KR101654192B1 - Method and Apparatus for Analyzing Touch Data, and Touch Data Analyzing System - Google Patents
Method and Apparatus for Analyzing Touch Data, and Touch Data Analyzing System Download PDFInfo
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- KR101654192B1 KR101654192B1 KR1020150103930A KR20150103930A KR101654192B1 KR 101654192 B1 KR101654192 B1 KR 101654192B1 KR 1020150103930 A KR1020150103930 A KR 1020150103930A KR 20150103930 A KR20150103930 A KR 20150103930A KR 101654192 B1 KR101654192 B1 KR 101654192B1
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Abstract
A touch data analysis apparatus, method, and system are disclosed. The embodiments of the present invention provide a touch data analysis method and apparatus and a touch data analysis system that generate more accurate and scientific analysis data by using a big data technique and provide the same to a client, By accumulating and analyzing the touch data of the visitors based on the information, it is possible to measure the reaction effect of the more precise contents by grasping the immediate reaction of the visitor to the contents included in the web page. Provide key information needed for improvement.
Description
The present invention relates to touch data analysis technology in various smart devices, and more particularly, to a touch data analysis method, apparatus, and system for generating meaningful touch data analysis data through analysis of big data.
As the touch screen is applied not only to mobile devices but also to tablets, clocks, home appliances, and automobiles, the touch experience of visitors is attracting attention as an important factor (refer to FIG. 1). This is because the various touch data inputted through the touch screen represent the content response of the actual visitor. As various smart devices with a touch screen are developed, it is important to understand the reaction information of the visitor when the visitor experience is becoming important.
In addition, as smart devices equipped with touch screens become more popular, it is becoming more common for not only the current generation but also children to touch the display. This means that touch data analysis has more possibilities than simply being used as a usage pattern analysis tool. For example, it provides functions that provide useful and valuable information to various industries such as software vendors, advertising agencies, and web agency such as user reaction analysis of contents, UI / UX analysis, advertisement analysis, and furthermore, Which is expected to bring considerable research results.
Specifically, a visitor's reaction to various contents displayed on a browser of a web-based smart device provides meaningful feedback not only on content information but also on device design and design configuration. This is because visitors' responses can be different even if the contents of the running application match. Therefore, we can access the device-specific content configuration and UI / UX (User Interface & User Experience) design from a more scientific and systematic viewpoint.
In addition, with the smart revolution, as the amount of data produced by users increases and the data types become diversified, the big data that are generating new values by collecting, accumulating, analyzing and utilizing these data are developed every day. There is a need for a technology for generating meaningful analysis data by analyzing touch data more accurately and scientifically using Big Data technology.
SUMMARY OF THE INVENTION The present invention has been made in view of the technical background as described above, and it is an object of the present invention to provide a touch data analysis method for generating more accurate and scientific analysis data by using a big data technology such as Hadoop, And to provide a device and a touch data analysis system.
According to an aspect of the present invention, there is provided a touch data analysis method including a collection server linked with a collection database and an analysis server linked with a meta database, Receiving touch data of a visitor connected to the web page on a page, transmitting the input touch data to a collection server and storing the received touch data in a collection database, processing the touch data stored in the collection database Wherein the analyzing server extracts an analysis result corresponding to the request from the meta database when the analysis server stores the analysis result in the meta database and receives a request for the analysis result from an arbitrary visitor, To the above-mentioned arbitrary visitor Step; provides touch data analysis method comprising a.
According to the present invention, by accumulating and analyzing the visitor's touch data based on the web page, it is possible to measure the reaction effect of the more precise contents by grasping the immediate reaction of the visitor to the contents included in the web page, (User Interfaces & User Experience).
In addition, the user can grasp the scroll pattern of the visitor through the web-based touch data analysis result, thereby making it possible to more effectively construct the advertisement and to select the advertisement position.
Also, by analyzing the visitor's web-based touch data analysis, it is possible to analyze the advertising effect more accurately by grasping the distribution of the leading pattern of the visitor, and it is possible to verify the actual quality of PV (Page View).
In addition, by allowing visitors to check the time spent by each web page section, the visitor's psychological needs such as visitor interest can be grasped.
In addition, it can prevent illegal clicks and fraudulent traffic.
1 is a diagram showing a system flow according to an embodiment of the present invention.
2 is a diagram illustrating a method of dividing a web page according to an embodiment of the present invention.
3 is a diagram schematically illustrating an Area Flow analysis according to an embodiment of the present invention.
4 is a diagram illustrating exemplary ratios of touch data reading patterns according to an embodiment of the present invention.
5 is a diagram schematically showing a basic data graph of area flow analysis of touch data according to an embodiment of the present invention.
6 is a diagram schematically illustrating a grouping step of area flow analysis of touch data according to an embodiment of the present invention.
7 is a diagram schematically showing a step of deriving a result of an area flow analysis of touch data according to an embodiment of the present invention.
8 is a diagram schematically showing area rank analysis according to an embodiment of the present invention.
Figure 9 is a schematic representation of a finish line analysis according to an embodiment of the present invention.
10 is a diagram illustrating the number of page repetitions according to an embodiment of the present invention.
11 is a diagram illustrating a comparison of a PC to a mobile session according to an embodiment of the present invention.
12 is a diagram illustrating the total number of unique visitors according to an exemplary embodiment of the present invention.
13 is a graph showing the total average residence time according to an embodiment of the present invention.
FIG. 14 is a diagram illustrating a left-handed right-handed touch data ratio according to an embodiment of the present invention.
FIG. 15 is a diagram showing a touch data ratio according to an embodiment of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention, and the manner of achieving them, will be apparent from and elucidated with reference to the embodiments described hereinafter in conjunction with the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. And is intended to enable a person skilled in the art to readily understand the scope of the invention, and the invention is defined by the claims. It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. It is noted that " comprises, " or "comprising," as used herein, means the presence or absence of one or more other components, steps, operations, and / Do not exclude the addition.
The embodiments of the present invention provide a touch data analysis method and apparatus and a touch data analysis system that generate more accurate and scientific analysis data by using a big data technique and provide the same to a client, By accumulating and analyzing the touch data of the visitors based on the information, it is possible to measure the reaction effect of the more precise contents by grasping the immediate reaction of the visitor to the contents included in the web page. And provide key information necessary for improvement.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings.
1 is a diagram showing a system flow according to an embodiment of the present invention.
The system of the present invention may include a
The acquisition server (touch server) receives the touch data from the mobile terminal, stores it in the database, transmits the stored touch data to the analysis server, and the analysis server receives the touch data transmitted from the collection server, Can be processed and provided.
The collection server is interlocked with a collection database storing the touch data, and the analysis server is interlocked with a meta database storing analysis results of the touch data.
The present invention can include a WAS (Web Application Server), and the WAS can serve as a server for providing analysis results to members.
When a touch occurs, it collects the page position information of the visitor by the time point of the visitor based on the top point (or the bottom point, the middle point) of the smartphone screen. During the visitor's dragging gesture, a phase of Start-Move-End occurs while the start phase is recognized at the moment the hand touches the screen, The finger is recognized while moving the finger in one state, and the ending step is recognized when the finger is removed from the screen. At each step, a total of N + 2 pieces of touch data are generated: 1 start, N movements, 1 end. Each touch data includes various information, and representative information is time stamp and position coordinates. The set of touch data generated in this manner is the touch data set, and the touch data set becomes the data set of the drag gesture once. On the actual mobile, dozens of hundreds or more sets of drag gestures can be collected until one visitor stays on the page and leaves the page.
The general flow of the present invention will be described with reference to the schematic system flow diagram of FIG. ii) the tracking code sends the touch data and page information to the collection server; iii) the collection server once stores the touch data in the collection database; And iv) the analysis server processes the touch data, analyzes the result, stores the result in the meta database, and v) when the customer requests the analysis data, requests the necessary data from the meta database and provides the stored analysis result .
Hereinafter, detailed embodiments of how touch data is processed and analyzed through the analysis server will be described.
FIG. 2 is a diagram illustrating a method of dividing a web page according to an embodiment of the present invention. FIG. 3 is a schematic diagram illustrating an area flow analysis according to an embodiment of the present invention. FIG. Is an exemplary ratio of touch data reading patterns according to an example. In FIG. 4, the number of visitors per each reading pattern is exemplified by using about 6 reading patterns.
The steps of analyzing a reading pattern of a specific visitor according to the present invention are as follows. First, based on a system including a collection server linked with a collection database and an analysis server linked with a meta database, touch data of a visitor who accesses the web page is input in a web page in which a tracking code is inserted. The input touch data is transmitted to a collection server and stored in a collection database. The analysis server processes and analyzes the touch data stored in the collection database, and the analysis server stores the analysis result in the meta database.
When receiving a request for the analysis result from an arbitrary visitor, the analysis server extracts an analysis result corresponding to the request from the meta database and delivers the extracted analysis result to the arbitrary visitor.
The touch data may include touch data coordinates (X, Y), touch data element information (e.g., Document Object Model information), touch data time stamp (Timestamp), browser information, have. The touch data coordinates (X, Y) are coordinates indicating which part of the web page the visitor has touched. The touch data time stamp is data in which the time when the visitor touches the web page is recorded. The touch data element information is data showing which one of the various components (for example, a banner, a link, a photograph, a moving picture, etc.) constituting a web page is communicated by a visitor. The above data can be a starting point for understanding how and how visitors use web pages.
When analyzing the visitor's web page reading pattern, the analysis server performs more detailed steps in processing and analyzing the touch data stored in the collection database. The analysis server divides the interval of the web page into N partial pages having the same length, that is, the entire web page can be divided into N equal parts. For example, the analysis server divides the interval of the web page into four partial pages having the same length, that is, the entire web page can be divided into quadrants.
The formula for calculating the number of kinds of leading patterns that can be obtained when dividing into N is as follows.
The number of kinds of reading patterns is 2 (1) + 2 ^ (2) + 2 ^ (3) + 2 ^ (4) + ... + 2 ^ (N-2) + 2 ^ (N-1) + 2 ^ (N)
For example, if the screen is divided into 5 pieces, the number of types of reading patterns that can be generated is 2 ^ (1) + 2 ^ (2) + 2 ^ (3) + 2 ^ (4) + 2 ^ 62.
Thereafter, the analysis server analyzes the touch data according to the divided web page section, and determines whether the divided partial pages are read by the visitor.
In order to determine whether each partial page has been read by a visitor, it is necessary to determine whether the total page stay time of the entire visitor, the partial page stay time of the entire visitor, the total page stay time of the specific visitor, Data is needed.
The step of analyzing and analyzing the touch data stored in the collection database by the analysis server may determine whether each partial page is read by a visitor through the following steps.
First, the total page stay time average (X t ) of all the visitors, the partial page stay time average (X p ) of each partial page of the total visitor, the total page stay time (x t ) p .
(X p / x t ) of each partial page residence time (x p ) of a specific visitor to the total page residence time (x t ) of a specific visitor and a residence time average (X p ) (X p / X t ) for each partial page stay time average (X t ) of all the visitors to the page.
According to the comparison of the above ratio values, the pattern can be divided into three types depending on whether a specific visitor has read the page, read it, or scrolled it completely.
The analysis server determines that the specific visitor has read the partial page and sets it as the read pattern (Y) if x p / x t > = X p / X t . If x p / x t <X p / X t , it is determined that the specific visitor did not read the partial page and is set as the unread pattern (N). If there is no residence time data for the partial page, the analysis server determines that the specific visitor has not scrolled and sets the non-scroll movement pattern (X).
In this method, a ratio (a) of the staying time consumed by a specific page among the time when the entire visitor stays on the entire page is obtained, and then the ratio (b) of the staying time consumed by the specific page to the divided page If b is equal to or greater than a, it is determined that the divided page has been read. If b is smaller than a, it is determined that the divided page is not read.
As an example, assume that a section of a web page is divided into four partial pages of equal length, and the total page stay time average and partial page stay time of the entire visitor are as follows.
X t = 10 seconds, X 0 = 2 seconds, X 1 = 5 seconds, X 2 = 1 second, X 3 = 1 second,
Given that the total page stay time average and partial page stay time of a particular visitor are as follows,
x t = 20 seconds, x 0 = 5 seconds, x 1 = 5 seconds, x 2 = 5 seconds, x 3 = 5 seconds
This person 's reading pattern is identified as Y - N - Y - Y.
The analysis server collects the determined patterns, generates a reading pattern of a visitor for each page area, and the reading pattern can be organized into the following list.
ranking
Reading Type
Reading Type
Explanation
One
NXXX
The user scrolls from the top of the page to the point of 25% of the total length, and the value corresponding to the [partial stay time / total stay time] of any visitor is [partial average stay time / total average stay time ] Is less than
2
YXXX
The user scrolls from the top of the page to the point of 25% of the total length, and the value corresponding to the [partial stay time / total stay time] of any visitor is [partial average stay time / total average stay time ] Or more
3
YYXX
Scrolling was performed from the top of the page to 50% of the total length, and the value corresponding to the [partial residence time / total residence time] of any visitor was 0 to 25% / Total average residence time] or more
4
YNXX
Scrolling was performed from the top of the page to 50% of the total length, and the value corresponding to [partial stay time / total stay time] of any visitor was [partial average stay time / total average stay time ], And in the interval of 25 to 50%, [partial average residence time / total average residence time] is less than
5
NNXX
Scrolling was performed from the top of the page to 50% of the total length, and the value corresponding to the [partial residence time / total residence time] of any visitor was 0 to 25% / Total average residence time]
6
NYXX
Scrolling was performed from the top of the page to 50% of the total length, and the value corresponding to [partial stay time / total stay time] of any visitor was [partial average stay time / total average stay time ], And in the interval of 25 to 50%, [partial average residence time / total average residence time] or more
7
YYYX
I scrolled from the top of the page to 75% of the total length, and the value of [partial stay time / total stay time] of any visitor was 0 to 25%, 25 to 50% In case of 50 ~ 75% interval [partial average residence time / total average residence time] or more
8
YYNX
Scrolling was performed from the top of the page to the point corresponding to 75% of the total length, and the value corresponding to the [partial residence time / total residence time] of any visitor was 0 to 25% / Total average residence time] and less than [partial average residence time / total average residence time] in the range of 50 to 75%
9
YNNX
Scrolling was performed from the top of the page to the point corresponding to 75% of the total length, and the value corresponding to the [partial stay time / total stay time] of any visitor was [partial average stay time / total average stay time ], And in the interval of 25 to 50% and 50 to 75%, [partial average residence time / total average residence time] is less than
10
NYYX
The user scrolls from the top of the page to the point corresponding to 75% of the total length, and the value corresponding to the [partial residence time / total residence time] of any visitor is 25 to 50% / Total average residence time] and less than [partial average residence time / total average residence time] in the range of 0 to 25%
11
NYNX
Scrolling was performed from the top of the page to 75% of the total length, and the value corresponding to the [partial residence time / total residence time] of any visitor was [partial average residence time / total average residence time ], And in the range of 0 to 25% and 50 to 75%, [partial average residence time / total average residence time] is less than
12
NNYX
The user scrolls from the top of the page to the point corresponding to 75% of the total length, and the value corresponding to the [partial stay time / total stay time] of any visitor is [partial average stay time / total average stay time ], And in the range of 0 to 25% and 25 to 50%, [partial average residence time / total average residence time] is less than
13
NNNX
Scroll down from the top of the page to 75% of the full length, and the value corresponding to [partial stay time / total stay time] of any visitor is 0 to 25%, 25 to 50%, 50 to 75% [Partial mean residence time / total mean residence time]
14
YNYX
The user scrolls from the top of the page to the point corresponding to 75% of the total length, and the value corresponding to [partial residence time / total residence time] of arbitrary visitor is 0 to 25% / Total average residence time] and less than [partial average residence time / total average residence time] in the range of 25 to 50%
15
YYYY
I scrolled from the top of the page to 100% of the full length, and the value corresponding to [partial residence time / total residence time] of any visitor was 0 to 25%, 25 to 50%, 50 to 75% ~ 100% interval is above [partial average residence time / total average residence time]
16
YYNY
Scrolling from the top of the page to 100% of the full length, and the value corresponding to [partial stay time / total stay time] of any visitor is 0 to 25%, 25 to 50%, 75 to 100% [Partial average residence time / total average residence time], and in the range of 50 to 75%, [partial average residence time / total average residence time] is less than
17
YYNNN
Scrolling down from the top of the page to 100% of the full length, the value corresponding to the [partial residence time / total residence time] of any visitor is 0 to 25% and the partial average residence time / Total average residence time], and in the range of 50 to 75% and 75 to 100%, it is less than [partial average residence time / total average residence time]
18
YNYY
Scroll down from the top of the page to 100% of the full length, and the value corresponding to the [Partial Dwell Time / Total Dwell Time] of any visitor is 0 to 25%, 50 to 75%, 75 to 100% [Partial average residence time / total average residence time], and in the range of 25 to 50%, [partial average residence time / total average residence time] is less than
19
YNYN
Scrolling from the top of the page to 100% of the full length, the value corresponding to the [partial residence time / total residence time] of any visitor is 0 to 25%, and the partial average residence time / Total average residence time], and in the range of 25 to 50% and 75 to 100%, [partial average residence time / total average residence time] is less than
20
YNNY
In the interval of 100% of the total length from the top of the page, the value corresponding to [partial residence time / total residence time] of any visitor is 0 to 25% / Total average residence time], and in the range of 25 to 50% and 50 to 75%, [partial average residence time / total average residence time] is less than
21
YNNN
Scrolling from the top of the page to 100% of the full length, and the value of [partial stay time / total stay time] of any visitor is [partial average stay time / total average stay time ], And it is less than [partial average residence time / total average residence time] in the range of 25 to 50%, 50 to 75%, and 75 to 100%
22
NYYY
Scrolling from the top of the page to 100% of the full length, and the value corresponding to the [partial residence time / total residence time] of any visitor is 25-50%, 50-75%, 75-100% [Partial average residence time / total average residence time], and in the range of 0 to 25%, [partial average residence time / total average residence time] is less than
23
NYYN
The user scrolls from the top of the page to the point corresponding to 100% of the total length, and the value corresponding to the [partial residence time / total residence time] of the arbitrary visitor is 25 to 50% / Total average residence time], and in the range of 0 to 25% and 75 to 100%, it is less than [partial average residence time / total average residence time]
24
NYNY
In the interval from the top of the page to 100% of the whole length, the value corresponding to [partial residence time / total residence time] of any visitor is 25 to 50% / Total average residence time] and less than [partial average residence time / total average residence time] in the range of 0 to 25% and 50 to 75%
25
NYNN
In the 25% to 50% interval, the value corresponding to the [partial residence time / total residence time] of any visitor scrolls from the top of the page to the point corresponding to 100% of the whole length, and [partial average residence time / total average residence time ] And less than [partial average residence time / total average residence time] in the range of 0 to 25%, 50 to 75%, and 75 to 100%
26
NNYY
Scrolling was performed from the top of the page to 100% of the whole length, and the value corresponding to the [partial residence time / total residence time] of any visitor was 50 to 75%, and the partial average residence time / Total average residence time], and in the range of 0 to 25% and 25 to 50%, it is less than [partial average residence time / total average residence time]
27
NNYN
Scrolling was performed from the top of the page to 100% of the total length, and when the value corresponding to [partial residence time / total residence time] of an arbitrary visitor is 50 to 75%, [partial average residence time / total average residence time ] And less than [partial average residence time / total average residence time] in the range of 0 to 25%, 25 to 50%, and 75 to 100%
28
NNNY
Scrolling from the top of the page to 100% of the total length, and the value corresponding to [partial stay time / total stay time] of any visitor is [partial average stay time / total average stay time ] And less than [partial average residence time / total average residence time] in the range of 0 to 25%, 25 to 50%, and 50 to 75%
29
NNNN
I scrolled from the top of the page to 100% of the full length, and the value corresponding to [partial residence time / total residence time] of any visitor was 0 to 25%, 25 to 50%, 50 to 75% ~ 100% interval is less than [partial average residence time / total average residence time]
30
YYYN
Scroll down from the top of the page to 100% of the full length, and the value corresponding to [Partial Dwell Time / Total Dwell Time] for any visitor is 0-25%, 25-50% In case of 50 ~ 75% interval, [partial average residence time / total average residence time] is over, and in 75 ~ 100% interval, [partial average residence time / total average residence time]
FIGS. 5 to 7 are diagrams schematically showing basic data graphs for analyzing area flows of touch data according to an embodiment of the present invention. FIG. 3 is a diagram showing an area flow of touch data according to an embodiment of the present invention. Flow analysis.
The analysis server may generate a line graph that allows the visitor to determine the scroll flow. The Area Flow analyzes the touch data for each section of the divided web page, and can grasp the strength and direction of the scroll for each section.
The step of analyzing and analyzing the touch data stored in the collection database by the analysis server may further include the following steps for area flow analysis.
First, in order to grasp the scroll flow for the web page of the visitors, a graph is generated in which the residence time (sec) of the visitor is set as the X axis and the distance (px) where the visitor scrolls is set as the Y axis. Then, the touch data among the touch data is indexed to extract the progress ratio of the visitor, and the distance px scrolled by the visitor is extracted from the touch data coordinates. When a visitor accesses a plurality of web pages, the visitor is sequentially grouped and averaged according to the rate at which the visitor scrolls at the top of the page. Then, a flow graph image is generated using the extracted progress ratio and distance.
The detailed method for calculating the area flow is described below.
1. Data filtering
Since a plurality of touch data are included in each drag set, there are cases where there is almost no time difference in the move step. Therefore, the difference between the previous touch data and the current touch data is compared with a predetermined time (for example, 50 ms), the corresponding data is excluded (data filtering). And then stores the filtered data as an array arranged sequentially in time zones.
2. Indexing by page length
Assuming that the page length is divided into specific units and is divided into, for example, 10 px units and the total length is 1,500 px, the total number of indexes is set to 150. The values used for such indexing are arbitrary, so other indexing values can be utilized. If the total length of the page is 150 indexes as in the previous example, index numbers from 0 to 149 are assigned to each page section. The data filtered in step 1 (sequential array) also have index numbers because of their respective positional coordinates (px).
3. Scroll progress ratio conversion
Through the
4. Averaging the scroll flow of many visitors
A step of averaging the scroll flow of a plurality of visitors of two or more to represent one representative scroll flow is necessary. This step usually proceeds with step 3. First, a grouping operation is required to classify the average objects, and the average target group can be classified according to the coordinate position of scrolling in the scroll that many visitors have. FIG. 6 schematically shows a step of grouping the touch scroll flows of the plurality of visitors.
The index array generated as a result of
8 is a diagram schematically showing Area Rank analysis according to an embodiment of the present invention.
The area rank is a graph showing the residence time for each section. The step of analyzing and analyzing the touch data stored in the collection database by the analysis server may further include the following steps for obtaining the area rank.
To represent the residence time for each section of the web page, a graph is created in which the residence time (sec) of the visitor is set to the X axis and the length (px) of the web page is set to the Y axis. The instantaneous staying time is calculated by calculating the time difference between the current touch data time and the previous touch data time as the instantaneous staying time (instantaneous staying time = current touch data time-previous touch data time).
The data used to measure the current screen area is as follows.
1. Touch Coordinates (x, y, y coordinates) - coordinates of the upper left corner of the instrument screen (0, 0)
2. Page coordinates (x, y, y coordinates) - Coordinates (y coordinate among them) to be collected based on the whole page,
3. Length of instrument screen
These three pieces of data can be used to determine where the current exposure area spans from page coordinates. 2 (page coordinates) - 1 (touch coordinates) is the starting point of the current exposure section, and calculating the current starting point + 3 (length of the instrument screen) is the last point of the current exposure section.
When each of the calculated instantaneous stay time is added to the stay section in which the visitor stays during the instant stay time, it can be determined how long visitors stayed in each stay section.
Figure 9 is a schematic representation of a finish line analysis according to an embodiment of the present invention. The visitor can collect data about where he / she ends the visit to the page and display the portion where the visitor has left. These finish lines can be used to identify the number of ending visitors per page section and as a basis for understanding what led visitors to leave the page.
10 is a diagram illustrating the number of page repetitions according to an embodiment of the present invention. When a page visitor raises the page again while scrolling the page, that is, when the page is read back to a specific part of the entire page.
Figure 11 is a comparison of the number of PC-to-mobile sessions according to an embodiment of the present invention. FIG. 11 can compare the number of visitors per route by determining whether the visitor of the page visited through the PC or the mobile terminal.
FIG. 12 is a diagram illustrating the total number of unique visitors visited on a page according to an exemplary embodiment of the present invention. FIG. 13 is a graph showing the total number of unique visitors visiting a corresponding page according to an exemplary embodiment of the present invention. And the average residence time.
FIG. 14 is a diagram illustrating a left-handed right-handed touch data ratio according to an embodiment of the present invention.
Assuming a hypothetical line dividing the screen of the mobile terminal from the upper center to the lower center, the left part of the mobile terminal screen is likely to be touched with the left hand finger, and the right part of the screen is likely to be touched with the right hand finger. (Refer to the mobile terminal screen on the left side of FIG. 14). When the left side of the screen of the mobile terminal is touched under the above assumption, the left hand touch data is stored. When the right side of the screen is touched, have. (See the ratio graph on the right side of Fig. 14)
Specifically, when the left-hand-touch data and the right-hand-touch data are individual touch data that is not touch data sequentially input, the left-hand-touch data can be compared with the right-hand-touch data to calculate the left-to-right touch ratio.
Also, when the left-hand-touch data and the right-hand-touch data are sequentially input touch data, sequentially inputted touch data can be recognized as drag data. Then, the left hand drag data is compared with the right hand drag data. When the left hand drag data is inputted relatively more, it is judged as left hand. When the right hand drag data is inputted relatively more, the right hand grip is judged. Can be calculated. When the touch data dragged in the left or right direction has a share of more than a certain value (for example, 70%), the position of all the touch data is detected until the visitor visits the page in one session, Can be recognized as a handle in the corresponding direction. The above example of 70% can be adjusted according to the purpose.
From this, mobile visitors can see left / right handed and left / right touch ratio.
FIG. 15 is a diagram showing a touch data ratio according to an embodiment of the present invention. For example, assuming that the visitors access the page using five mobile terminal models A, B, C, D, and E, each mobile terminal model can grasp the total usage time using the page. If the total page usage time of each mobile terminal model is divided by the number of visitors of each mobile terminal model, the average usage time of each model can be obtained. If the total usage time is accumulated, the cumulative usage time can be obtained. Fig. 15 shows a graph showing such an embodiment.
The touch data analysis method according to the present invention may also include analysis of the touch data by date and country. In the above steps, the content calculated through one cycle is i) the data input in one page, ii) in some countries, iii) in a few months, iv) in one time using a certain model.
For example, if you want to know how visitors to your A-type terminal visit a specific web page, you can use the "Date and Time Range" as "visitors from Korea and Japan from January 1 to June 1" Can be set. This process is implemented in such a manner that all the data corresponding to the set country and date among all the result values of the 'A type terminal / specific web page' touch data is averaged in the backend.
As described above, the embodiment of the present invention provides a touch data analysis method and apparatus and a touch data analysis system that generate more accurate and scientific analysis data by using the big data technology, and provide the same to the client By accumulating and analyzing the visitor's touch data based on the web page, the reaction of the visitor to the contents included in the web page can be grasped and the reaction effect of the more accurate contents can be measured, and the UI / UX & User Experience) to provide essential information for improvement.
Meanwhile, the data flow-based large-scale data stream processing method according to the embodiment of the present invention described above can be implemented as a computer-readable code on a computer-readable recording medium. The computer-readable recording medium includes all kinds of recording media storing data that can be decoded by a computer system. For example, there may be a ROM (Read Only Memory), a RAM (Random Access Memory), a magnetic tape, a magnetic disk, a flash memory, an optical data storage device and the like. The computer-readable recording medium may also be distributed and executed in a computer system connected to a computer network and stored and executed as a code that can be read in a distributed manner.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. Therefore, the scope of the present invention should not be limited by the illustrated embodiments, but should be determined by the scope of the appended claims and equivalents thereof.
Claims (12)
Receiving touch data of a visitor who accesses the web page from a web page in which a tracking code is inserted;
The input touch data is transmitted to a collection server and stored in a collection database;
Processing the analysis data by processing the touch data stored in the collection database;
Storing the analysis result in a meta database; And
When receiving a request for the analysis result from an arbitrary visitor, the analysis server extracts an analysis result corresponding to the request from the meta database and transmits the extracted analysis result to the arbitrary visitor,
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
Dividing the section of the web page into N partial pages of equal length; And
And analyzing the touch data according to the divided web page section to determine whether each of the divided partial pages has been read by the visitor.
Wherein the touch data includes touch data coordinates (X, Y), touch data element information, a touch data time stamp (Timestamp), browser information, visitor information, and page information.
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
(X t ), the total page residence time (x p ) of each visitor, the total page residence time (x t ) of a specific visitor, the page residence time (x p) ≪ / RTI > And
Calculating a ratio value of the determined time information and analyzing the touch data according to the calculation result;
Further comprising the steps of:
Calculating the ratio value of the determined time information and analyzing the touch data according to the calculation result,
(X p / x t ) of each partial page residence time (x p ) of a specific visitor to the total page residence time (x t ) of a specific visitor and the residence time average (X p ) of the partial page of the entire visitor Comparing a ratio value (X p / X t ) of each partial page stay time average (X t ) of all visitors to the partial page stay time average (X t );
determining that the particular visitor has read the partial page and setting the read pattern as a read pattern (Y) if x p / x t > = X p / X t ;
if x p / x t <X p / X t , determining that the particular visitor did not read the partial page and setting the partial page as an unread pattern (N);
Determining that the specific visitor has not scrolled if there is no residence time data for the corresponding partial page and setting it to the no scroll movement pattern (X); And
Collecting the determined pattern and generating a reading pattern of a visitor for each page area;
And analyzing the touch data.
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
Generating a touch data array composed of a scroll progress time (seconds) of the visitor and a scroll coordinate position (px) of the visitor in order to grasp the scroll flow of the web page of the visitors;
Indexing the web page length in units of a predetermined distance (px);
Converting the position of the scroll coordinate among all input touch data elements into an index number corresponding thereto;
Creating a new touch data group by grouping the pairs of index numbers existing in the converted touch data array and the scroll progress time with the same index number;
Extracting touch data latest in time from each of the generated touch data groups;
Reproducing a new touch data group by grouping the pairs of the index numbers included in the extracted touch data and the scroll progress time with the same index number when the extracted touch data is a plurality;
Averaging the scroll progress time (seconds) included in each group in the regenerated touch data group; And
Generating a flow graph image by matching the input total touch data with a scroll progress ratio reference;
And analyzing the touch data.
Performing data filtering by excluding the corresponding touch data from the inputted touch data when the difference of the touch time difference between the previous touch data and the current touch data is less than a predetermined value; And
And storing the filtered data in an array arranged in order by time zone.
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
Generating a graph in which a residence time (sec) of a visitor is set as an X-axis and a length (px) of a web page is set as a Y-axis to represent a residence time of each section of the web page;
The point obtained by subtracting the touch coordinates from the page coordinates is set as the start point of the current exposure section and the point where the current start point is added to the length of the apparatus screen is set as the last point of the current exposure section, To a web page section exposed to a visitor;
Calculating a time difference between a current touch data time and a previous touch data time as an instant staying time and calculating a web page section exposed to a visitor from a previous touch data time to a current data time as an instant staying time; And
And summing each of the calculated instantaneous residence times to a stay interval that is exposed to a visitor during an instantaneous residence time.
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
Dividing a web page touch area by horizontally dividing a web page displayed on a mobile terminal screen into a virtual line formed by dividing the center of the web page from a center of the upper center to a lower center;
Storing left hand touch data when the left side is touched in the left and right divided touch regions and right hand side touch data when the right side is touched;
The left-hand-touch data and the right-hand-touch data are compared with each other when the touch data is the sequential input of the left-hand-touch data or when the right-hand-touch data is sequentially input, ; And
When the touch data is the touch data sequentially inputting the left-hand touch data or the touch data sequentially inputting the right-hand touch data, the sequentially inputted touch data is recognized as the drag data, and the left-hand drag data and the right- And calculating a left-handed and a right-handed ratio by comparing the left-handed and right-handed ratios.
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
Confirming a terminal type of a visitor accessing a web page;
Collecting and storing the entire page usage time of each terminal model;
Confirming the number of visitors using each terminal model; And
Further comprising calculating the average usage time of each terminal type by dividing the total page usage time of each terminal type by the number of visitors using each terminal type.
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
Collecting and storing a position where each visitor out of the web page leaves the page;
Counting the number of times a visitor leaves the web page for each location of the web page; And
And generating a graph in which a number of times a visitor leaves the web page is set as an X-axis and a length (px) of an entire web page is set as a Y-axis.
Wherein the analysis server processes and analyzes the touch data stored in the collection database,
Collecting and storing locations where each visitor scrolls and returns to another location in the web page;
Counting the number of times the visitor has moved to another location and returned to each location of the web page; And
Generating a graph in which a number of times a visitor repeats a specific position of the web page is set as an X-axis and a length (px) of an entire web page is set as a Y-axis; .
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| Qi Guo 외 4. Mining Touch Interaction Data on Mobile Devices to Predict Web Search Result Relevance. 2013.8.* * |
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| CN113886449A (en) * | 2021-08-30 | 2022-01-04 | 帝杰曼科技股份有限公司 | Big data information analysis system based on Internet of things |
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