A method and device for analyzing the correlation between product webpage capacity and customer perception

Abstract

The present invention discloses a method for analyzing the association between product webpage capacity and customer perception, the method comprising: obtaining online behavior data of an e-commerce platform from a DPI system; extracting e-commerce platform webpage data and customer interaction perception data from the online behavior data; obtaining the product page capacity within a set period according to the product page data of each product in the e-commerce platform webpage data; obtaining the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data; obtaining the customer perception of the product according to the capacity of each product page within the set period and the attention frequency of each product within the set period; and setting the product page capacity threshold according to the customer perception of the product. The present invention also discloses a device for analyzing the association between product webpage capacity and customer perception. The present invention can realize the reasonable setting of product webpage capacity according to customer perception, thereby improving the success rate of product sales.

Description

Technical Field

[0001] The present invention belongs to the field of computer software and relates to analysis and management technology of e-commerce commodity web pages. Background Art

[0002] Shopping on e-commerce platforms has become one of people's daily life activities. People browse product pages and decide whether to buy products. In order to increase sales, merchants try to display all the features of products on product pages. In order to display products, merchants need to load many product pictures on product pages. Too many product pictures make the product page larger, which makes the product page display too long in people's browsers, which will make people give up buying products. Therefore, understanding the relationship between product page size and people's purchasing behavior has become the key to designing a reasonable product page size.

[0003] Although existing technical solutions focus on customer behavior data, most of them predict customer interest in products based on customer behavior data and then push relevant advertisements. There is no technical solution that correlates and analyzes customer behavior data with product page size data.

[0004] Therefore, a technology that can assist e-commerce website developers in developing reasonable product pages based on customer perception and improve the success rate of e-commerce product sales is in urgent need of emergence. Summary of the invention

[0005] The purpose of the present invention is to provide a method for determining the capacity of an e-commerce product page according to the customer's Internet behavior, so as to display products with a reasonable web page capacity and improve the success rate of product sales.

[0006] In order to achieve the above object, the present invention provides a method for analyzing the correlation between product page capacity and customer perception, the method comprising:

[0007] Obtain online behavior data of e-commerce platforms from the DPI system;

[0008] Extracting e-commerce platform web page data and customer interaction perception data from the online behavior data;

[0009] Acquire the product page capacity within a set period according to the product page data in the e-commerce platform webpage data;

[0010] Obtaining the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data;

[0011] Acquire customer perception of the product according to the capacity of each product page in a set period and the attention frequency of each product in the set period;

[0012] A product page capacity threshold is set based on customer perception of the product.

[0013] Specific:

[0014] The e-commerce platform web page data includes the URL of each product page, the URL embedded in the product page, and the size of the product page URL and the size of the product page embedded URL;

[0015] The customer interaction perception data includes customer basic information, time information, URL of the product page browsed by the customer, and URL of adding to the shopping cart.

[0016] More specific:

[0017] Within a set period, according to the size of the product page URL in the e-commerce platform webpage data and the total value of the size of the embedded URL of the product page, obtain the average of the total value of the product page and its embedded URL;

[0018] The average of the total values ​​is the capacity of the product page within the period, denoted by A.

[0019] Further:

[0020] The click frequency of the customer on the product webpage URL and the click frequency of the add to shopping cart URL within a set period is obtained according to the customer interaction perception data.

[0021] Further:

[0022] Obtain the sum of click frequencies of the add-to-cart URLs of products whose total value of the size of the product page URL and the size of the embedded URLs on the product page is less than A;

[0023] Obtain the sum of the click frequencies of the product page URLs of the products whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A;

[0024] The customer perception of the product is the ratio of the sum of click frequencies of the add-to-shopping-cart URL of the product to the sum of click frequencies of the product page URL.

[0025] The present invention also provides a device for analyzing the association between commodity webpage capacity and customer perception, characterized in that the device comprises:

[0026] An online behavior data acquisition unit, used to acquire online behavior data of the e-commerce platform from the DPI system;

[0027] A data extraction unit, used to extract e-commerce platform web page data and customer interaction perception data from the online behavior data acquired by the online behavior data acquisition unit;

[0028] A product webpage capacity acquisition unit, used to acquire the product page capacity within a set period according to the product page data in the e-commerce platform webpage data;

[0029] A customer perception information acquisition unit, configured to acquire the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data;

[0030] The product page capacity threshold setting unit is used to obtain the customer perception of the product according to the size of each product page in a set period and the attention frequency of each product in a set period; and set the product page capacity threshold according to the customer perception of the product.

[0031] The data extraction unit further comprises:

[0032] An e-commerce platform web page data extraction module, used to extract the URLs of each product page, the URL embedded in the product page, and the size of the product page URL and the size of the product page embedded URL contained in the e-commerce platform web page data according to the online behavior data of the e-commerce platform;

[0033] The customer interaction perception data extraction module is used to extract the customer basic information, time information, customer browsing product page URL, and shopping cart URL contained in the customer interaction perception data according to the online behavior data of the e-commerce platform.

[0034] Specifically, the method for the product webpage capacity acquisition unit to acquire the product page capacity is as follows:

[0035] Within a set period, according to the size of the product page URL in the e-commerce platform webpage data and the total value of the size of the embedded URL of the product page, obtain the average of the total value of the product page and its embedded URL;

[0036] The average of the total values ​​is the capacity of the product page within the period, denoted by A.

[0037] Furthermore, the method for the customer perception information acquisition unit to acquire the attention frequency of each product within a set period is specifically as follows:

[0038] The click frequency of the customer on the product webpage URL and the click frequency of the add to shopping cart URL within a set period is obtained according to the customer interaction perception data.

[0039] Specifically, the commodity page capacity threshold setting unit further includes:

[0040] The first calculation module is used to obtain the sum of click frequencies of the add to shopping cart URL of the product whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A; obtain the sum of click frequencies of the product page URL of the product whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A;

[0041] The second calculation module is used to obtain the ratio of the sum of click frequencies of the add-to-shopping-cart URL of the product to the sum of click frequencies of the product page URL, and use the ratio as the customer perception of the product;

[0042] The threshold setting module is used to set the customer perception of the product as the product page capacity threshold.

[0043] The present invention discloses a method and device for analyzing the association between commodity webpage capacity and customer perception. The method measures customer perception by using the rate at which customers click an "add to shopping cart" button, analyzes the relationship between the size of e-commerce commodity webpage and customer perception, determines the optimal e-commerce commodity webpage size, and improves the sales success rate of e-commerce commodities. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic diagram of a method for analyzing the correlation between product webpage capacity and customer perception provided in the first embodiment of the present application;

[0046] Figure 2 A flow chart of the method provided in Example 2 of the present application;

[0047] Figure 3 A schematic diagram of the structure of a device for analyzing the association between product webpage capacity and customer perception provided in the fourth embodiment of the present application;

[0048] Figure 4 This is a structural diagram provided for Example 5 of the present application. DETAILED DESCRIPTION

[0049] 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.

[0050] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0051] like Figure 1 As shown, it is a flow chart of a method for analyzing the association between product page capacity and customer perception. This embodiment includes the following steps:

[0052] Step S01: Obtain online behavior data of the e-commerce platform from the DPI system.

[0053] DPI is a deep detection technology based on data packets, which can perform deep detection on different network application layer loads to obtain Internet behavior data, such as jump source, button address, dwell time, page size, user ID and other information.

[0054] Step S02: extracting e-commerce platform web page data and customer interaction perception data from the online behavior data.

[0055] The e-commerce platform web page data includes the URL of each product page, the URL embedded in the product page, and the size of the product page URL and the size of the product page embedded URL.

[0056] It should be noted that the URLs of product pages of different e-commerce companies may be different, but they all contain information such as the e-commerce address and product logo. The embedded URLs of product pages include the URLs of embedded web pages and embedded images; and the sizes of these URLs.

[0057] The customer interaction perception data includes customer basic information, time information, URL of the product page browsed by the customer, and URL of adding to the shopping cart.

[0058] Basic customer information refers to information that can identify the customer, such as customer ID and customer type, and usually includes mobile phone number, user name, etc.

[0059] Since the URLs of e-commerce platforms are different, the product identifiers and the e-commerce platforms they belong to can be extracted from these URLs.

[0060] Step S03: acquiring the product page capacity within a set period according to the product page data in the e-commerce platform webpage data.

[0061] Product page capacity usually refers to the total size of the product webpage and its embedded URL. In order to display products, merchants need to load many product images on the page. Too many product images will make the product page too large, increase the customer's browsing time, and make customers give up buying products. Therefore, there is an inevitable connection between product page capacity and customer purchasing behavior.

[0062] Step S04: Obtain the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data.

[0063] The frequency of attention to each product is obtained by the number of clicks on the product page URL and the shopping cart URL by customers within a certain period.

[0064] The cycle can be set according to actual conditions, usually one week, one month, or one year. The cycle can also be modified according to actual conditions and set in hours or minutes.

[0065] Step S05: Acquire the customer perception of the product according to the capacity of each product page within a set period and the attention frequency of each product within the set period.

[0066] Get the relationship between product page capacity and product attention frequency, and then get customer perception of the product.

[0067] Step S06: setting a product page capacity threshold according to the customer perception of the product.

[0068] The present invention measures customer perception by using the rate at which customers click on an "add to cart" button, analyzes the relationship between the size of an e-commerce webpage commodity and customer perception, determines the optimal e-commerce commodity webpage size, and improves the sales success rate of e-commerce commodities.

[0069] In order to better illustrate the present invention, Example 2 is given to explain the working principle of each step in detail. Figure 2 shown.

[0070] Step S201: Obtain online behavior data of the e-commerce platform from the DPI system.

[0071] Step S202: extracting e-commerce platform web page data and customer interaction perception data from the online behavior data.

[0072] The e-commerce platform web page data includes the URL of each product page, the URL embedded in the product page, and the size of the product page URL and the size of the product page embedded URL;

[0073] The customer interaction perception data includes customer basic information, time information, URL of the product page browsed by the customer, and URL of adding to the shopping cart, as shown in Table 1, Table 2, Table 3, and Table 4.

[0074] Table 1

[0075] Product page URL Embedded URL

[0076] Table 2

[0077] URL Size (kb)

[0078] Table 3

[0079] Phone number time Product page URL

[0080] Table 4

[0081] Phone number time "Add to Cart" button URL

[0082] The URLs of product pages of different e-commerce companies may be different.

[0083] The URL of the product page of merchant AA is https: / / item.AA.XXX / YYYYY.html, where XXX and YYYYY can be any numbers and letters, and YYYYY represents the product ID.

[0084] The URL of the product page of merchant BB is http: / / product.BB.com / YYYYY.html, where YYYYY can be any number or letter, and YYYYY represents the product identifier. The present invention does not involve analyzing the product URL features of each e-commerce website.

[0085] The URL embedded in the product web page includes the URL of the embedded web page and the URL of the image.

[0086] The URL of the Add to Cart button may be different for different e-commerce companies.

[0087] Merchant AA is:

[0088] https: / / cart.AA.com / addToCart.html? pid=YYYY&……, YYYYY can be any number or letter, and YYYYY represents the product ID.

[0089] Merchant BB is:

[0090] http: / / shopping.BB.com / shoppingcart / cart_append_new?product_ids={"product":[{"productId":YYYY,……, YYYYY can be any number or letter, YYYYY represents the product ID.

[0091] Each merchant URL feature can be identified according to actual conditions. The present invention does not involve analyzing the shopping cart button URL features of each e-commerce website.

[0092] Step S203: within a set period, according to the size of the product page URL in the e-commerce platform webpage data and the total size of the product page embedded URL, obtain the average of the total value of the product page and its embedded URL.

[0093] The average of the total values ​​is the capacity of the product page within the period, denoted by A.

[0094] Extract the product ID and the e-commerce platform it belongs to from the product webpage URL, and periodically (for example, every month) calculate the total size of each product page and its embedded URL. Generally, a product webpage URL will be clicked multiple times, so the average total size of the product webpage and its embedded URL can be calculated, as shown in Table 5:

[0095] Table 5. Average total size of web pages

[0096] E-commerce platform Product page URL Product identification Statistics start time Statistics end time Average page size (kb)

[0097] Step S204: obtaining the customer's click frequency on the product webpage URL and the click frequency on the add-to-shopping-cart URL within a set period according to the customer interaction perception data.

[0098] The product ID and the e-commerce platform to which it belongs are extracted from the product webpage URL and the "Add to Cart" button URL; the number of clicks on the "Add to Cart" button URL and the product webpage URL of each product are counted periodically (for example, every month), as shown in Table 6.

[0099] Table 6 Obtaining attention frequency

[0100]

[0101] Step S205: Obtain the sum of click frequencies of the add-to-cart URLs of the products whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A.

[0102] The sum of the click frequencies of the Add to Cart URL for products whose total value is less than A is: SUM (the number of clicks on the "Add to Cart" button URL of the web page less than or equal to A), where SUM means accumulation.

[0103] Step S206: Obtain the sum of click frequencies of the product page URLs of the products whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A.

[0104] The sum of the product page URL click frequencies of products whose total size is less than A is: SUM (the number of product page URL clicks of web pages less than or equal to A), where SUM means accumulation.

[0105] Step S207: Determine the customer perception of the product as the ratio of the sum of click frequencies of the add-to-shopping-cart URL of the product to the sum of click frequencies of the product page URL.

[0106] The customer perception is SUM (the number of clicks on the URL of the "Add to Cart" button of the web page that is less than or equal to A) / SUM (the number of clicks on the URL of the product web page that is less than or equal to A), where SUM means accumulation.

[0107] Step S208: setting a product page capacity threshold according to the customer perception of the product.

[0108] This technical solution makes it easy to obtain customer Internet behavior data from the DPI system, and then measure customer perception by the customer click rate of the Add to Cart button, and then analyze the relationship between the product webpage size and customer perception, and then determine the optimal e-commerce product webpage capacity. This avoids the waste of webpage capacity and improves the success rate of product sales.

[0109] In order to better illustrate the method of the present invention, embodiment 3 of the present invention is given in combination with actual conditions.

[0110] The DPI system collects customer online behavior data on e-commerce platforms, as shown in Tables 3-1 to 3-4:

[0111] Table 3-1 Collected Internet behavior data

[0112]

[0113]

[0114] Table 3-2 Collected Internet Behavior Data

[0115]

[0116] Table 3-3 Collected Internet behavior data

[0117] Phone number time Product page URL 139aaaabbbb 20190324 14:41:00 https: / / item.AA.com / 34280269098.html …… …… ……

[0118] Table 3-4 Collected Internet behavior data

[0119]

[0120] Extract the product ID and the e-commerce platform it belongs to from the product webpage URL, and calculate the total size of each product page and its embedded URL at the end of each month. Generally, a product webpage URL will be clicked multiple times, so the average total size of the product webpage and its embedded URL can be calculated. As shown in Table 3-5:

[0121] Table 3-5 Average total size of web pages

[0122]

[0123] Extract the product ID and the e-commerce platform it belongs to from the product webpage URL and the "Add to Cart" button URL; count the number of clicks on the "Add to Cart" button URL and the product webpage URL for each product at the end of each month. See Table 3-6:

[0124] Table 3-6 Customer Perception Frequency

[0125]

[0126] The correlation analysis of "e-commerce platform", "product identification", "statistical start time" and "statistical end time" yields the data in Table 3-7:

[0127] Table 3-7 Correlation between product page information and customer perception

[0128]

[0129]

[0130] Sort the latest statistics in Table 3-7 by the value of "Average Web Page Size (kb)", and count the click rate of the "Add to Cart" button for each "Average Web Page Size (kb)" (hereinafter referred to as A):

[0131] Click rate of "Add to Cart" button = SUM (number of clicks on the URL of the "Add to Cart" button of web page less than or equal to A) / SUM (number of clicks on the URL of the product web page of web page less than or equal to A).

[0132] In the above formula, SUM means accumulation.

[0133] In Table 3-7: The 1kb web page is the smallest web page in Table 7, the number of clicks on the product web page URL is 150, and the number of clicks on the "Add to Cart" button URL is 145, then the click rate of the "Add to Cart" button of the 1kb web page = 145 / 150 = 96.7%.

[0134] The 1.5kb web page is the third web page in Table 3-7. The product web page URL has 200 clicks and the "Add to Cart" button URL has 190 clicks. It can be calculated that the click rate of the "Add to Cart" button for the 1.5kb web page is 95.7%.

[0135] And so on...

[0136] The rate of clicking the "Add to Cart" button is customer perception.

[0137] Table 3-8 Customer Perception List

[0138] Statistics start time Statistics end time Average page size (kb) Click "Add to Cart" button rate 20190301 20190331 1 96.7％ 20190301 20190331 1.5 95.7％ …… …… …… ……

[0139] According to the data in Table 3-8, e-commerce website developers can obtain the upper limit of the product web page size based on the required click rate of the "Add to Cart" button.

[0140] The present invention also discloses a device for analyzing the association between product webpage capacity and customer perception. Embodiment 4 of the present invention is given. Figure 3 As shown, it is used to illustrate the structural features of the device.

[0141] The device includes:

[0142] The online behavior data acquisition unit 1 is used to acquire the online behavior data of the e-commerce platform from the DPI system.

[0143] The data extraction unit 2 is used to extract the e-commerce platform web page data and the customer interaction perception data from the online behavior data acquired by the online behavior data acquisition unit.

[0144] The product webpage capacity acquisition unit 3 is used to acquire the product page capacity within a set period according to the product page data in the e-commerce platform webpage data.

[0145] The customer perception information acquisition unit 4 is used to acquire the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data.

[0146] The product page capacity threshold setting unit 5 is used to obtain the customer perception of the product according to the size of each product page in a set period and the attention frequency of each product in a set period; and set the product page capacity threshold according to the customer perception of the product.

[0147] This paper proposes a device for analyzing the correlation between product webpage capacity and customer perception. This device uses the customer click rate of the "add to shopping cart" button to measure customer perception, analyzes the relationship between the size of the e-commerce product webpage and customer perception, and determines the optimal e-commerce product webpage size.

[0148] In order to better illustrate the structure and working mode of each part of the device, the fifth embodiment of the present invention is given. Figure 4 shown.

[0149] Specifically, the sample acquisition unit 1 further includes:

[0150] The online behavior data acquisition unit 1 is used to acquire the online behavior data of the e-commerce platform from the DPI system.

[0151] The data extraction unit 2 further comprises:

[0152] The e-commerce platform web page data extraction module 21 is used to extract the URLs of each product page, the URL embedded in the product page, and the size of the product page URL and the size of the product page embedded URL contained in the e-commerce platform web page data according to the online behavior data of the e-commerce platform.

[0153] The customer interaction perception data extraction module 22 is used to extract the customer basic information, time information, customer browsing product page URL, and shopping cart URL contained in the customer interaction perception data according to the online behavior data of the e-commerce platform.

[0154] The product webpage capacity acquisition unit 3 is used to acquire the product page capacity within a set period according to the product page data in the e-commerce platform webpage data.

[0155] The product webpage capacity acquisition unit acquires the average of the total value of the product page and its embedded URL according to the size of the product page URL and the total value of the size of the embedded URL in the product page in the e-commerce platform webpage data within a set period.

[0156] The average of the total values ​​is the capacity of the product page within the period, denoted by A.

[0157] The customer perception information acquisition unit 4 is used to acquire the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data.

[0158] The click frequency of the customer on the product webpage URL and the click frequency of the add to shopping cart URL within a set period is obtained according to the customer interaction perception data.

[0159] The commodity page capacity threshold setting unit 5 further comprises:

[0160] The first calculation module 51 is used to obtain the sum of the click frequencies of the add to shopping cart URL of the product whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A; obtain the sum of the click frequencies of the product page URL of the product whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A.

[0161] The second calculation module 52 is used to obtain the ratio of the sum of click frequencies of the add-to-shopping-cart URL of the product to the sum of click frequencies of the product page URL, and use the ratio as the customer perception of the product.

[0162] The threshold setting module 53 is used to set the customer perception of the product as the product page capacity threshold.

[0163] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the corresponding processes in the above-described device embodiments can refer to the specific working processes of the aforementioned methods, which will not be repeated here.

[0164] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0165] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly with hardware, a software module executed by a processor, or a combination of the two. The software module can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the technical field. The terms "first", "second", "third", "fourth", etc. (if present) in the specification and claims and the above-mentioned drawings are used to distinguish similar parts, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described here can be implemented in a sequence other than that illustrated here.

[0166] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing the correlation between product webpage capacity and customer perception, characterized in that: The method comprises: Obtain online behavior data of e-commerce platforms from the DPI system; Extracting e-commerce platform web page data and customer interaction perception data from the online behavior data; Acquire the product page capacity within a set period according to the product page data in the e-commerce platform webpage data; Obtaining the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data; Acquire customer perception of the product according to the capacity of each product page in a set period and the attention frequency of each product in the set period; A product page capacity threshold is set based on customer perception of the product.

2. The method according to claim 1, characterized in that: The e-commerce platform web page data includes the URL of each product page, the URL embedded in the product page, and the size of the product page URL and the size of the product page embedded URL; The customer interaction perception data includes customer basic information, time information, URL of the product page browsed by the customer, and URL of adding to the shopping cart.

3. The method according to claim 2, characterized in that: The method for obtaining the capacity of the product page within a set period is specifically as follows: Within a set period, according to the size of the product page URL in the e-commerce platform webpage data and the total value of the size of the embedded URL of the product page, obtain the average of the total value of the product page and its embedded URL; The average of the total values ​​is the capacity of the product page within the period, denoted by A.

4. The method according to claim 3, characterized in that The method for obtaining the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data is specifically: The click frequency of the customer on the product webpage URL and the click frequency of the add to shopping cart URL within a set period is obtained according to the customer interaction perception data.

5. The method according to claim 4, characterized in that The method for obtaining the customer perception of the product according to the capacity of each product page in a set period and the attention frequency of each product in a set period is specifically: Obtain the sum of click frequencies of the add-to-cart URLs of products whose total value of the size of the product page URL and the size of the embedded URLs on the product page is less than A; Obtain the sum of the click frequencies of the product page URLs of the products whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A; The customer perception of the product is the ratio of the sum of click frequencies of the add-to-shopping-cart URL of the product to the sum of click frequencies of the product page URL.

6. A device for analyzing the correlation between product webpage capacity and customer perception, characterized in that: The device comprises: An online behavior data acquisition unit, used to acquire online behavior data of the e-commerce platform from the DPI system; A data extraction unit, used to extract e-commerce platform web page data and customer interaction perception data from the online behavior data acquired by the online behavior data acquisition unit; A product webpage capacity acquisition unit, configured to acquire the product page capacity within a set period according to the product page data in the e-commerce platform webpage data; A customer perception information acquisition unit, configured to acquire the attention frequency of each product within a set period according to the product page data combined with the customer interaction perception data; The product page capacity threshold setting unit is used to obtain the customer perception of the product according to the size of each product page in a set period and the attention frequency of each product in a set period; and set the product page capacity threshold according to the customer perception of the product.

7. The device according to claim 6, characterized in that The data extraction unit further comprises: An e-commerce platform web page data extraction module, used to extract the URLs of each product page, the URL embedded in the product page, and the size of the product page URL and the size of the product page embedded URL contained in the e-commerce platform web page data according to the online behavior data of the e-commerce platform; The customer interaction perception data extraction module is used to extract the customer basic information, time information, customer browsing product page URL, and shopping cart URL contained in the customer interaction perception data according to the online behavior data of the e-commerce platform.

8. The device according to claim 7, characterized in that The method for the commodity webpage capacity acquisition unit to acquire the commodity page capacity is specifically as follows: Within a set period, according to the size of the product page URL in the e-commerce platform webpage data and the total value of the size of the embedded URL of the product page, obtain the average of the total value of the product page and its embedded URL; The average of the total values ​​is the capacity of the product page within the period, denoted by A.

9. The device according to claim 8, characterized in that: The method for the customer perception information acquisition unit to acquire the attention frequency of each product within a set period is specifically as follows: The click frequency of the customer on the product webpage URL and the click frequency of the add to shopping cart URL within a set period is obtained according to the customer interaction perception data.

10. The device according to claim 9, characterized in that The commodity page capacity threshold setting unit further comprises: The first calculation module is used to obtain the sum of click frequencies of the add to shopping cart URL of the product whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A; obtain the sum of click frequencies of the product page URL of the product whose total value of the size of the product page URL and the size of the embedded URL of the product page is less than A; The second calculation module is used to obtain the ratio of the sum of click frequencies of the add-to-shopping-cart URL of the product to the sum of click frequencies of the product page URL, and use the ratio as the customer perception of the product; The threshold setting module is used to set the customer perception of the product as the product page capacity threshold.

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