KR102522676B1 - Data analysis server, method of calculating an expected sales degree for commerce content scheduled to be transmitted and computer program - Google Patents

Abstract

According to embodiments of the present disclosure, analysis data of commerce content is generated using viewing data obtained through one or more media providing servers, external environment data corresponding to order data and programming time, and presentation data included in commerce content. And, by using the analysis data of the commerce contents, it is possible to calculate the expected sales level for the commerce contents that are not yet scheduled to be transmitted but are scheduled to be transmitted in the future.

Description

Data analysis server, method for calculating expected sales of commerce contents scheduled to be transmitted, and computer program

Embodiments of the present disclosure relate to a data analysis server, a method for calculating an expected sales level for commerce content scheduled to be transmitted, and a computer program.

With the development of today's digital technology, television is gradually becoming digital, and on the other hand, the convergence of broadcasting and communication, which provided contents of different characteristics through a separate network, is accelerating. That is, the initiative is shifting from one-way analog broadcasting to interactive digital broadcasting that communicates with viewers. In addition, methods by which users can use content are becoming more diverse, such as providing broadcasting content and commerce content through digital multimedia broadcasting (DMB) and the Internet.

A conventional viewer rating calculation method has used a method of selecting a limited number of panels, installing dedicated equipment necessary for calculating viewer ratings in the households of the selected panels, and collecting data of channels watched by the household for each day to calculate viewer ratings. In particular, since the viewing panel-related data is uniformly transmitted to a server that calculates viewer ratings in a specific time period (eg, 4 to 5 am), it is impossible to calculate viewer ratings in real time.

In addition, since an audience rating calculation device installed in a home cannot determine who among household members is currently watching a channel, it cannot calculate viewer rating data with high reliability. In addition, since the data provided by the viewer rating calculation device includes only information about which channel among channels currently being broadcasted in real time is being watched, information on various events associated with transmitted content cannot be collected.
[Prior literature] KR 10-2018-0071906 (published on June 28, 2018)

In order to solve the above problems, embodiments of the present disclosure are a data analysis server capable of generating an expected sales level for commerce content scheduled to be transmitted based on viewing data and order data for previously transmitted commerce content, and commerce scheduled to be transmitted. It is intended to provide a method and a computer program for calculating an expected sales level for content.

A method according to embodiments of the present disclosure may include receiving, by a data analysis server, a signal requesting an estimated sales level for commerce content scheduled to be transmitted from a first user terminal; In response to the signal, the data analysis server extracts a code of a product sold in the commerce content and a time slot of the commerce content, and determines presentation data obtained by analyzing the commerce content; generating, by the data analysis server, first past analysis data related to product codes of commerce contents; determining, by the data analysis server, expected external environment data related to an expected programming time slot in which commerce content is organized; generating, by the data analysis server, second past analysis data including viewing data or purchasing data related to presentation data included in commerce contents; and calculating, by the data analysis server, an expected sales level for the commerce contents in consideration of the first historical analysis data, the second historical analysis data, and the expected external environment data; may be a method for calculating .

The generating of the first past analysis data may include searching for one or more similar commerce contents selling the same product or similar products based on the product code of the commerce contents, and viewing data and order for the one or more similar commerce contents. It may include at least one of the past number of viewers, the past order amount, and the past purchase conversion rate generated based on data and external environment data.

The step of determining the expected external environment data may include receiving one or more external environment data corresponding to the organizing time of the commerce content from one or more external servers, and determining the expected external environment data by combining the one or more external environment data. there is.

The generating of the second historical analysis data may include retrieving viewing data or order data for each presentation information included in presentation data included in the commerce content, and based on the viewing data or order data for each presentation information. Second historical analysis data including at least one of the number of past viewers, the past order amount, and the past purchase conversion rate for the commerce content may be generated.

The order data may include call-in data including at least one of pre-ordered phone data and the number of simultaneous consultations for one or more previously transmitted commerce contents.

The viewing data includes at least one of the number of inflows, the number of exits, the number of views, and the number of viewers, and may be counted through an access signal from the set-top box while one or more previously transmitted commerce contents are being transmitted.

A computer program according to an embodiment of the present disclosure may be stored in a medium to execute any one of the methods according to an embodiment of the present disclosure using a computer.

In addition to this, another method for implementing the present invention, another system, and a computer readable recording medium recording a computer program for executing the method are further provided.

Other aspects, features and advantages other than those described above will become apparent from the following drawings, claims and detailed description of the invention.

A data analysis server according to embodiments of the present disclosure, a method for calculating an expected sales level for commerce content scheduled to be transmitted, and a computer program are external data corresponding to viewing data obtained through one or more media providing servers, order data, and programming time. Using environmental data and production data included in commerce content, commerce content analysis data is generated, and commerce content analysis data is used to determine the expected sales level for commerce content that is not yet scheduled to be transmitted but is scheduled to be transmitted in the future. can be calculated

1 is a block diagram of a data processing system generating processed data by analyzing viewing data of users according to embodiments of the present disclosure.
2 is a diagram of a data analysis server communicating with one or more media presentation servers.
3 is a block diagram of a data analysis server 100 according to embodiments of the present disclosure.
4 is a block diagram of the data processing unit 130.
5 is flowcharts of a method for calculating expected sales levels for commerce contents scheduled to be transmitted according to embodiments of the present disclosure.
6 to 11 are exemplary diagrams of a user interface generated by the data analysis server 100.

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

Since the present invention can apply various transformations and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. Effects and features of the present invention, and methods for achieving them will become clear with reference to the embodiments described later in detail together with the drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various forms.

Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings, and when describing with reference to the drawings, the same or corresponding components are given the same reference numerals, and overlapping descriptions thereof will be omitted. .

In this specification, terms such as “learning” and “learning” are terms that refer to performing machine learning through computation according to procedures, which are not intended to refer to mental operations such as human educational activities. interpret

In the following embodiments, terms such as first and second are used for the purpose of distinguishing one component from another component without limiting meaning.

In the following examples, expressions in the singular number include plural expressions unless the context clearly dictates otherwise.

In the following embodiments, terms such as include or have mean that features or components described in the specification exist, and do not preclude the possibility that one or more other features or components may be added.

In the drawings, the size of components may be exaggerated or reduced for convenience of description. For example, since the size and thickness of each component shown in the drawings are arbitrarily shown for convenience of description, the present invention is not necessarily limited to the illustrated bar.

When an embodiment is otherwise implementable, a specific process sequence may be performed differently from the described sequence. For example, two processes described in succession may be performed substantially simultaneously, or may be performed in an order reverse to the order described.

A set-top box is a hardware device necessary for viewing satellite broadcasting or cable broadcasting at home. Data such as satellite broadcasting, cable broadcasting, or internet broadcasting is transmitted to the set-top box as data processed through compression and modulation. The set-top box processes the received data by demodulation and decompression, It plays a role to enable implementation in terminals such as TVs.

1 is a block diagram of a data processing system generating processed data by analyzing viewing data of users according to embodiments of the present disclosure.

As shown in FIG. 1, the data processing system includes a data analysis server 100, a media providing server 200, a content transmission server 300, a first set-top box 401, a second set-top box 402, 3 set-top box 403, . . . , an n-th set-top box 40n, and one or more user terminals (T1, T2, T3, ..., Tn, hereinafter, T).

The data analysis server 100 receives viewing data, call-in data, and/or order data for content and/or output data related to the content through the media providing server 200, and receives the received viewing data and call-in data. and/or order data to analyze content-related order amount, expected handling amount, order amount relative to viewers, number of repeat visits, average viewing duration, number of inflows, number of exits, number of views compared to previous years, number of inflows, number of exits from a segment, and number of return visits to a segment , Data processing data or analysis data such as average viewing duration of a section, order amount for a section, expected handling amount per minute, order amount per viewer for a section, etc. may be generated. The data analysis server 100 is based on the time series change pattern generated by linking the viewing data and order data, the highest handling amount target time zone, the lowest handling amount target time period, the highest number of viewers time period, the lowest number of viewers time period, the highest purchase conversion rate time period, and the lowest It can be calculated for the purchase conversion rate time zone, the highest handling amount per customer price time zone, the lowest handling amount per customer price time zone, the highest expected handling amount time zone, and the lowest expected handling amount time zone.

The media providing server 200 may generate output data for broadcasting and transmit the output data for broadcasting to one or more set-top boxes 400 . At this time, the media providing server 200 may transmit output data for broadcasting to one or more set-top boxes 400 using a cloud service. The set-top box 400 may execute various user interfaces by driving applications provided by the media providing server 200 . The set-top box 400 can output content through each connected output device. The set-top box 400 may access the content transmission server 300 and output content included in broadcasting output data. The user terminal (T) refers to an electronic device that each user has separately from the set-top box 400, and various types of devices having display functions such as smart phones, mobile phones, wearable devices, PMPs, tablets, desktops, laptops, etc. can

The media providing server 200 may transmit commerce content providing guidance on products for sale, a function to order products by phone, a function to order a remote control, and a function to order a homepage for the products to be sold to one or more set-top boxes 400 .

Meanwhile, the media providing server 200 may control the set-top box 400 through a program installed in the set-top box 400 . The media providing server 200 may perform a function of reading information of the set top box 400 . The media providing server 200 may receive a platform code of the set-top box 400, a set-top box ID (STP ID), access information, and the like. The media providing server 200 may generate output data of the set-top box 400 based on information about the set-top box 400 . The media providing server 200 may regenerate output data for broadcasting in response to a user input acquired through the set-top box 400 and transmit it to the set-top box 400 .

The media providing server 200, instead of the information itself including various contents such as operation through a cloud solution, captures information of such contents to the screen of the output devices 411, 412, 413, ... 41n through the set-top box 400. can be displayed. The media providing server 200 may be implemented using a cloud server, and may control conversion of at least one first cloud server and a second cloud server. The media providing server 200 implements the screens of the output devices 411, 412, 413, ... 41n by driving an application corresponding to a selected channel through the first cloud server, and can accommodate multiple users through this.

The set-top box 400 communicates with the media providing server 200 to receive output data, analyzes and reads (encodes, etc.) the received output data to output devices 411, 412, 413, ... 41n such as TVs. can be output through The set-top box 400 may output the broadcast output data received from the media providing server 200 through the output devices 411, 412, 413, ... 41n. The set-top box 400 may obtain a user input through the provided input device and transmit a request signal corresponding to the user input to the media providing server 200 .

The content transmission server 300 transmits broadcast images of one or more broadcasting companies or broadcasting channels to the set-top boxes 400 . The content transmission server 300 may be implemented to broadcast content received from the content providing server. The content delivery server 300 may convert and store the content according to platforms or formats of one or more broadcasting companies, portal companies, and content producers. This is also the case in the embodiments described later and modifications thereof.

The data processing system includes a first set-top box 401, a second set-top box 402, a third set-top box 403, ... , the first output device 411, the second output device 412, the third output device 413, respectively connected to the nth set-top box 40n, ... An nth output device 41n may be further included. The first set-top box 401, the second set-top box 402, the third set-top box 403, ... , At least one of the nth set-top boxes 40n may be referred to as the set-top box 400. The first terminal (T1), the second terminal (T2), the third terminal (T3), ... , at least one of the nth terminals Tn may be referred to as a terminal T.

In the data processing system, a data analysis server 100, a media providing server 200, a content transmission server 300, a first set-top box 401, a second set-top box 402, a third set-top box 403, ... , the nth set-top box 40n, and one or more user terminals T1, T2, T3, ..., Tn, hereinafter, T) may communicate with each other through a network. The communication method of the network of the present disclosure includes wired or wireless communication, but is not limited thereto, but the network according to some embodiments may use short-range wireless communication. In the data processing system, the set-top box 400 and the output devices 411, 412, ... 41n may be connected through a network or an electric line. In addition, the set-top box 400 and the output devices 411, 412, 413, ... 41n may be implemented as one piece of hardware. The media providing system 10 may further include a remote control device (eg, a remote control) for communicating with the set-top box 400 and/or the output devices 411, 412, 413, ... 41n. The set-top box 400 and/or the output device 411, 412, 413, ... 41n may communicate with the remote controller through a local area network.

2 is a diagram of a data analysis server communicating with one or more media presentation servers.

As shown in FIG. 2, the data analysis server 100 includes a first media providing server 201, a second media providing server 202, ... , viewing data, call-in data, and/or order data may be received from the n-th media providing server 20n.

The data processing system provides content through one media providing server 200 as shown in FIG. 1, but as shown in FIG. 2, the first media providing server 201 and the second media providing server 202 ), … , Content may be provided through the n-th media providing server 20n.

The data analysis server 100 includes a first media providing server 201, a second media providing server 202, ... , viewing data, call-in data, and/or order data for the contents provided through the n-th media providing server 20n may be received.

3 is a block diagram of a data analysis server 100 according to embodiments of the present disclosure.

The processor 110 is a component for controlling the data analysis server 100 as a whole. Specifically, the processor 110 controls the overall operation of the data analysis server 100 using various programs stored in the data processing unit 130 of the data analysis server 100 . For example, the processor 110 may include a CPU, RAM, ROM, and a system bus. Here, the ROM is a configuration in which a command set for system booting is stored, and the CPU copies the stored operating system of the media providing server 200 to the RAM according to the command stored in the ROM, and executes the O/S to boot the system. When booting is completed, the CPU copies various applications stored in RAM and executes them to perform various operations. In the above, the data analysis server 100 has been described as including only one CPU, but may be implemented with a plurality of CPUs (or DSPs, SoCs, etc.) at the time of implementation.

According to an embodiment of the present disclosure, the processor 110 may be implemented as a digital signal processor (DSP) that processes digital signals, a microprocessor, or a time controller (TCON). However, it is not limited thereto, and a central processing unit (CPU), a micro controller unit (MCU), a micro processing unit (MPU), a controller, an application processor (AP), or It may include one or more of a communication processor (CP) and an ARM processor, or may be defined by the term. In addition, the processor 110 may be implemented as a system on chip (SoC) having a processing algorithm, a large scale integration (LSI), or a field programmable gate array (FPGA).

The communication unit 120 is a component for transmitting and receiving data with devices such as the media providing server 200, the content transmission server 300, one or more set-top boxes 401, 402, 403, ... 40n, and the user terminal T. . The communication unit 120 includes a Bluetooth communication unit, a Bluetooth Low Energy (BLE) communication unit, a Near Field Communication unit (WLAN) communication unit, a Zigbee communication unit, an infrared data association (IrDA) communication unit, and a WFD ( It may include a Wi-Fi Direct) communication unit, a UWB (ultra wideband) communication unit, a short-range communication unit such as an Ant+ communication unit, a mobile communication network, or a wired Ethernet network.

4 is a block diagram of the data processing unit 130.

Data processing unit 130 includes viewing data acquisition unit 131, external environment data acquisition unit 132, call-in data acquisition unit 133, order data acquisition unit 134, commerce content analysis unit 135, real-time It may include a data analysis unit 136, a statistical data analysis unit 137, and a prediction data generation unit 138.

The viewing data acquisition unit 131 may receive viewing data for content displayed on the set-top box acquired through the media providing server 200 . Viewing data is related to behavioral patterns of one or more users who watch content transmitted through the media providing server 200, and may be collected by the media providing server 200 through the set-top box 400. The viewing data acquisition unit 131 receives the inflow time and exit time of the commerce content to be transmitted recorded in the media providing server 200, records the inflow time and exit time for each user, and records the commerce content by time. Viewing data such as the number of inflows, the number of exits, the number of viewers, the number of revisits, and the average viewing duration can be generated. In addition, the viewing data may include identification information of the set-top box, and may be classified according to viewers viewing commerce content using the identification information of the set-top box. In the case of a set-top box used by a plurality of users, a viewer of commerce content may be identified based on an order signal made during viewing. Through this, the viewing data acquisition unit 131 may calculate a point of entry into the commerce content and a point of departure for each identified viewer.

Viewing data may include information about a viewer, and may include information about a viewer's viewing area, user identification information, set-top box identification information, and the like. Here, the number of arrivals and/or departures may be counted through an access signal to the set-top box while the commerce content is being transmitted. The number of views compared to the previous one refers to the number of viewers compared to the commerce content that sells the same product as the product of the corresponding commerce content. The number of revisits may be a value obtained by counting the number of times an access signal is received again from a set-top box that has been introduced and left. The average viewing duration may be calculated as a difference value between an entry point and an exit point. The order amount relative to viewers may be calculated by dividing the order amount generated while the commerce content is being transmitted by the total number of viewers. The number of viewers may be determined by the number of connected set-top boxes.

The external environment data acquisition unit 132 may acquire external environment data for a time when commerce content is transmitted. The external environment data acquisition unit 132 transmits weather information (temperature, precipitation, rainfall, wind information, etc.), real-time news information, real-time event information (sports games, parades, festivals, etc.) It can be obtained by requesting from the server of The external environment data acquisition unit 132 may obtain corresponding information by searching for weather information, real-time news information, real-time event information, and the like, and reading search result pages. The external environment data acquisition unit 132 may correspond and store weather information, real-time news information, real-time event information, and the like for each time the commerce content is transmitted.

The call-in data acquiring unit 133 may obtain call-in data for commerce content from the media providing server 200 and/or the data server 500 . The call-in data may be data obtained by analyzing the number of call-in calls by time while the commerce content is transmitted.

The order data acquisition unit 134 may acquire order data for commerce content from the media providing server 200 and/or the data server 500 . The order signal may be introduced through a set-top box (400 in FIG. 1) through which commerce contents are transmitted, or may be introduced through a user terminal (T in FIG. 1) possessed by the user. The media providing server 200 may generate and store order data corresponding to the order signal. The order data acquisition unit 134 transmits a signal requesting order data for commerce contents to the media providing server 200 and/or data server 500 and receives a response signal thereto, thereby ordering commerce contents. data can be obtained. The order data refers to order-related information for products or services sold by commerce contents, and may include an order amount per time for commerce contents, an order amount for each order medium provided in commerce contents, and the like.

The order data may be data ordered by an ordering method such as a remote control order, a telephone order, an on-demand order, or a simple mobile order while commerce contents are being transmitted.

The commerce content analyzer 135 may generate time-specific presentation data for transmitted commerce content. The presentation data for the commerce content is information related to the content of the commerce content, and may include presentation information. For example, the presentation information of the first section of the commerce content is content information in which the show host explains the product, the presentation information of the second section is content information that enlarges and shows the product, and the presentation information of the third section is the model Content information using the product may be included.

Directing information of commerce contents may be generated by a PD or a planner who plans the contents. In this case, production information corresponding to a corresponding section for each section of commerce content may be tagged by a PD or a planner.

The commerce content analysis unit 134 may analyze and obtain object information, situation information, etc. obtained from the image when image information included in each section is included in the presentation information of the commerce content.

The real-time data analysis unit 136 may analyze data acquired while commerce content is transmitted in real time. The real-time data analysis unit 136 receives viewing data and/or order data from the viewing data obtaining unit 131 and/or order data obtaining unit 134 while the commerce contents are being transmitted, and the call-in data obtaining unit ( Call-in data may be received from 133), external environment data may be received from the external environment data acquisition unit 132, and presentation data for commerce content may be received from the commerce content analysis unit 135. The real-time data analysis unit 136 may correspond the received data to each time of commerce content. For example, the real-time data analysis unit 136 combines the viewing data of the first time interval included in the viewing data, the order data, the external environment data, and the production data for the commerce content with respect to the currently transmitted commerce content, Real-time analysis data such as the number of viewers in a time interval, the amount of orders, the purchase conversion rate versus the number of viewers, the order amount versus the number of viewers, the order amount according to weather, the purchase conversion rate according to weather, and the viewing time according to weather may be generated.

The statistical data analysis unit 137 uses viewing data, order data, external environment data, and production data for one or more commerce contents that have been transmitted, and viewing data corresponding to programming time, order data corresponding to programming time, and production Viewing data corresponding to the information, order data corresponding to the production information, and the like may be generated. The statistical data analysis unit 137 statistically analyzes one or more commerce contents that have been transmitted to generate past analysis data including viewing data or order data corresponding to at least one of organizing time, production information, and external environment data. there is.

Real-time analytics data or historical analytics data includes the number of views, the number of viewers, the number of churns, the number of repeat visits, the average duration, the number of orders, the purchase conversion rate, the transaction price per customer, the number of views versus the amount of orders, the amount of orders versus viewers, the highest transaction amount target time period, and the lowest transaction amount. Target time zone, highest number of viewers time zone, lowest number of viewers time zone, highest conversion rate time zone, lowest purchase conversion rate time zone, highest transaction price per customer time zone, lowest transaction price per customer time zone, highest expected transaction value time zone, lowest expected transaction value time zone, highest number of viewers Directing information , Minimum number of viewers presentation information, highest purchase conversion rate presentation information, lowest purchase conversion rate presentation information, highest handling per customer price presentation information, lowest handling amount per customer presentation information, highest expected handling amount presentation information, lowest expected handling amount presentation information, etc. . Real-time analysis data or historical analysis data may be generated in units of a predetermined period, for example, 1 day, 1 week, 1 month, and the like. Real-time analysis data or historical analysis data may be generated for each commerce content unit.

The prediction data generation unit 138 may generate an estimated sales level for commerce content scheduled to be broadcasted. The predictive data generation unit 138 includes past analysis data including viewing data or order data corresponding to at least one of programming time, production information, and external environment data generated through the statistical data analysis unit 136 and/or In consideration of the real-time analysis data, an estimated sales level for the commerce content may be generated.

The prediction data generation unit 138 generates expected external environment data based on the organizing time of the commerce content, analyzes the commerce content, generates directing data, and organizes the commerce content organizing time, directing data, and predicted external environment data. An estimated sales level for commerce content may be generated by considering at least one of the above.

The predictive data generator 138 determines the number of viewers, order amount, and/or purchase conversion rate for each time section of the commerce content based on at least one of programming time, production data, and expected external environment data, and determines viewers for each time section of the commerce content. An estimated sales level for the commerce content may be generated by considering at least one of the number of orders, the order amount, and the purchase conversion rate.

The prediction data generation unit 138 may calculate the expected sales level for the commerce content in consideration of past analysis data and/or real-time analysis data for the commerce content that sold the same product or a similar product to the product sold in the commerce content. there is. The expected sales degree may be calculated for the entire time of the commerce content by considering the number of views, the number of viewers, the order amount, and the purchase conversion rate for each unit time.

The prediction data generation unit 138 may receive a signal requesting an estimated sales level for commerce content scheduled to be transmitted through the communication unit 120 . The prediction data generating unit 137 may extract a code of a product sold in the commerce content and an organized time slot of the commerce content in response to the requested signal, and may analyze the commerce content to determine presentation data obtained.

The prediction data generation unit 138 may generate first past analysis data related to product codes of commerce contents. The prediction data generation unit 138 searches for one or more similar commerce contents selling the same product or similar products based on the product code of the commerce contents, and views data, order data, and external environment data for one or more similar commerce contents. First historical analysis data including at least one of a past number of viewers, a past order amount, and a past purchase conversion rate may be generated based on. The predictive data generator 138 searches for one or more similar commerce contents selling the same product or similar products based on the product code of the commerce contents, and makes a call for one or more similar commerce contents. Incoming data may be detected, and call-in prediction information for the corresponding commerce content may be generated based on the call-in data.

The prediction data generation unit 138 may determine expected external environment data related to the expected programming time slot in which the commerce content is organized. The prediction data generation unit 138 may receive one or more external environment data corresponding to the organizing time of the commerce content from one or more external servers, and determine expected external environment data by combining the one or more external environment data.

The prediction data generator 138 may generate second past analysis data including viewing data or purchase data related to presentation data included in commerce content. The prediction data generation unit 138 searches for viewing data or order data for each presentation information included in the presentation data included in the commerce content, and based on the viewing data or order data for each presentation information, the past for the commerce content. Second past analysis data including at least one of the number of viewers, past order amount, and past purchase conversion rate may be generated.

The prediction data generating unit 138 may calculate an expected sales level for the commerce content in consideration of the first historical analysis data, the second historical analysis data, and the expected external environment data.

In an optional embodiment, the predictive data generating unit 138 responds to the request signal requested by the user terminal, the number of inflows, the number of exits, the number of views compared to the previous one, the number of revisits, the expected handling amount, and the number of viewers while the commerce content is being transmitted. Order Amount, Ordered Options Composition Ratio, Highest Handling Target Time Zone, Lowest Handling Target Time Zone, Highest Number of Viewers Time Zone, Lowest Number of Viewers Time Zone, Highest Purchase Conversion Rate Time Zone, Lowest Purchase Conversion Rate Time Zone, Highest Handling Price Per Customer Time Zone, Lowest Handling Price Per Customer Time Zone, Highest It is possible to calculate and provide information such as the expected handling amount time period and the lowest expected handling amount time period.

Through this, the data analysis server 100 uses viewing data acquired through one or more media providing servers 200, external environment data corresponding to order data and programming time, and directing data included in commerce contents to provide information about commerce contents. It is possible to generate analysis data and calculate an estimated sales level for commerce contents that are not yet scheduled to be transmitted but are scheduled to be transmitted in the future using the analyzed data of the commerce contents.

5 is flowcharts of a method for calculating expected sales levels for commerce contents scheduled to be transmitted according to embodiments of the present disclosure.

In S110, the data analysis server 100 may receive a signal requesting an estimated sales level for commerce content scheduled to be transmitted from the first user terminal.

In S120, the data analysis server 100 extracts the code of a product sold in the commerce content and the organization time of the commerce content, and determines presentation data obtained by analyzing the commerce content.

In S130, the data analysis server 100 generates first past analysis data related to the product code of the commerce content, determines expected external environment data related to the expected programming time slot in which the commerce content is organized, and directs data included in the commerce content. Second past analysis data including viewing data or purchase data related to may be generated.

In operation S140 , the data analysis server 100 may calculate an expected sales level for the commerce content in consideration of the first historical analysis data, the second historical analysis data, and the expected external environment data.

The data analysis server 100 calculates the expected order amount, expected number of viewers, and expected purchase conversion rate for each presentation information included in the commerce content in consideration of the first past analysis data and the second past analysis data related to the product code, and the expected external environment Considering the data, it is possible to recalculate the expected order amount, the expected number of viewers, and the expected purchase conversion rate for each presentation information included in the commerce content.

The data analysis server 100 calculates the expected order amount, the expected number of viewers, and the expected purchase conversion rate of the commerce content based on the first past analysis data related to the product code, and calculates the second past analysis data and/or expected external environment data. The expected order amount, the expected number of viewers, and the expected purchase conversion rate can be recalculated by taking into account.

6 to 11 are exemplary diagrams of a user interface generated by the data analysis server 100.

As shown in FIG. 6, while commerce content (VOD) is displayed through the user's set-top box 400, the data analysis server 100 converts the number of views and order amount by time of the commerce content into time series data, such as FD1. can create

The data analysis server 100 considers the number of viewers who selected commerce content (VOD) from the set-top box 400, and calculates the number of inflows, the number of departures, the number of views compared to the previous one, the number of revisits, the expected handling amount, the amount of orders versus viewers, and the options ordered Analysis data such as composition ratio can be created like FD2.

As shown in FIG. 7, the data analysis server 100 divides the commerce content by time section, and the number of section inflows, section exits, section revisit count, section average viewing duration, section order amount, expected handling amount per minute, section viewers Analysis data (FD3) such as per order amount can be generated.

As shown in FIG. 8 , the data analysis server 100 may calculate analysis data WD including the number of views, the number of viewers, the number of departures, the number of revisits, and the average viewing duration in relation to viewing. . The number of views is a value obtained by counting access signals, and even if the same viewer enters and leaves, it can be counted twice. As for the number of viewers, viewers who came in and then left may be counted as one time.

The data analysis server 100 may calculate analysis data DD including the number of orders, the purchase conversion rate, the handling amount per customer, the number of views compared to the order amount, and the number of viewers compared to the order amount by linking viewing and ordering.

The data analysis server 100 may visually generate the strength of the viewing rate for each week and each time period (DR). The intensity for the viewing rate is set as a constant between 0 and 5, and the higher the value of the intensity, the greater the number of views.

As shown in FIG. 9, the data analysis server 100 can create visual data (VTD) of time-series change patterns of the handling amount target, expected handling amount, number of viewers, purchase conversion rate, and handling amount unit price per time zone on a daily basis. there is.

Data analysis server 100, based on the time series change pattern generated by linking the viewing data and order data, the highest handling amount target time zone, the lowest handling amount target time period (TD1), the highest number of viewers time period, the lowest number of viewers time period (TD2), It can be calculated for the highest purchase conversion rate time zone, the lowest purchase conversion rate time zone (TD3), the highest transaction amount per customer time period, the lowest transaction amount per customer price time period (TD4), the highest expected transaction amount time period, and the lowest expected transaction amount time period (TD5). TD1, TD2, TD3, TD4, TD5 can be provided including time value and amount.

As shown in FIG. 10 , real-time analysis data may be generated for commerce content (VOD) provided to each set-top box through the media providing server 200, such as RTD1, RTD2, RTD3, and RTD4.

The data analysis server 100 calculates the number of viewers, the number of views, the purchase conversion rate, the unit price of handling amount, the total order amount, the expected amount of handling, the expected handling amount per minute, the expected handling amount versus the number of views, and the expected handling amount versus the number of viewers for the commerce content to be transmitted. The number of viewers, the number of views, the purchase conversion rate, the handling amount per customer, the total order amount, the expected handling amount, the expected handling amount per minute, the expected handling amount compared to the number of views, and the expected handling amount compared to the number of viewers In contrast, it can be provided.

As shown in FIG. 11 , the data analysis server 100 may generate analysis data for comparison between commerce contents.

The data analysis server 100 may generate average data for one or more commerce contents based on the organization group or sales product. For example, an average value of analysis data (number of views per minute, number of exits per minute, number of revisits per minute, average viewing duration, etc.) for a predetermined number of times, such as 4 times, 12 times, etc., may be generated.

The data analysis server 100 may generate average analysis data of commerce contents selling the same or similar products, such as an average trend of 4 times, an average trend of 12 times, and the like. For commerce contents selling the same or similar products, the data analysis server 100 generates average analysis data of the commerce contents transmitted during the same time period, such as the number of views per minute, the number of exits per minute, the number of revisits per minute, and the average duration of viewing. can do. The data analysis server 100 may generate analysis data of commerce content immediately before the sale of the same product. The data analysis server 100 uses average analysis data of commerce contents selling the same or similar product, average analysis data of commerce contents transmitted during the same time period, and analysis data of commerce contents immediately before selling the same product, Future order data for commerce content scheduled to be broadcast may be generated.

The devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, devices and components described in the embodiments may include, 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), microprocessor, or any other device capable of executing and responding to instructions. The processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. You can command the device. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

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. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited examples and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

100: data analysis server 110: processor
120: communication unit 130: data processing unit
131: viewing data acquisition unit 132: external environment data acquisition unit
133: call-in data acquisition unit 134: order data acquisition unit
135: commerce content analysis unit 136: real-time data analysis unit
137: statistical data analysis unit 138: prediction data generation unit
200: media provision server
300: content transmission server 400: set-top box
411, 412, 413, ... 41n: output device 500: data server

Claims (13)

The data analysis server receives viewing data for commerce content that has been transmitted from one or more set-top boxes obtained through the media providing server, receives the entry point and exit point for the commerce content based on the viewing data, and the entry time point and recording departure times for each user and generating viewing data of the number of inflows, the number of departures, the number of viewers, the number of revisits, and the average viewing duration for the commerce content by time.
Acquiring, by the data analysis server, order data for the commerce contents that have been transmitted, from the media providing server or the data server - the order data includes an order amount per hour for goods or services sold by commerce contents, and an order medium Include any order amount;
Calculating, by the data analysis server, analysis data including the number of orders, the purchase conversion rate handling amount per customer, the number of views versus the order amount, and the order amount versus the viewer by linking the viewing data and the order data;
Receiving, by the data analysis server, a signal requesting an estimated sales level for commerce content scheduled to be transmitted from a first user terminal;
The data analysis server, in response to the signal, extracts the code of the product sold in the commerce content and the time zone of the commerce content, and analyzes the commerce content to analyze the commerce content, and display information and objects for each section of the commerce content. generating presentation data including information or situation information;
The data analysis server generates first past analysis data for commerce content that sold the same or similar product as the product code of the commerce content, viewing data for similar commerce content that sold the same or similar product, order data, generating first historical analysis data based on external environment data;
receiving, by the data analysis server, one or more external environment data corresponding to an organized time slot in which commerce content is organized, and determining expected external environment data including the one or more external environment data;
The data analysis server includes at least one of the number of past viewers, past order amount, and past purchase conversion rate for each presentation information using viewing data and order data for each presentation information included in the presentation data included in the commerce content. generating second historical analysis data; and
The data analysis server calculates an expected order amount, an expected number of viewers, and an expected purchase conversion rate for each presentation information included in the commerce content scheduled to be transmitted in consideration of the first past analysis data and the second past analysis data,
A method for calculating an expected sales level for commerce content scheduled to be transmitted, further comprising recalculating the expected order amount for each presentation information, the expected number of viewers, and the expected purchase conversion rate in further consideration of the expected external environment data. According to claim 1,
Wherein the first historical analysis data includes at least one of a past number of viewers, a past order amount, and a past purchase conversion rate. delete delete According to claim 1,
The order data is
As data about an order signal made while watching one or more previously transmitted commerce contents, the expected sales level for the commerce contents scheduled to be transmitted, including call-in data including at least one of previously generated order phone data and the number of simultaneous consultations. How to calculate . According to claim 1,
The viewing data is
Information about one or more set-top boxes that watch the commerce content transmitted through the media providing server, including at least one of the number of inflows, the number of exits, the number of views, and the number of viewers, and one or more previously transmitted commerce contents are transmitted In the meantime, a method for calculating an expected sales level for commerce content scheduled to be transmitted, which is counted through an access signal from each of the one or more set-top boxes. In the data analysis server including a communication unit and a processor,
The processor
Receive viewing data for commerce contents that have been transmitted from one or more set-top boxes obtained through a media providing server, receive inflow and outflow times for commerce contents based on the viewing data, and determine inflow and outflow times Records per user to generate the number of inflows, the number of departures, the number of viewers, the number of revisits, and the average viewing duration for each hour of the commerce content;
Acquire order data for commerce contents that have been transmitted from the media providing server or data server - the order data includes an order amount per hour for goods or services sold by commerce contents, and an order amount per order medium,
By linking viewing data and order data, analysis data including the number of orders, purchase conversion rate handling amount, unit price per customer, order amount compared to number of views, and order amount compared to viewers is calculated,
Receiving a signal requesting an estimated sales level for commerce content scheduled to be transmitted from a first user terminal;
In response to the signal, the code of a product sold in the commerce content and the time zone of the commerce content are extracted, and the commerce content is analyzed to obtain presentation information, object information, or situation information for each section of the commerce content. Create directing data that includes
First historical analysis data for commerce content that sold the same or similar product as the product code of the commerce content is generated, but based on viewing data, order data, and external environment data for similar commerce content that sold the same or similar product To generate first past analysis data,
Receiving one or more external environment data corresponding to a programming time zone in which commerce content is organized, determining expected external environment data including the one or more external environment data;
Second historical analysis data including at least one of the number of past viewers, the past order amount, and the past purchase conversion rate for each presentation information using the viewing data and order data for each presentation information included in the presentation data included in the commerce content create,
An expected order amount, an expected number of viewers, and an expected purchase conversion rate for each production information included in the commerce content scheduled to be transmitted are calculated in consideration of the first historical analysis data and the second historical analysis data, and further considering the expected external environment data. The data analysis server for recalculating the expected order amount for each presentation information, the expected number of viewers, and the expected purchase conversion rate. According to claim 7,
The first historical analysis data includes at least one of a past number of viewers, a past order amount, and a past purchase conversion rate, data analysis server. delete delete According to claim 7,
The order data is
A data analysis server comprising call-in data including at least one of pre-ordered phone data and the number of simultaneous consultations as data for an order signal made while watching one or more previously transmitted commerce contents. According to claim 7,
The viewing data is
Information about one or more set-top boxes that watch the commerce content transmitted through the media providing server, including at least one of the number of inflows, the number of exits, the number of views, and the number of viewers, and one or more previously transmitted commerce contents are transmitted During, counting through the connection signal from each of the one or more set-top boxes, data analysis server. A computer program stored in a computer readable storage medium to execute the method of any one of claims 1 to 2 using a computer.

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