KR101868428B1 - Method of Optimizing Search Keyword - Google Patents

Abstract

Disclosed is a search keyword optimizing method which comprises the following steps: extracting information on raw data with respect to queries, clicks, impressions, a click-through ratio (CTR), and an average position of a specific period from a Google search console in a specific file form for each relevant keyword; loading and inputting an information file extracted in the specific file form; calculating a standard normal distribution-based Z-score from the input raw data information, and analyzing the same; displaying the entire graph and individual graphs of the Z-score for nine level average locations; selecting a lower keyword less than specific 50% of the Z-score compared to the CTR; and providing an optimization score for the lower keyword by calculating a page URL, page tag information, page speed score information, and keyword density information in a page body text exposed when the selected keyword is searched. A keyword having poor keyword efficiency is selected, and a cause for the poor keyword efficiency is analyzed in detail, such that deletion, modification, or additional action to the keyword can be induced.

Description

{Method of Optimizing Search Keyword}

The present invention relates to a search keyword optimization technique, and more particularly, to a search keyword optimization technique for analyzing keyword efficiency with a specific index and providing cause analysis information for improving keyword efficiency of a keyword having poor keyword efficiency. ≪ / RTI >

As is known, in a keyword search advertisement, a commonly used method for measuring an advertisement efficiency measures a click effect of a keyword on an exposure using an index called CTR (Click Through Rate).

Korean Patent Publication No. 10-2014-0076683 (method and apparatus for measuring efficiency of keyword advertisement) has been disclosed as a prior art related to keyword search advertisement. A conventional method and apparatus for measuring the efficiency of keyword advertisement, A step of extracting the number of queries and CTR data for a specific period for a plurality of keywords, classifying the number of extracted queries and the CTR into a plurality of classes, And providing the calculated sum as an index indicating the efficiency of the keyword, thereby realizing the objective efficiency measurement of the keyword.

However, the above-described prior art has a limitation in that it can not provide a countermeasure for a keyword having poor keyword efficiency because it is limited to simply provide keyword information by calculating a keyword efficiency index.

Korean Patent No. 10-2014-0076683 (Method and Apparatus for Measuring Efficiency of Keyword Advertisement)

The technical problem to be solved by the present invention is to provide a search keyword optimization method capable of analyzing a keyword having poor keyword efficiency by a specific index so as to improve keyword efficiency and providing cause analysis information for the keyword There is.

In order to achieve the above object, the present invention provides a method for searching a specific file, the method comprising: extracting a query, a click count, an exposure count, a CTR and a level average position raw data information of a specific period from a Google search console, A step of calculating and analyzing a standard normal distribution based Z-score from the input raw data information, and a step of calculating a Z- A step of selectively displaying each Z-score in a separate graph for each of nine level average positions, selecting and providing keywords less than 50% of Z-score sub-specific to the CTR, , Page tag information, page speed score information, and keyword density information in the page body. The search keyword optimization comprises the step of providing optimization score for the sub-keyword.

Here, the Z-score can be calculated as a value obtained by dividing the difference value of the keyword individual CTR and the average CTR at each level by the CTR standard deviation at each level.

In addition, the page tag information may include a title tag, a meta description, and keyword use ratio information in the H1 tag.

The optimization score is set to 0 when the keyword is not included in the page URL and is set to 20 when the keyword is included. When the keyword is not included in the title tag, the optimization score is set to 0, and when the meta description is included, Is set to 0 at the time of inclusion and is set to 10 points at the time of inclusion and is set to 0 at the time of not including within the H1 tag and is set at 10 when inclusive and the keyword density in the page body is within the range of 3% And if it is less than 3%, the score is set proportionally from 0% to 3%, and the optimization score can be calculated by totaling the scores.

In addition, if the computed optimization score is less than 70, guide information for improving keyword efficiency according to the calculated score can be transmitted to the client terminal.

Also, the method may further include exporting keyword information of less than 50% of the Z-score subspecificity to the CTR and the number of impressions of 100 or more in an Excel file format.

In addition, the method may further include displaying the keyword when selecting the analyzed Z-score on the graph to be individually displayed.

According to the present invention, it is possible to select a keyword having poor keyword efficiency and carry out a low-level cause analysis in detail so as to induce a response to improve the keyword efficiency of deletion, correction, or addition of keywords.

Further, there is an effect that the keyword corresponding to the coordinates analyzed as the index can be displayed, and the keyword information according to the Z-score information for each level average position can be visually immediately recognized.

Furthermore, an optimization score for the keyword efficiency sub-keyword is given, so that it is possible to easily grasp a plurality of related sub-keywords in descending order.

1 schematically shows a flowchart of a search keyword optimization method according to the present invention.
FIGS. 2A to 2D illustrate user interface screens according to the search keyword optimization method of FIG. 1, respectively.
FIG. 3A illustrates a Z-score result of each search keyword in the search keyword optimization method of FIG. 1, and FIG. 3B illustrates a Z-score result of each search keyword by each level.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 schematically shows a flowchart of a search keyword optimization method according to the present invention. FIGS. 2A to 2D illustrate user interface screens according to the search keyword optimization method of FIG. 1, A Z-score result for each search keyword in the keyword optimization method, and FIG. 3B illustrates a Z-score result for each search keyword in each level.

1, a search keyword optimization method according to the present invention includes extracting raw data information (S110), extracting information file loading step (S120), Z-score calculating step (S130) S140), a separate display step S150, a keyword selection step S160, and an optimization score calculation and provision step S170.

First, in the raw data information extraction step (S110), the query, the number of clicks, the number of impressions, the CTR, and the level average position raw data information of a specific period are extracted from the Google search console in the form of a specific file for each keyword.

For example, as shown in FIG. 2A, a query, a click, an impression, a click through ratio (CTR), and a level average position (Avg.Position) related to a Samsung smartphone and a tablet PC- ) Information can be extracted as an Excel file.

Next, as shown in FIG. 2B, in the extracting information file loading step (S120), an information file extracted in a specific file form is loaded and inputted. That is, the path of the above-described Excel file can be input in the 'path setting for importing'.

Next, as shown in FIG. 2B, in the Z-score calculation step (S130), the input raw data information is recognized and the standard normal distribution based Z-score is calculated and analyzed. That is, the Z-score is calculated for each level average position in the 'analysis start'.

For example, as exemplified in FIG. 3A, the Z-score can be calculated as a value obtained by dividing the keyword individual CTR and the difference value of the average CTR at each level by the CTR standard deviation at each level.

Next, as shown in FIG. 2C, in the entire display step S140, the calculated level average position and Z-score information are displayed as a Z-score total graph for each of nine level average positions.

For example, it is possible to grasp the pre-grasping detailed information, the level average position, and the entire Z-score information in advance by briefly checking the 'whole graph'.

Next, as shown in FIG. 2D, in the individual display step S150, Z-scores are selectively displayed as individual graphs for each of nine level average positions for detailed information confirmation.

Here, if the analyzed Z-score on the graph to be individually displayed is selected, the corresponding keyword can be displayed. For example, if a specific figure on the level 1 level coordinate is selected, the keyword corresponding to the coordinate can be displayed, and the keyword corresponding to the Z-score information for each level average position can be visually immediately recognized.

For example, depending on the size of the number of impressions, the display may be sequentially displayed in the order of blue, green, and yellow.

For reference, when displaying a graph, the average position on the vertical axis can be displayed by dividing by 0.1 unit. Level 1 is between 1 and 1.999 in average position, and level 9 is between 9 and 9.999 in average position sequentially.

Next, in the keyword selection step S160, keywords less than 50% of Z-score subspecificity relative to CTR are selected. That is, a keyword corresponding to a specific CTR value with poor keyword efficiency is extracted and selected.

For example, as illustrated in FIG. 3B, keyword information of less than 50% of the Z-score sub-CTR versus level 1 of the number of impressions of 100 or more can be exported in the form of an Excel file.

Next, in the optimization score calculation and provision step (S170), the page URL, the page tag information, the page speed score information, and the keyword density information in the page body, which are exposed at the time of searching the selected keyword, Provide a score.

Here, the page tag information may include the title tag, the meta description, and the keyword use ratio information in the H1 tag.

On the other hand, the optimization score is set to 0 points when the keyword is not included in the page URL, and is set to 20 points when the keyword is included. When the keyword is not included in the title tag, the optimization score is set to 0 point. Set to 10 points when included, set to 0 when not included in H1 tag, set to 10 points when included, set to 40 if the keyword density in the body of the page is in the range of 3% to 5% If it is less than 3%, the score can be set proportionally from 0% to 3%, and the optimization score can be calculated based on the total of 100 points.

If the computed optimization score is less than 70, the guide information for improving the keyword efficiency according to the calculated score is transmitted to the client terminal (S180).

For example, after selecting keywords with less than 50% of the Z-score subspecificity versus CTR and 100 or more of the number of impressions, the cause analysis for the keywords having an optimization score of less than 70, namely, The keyword density information in the page body may be analyzed in detail to induce a corresponding deletion, correction, or addition of a keyword having poor efficiency.

Therefore, the keyword having low keyword efficiency is selected by the search keyword optimization method as described above, and the cause analysis is performed in detail following the poor reason, so that the keyword can be deleted, corrected, The keyword corresponding to the coordinates analyzed as the index can be displayed so that the keyword information according to the Z-score information for each level average position can be visually immediately recognized and the optimization score for the keyword efficiency sub-keyword So that a plurality of related sub-keywords can be easily grasped numerically.

The embodiments described in the present specification and the configurations shown in the drawings are merely the most preferred embodiments of the present invention and are not intended to represent all of the technical ideas of the present invention and therefore various equivalents It should be understood that water and variations may be present.

S110: Raw data information extraction step
S120: Extraction information file loading step S130: Z-score calculation step
S140: full display step S150: individual display step
S160: Keyword selection step
S170: Optimization score calculation and provision step

Claims (7)

Extracting query, click count, number of impressions, CTR and level average position raw data information of a specific period from a Google search console into a specific file type per related keyword,
Loading the information file extracted in the specific file format and inputting the information file,
Calculating and analyzing a standard normal distribution based Z-score from the input raw data information,
Displaying an overall graph of Z-scores for each of the nine level average positions,
Selectively displaying the Z-scores for each of the nine level average positions in an individual graph,
Selecting and providing keywords having less than 50% of Z-score sub-specific to the CTR;
And a step of calculating an index of a page keyword, an index of a page speed, and a keyword density information in a page body to provide an optimization score for the lower keyword, .
The method according to claim 1,
Wherein the Z-score is calculated as a value obtained by dividing a keyword individual CTR and a subtraction value of an average CTR at each level by a CTR standard deviation at each level.
The method according to claim 1,
Wherein the page tag information includes title tag, meta description, and keyword use ratio information in the H1 tag.
The method of claim 3,
The optimization score is set to 0 points when the keyword is not included in the page URL and is set to 20 points when the keyword is included. When the keyword is not included in the title tag, the optimization score is set to 0 point. When the keyword is not included in the meta description, Set to 0 at the time of inclusion and set to 0 at the time of inclusion in the H1 tag and set to 10 at inclusion and if the keyword density in the page body is within the range of 3% to 5% Point, and if the value is less than 3%, the score is set proportionally from 0% to 3%, and the optimization score is calculated by totally summing the scores.
5. The method of claim 4,
And if the computed optimization score is less than 70 points, guide information for improving keyword efficiency according to the calculated score is transmitted to the client terminal.
The method according to claim 1,
Further comprising: exporting keyword information of less than 50% of the Z-score subspecificity to the CTR and the number of impressions of 100 or more in an Excel file format.
The method according to claim 1,
Selecting an analyzed Z-score on the graph to be displayed separately, and displaying the keyword.

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