Apparatus for analayzing business performance using artificial intelligence and control method thereof

Abstract

A store operation performance analysis device according to one embodiment may include at least one processor and may be implemented by said at least one processor. The store operation performance analysis device may include a data collection unit that collects detailed data including at least one of keyword exposure frequency, location display frequency, and content usage information based on user input; an indicator calculation unit that calculates an analysis indicator including a search exposure indicator, a behavioral response indicator, and a competition indicator based on the collected detailed data; and a performance analysis unit that calculates a comprehensive performance index based on the analysis indicator.

Description

Technology Field

[0001] The present invention relates to a business performance analysis technology and a store operation performance analysis technology utilizing artificial intelligence, and more specifically, to a performance analysis device and method capable of quantitatively evaluating store performance by collecting related keyword exposure frequency, content response, competitive status, etc., and analyzing performance based on the collected data. Background Technology

[0002] With the recent rise of online platforms and social media, offline-based businesses, such as restaurants, retailers, and service providers, are increasingly utilizing digital data to formulate marketing and sales strategies. In particular, exposure across various channels and engagement with content are significantly impacting sales.

[0003] In this environment, business owners need to formulate strategies by considering factors such as keyword search volume for their stores, location-based exposure rankings, and user content responses. However, existing commercial area analysis tools and advertising solutions have limitations in that they make comprehensive performance analysis difficult because they provide only fragmentary metrics or are based on data limited to specific platforms.

[0004] Furthermore, there is a lack of functionality to compare multiple keywords simultaneously, analyze time-series trends such as ranking fluctuations over time, or quantitatively analyze correlations with sales by integrating store-level search exposure, behavioral responses, and competitive performance. Accordingly, to enable store operators to make more practical and objective decisions, a new type of analysis device is required that comprehensively collects and analyzes exposure and content response data and provides this information in a visually easy-to-understand format. Prior art literature

[0005] Korean Patent Publication No. 10-2722261 (October 22, 2024) The problem to be solved

[0006] The technical problem that the present invention aims to solve is to provide a store operation performance analysis device and a control method thereof. Specifically, the invention aims to provide a store operation performance analysis device that quantitatively diagnoses the operation performance of individual stores by integrating and analyzing various data, and includes time-series-based ranking fluctuation trends, comparisons with competing stores, and user-customized review responses.

[0007] Specifically, we can provide a store operations analysis system that integrates search exposure, behavioral responses, and competitive metrics to analyze their correlation with sales and calculate a comprehensive performance index. In particular, by integrating various functions such as time-series-based ranking tracking, analysis of high-volume keyword searches, and personalized review responses, we can support the formulation of practical marketing strategies. Through this, the goal is to go beyond the provision of simple statistics and offer quantitative and strategic operational analysis directly linked to sales performance.

[0008] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0009] According to embodiments of the present invention for achieving the above-mentioned objectives, a store operation performance analysis device includes at least one processor and can be implemented by said at least one processor.

[0010] A store operation performance analysis device may include a data collection unit that collects detailed data including at least one of keyword exposure frequency, location display frequency, and content usage information based on user input, an indicator calculation unit that calculates analysis indicators including search exposure indicators, behavioral response indicators, and competition indicators based on the collected detailed data, and a performance analysis unit that calculates a comprehensive performance index based on the analysis indicators.

[0011] An indicator calculation unit according to one embodiment can calculate a map-based exposure ranking for target keywords obtained based on user input using location display frequency and keyword exposure frequency.

[0012] The metric calculation unit can calculate search exposure metrics by cumulatively summing the values ​​obtained by multiplying the inverse weight of the map-based exposure ranking for the target keyword by the keyword exposure frequency at each ranking.

[0013] The indicator calculation unit can calculate a behavioral response indicator by calculating at least one of the views, shares, and saves included in the content usage information as a response rate for the content exposure frequency.

[0014] The indicator calculation unit can generate a group of similar stores in which keyword similarity and location similarity are greater than or equal to a predetermined value. The indicator calculation unit can calculate competitive indicators by calculating a relative ranking based on a comparison of search exposure indicators and behavioral response indicators with the said group of similar stores.

[0015] A performance analysis unit according to one embodiment can analyze keyword exposure frequency, location display frequency, and content usage information on a time-series basis for a target keyword to analyze ranking changes of the target keyword and the history of ranking conversions within a similar store group.

[0016] The performance analysis department may assign a first weight to the correlation coefficient between the search exposure indicator and sales performance, assign a second weight to the correlation coefficient between the behavioral response indicator and sales performance, and assign a third weight to the correlation coefficient between the competition indicator and sales performance.

[0017] The performance analysis department can calculate the overall performance index based on the first to third weights.

[0018] According to one embodiment, the store operation performance analysis device may further include a review response unit that generates a response to a customer review based on predetermined user information.

[0019] The above user information may include user age, gender, tone, and expression style information.

[0020] The review response unit can classify at least one customer review into sentiment type and purpose type. The review response unit can select at least one of predefined response strategies based on the classified review type. The review response unit can generate a response sentence by combining customized sentence patterns according to the selected strategy.

[0021] According to one embodiment, a store operation performance analysis device can adjust the weights of analysis indicators when there are pre-set goals. Pre-set goals may include attracting new visitors, inducing repeat visits, and increasing content response. Effects of the invention

[0022] According to embodiments of the present invention, the store operation performance analysis device of the present invention can objectively evaluate the performance of store operations in quantitative terms by integrating search exposure indicators, behavioral response indicators, and competition indicators to calculate a comprehensive performance index based on a correlation coefficient with sales.

[0023] In particular, the store operation performance analysis device of the present invention can provide basic data that can be utilized for establishing target marketing strategies by analyzing keyword exposure rankings, content response rates, comparisons of similar stores, etc., in a time series, or by analyzing keyword search volume, click counts, competition levels, etc., in large quantities to provide age and gender-based segmented statistics.

[0024] Furthermore, the store operation performance analysis device of the present invention generates automatic review responses using tone and templates that match user information set by the user, thereby improving the quality of response. Moreover, by analyzing trends based on response data to various content and recommending related content, it can effectively contribute to the dissemination of brand image and the response to trends. Through this, the store operation performance analysis device of the present invention can help the user comprehensively monitor the overall status of store operations and utilize this information for decision-making. Brief explanation of the drawing

[0025] FIG. 1 is a block diagram illustrating, in exemplary fashion, a system in which a store operation performance analysis device according to one embodiment of the present invention operates. FIG. 2 is a block diagram illustrating an exemplary store operation performance analysis device according to one embodiment of the present invention. FIG. 3 is an example diagram for registering a ranking tracking target place according to one embodiment of the present invention. FIG. 4 is an example diagram showing the daily exposure ranking and content usage information of a ranking tracking target place in a time series according to one embodiment of the present invention. FIG. 5 is an example diagram illustrating a plurality of store lists and time-series change data according to registered tracking keywords according to an embodiment of the present invention. FIG. 6 is an example diagram illustrating the search and results of top-exposure companies by keyword according to one embodiment of the present invention. FIG. 7 is an example diagram illustrating the time-series changes in the exposure ranking and content response indicators of top companies by keyword according to one embodiment of the present invention. FIG. 8 is an example diagram illustrating monthly and period-based search volume statistics of keywords according to an embodiment of the present invention. FIG. 9 is an example traffic analysis diagram showing the time-series change trend of keyword search volume according to one embodiment of the present invention. FIG. 10 is an example diagram visualizing the age group and gender ratio of keyword searchers according to one embodiment of the present invention. FIG. 11 is an example diagram showing daily search volume according to one embodiment of the present invention. FIG. 12 is an example diagram showing usage response information of popular content according to one embodiment of the present invention visualized in a time series. FIG. 13 is an illustrative diagram for explaining the operation of a review response unit according to an embodiment of the present invention. FIG. 14 is an example diagram showing the operation screen of a bulk search volume inquiry function according to one embodiment of the present invention. Specific details for implementing the invention

[0026] Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

[0027] The embodiments of the present invention described below are provided to more clearly explain the present invention to those skilled in the art, and the scope of the present invention is not limited by the following embodiments, and the following embodiments may be modified in various other forms.

[0028] The terms used herein are for describing specific embodiments and are not intended to limit the invention. Terms used herein in the singular form may include plural forms unless the context clearly indicates otherwise. Additionally, the terms “comprise” and / or “comprising” used herein specify the presence of the mentioned features, steps, numbers, actions, components, elements, and / or groups thereof, and do not exclude the presence or addition of one or more other features, steps, numbers, actions, components, elements, and / or groups thereof. Furthermore, the term “connected” used herein means not only that components are directly connected, but also includes the concept of indirectly connecting components through the interposition of additional components between them.

[0029] Furthermore, when a component is described in this specification as being located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components. The term "and / or" as used in this specification includes any one of the listed items and all combinations of one or more thereof. Additionally, terms of degree such as "about" and "substantially" as used in this specification are used to mean a range of numerical values ​​or degrees or approximate values, taking into account inherent manufacturing and material tolerances, and are used to prevent an infringer from unfairly exploiting the disclosures in which precise or absolute figures provided to aid in understanding this specification are mentioned.

[0030] Embodiments of the present invention will be described in detail below with reference to the attached drawings. The sizes or thicknesses of the areas or parts depicted in the attached drawings may be slightly exaggerated for the clarity of the specification and convenience of explanation. Throughout the detailed description, the same reference numerals indicate the same components.

[0031] A store operation performance analysis device and a control method according to one embodiment of the present invention collect detailed data including at least one of keyword exposure frequency, location display frequency, and content usage information based on user input, calculate an analysis indicator including a search exposure indicator, a behavioral response indicator, and a competition indicator based on the collected detailed data, and calculate a comprehensive performance index based on the analysis indicator.

[0032] According to one embodiment, the user may be a person who requires the establishment of practical marketing and management strategies, such as a store operator. The operational performance analysis device can provide analysis results to the user by intuitively and quantitatively analyzing external response data that has a high correlation with sales performance. Based on the provided analysis results, the user can effectively make various decisions, such as improving advertising efficiency, setting the direction of content, and securing a competitive advantage over rival stores.

[0033] In addition, the operational performance analysis device of the present invention may further include a review response unit that generates responses to customer reviews based on predetermined user information. By automatically generating customized response sentences that reflect propensity information such as age, gender, and tone, the review response unit can improve the quality of communication with customers and contribute to enhancing brand image and customer satisfaction. In particular, the review response unit can establish a more strategic and consistent customer response system by selecting an appropriate response strategy according to the emotion type (e.g., praise, complaint, suggestion, etc.) and purpose type (e.g., inquiry, request, etc.) of the review.

[0034] A store operation performance analysis device according to one embodiment of the present invention can analyze correlations with sales by integrating search exposure, behavioral responses, and competitive indicators. In particular, the store operation performance analysis device can support the establishment of practical marketing strategies by integrating various functions such as time-series-based ranking tracking, analysis of large keyword search volume, and user-customized review responses. Through this, the store operation performance analysis device can provide quantitative and strategic operational performance analysis directly linked to sales performance, going beyond the provision of simple statistics.

[0035] According to one embodiment, the operational performance analysis device can analyze operational performance based on relationships with various external response indicators. The operational performance analysis device can provide the causes of the rise or fall of specific indicators through time-series-based trend analysis. Through this, the user can comprehensively understand various data, such as changes in search exposure, content response rates, and relative position with competing stores.

[0036] Furthermore, the operational performance analysis device can provide foundational data to establish practical operational strategies, such as keyword strategies, content distribution timing, and target marketing directions, based on the analysis results. Users can derive actionable strategies, such as adjusting advertising investments, improving services, and changing customer response methods, based on the difference from target performance provided by the analysis device.

[0037] Furthermore, the operational performance analysis device can adjust analysis weights based on relationships with various external response indicators. Through this, the device can flexibly derive analysis results according to store-specific goals or marketing directions, and can also be effective in establishing proactive response strategies, such as detecting anomalies and optimizing content.

[0038] FIG. 1 is a block diagram illustrating, in exemplary fashion, a system in which a store operation performance analysis device according to one embodiment of the present invention operates.

[0039] Referring to FIG. 1, a system in which a store operation performance analysis device operates may include a communication network (10), a user terminal (20), and a store operation performance analysis device (100).

[0040] The communication network (10) may be a wired or wireless communication network. The communication network (10) may be a short-range communication network such as Bluetooth, RFID (radio frequency identification), infrared communication (Infra Data Association; IrDA), UWB (Ultra-Wideband), ZigBee, Wi-Fi (Wireless Fidelity), an IoT (Internet of Things) network such as LTE-M, LoRA (Long Range), a mobile communication network such as LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced), 5G, a wireless network such as Wide Area Networks (WANs), Metropolitan Area Networks (MANs), Wireless LANs, or a satellite communication network, or a wired network such as Integrated Service Digital Networks (ISDNs). The store operation performance analysis device (100) and the user terminal (20) can be connected using a communication network (10) and can transmit and receive data for analyzing store operation performance.

[0041] The user terminal (20) may refer to a device intended to analyze store operation performance and receive information regarding it through the store operation performance analysis device (100) according to one embodiment of the present invention. The user terminal (20) may be any information processing terminal capable of realizing the functions described in each embodiment. The user terminal (20) may include, exemplarily but not limited to, a smartphone, a mobile phone, a desktop computer, a laptop computer, a tablet PC, a PDA (Personal Digital Assistant), a wearable device (smart watch, smart glasses, etc.), or other types of computing devices.

[0042] According to one embodiment, a user can analyze store operation performance by entering keywords or store names, etc. Additionally, the user can input information such as industry type, location, analysis target period, and analysis purpose through a user terminal (20).

[0043] According to one embodiment, the user may pre-register preference information required for generating a review response or adjust the settings of the review response method according to store characteristics.

[0044] These user inputs can be used as analysis conditions for the store operation performance analysis device (100) to contribute to providing customized indicator calculations and visualization results tailored to the user's needs.

[0045] The store operation performance analysis device (100) can collect detailed data such as keyword exposure frequency, location display frequency, and content reaction logs from external servers, search portals, social media platforms, etc., through a communication network (10).

[0046] Detailed data may be collected in the form of, for example, map-based exposure information related to target keywords entered by the user, reaction information to blog and short-form content, competitive indicators of similar store groups, search volume and click counts of multiple keywords, etc. Detailed data may be updated in real time or at regular intervals via a communication network (10) and provided to a store operation performance analysis device (100).

[0047] The store operation performance analysis device (100) can provide analysis indicators based on collected detailed data.

[0048] The store operation performance analysis device (100) can calculate and perform integrated analysis of search exposure indicators, behavioral response indicators, and competition indicators based on information input from the user terminal (20). The store operation performance analysis device (100) can derive a comprehensive performance index that matches the analysis purpose input by the user.

[0049] In addition, the store operation performance analysis device (100) can generate automatic responses to customer reviews based on user review response tendency information, or analyze short-form content response data to help establish brand trend changes and content strategies.

[0050] The store operation performance analysis device (100) can provide intelligent analysis results that go beyond simple data visualization tools and support user-customized performance interpretation and strategy formulation.

[0051] FIG. 2 is a block diagram illustrating an exemplary store operation performance analysis device according to one embodiment of the present invention.

[0052] Referring to FIG. 2, the store operation performance analysis device (100) may include a data collection unit (110), an indicator calculation unit (120), a performance analysis unit (130), and a review response unit (140).

[0053] The data collection unit (110) can collect detailed data including at least one of keyword exposure frequency, location display frequency, and content usage information based on user input. The data collection unit (110) can collect data from external search portals, social media, content platforms, etc., through a communication network (10). The detailed data can be configured in various formats, such as map-based exposure information, content reaction logs (e.g., views, shares, saves), search volume and clicks by keyword, and indicators of competitor store groups.

[0054] The data collection unit (110) can, for example, access an external database based on keywords, store names, business types, location information, etc. entered by the user to collect relevant data, preprocess the data into a standardized format, and transmit it to the indicator calculation unit (120).

[0055] Additionally, the data collection unit (110) may include the function of periodically updating the collection target and period, or receiving data in a real-time streaming manner.

[0056] The indicator calculation unit (120) can calculate one or more analysis indicators, including search exposure indicators, behavioral response indicators, and competition indicators, based on external data received from the data collection unit (110).

[0057] The indicator calculation unit (120) can calculate a map-based exposure ranking for a target keyword set according to user input using location display frequency and keyword exposure frequency. The calculated ranking is given an inverse weight, multiplied by the keyword exposure frequency at each ranking, and then cumulatively summed to derive a search exposure indicator.

[0058] The search exposure indicator may be an indicator that quantitatively expresses the keyword competitiveness and location-based visibility of the store. The indicator calculation unit (120) can provide the user with a basis for establishing an effective keyword strategy by converting the map exposure effect for a specific keyword into a numerical value.

[0059] According to one embodiment, when the exposure frequency is high but the map ranking is low for the same keyword, the search exposure indicator can be used as an insight to suggest strategies such as improving map registration information or securing the number of reviews.

[0060] Additionally, the indicator calculation unit (120) can calculate a behavioral response indicator by normalizing at least one of the number of views, shares, and saves included in the content usage information into a response rate relative to the exposure frequency of the content. Through this, the store operation performance analysis device (100) can quantitatively compare and analyze user responses by content.

[0061] Behavioral response metrics are indicators representing the level of user response relative to exposure to each piece of content, and can be used to objectively evaluate the quality, delivery, and attention of marketing content.

[0062] According to one embodiment, content with a high number of views but low sharing or saving can be interpreted as having induced short-term interest but low sustained interest or diffusion power, while conversely, content with high sharing or saving can be evaluated as effective in strengthening brand image and inducing revisits.

[0063] The indicator calculation unit (120) can form a similar store group based on other stores that have keyword similarity and location similarity above a certain standard, and calculate a competitive indicator by comparing search exposure indicators and behavioral response indicators within the store group.

[0064] That is, the indicator calculation unit (120) can evaluate the relative ranking by comparing search exposure indicators and behavioral response indicators between stores using similar keywords within the same commercial area.

[0065] Competitive indicators enable the identification of whether a company's store's exposure and response performance are superior to that of similar stores, thereby allowing for the derivation of strategies to secure a strategic competitive advantage.

[0066] The indicator calculation unit (120) manages the target keyword or store information based on a time series, and can dynamically calculate changes in indicators, ranking trends, and changes in competitive landscape over time.

[0067] In addition, the store operation performance analysis device (100) according to the present invention can provide a top exposure analysis function by utilizing the results of the competition indicator calculation.

[0068] According to one embodiment of the present invention, the performance analysis unit (130) can perform the function of quantitatively analyzing store operation performance based on search exposure indicators, behavioral response indicators, and competition indicators calculated by the indicator calculation unit (120), and converting this into a comprehensive performance index.

[0069] According to one embodiment, the performance analysis unit (130) can calculate a statistical correlation coefficient between past sales performance data and each analysis indicator, and assign first, second, and third weights in proportion to this correlation coefficient.

[0070] For example, the performance analysis unit (130) assigns a relatively high weight to the search exposure indicator when the search exposure indicator has the highest correlation with sales, so that it is reflected in the performance evaluation.

[0071] The performance analysis unit (130) can calculate the overall performance index by multiplying each indicator by the assigned weight and then summing them up.

[0072] According to another embodiment, the performance analysis unit (130) can convert the overall performance index into a percentage index or the like and visualize it. Through this, the store operation performance analysis device (100) can provide an intuitive performance comparison effect.

[0073] The performance analysis unit (130) can analyze the pattern of change in the performance index over time using keyword-based time series data input from the indicator calculation unit (120). For example, if a rapid rise or fall is detected within a certain period, the performance analysis unit (130) can determine that it is an abnormal fluctuation and provide a notification to the manager or output a warning message to the user terminal (20). Through this, risks such as data manipulation can be prevented.

[0074] According to one embodiment, if there is an analysis goal set by the user (e.g., strengthening new inflow, inducing revisits, improving content response rate, etc.), the performance analysis unit (130) can calculate a customized performance index by adjusting the weight of each indicator according to the goal. Through this, the performance analysis unit (130) can perform a multi-faceted performance evaluation that aligns with strategic goals, rather than an evaluation centered on a single indicator.

[0075] For example, the performance analysis unit (130) can calculate a comprehensive performance index by giving additional weight to the search exposure indicator when the goal of the analysis is to strengthen new inflow.

[0076] The store operation performance analysis device (100) can provide a function to trace back the cause of top exposure by extracting competing stores that are ranked high in exposure for a specific keyword and analyzing additional indicators such as the number of reviews, saves, and blog mentions of those stores. Through this, the user can specifically identify the elements that need to be strengthened (e.g., review activation, content diffusion, etc.) for their store to be ranked high.

[0077] Furthermore, the store operation performance analysis device (100) can automatically summarize and provide the analyzed indicators and comprehensive performance index in the form of a report. The store operation performance analysis device (100) can provide a report including the relative ranking with competing stores, key performance indicators (KPIs), and recommended actions.

[0078] According to one embodiment of the present invention, the review response unit (140) can automatically generate a response sentence for a customer review based on user input or store characteristic information.

[0079] According to one embodiment, the review response unit (140) can classify the type of review by analyzing the sentiment and purpose of the review based on the review data collected through the data collection unit (110).

[0080] The review response unit (140) receives the original review text collected from the data collection unit (110) as input and can automatically classify the emotional characteristics (e.g., positive, negative, neutral) and purposeful characteristics (e.g., complaints, praise, suggestions, inquiries, etc.) of the review into one of multiple categories using a Natural Language Processing (NLP) based algorithm. These classification results can serve as criteria for selecting a response generation strategy thereafter.

[0081] The review response unit (140) may select at least one of the predefined response strategies corresponding to the classified review types. For example, if a particular review is classified as negative, the review response unit (140) may select an apology strategy.

[0082] After selecting a strategy, the review response unit (140) can generate a final response sentence by combining sentences by referencing a response template or multiple sentence patterns that match the strategy from a database or built-in model.

[0083] Additionally, the review response unit (140) can generate a natural response sentence that reflects the user's customized tone and expression style by referring to user information registered in advance from the user (e.g., age, gender, tone tendency, brand image, etc. of the store operator).

[0084] According to one embodiment, the review response unit (140) may select at least one of a predefined response strategy (e.g., apology, thanks, information provision, etc.) based on the review analysis result, and may construct a response sentence by combining a pre-learned template or sentence pattern based on the selected strategy.

[0085] Through this, the store operation performance analysis device (100) can automate repetitive review response tasks, thereby saving the user's time. In addition, the store operation performance analysis device (100) can maintain a consistent tone and response quality, thereby providing a positive effect on brand image management.

[0086] FIG. 3 is an example diagram for registering a ranking tracking target place according to one embodiment of the present invention.

[0087] Referring to FIG. 3, the store operation performance analysis device (100) can register standard keywords for measuring place search rankings based on user input (310). The store operation performance analysis device (100) can receive the name of a specific store or business that wants to track rankings (320).

[0088] That is, the store operation performance analysis device (100) can acquire and register target keywords and place information through user input. The store operation performance analysis device (100) can analyze map-based exposure rankings and ranking change trends based on the input keywords or place information.

[0089] FIG. 4 is an example diagram showing the daily exposure ranking and content usage information of a ranking tracking target place in a time series according to one embodiment of the present invention.

[0090] Referring to FIG. 4, the store operation performance analysis device (100) can provide information on search ranking, number of saves, number of blog mentions, and amount of receipts collected, along with the name (410) of the place to be analyzed, registrant information, place ID, location information on a map, and affiliated category (420).

[0091] According to one embodiment, content usage information may include the number of saves, the number of blogs, and the number of receipts. The store operation performance analysis device (100) may provide daily ranking changes and content usage information for a place to be analyzed arranged in a time series, thereby enabling an intuitive understanding of performance trends over time. Specifically, the store operation performance analysis device (100) may provide trends in the number of saves, blog mentions, and receipts changing based on a specific date, thereby enabling real-time detection of marketing campaign effects or changes in customer response.

[0092] According to one embodiment, the performance analysis unit (130) can perform time-series-based analysis of search exposure indicators, behavioral response indicators, and competition indicators. The analysis period can be set, for example, on a daily, weekly, or monthly basis. Through this, the performance analysis unit (130) can track changes in the exposure ranking of target keywords or stores, content response trends, and changes in relative indicators with competing stores over time. For example, if the exposure ranking of a specific keyword is declining on a weekly basis, the responsiveness of content related to that keyword can be checked or changes to the advertising strategy can be reviewed.

[0093] FIG. 5 is an example diagram illustrating a plurality of store lists and time-series change data according to registered tracking keywords according to an embodiment of the present invention.

[0094] Referring to FIG. 5, the store operation performance analysis device (100) can provide the ranking, store name, number of saves, number of blog mentions, and number of receipts for top-ranking businesses for the tracked keyword (510). The store operation performance analysis device (100) can be sorted in real-time or by a specific date and can provide a search function through keywords and business names.

[0095] The store operation performance analysis device (100) can provide time-series ranking changes and storage count changes for the target store (520, 530). Through this, the user can intuitively analyze how the exposure performance of the target store has changed over time.

[0096] Through this, the store operation performance analysis device (100) can provide a competitive landscape within the entire market for tracking keywords and help quantitatively analyze the change patterns of a specific place. The store operation performance analysis device (100) can be used for establishing marketing strategies, responding to a decline in rankings, benchmarking competitor stores, etc.

[0097] FIG. 6 is an example diagram illustrating the search and results of top-exposure companies by keyword according to one embodiment of the present invention.

[0098] Referring to FIG. 6, the store operation performance analysis device (100) can provide a list of businesses that appear at the top when searching for a target keyword entered by a user. Specifically, the store operation performance analysis device (100) can adjust the number of stores viewed through a dropdown menu (610).

[0099] Figure 7 is an example of a time-series change of exposure rankings and content response indicators of top companies by keyword according to one embodiment of the present invention.

[0100] The store operation performance analysis device (100) can provide a time series graph showing changes in daily rankings, number of saves, number of blog mentions, and number of receipts collected for top-ranked companies regarding the target keyword.

[0101] Referring to FIG. 7, the red line (710) may indicate changes in daily exposure rankings. The blue line (720) is the number of receipts based on actual purchase history. The yellow line (730) may indicate content response based on blog mention volume. The green line (740) is the number of saves and may be an indicator of user interest and intention to revisit.

[0102] The store operation performance analysis device (100) can visually provide correlations between the trends and rankings of each indicator so that multiple competitors can be compared based on a single keyword. Through this, the user can comprehensively assess the marketing effect, content response, and volatility of the competitive situation at a specific point in time, and can utilize this for strategic response and adjustment of advertising investment.

[0103] FIG. 8 is an example diagram that visually provides monthly and period-based search volume statistics of keywords according to one embodiment of the present invention.

[0104] Referring to FIG. 8, the store operation performance analysis device (100) can provide time-series-based search volume statistics for a target keyword. Specifically, the store operation performance analysis device (100) can provide the search volume for the target keyword over the past month (810), the monthly average search volume over the past year (820), the daily average search volume over the past month (830), and the weekly average search volume over the past six months (840).

[0105] The store operation performance analysis device (100) can enable the derivation of strategies for different periods, such as seasonality, campaign effects, and issue response, by providing average values ​​for various time units for the analysis target keywords set by the user.

[0106] FIG. 9 is an example traffic analysis diagram showing the time-series change trend of keyword search volume according to one embodiment of the present invention.

[0107] Referring to FIG. 9, the store operation performance analysis device (100) can visually display and provide changes in keyword search volume for a specific period set by the user (910).

[0108] Specifically, the store operation performance analysis device (100) can provide search volume figures recorded by date in the form of a linear chart. Through this, the store operation performance analysis device (100) can intuitively identify a surge or decrease phenomenon at a specific point in time.

[0109] According to one embodiment, using a store operation performance analysis device (100), a user can analyze the pattern of fluctuation in search demand for a specific keyword and identify changes in search volume due to marketing campaigns, the occurrence of issues, seasonal factors, etc. The store operation performance analysis device (100) can provide criteria useful for setting the timing of content or advertising strategies.

[0110] Furthermore, the store operation performance analysis device (100) can help intuitively identify trend changes regarding the target keyword. Users can use the increase or decrease in the search volume of the target keyword at a specific point in time as an indicator for establishing marketing strategies or improving content.

[0111] FIG. 10 is an example diagram visualizing the age group and gender ratio of keyword searchers according to one embodiment of the present invention.

[0112] Referring to FIG. 10, the store operation performance analysis device (100) can derive age group and gender statistics of searchers who have performed a specific keyword or store-related search and display them visually.

[0113] Specifically, the store operation performance analysis device (100) can provide search ratios by age group for searchers in their teens, 20s, 30s, 40s, and 50s or older (1010). More specifically, if the 20s and 30s account for the highest search proportions at 37.3% and 37.8%, respectively, this may suggest that the keyword is receiving attention mainly from the younger generation.

[0114] The store operation performance analysis device (100) can provide a search gender ratio (1020). Through this, the store operation performance analysis device (100) can provide the distribution of the main age groups and genders of the marketing target. Based on this, the user can establish a target-tailored content strategy and a customer-tailored response strategy.

[0115] FIG. 11 is an example diagram showing daily search volume according to one embodiment of the present invention.

[0116] Referring to FIG. 11, the store operation performance analysis device (100) can provide daily search volume data (1120) for a set search keyword by date (1110). The store operation performance analysis device (100) can provide a search volume trend over time. The search volume trend over time can be used as basic data for detecting anomalies and quantitatively identifying trend changes.

[0117] The store operation performance analysis device (100) can provide real-time search volume trends to the user intuitively by visually and numerically supporting keyword traffic monitoring and search trend analysis functions.

[0118] FIG. 12 is an example diagram showing usage response information of popular content according to one embodiment of the present invention visualized in a time series.

[0119] The store operation performance analysis device (100) can collect content usage information including the number of views, likes, and comments of the content. The store operation performance analysis device (100) can visualize and provide daily change trends in the form of a time series chart.

[0120] Specifically, the store operation performance analysis device (100) can provide the total number of views, total number of likes, and total number of comments, along with the respective growth rate (%) (1210). The store operation performance analysis device (100) can provide linear graphs of the trend in the number of views (1220) and the trend in the number of likes (1230) over a specific period. The store operation performance analysis device (100) enables quantitative analysis of changes in user reactions to content, and in particular, supports visual identification of the rising or falling trend in the popularity of specific content.

[0121] According to one embodiment, the indicator calculation unit (120) can calculate a behavioral response indicator by normalizing at least one of the number of views, shares, and saves included in the content response log into a response rate relative to the exposure frequency of the content.

[0122] The normalization method can be implemented, for example, as a ratio of the number of exposures to the number of reactions or an indexing method based on the number of reactions relative to the number of unit exposures. Through this, the indicator calculation unit (120) can provide a comparative response efficiency for each content.

[0123] FIG. 13 is an illustrative diagram for explaining the operation of a review response unit according to an embodiment of the present invention.

[0124] Referring to FIG. 13, the store operation performance analysis device (100) can receive input from a user regarding MID (1310), age (1320), gender (1330), and the number of reviews to display (1340), and generate an optimized AI review response sentence based on this.

[0125] More specifically, the store operation performance analysis device (100) can collect review data written on an external platform. The store operation performance analysis device (100) can remove unnecessary special characters, etc. from the collected reviews and preprocess them into a form suitable for natural language analysis.

[0126] The review response unit (140) can classify preprocessed reviews into sentiment types such as positive, negative, or neutral through a sentiment analysis module. The review response unit (140) can determine whether the review is of a specific purpose type, such as praise, complaint, inquiry, or suggestion, through context-based analysis. The review response unit (140) can determine the response style based on user profile information entered by the user, such as age, gender, and tone (e.g., polite tone, informal tone).

[0127] The review response unit (140) can select at least one of the predefined response strategy templates according to the emotion type and purpose type. Based on the selected template, the review response unit (140) can generate a customized response sentence by combining sentence patterns.

[0128] According to one embodiment, the review response unit (140) can generate natural response sentences based on the age, gender, and tone setting values ​​registered by the user. For example, the review response unit (140) can generate a review based on soft and casual expressions when set to a female in her 20s, and generate a review using concise and polite expressions when set to a male in his 50s.

[0129] The store operation performance analysis device (100) can automatically provide AI-based review responses tailored to each user, thereby reducing the burden on users in managing reviews and improving the quality of customer response.

[0130] Furthermore, the store operation performance analysis device (100) can provide the generated response sentence to the user as a preview. The user can register a review after automatic registration or modification.

[0131] FIG. 14 is an example diagram showing the operation screen of a bulk search volume inquiry function according to one embodiment of the present invention.

[0132] When a user inputs multiple keywords, the store operation performance analysis device (100) can provide search volume statistics and competition information for each keyword. Specifically, the store operation performance analysis device (100) can provide monthly search volume (distinguishing between PC and mobile), daily average search volume, click volume and click-through rate, competition indicators, and monthly average number of exposed advertisements. Based on this, the store operation performance analysis device (100) enables quantitative analysis of market demand and advertising competition by keyword, and the user can utilize this for establishing marketing strategies, selecting advertising keywords, or analyzing the feasibility of opening a new store.

[0134] According to an additional embodiment, the performance analysis unit (130) according to the present embodiment is a search exposure indicator. The performance analysis unit (130) according to the present embodiment is a search exposure indicator. , behavioral response indicators and competitive indicators time series Collected according to, and each indicator and sales performance Comprehensive performance index by analyzing the correlation structure between It can calculate the comprehensive performance index according to the following mathematical formulas 1 and 2. Specifically, the performance analysis department (130) calculates the comprehensive performance index according to the following mathematical formulas 1 and 2. can calculate.

[0135]

[0136] Here, each is a non-linear function that reflects the sensitivity and reference threshold of the corresponding indicator, and is defined according to Equation 2.

[0137]

[0138] The above is the weight for each indicator, the correlation coefficient between each indicator and sales performance It is set in proportion to. is the sensitivity adjustment coefficient, is a reference threshold value, and can be automatically corrected based on the average or median value of the indicator.

[0139] For example, search exposure metrics a sales performance If it shows a high positive correlation with, The value is set relatively high, and the overall performance index It will have a strong impact on. Conversely, competitive indicators If the correlation is low, It is set low, so the impact is reduced.

[0140] As such, this embodiment reflects the mutual non-linear relationship between each indicator, enabling more realistic and flexible performance analysis than the simple weighted average method, and can provide quantitative analysis results that consider both data variability and sensitivity.

[0141] As such, this embodiment reflects the mutual non-linear relationship between each indicator, enabling more realistic and flexible performance analysis than the simple weighted average method, and can provide quantitative analysis results that consider both data variability and sensitivity.

[0142] According to an additional embodiment, the indicator calculation unit (120) calculates a Reaction Efficiency Index that evaluates the relative efficiency of each keyword by integrating the reaction rate calculated from content usage information and the competition intensity corresponding to the same keyword.

[0143] Specifically, the indicator calculation unit (120) is individual content Exposure count regarding , reaction number Basic reaction rate using It can be calculated as shown in the following mathematical formula 3.

[0144]

[0145] Here includes at least one of views, likes, shares, and saves per content, and may refer to the total number of impressions of the content.

[0146] Subsequently, the indicator calculation unit (120) determines the competitive intensity within the same keyword or the same industry. Reaction efficiency index reflecting the degree of competition correction factor, considering Calculate according to the following mathematical formula 4.

[0147]

[0148] In mathematical formula 4 It adjusts the degree of influence of competition intensity as a competition mitigation coefficient, and is a competitive sensitivity control constant that mitigates the decrease in efficiency indicators as competition intensity increases.

[0149] For example, the intensity of competition for a specific keyword Even if it rises rapidly, If the value is large The fluctuation range is kept gradual so that efficiency remains stable despite competitive situations when the actual content quality is high.

[0150] The performance analysis unit (130) is calculated for multiple contents or keywords The standardized mean value of the overall efficiency distribution by normalizing the values Calculate as shown in the following mathematical formula 5.

[0151]

[0152] Here is the total number of content items subject to evaluation, is the average efficiency index of the entire content, is the standard deviation. Through this, the store operation performance analysis device (100) can quantitatively evaluate relative efficiency according to the level of competition and can distinguish high-quality content even in a highly competitive market.

[0153] Furthermore, the performance analysis unit (130) is a time series According to rate of change By monitoring it in real time, it is possible to automatically detect periods of decreased or rapidly increasing response efficiency due to changes in the competitive environment and provide notifications when the rate of change exceeds a preset threshold.

[0154] This integrated competition-response rate model goes beyond simple click-through rate or response rate analysis and enables dynamic efficiency evaluation that reflects the market competition structure, thereby providing a means to optimize the advertising efficiency and content strategies of actual store operators.

[0155] According to an additional embodiment, the performance analysis unit (130) automatically detects abrupt changes in performance indicators or data distortion caused by external events from time series data, at each point in time Search exposure metrics in , behavioral response indicators , competition indicators Normalized fluctuation vector It can generate.

[0156] Normalization is the average value of each indicator and standard deviation Based on , it is performed according to the following mathematical formula 6.

[0157]

[0158] The performance analysis unit (130) uses the normalized vector to time points Distance indicating the degree of anomaly Calculate as in mathematical formula 7.

[0159]

[0160] Here, is the reference mean vector, and is a covariance matrix that reflects the correlation.

[0161] a threshold If it exceeds, the performance analysis unit (130) determines the corresponding section as an abnormal fluctuation and the correction coefficient Comprehensive performance index by applying It can be corrected as shown in the following mathematical formula 8.

[0162]

[0163] The above correction formula is the greater the abnormal variation It operates by decreasing exponentially to reflect normal data from the previous point in time with weight. Accordingly, this embodiment provides a means to automatically mitigate abrupt changes in indicators caused by external campaigns, surges in advertising, data errors, etc., thereby enabling stable performance analysis based on long-term trends.

[0164] Therefore, the present embodiment can implement an AI-based performance evaluation model equipped with data anomaly detection and self-correction functions beyond simple statistics-based analysis, and can support store operators in making highly reliable trend-based decisions.

[0165] According to an additional embodiment, the performance analysis unit (130) according to the present embodiment is a search exposure indicator collected based on a time series. , behavioral response indicators , competition indicators and sales performance An artificial intelligence regression prediction model can be constructed using [data] as training data.

[0166] In particular, in this embodiment, a multi-layer perceptron (MLP)-based prediction model is used to reflect the non-linear correlation between each indicator. Construct , and the overall performance prediction value according to the following mathematical formula 9 It can produce.

[0167]

[0168] Here, is the input vector, and is the learnable weight and bias vector, is an activation function (ReLU or Sigmoid).

[0169] Model training is based on actual sales data To minimize the error with, the loss function of the following Equation 10 It is performed in a direction that minimizes .

[0170]

[0171] Here, is a normalization coefficient to prevent overfitting.

[0172] A trained artificial intelligence model new input Future sales forecast values ​​for It can derive and can also be used to detect abnormal fluctuations by analyzing the residual between the prediction results and the actual data.

[0173] Furthermore, the performance analysis unit (130) outputs the model Wow, actual sales By cumulatively analyzing the error patterns over time, it is possible to inversely calculate which factor among advertising efficiency, content responsiveness, and competitiveness contributed most significantly to the prediction error, and provide the results. Through this, store operators can quantitatively interpret the causes of performance decline from the AI ​​model and derive strategic improvement directions.

[0174] As such, this embodiment can support data-driven decision-making by learning non-linear interactions between indicators through an AI-based weighted regression model and predicting sales and performance with higher precision compared to simple statistical analysis.

[0175] The embodiments described above may be implemented as hardware components, software components, and / or combinations of hardware and software components. For example, the devices, methods, and components described in the embodiments may be implemented using one or more general-purpose or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing unit may execute an operating system (OS) and one or more software applications executed on said operating system. Additionally, the processing unit may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing unit may be described as being used as a single unit, but those skilled in the art will understand that the processing unit may include multiple processing elements and / or multiple types of processing elements. For example, the processing unit may include multiple processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

[0176] Software may include computer programs, code, instructions, or a combination of one or more of these, and may configure a processing unit to operate as desired or command the processing unit independently or collectively. Software and / or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal wave so as to be interpreted by the processing unit or to provide instructions or data to the processing unit. Software may be distributed over networked computer systems and may be stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

[0177] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the computer-readable medium may be those specifically designed and configured for the embodiment, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

[0178] Although the embodiments have been described above with reference to the limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, suitable results may be achieved even if the described techniques are performed in a different order than described, and / or if the components of the described system, structure, device, circuit, etc. are combined or assembled in a form different from described, or replaced or substituted by other components or equivalents.

[0179] This specification discloses preferred embodiments of the present invention. Although specific terms have been used, they are used merely in a general sense to facilitate the explanation of the technical content of the invention and to aid in understanding the invention, and are not intended to limit the scope of the invention. It is obvious to those skilled in the art that, in addition to the embodiments disclosed herein, other variations based on the technical concept of the present invention are possible. Those skilled in the art will understand that various substitutions, changes, and modifications can be made within the scope of the technical concept of the store operation performance analysis device according to the embodiments described with reference to FIGS. 1 to 14. Therefore, the scope of the invention should not be determined by the described embodiments but by the technical concept described in the patent claims.

Claims

Claim 1 A store operation performance analysis device comprises at least one processor, and is implemented by the at least one processor: a data collection unit that collects detailed data including at least one of keyword exposure frequency, location display frequency, and content usage information based on user input; an indicator calculation unit that calculates an analysis indicator including a search exposure indicator, a behavioral response indicator, and a competition indicator based on the collected detailed data; and a performance analysis unit that calculates a comprehensive performance index based on the analysis indicator, wherein the indicator calculation unit calculates a map-based exposure ranking using location display frequency and keyword exposure frequency for a target keyword obtained based on the user input, calculates a search exposure indicator by cumulatively summing the value obtained by multiplying the inverse weight of the map-based exposure ranking for the target keyword by the keyword exposure frequency at each ranking, calculates a behavioral response indicator by calculating at least one of views, shares, and saves included in the content usage information as a response rate for the content exposure frequency, generates a group of similar stores in which keyword similarity and location similarity are greater than or equal to a predetermined value, and calculates a competition indicator by calculating a relative ranking based on a comparison of the search exposure indicator and behavioral response indicator with the group of similar stores, and the performance analysis unit for a target keyword The method is characterized by analyzing keyword exposure frequency, location display frequency, and content usage information based on a time series to analyze the ranking fluctuations of target keywords and the history of ranking conversions within similar store groups, and by assigning a first weight to the correlation coefficient between the search exposure indicator and sales performance, assigning a second weight to the correlation coefficient between the behavioral response indicator and sales performance, assigning a third weight to the correlation coefficient between the competition indicator and sales performance, and calculating a comprehensive performance index based on the first to third weights, and further includes a review response unit that generates a response to a customer review based on predetermined user information, wherein the user information includes user age, gender,The review response unit includes information on tone and expression style, classifies at least one customer review into sentiment type and purpose type, selects at least one of a predefined response strategy according to the classified review type, and generates a response sentence by combining a customized sentence pattern according to the selected strategy, and is characterized by adjusting the weight of the analysis indicator if there is a pre-set goal, wherein the pre-set goal includes influx of new visitors, induction of revisits, and increase in content response, and the performance analysis unit, based on mathematical formula 1 and mathematical formula 2, time series, Search exposure metrics collected according to , behavioral response indicators and competitive indicators Wow, sales performance Comprehensive performance index based on the correlation structure between Calculate and provide, and the above mathematical formula 1 is, and, above is a non-linear function reflecting the sensitivity and reference threshold of each indicator, defined according to the above mathematical formula 2, and the above mathematical formula 2 is, and, above is the weight for each indicator, the correlation coefficient between each indicator and sales performance It is set in proportion to, and the above is a sensitivity adjustment coefficient, and the above A store operation performance analysis device that automatically corrects based on the average or median value of an indicator as a reference threshold. Claim 2 delete Claim 3 delete

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