. The specific formula is:

[0017] Number of followers = number of products on the shelves * 0.3 * o / n;

[0018] Get the common object again and get the common number according to the formula: number of products on the shelf * 0.2 * o / n;

[0019] SS3: According to the number of objects, the number of followers and the number of common items, the corresponding items with the highest ranking and the corresponding value are obtained in sequence, and the obtained items are marked as recommended items.

[0020] Furthermore, the specific method of the listing analysis in step one is:

[0021] S1: Obtain information of all viewers in the current live broadcast room, mark them as target objects, and all target objects constitute a target object group Bi, i=1...n, indicating that there are n target objects, i.e., n viewers;

[0022] S2: Then, the number of people who have purchased goods in the current live broadcast room is obtained, and it is marked as the purchase single value Gd; the number of people who join the live broadcast room for the first time is obtained simultaneously, and it is marked as the potential single value Qd;

[0023] S3: Then, the purchase number Mi and purchase amount Ei of all target objects in the live broadcast room are obtained, i=1...n, where Mi and Ei are in a one-to-one correspondence with Bi;

[0024] S4: After that, all live broadcast data of the past two weeks are obtained, and the total number of people entering the live broadcast room for the first time and the number of people placing orders in each live broadcast data are obtained. The number of people placing orders is divided by the total number of people entering the live broadcast room for the first time, and the value obtained after multiplying by 0.75 is marked as the proposed conversion value Nz;

[0025] S5: Then use the formula to calculate the open value K. The specific calculation formula is:

[0026]

[0027] S6: When the K value exceeds X1, a shelf-putting signal is generated, and when the shelf-putting signal is generated, the user is reminded that the product can be put on the shelf; X1 is a preset value.

[0028] Furthermore, the purchase number in step S3 refers to the number of products purchased by the corresponding target object in the live broadcast room, and the purchase amount refers to the amount paid by the corresponding user for the goods.

[0029] Furthermore, the specific method of dividing personnel in step S01 is:

[0030] The target objects that enter the live broadcast room for the first time are re-marked as potential objects Qi, i=1...o; the target objects that have purchased goods in the live broadcast room are re-marked as habitual customers Gi, i=1...m, and the remaining target objects are marked as regular objects Ci, i=1...l; where o+m+l=n.

[0031] Furthermore, the specific method of inertia analysis in step S02 is:

[0032] S021: Obtain all the inertial customers Gi, and the products they have purchased in the live broadcast room, mark them as target products, and then obtain the purchase image value, purchase times and profit value of all target products. The purchase image value refers to the number of people who purchased the target product, and the purchase times refers to the total number of times the target product was purchased; the profit value refers to the profit obtained by purchasing the product, and the profit refers to the sales volume minus the cost, or the profit that the corresponding anchor can obtain by purchasing the product;

[0033] S022: Calculate the check value of all target products according to the formula. The specific calculation method of the check value is:

[0034] Check value = 0.28*purchase value + 0.31*purchase times + 0.41*interest value;

[0035] S023: Then, the check values ​​of all target products are obtained, and the target products are sorted in descending order according to the check values.

[0036] Furthermore, the specific method of obtaining the desired values ​​of all the attention objects in step S03 is: obtaining all the attention objects, obtaining the number of occurrences and the frequency of all the attention objects, the number of occurrences refers to the number of times the keyword of the attention object appears in the live broadcast room, and the frequency refers to the number of viewers who type the word of the attention object in the live broadcast room;

[0037] Then, the shopping records of all viewers are obtained, and the past purchase values ​​of all follow objects are obtained based on the shopping records. The past purchase value is automatically increased by 1 if any viewer who typed the word follow object has purchased the corresponding product of the follow object once or more, otherwise the past purchase value is increased by 0.5;

[0038] The desire value is calculated based on the number of appearances, frequency and purchase value of the object of interest. The calculation formula for the desire value is:

[0039] Desire value = 0.18*number of occurrences + 0.66*frequency + 0.16*purchase value.

[0040] Furthermore, the specific method for obtaining the demand value in step S04 is: synchronously obtain the number of occurrences and the number of frequency directions corresponding to the common object, and calculate the demand value according to the number of occurrences and the number of frequency directions. The specific calculation formula is:

[0041] Demand value = 0.35*number of occurrences + 0.65*number of frequency.

[0042] Furthermore, after completing step 3, the following steps are required:

[0043] Step 4: Put the corresponding recommended products on the shelves, conduct the corresponding shelf analysis of step 3 every T1 time interval, and update and rotate the corresponding recommended products.

[0044] Beneficial effects of the present invention:

[0045] The present invention performs a shelf analysis before the product is put on the shelf, generates a shelf signal according to the shelf analysis result, and reminds the user to put the product on the shelf; it can remind the anchor to put the product on the shelf at an appropriate time, which is convenient for the targeted analysis of the product; and then determines the weight ranking of the target product, the object of attention, and the ordinary object by performing product analysis;

[0046] Then the real-time number of habitual customers, potential objects and regular objects, o, m and l, are obtained; by means of proportion analysis and real-time number of people, suitable recommended products are determined, so that user needs can be met as much as possible. The present invention is simple, effective and easy to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0048] Figure 1 This is a flow chart of a store product conversion method based on live webcasting of the present invention;

[0049] Figure 2 Flowchart for the on-shelf analysis. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] like Figure 1-2 As shown, the present invention is a store product conversion method based on network live broadcast, and the method specifically includes the following steps:

[0052] Step 1: Before the product is put on the shelf, conduct a shelf analysis. The specific methods of shelf analysis are as follows:

[0053] S1: Obtain information of all viewers in the current live broadcast room, mark them as target objects, and all target objects constitute a target object group Bi, i=1...n, indicating that there are n target objects, i.e., n viewers;

[0054] S2: Then, the number of people who have purchased goods in the current live broadcast room is obtained, and it is marked as the purchase single value Gd; the number of people who join the live broadcast room for the first time is obtained simultaneously, and it is marked as the potential single value Qd;

[0055] S3: Then, the purchase number Mi and purchase amount Ei of all target objects in the live broadcast room are obtained, i=1...n, where Mi and Ei are in a one-to-one correspondence with Bi;

[0056] The number of purchases refers to the number of products purchased by the corresponding subject in the live broadcast room, and the purchase amount refers to the amount paid by the corresponding user for the goods;

[0057] S4: After that, all live broadcast data of the past two weeks are obtained, and the total number of people entering the live broadcast room for the first time and the number of people placing orders in each live broadcast data are obtained. The number of people placing orders is divided by the total number of people entering the live broadcast room for the first time, and the value obtained after multiplying by 0.75 is marked as the proposed conversion value Nz;

[0058] S5: Then use the formula to calculate the open value K. The specific calculation formula is:

[0059]

[0060] In the formula, 0.28, 0.35, 0.37, 0.45 and 0.55 are preset weights used to reflect the influence of different factors;

[0061] S6: When the K value exceeds X1, a listing signal is generated, and when the listing signal is generated, the user is reminded that the product can be listed. If the user has given permission in advance, the corresponding product can be listed by the system. Here, X1 is a preset weight. The specific value can be calculated by calculating the specific K value at the time of listing the product in each live broadcast with goods in the past two weeks, and then calculating the sales generated by each live broadcast with goods in the past two weeks. The user can determine a minimum K value based on the sales, which is the value of X1 here; of course, no specific details are given here;

[0062] Step 2: Conduct product analysis, specifically:

[0063] S01: First, obtain all target object groups Bi and perform preliminary personnel division, specifically:

[0064] The target object that enters the live broadcast room for the first time is re-marked as a potential object Qi, i=1...o; the target object that has purchased goods in the live broadcast room is re-marked as a habitual customer Gi, i=1...m, and the remaining target objects are marked as regular objects Ci, i=1...l; where o+m+l=n;

[0065] S02: After that, all the inertial customers Gi are obtained and inertial analysis is performed on them. The specific method of inertial analysis is as follows:

[0066] S021: Obtain all the inertial customers Gi, and the products they have purchased in the live broadcast room, mark them as target products, and then obtain the purchase image value, purchase times and profit value of all target products. The purchase image value refers to the number of people who purchased the target product, and the purchase times refers to the total number of times the target product was purchased; the profit value refers to the profit obtained by purchasing the product, and the profit refers to the sales volume minus the cost, or the profit that the corresponding anchor can obtain by purchasing the product;

[0067] S022: Calculate the check value of all target products according to the formula. The specific calculation method of the check value is:

[0068] Check value = 0.28*purchase value + 0.31*purchase times + 0.41*interest value;

[0069] In the formula, 0.28, 0.31 and 0.41 are all preset weights;

[0070] S023: Afterwards, the check values ​​of all target products are obtained, and the target products are sorted in descending order according to the check values;

[0071] S03: Afterwards, all potential objects Qi are obtained, all speeches of all potential objects Qi in the live broadcast room are obtained, all relevant words containing the product name are automatically identified, and the words are marked as objects of interest;

[0072] Then, all the attention objects are obtained, and the occurrence times and frequency times of all the attention objects are obtained. The occurrence times refer to the number of times the keyword "attention object" appears in the live broadcast room, and the frequency times refer to the number of viewers who type the word "attention object" in the live broadcast room.

[0073] Then, the shopping records of all viewers are obtained, and the past purchase values ​​of all follow objects are obtained based on the shopping records. The past purchase value is automatically increased by 1 if any viewer who typed the word follow object has purchased the corresponding product of the follow object once or more, otherwise the past purchase value is increased by 0.5;

[0074] The desire value is calculated based on the number of appearances, frequency and purchase value of the object of interest. The calculation formula for the desire value is:

[0075] Desire value = 0.18*occurrence times + 0.66*frequency times + 0.16*purchase value;

[0076] Reorder the objects of interest in descending order of desire value;

[0077] S04: After that, all the regular objects Ci are obtained. Similarly, the concerned objects of all the regular objects Ci are obtained in the same manner as in step S03. The concerned objects are marked as ordinary objects. The number of occurrences and the number of frequency directions corresponding to the ordinary objects are obtained simultaneously. The demand value is calculated according to the number of occurrences and the number of frequency directions. The specific calculation formula is:

[0078] Demand value = 0.35*occurrence times + 0.65*frequency times;

[0079] Sort common objects in descending order according to their demand values;

[0080] Step 3: Get the target products, attention objects and common objects in order; get the real-time number of habitual customers, potential objects and regular objects o, m and l; perform proportion analysis based on this. The specific method of proportion analysis is as follows:

[0081] SS1: Get the number of products on the shelves in this session. The number of products on the shelves refers to the total number of products on the shelves at the same time in this session;

[0082] SS2: After that, the target products are obtained, and the number of objects is obtained according to the formula: number of objects = number of products on the shelves * 0.5 * o / n;

[0083] Get the follow object again and get the number of followers according to the formula. The specific formula is:

[0084] Number of followers = number of products on the shelves * 0.3 * o / n;

[0085] Get the common object again and get the common number according to the formula: number of products on the shelf * 0.2 * o / n;

[0086] SS3: According to the number of objects, the number of followers and the number of common items, the corresponding items with the highest ranking and the corresponding values ​​are obtained in sequence, and the obtained items are marked as recommended items.

[0087] Step 4: Put the corresponding recommended products on the shelves, conduct the corresponding shelf analysis of step 3 every T1 time interval, and update and rotate the corresponding recommended products.

[0088] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A store product conversion method based on live webcasting, characterized in that: The method specifically comprises the following steps: Step 1: Before the product is put on the shelf, a shelf analysis is performed, and a shelf signal is generated based on the shelf analysis results to remind the user to put the product on the shelf; Step 2: Conduct product analysis, specifically: S01: First, all target object groups Bi are obtained, and preliminary personnel division is performed to obtain potential objects Qi, i=1...o, habitual customers Gi, i=1...m and regular objects Ci, i=1...l; S02: Afterwards, all the habitual customers Gi are obtained, and their inertia analysis is performed to obtain the check values ​​of all the target products, and the target products are sorted in descending order according to the check values; S03: Afterwards, all potential objects Qi are obtained, all speeches of all potential objects Qi in the live broadcast room are obtained, all related words containing the product name are automatically identified, the words are marked as objects of interest, the desire values ​​of all objects of interest are obtained simultaneously, and the objects of interest are reordered in descending order of the desire values; S04: After that, all the regular objects Ci are obtained, and the concerned objects of all the regular objects Ci are obtained in the same manner as in step S03, and the concerned objects are marked as ordinary objects, and the demand values ​​of the ordinary objects are obtained, and the ordinary objects are sorted in descending order according to the order of the demand values; Step 3: Get the target products, attention objects and common objects in order; get the real-time number of habitual customers, potential objects and regular objects o, m and l; perform proportion analysis based on this. The specific method of proportion analysis is as follows: SS1: Get the number of products on the shelves in this session. The number of products on the shelves refers to the total number of products on the shelves at the same time in this session; SS2: After that, the target products are obtained, and the number of objects is obtained according to the formula: number of objects = number of products on the shelves * 0.5 * o / n; Get the follow object again and get the number of followers according to the formula. The specific formula is: Number of followers = number of products on the shelves * 0.3 * o / n; Get the common object again and get the common number according to the formula: number of products on the shelf * 0.2 * o / n; SS3: According to the number of objects, the number of followers and the number of common items, the corresponding items with the highest ranking and the corresponding values ​​are obtained in sequence, and the obtained items are marked as recommended items. The specific method of the listing analysis in step 1 is as follows: S1: Obtain information of all viewers in the current live broadcast room, mark them as target objects, and all target objects constitute a target object group Bi, i=1...n, indicating that there are n target objects, i.e., n viewers; S2: Then, the number of people who have purchased goods in the current live broadcast room is obtained, and it is marked as the purchase single value Gd; the number of people who join the live broadcast room for the first time is obtained simultaneously, and it is marked as the potential single value Qd; S3: Then, the purchase number Mi and purchase amount Ei of all target objects in the live broadcast room are obtained, i=1...n, where Mi and Ei are in a one-to-one correspondence with Bi; S4: After that, all live broadcast data of the past two weeks are obtained, and the total number of people entering the live broadcast room for the first time and the number of people placing orders in each live broadcast data are obtained. The number of people placing orders is divided by the total number of people entering the live broadcast room for the first time, and the value obtained after multiplying by 0.75 is marked as the proposed conversion value Nz; S5: Then use the formula to calculate the open value K. The specific calculation formula is: S6: When the K value exceeds X1, a shelf-putting signal is generated, and when the shelf-putting signal is generated, the user is reminded that the product can be put on the shelf; X1 is a preset value; The specific method of inertia analysis in step S02 is: S021: Obtain all the inertial customers Gi and the products they have purchased in the live broadcast room, mark them as target products, and then obtain the purchase image value, purchase times and profit value of all target products. The purchase image value refers to the number of people who purchased the target product, and the purchase times refers to the total number of times the target product was purchased; the profit value refers to the profit obtained by purchasing the product, and the profit refers to the sales volume minus the cost, or the profit that the corresponding anchor can obtain by purchasing the product; S022: Calculate the check value of all target products according to the formula. The specific calculation method of the check value is: Check value = 0.28*purchase value + 0.31*purchase times + 0.41*interest value; S023: Then, the check values ​​of all target products are obtained, and the target products are sorted in descending order according to the check values.

2. The method for converting store products based on live webcasting according to claim 1, characterized in that: The purchase number in step S3 refers to the number of products purchased by the corresponding target object in the live broadcast room, and the purchase amount refers to the amount paid by the corresponding user for the goods.

3. The method for converting store products based on live webcasting according to claim 1, characterized in that: The specific method of dividing personnel in step S01 is: The target objects that enter the live broadcast room for the first time are re-marked as potential objects Qi, i=1...o; the target objects that have purchased goods in the live broadcast room are re-marked as habitual customers Gi, i=1...m, and the remaining target objects are marked as regular objects Ci, i=1...l; where o+m+l=n.

4. The method for converting store products based on live webcasting according to claim 1, characterized in that: The specific method of obtaining the desired values ​​of all the attention objects in step S03 is: obtaining all the attention objects, obtaining the number of occurrences and the frequency of all the attention objects, the number of occurrences refers to the number of times the keyword of the attention object appears in the live broadcast room, and the frequency refers to the number of viewers who type the word of the attention object in the live broadcast room; Then, the shopping records of all viewers are obtained, and the past purchase values ​​of all follow objects are obtained based on the shopping records. The past purchase value is automatically increased by 1 if any viewer who typed the word follow object has purchased the corresponding product of the follow object once or more, otherwise the past purchase value is increased by 0.5; The desire value is calculated based on the number of appearances, frequency and purchase value of the object of interest. The calculation formula for the desire value is: Desire value = 0.18*number of occurrences + 0.66*frequency + 0.16*purchase value.

5. The method for converting store products based on live webcasting according to claim 1, characterized in that: The specific method of obtaining the demand value in step S04 is: synchronously obtain the number of occurrences and the number of frequency directions corresponding to the common object, and calculate the demand value according to the number of occurrences and the number of frequency directions. The specific calculation formula is: Demand value = 0.35*number of occurrences + 0.65*number of frequency.

6. The method for converting store products based on live webcasting according to claim 1, characterized in that: After completing step 3, you need to perform the following steps: Step 4: Put the corresponding recommended products on the shelves, conduct the corresponding shelf analysis of step 3 every T1 time interval, and update and rotate the corresponding recommended products.

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