User operation efficiency determination method and device, equipment and storage medium

Abstract

The present disclosure provides a user operation efficiency determination method and device, equipment and storage medium, relates to the technical field of Internet, and particularly relates to the technical field of software front-end development. The specific implementation scheme is: obtaining interaction behavior data of a user in a human-computer interaction interface provided by a target platform, wherein the interaction behavior data includes operation duration and operation frequency of the interaction behavior; determining an interaction process corresponding to the interaction behavior data, and a material level and a marketing target associated with the interaction process; determining an operation efficiency index of the material level and an operation efficiency index corresponding to the marketing target according to the interaction behavior data. By determining the operation efficiency index of the material level and the operation efficiency index corresponding to the marketing target, the operation efficiency of the user in the human-computer interaction interface of the target platform is quantitatively obtained, and then the operation experience of the user in the human-computer interaction interface of the target platform can be optimized.

Description

Technical Field

[0001] This disclosure relates to Internet technology, and more particularly to the field of software front-end development technology, specifically to a method, apparatus, device, and storage medium for determining user operation efficiency. Background Technology

[0002] Currently, user experience surveys conducted on customers of websites and application platforms (such as B2B platforms for merchant management) have revealed that "low operational efficiency" is the most common feedback among all customer experience feedback. This indicates that users find the interactive operations on application platforms cumbersome and complex, resulting in low operational efficiency.

[0003] It's easy to understand that if user interactions on an application platform are always cumbersome and complicated, it will severely erode customer trust in the platform and lead to customer churn. Therefore, it's necessary to optimize the user experience.

[0004] However, the prerequisite for optimizing the customer experience is to objectively measure the efficiency of users' operations on the application platform, so as to use the quantitative results as data support to enable the optimization of the customer experience. Summary of the Invention

[0005] This disclosure provides a method, apparatus, device, and storage medium for determining user operation efficiency.

[0006] According to a first aspect of this disclosure, a method for determining user operation efficiency is provided, the method comprising:

[0007] Acquire user interaction behavior data on the human-computer interaction interface provided by the target platform, wherein the aforementioned interaction behavior data includes: operation duration and number of operations;

[0008] Determine the interaction flow corresponding to the above-mentioned interactive behavior data, as well as the material level and marketing objectives associated with the above-mentioned interaction flow;

[0009] Based on the aforementioned interactive behavior data, determine the operational efficiency index of the aforementioned material level and the operational efficiency index corresponding to the aforementioned marketing objectives.

[0010] Furthermore, acquire user interaction data on the human-computer interaction interface provided by the target platform, including:

[0011] The user's interaction behavior on the human-computer interaction interface is obtained, wherein the interaction behavior includes mouse click behavior and keyboard input behavior;

[0012] Obtain the operation duration and number of operations of the aforementioned interaction behavior at the end of the interaction behavior.

[0013] Furthermore, the number of operations for obtaining the above interactive behavior includes:

[0014] Get the number of clicks corresponding to the mouse click behavior and the number of key inputs corresponding to the keyboard input behavior;

[0015] Determine the normalization coefficients for normalizing the above keyboard input behavior;

[0016] Based on the above click count, the above normalization coefficient, and the above input count, the number of operations for the above interactive behavior is calculated.

[0017] Furthermore, the normalization coefficients for normalizing the above keyboard input behavior are determined, including:

[0018] Obtain the historical click count and historical input count of the above human-computer interaction interface within a historical time period;

[0019] Set weight values ​​for the aforementioned historical click counts and the aforementioned historical input counts, respectively;

[0020] Based on the aforementioned historical click counts and historical input counts, as well as the corresponding weight values ​​for each of the aforementioned historical click counts and historical input counts, the aforementioned normalization coefficients are calculated using a fitting method.

[0021] Furthermore, the interaction flow corresponding to the aforementioned interactive behavior data, as well as the material hierarchy and marketing objectives associated with the aforementioned interaction flow, are determined, including:

[0022] Determine the data collection scenarios corresponding to the aforementioned interactive behavior data;

[0023] Based on the above data collection scenario, the above interaction process, as well as the material level and marketing objectives associated with the above interaction process, are determined.

[0024] Furthermore, based on the aforementioned interaction behavior data, the operational efficiency index of the aforementioned material level is determined, including:

[0025] Obtain the number of materials created or edited in the interaction process corresponding to the above interactive behavior data;

[0026] Based on the above interactive behavior data, determine the overall operational efficiency index corresponding to the above interactive process.

[0027] Based on the overall operating efficiency index and the quantity of materials mentioned above, the average operating efficiency index of a single material in the material hierarchy is calculated.

[0028] Furthermore, based on the aforementioned interactive behavior data, the operational efficiency index corresponding to the above marketing objectives is determined, including:

[0029] Based on the above interactive behavior data, determine the overall operational efficiency index corresponding to the above interactive process.

[0030] Based on the overall operational efficiency index and the marketing objectives mentioned above, the operational efficiency index corresponding to the marketing objectives is calculated.

[0031] Furthermore, after determining the interaction flow corresponding to the aforementioned interactive behavior data, as well as the material hierarchy and marketing objectives associated with the aforementioned interaction flow, the above method also includes:

[0032] Determine whether the above interaction process was completed successfully;

[0033] If the above interaction process is not completed successfully, the user's interaction sinking data on the above human-computer interaction interface is determined based on the above interaction behavior data, so as to optimize the above human-computer interaction interface based on the above interaction sinking data.

[0034] If the above interaction process is successfully completed, the method steps for determining the operational efficiency index of the above material level and the operational efficiency index corresponding to the above marketing objectives based on the above interaction behavior data will be executed.

[0035] Furthermore, after determining the operational efficiency index of the aforementioned material level and the operational efficiency index corresponding to the aforementioned marketing objectives based on the aforementioned interaction behavior data, the above method also includes:

[0036] The operational efficiency index of the above material levels and the operational efficiency index corresponding to the above marketing objectives are visualized.

[0037] Based on the operational efficiency index of the above material level and the operational efficiency index corresponding to the above marketing objectives, the human-computer interaction interface of the above target platform is optimized.

[0038] According to a second aspect of this disclosure, a device for determining user operation efficiency is provided, the device comprising:

[0039] The acquisition unit is used to acquire user interaction behavior data on the human-computer interaction interface provided by the target platform, wherein the aforementioned interaction behavior data includes: operation duration and number of operations of the interaction behavior;

[0040] The first determining unit is used to determine the interaction process corresponding to the above-mentioned interaction behavior data, as well as the material level and marketing objectives associated with the above-mentioned interaction process.

[0041] The second determining unit is used to determine the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the aforementioned interactive behavior data.

[0042] Furthermore, the aforementioned acquisition unit includes:

[0043] The first acquisition module is used to acquire the user's interactive behavior on the human-computer interaction interface, wherein the interactive behavior includes mouse click behavior and keyboard input behavior.

[0044] The second acquisition module is used to acquire the operation duration and number of operations of the above-mentioned interactive behavior at the end of the above-mentioned interactive behavior.

[0045] Furthermore, the second acquisition module is also used to: acquire the number of clicks corresponding to the mouse click behavior and the number of inputs corresponding to the keyboard input behavior; determine the normalization coefficient for normalizing the keyboard input behavior; and calculate the number of operations of the interaction behavior based on the number of clicks, the normalization coefficient, and the number of inputs.

[0046] Furthermore, the second acquisition module is also used to: acquire the historical click count and historical input count of the human-computer interaction interface within a historical time period; set the weight values ​​corresponding to the historical click count and the historical input count respectively; and calculate the normalization coefficient based on the historical click count and the historical input count, as well as the weight values ​​corresponding to the historical click count and the historical input count respectively.

[0047] Furthermore, the aforementioned first determining unit includes:

[0048] The first determining module is used to determine the data collection scenario corresponding to the above-mentioned interactive behavior data;

[0049] The second determining module is used to determine the above-mentioned interaction process, as well as the material level and marketing objectives associated with the above-mentioned interaction process, based on the above-mentioned data collection scenario.

[0050] Furthermore, the aforementioned second determining unit includes:

[0051] The third acquisition module is used to acquire the quantity of newly created or edited materials in the interaction process corresponding to the above-mentioned interaction behavior data;

[0052] The third determining module is used to determine the total operational efficiency index corresponding to the above-mentioned interactive process based on the above-mentioned interactive behavior data.

[0053] The first calculation module is used to calculate the average operating efficiency index of a single material in the above material level based on the above total operating efficiency index and the above material quantity.

[0054] Furthermore, based on the aforementioned interactive behavior data, the operational efficiency index corresponding to the above marketing objectives is determined, including:

[0055] The fourth determining module is used to determine the overall operation efficiency index corresponding to the above interaction process based on the above interaction behavior data.

[0056] The fifth determining module is used to calculate the operational efficiency index corresponding to the marketing objectives based on the overall operational efficiency index and the marketing objectives.

[0057] Furthermore, the aforementioned device also includes:

[0058] The third determining unit is used to determine whether the above interaction process has been successfully completed.

[0059] The fourth determining unit is used to determine the user's interaction sinking data on the human-computer interaction interface based on the interaction behavior data if the above interaction process is not successfully completed, so as to optimize the human-computer interaction interface based on the interaction sinking data.

[0060] If the above interaction process is successfully completed, the method steps for determining the operational efficiency index of the above material level and the operational efficiency index corresponding to the above marketing objectives based on the above interaction behavior data will be executed.

[0061] Furthermore, the aforementioned device also includes:

[0062] The display unit is used to visually display the operational efficiency index of the above-mentioned material levels, as well as the operational efficiency index corresponding to the above-mentioned marketing objectives;

[0063] The optimization unit is used to optimize the human-computer interaction interface of the target platform based on the operational efficiency index of the above-mentioned material level and the operational efficiency index corresponding to the above-mentioned marketing objectives.

[0064] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0065] At least one processor; and

[0066] A memory communicatively connected to at least one of the aforementioned processors; wherein,

[0067] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform any of the methods described above.

[0068] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method according to any one of the above claims.

[0069] According to a fifth aspect of this disclosure, a computer program product is provided, comprising: a computer program stored in a readable storage medium, wherein at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the method described in the first aspect.

[0070] According to the technology disclosed herein, user interaction behavior data on the human-computer interaction interface provided by the target platform is obtained. The interaction behavior data includes: operation duration and number of operations; determining the interaction process corresponding to the interaction behavior data, as well as the material level and marketing objective associated with the interaction process; and determining the operation efficiency index of the material level and the operation efficiency index corresponding to the marketing objective based on the interaction behavior data.

[0071] This embodiment of the disclosure acquires user interaction behavior data on the human-computer interaction interface, including not only the operation duration but also the number of operations, which accurately quantifies the user's operational efficiency. Then, based on this interaction behavior data, the user's interaction flow on the human-computer interaction interface is determined, such as creating or editing a flow, as well as the material level and marketing objectives associated with the interaction flow. By determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objectives, the user's operational efficiency on the target platform's human-computer interaction interface is quantified. Furthermore, the operational efficiency indices of the material level and the marketing objectives can be used as data support to enable optimization of the user's operational experience on the target platform's human-computer interaction interface.

[0072] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0073] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0074] Figure 1 This is a flowchart of a method for determining user operation efficiency according to an embodiment of the present disclosure;

[0075] Figure 2 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of the present disclosure;

[0076] Figure 3 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of the present disclosure;

[0077] Figure 4This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of the present disclosure;

[0078] Figure 5 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of the present disclosure;

[0079] Figure 6 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of the present disclosure;

[0080] Figure 7 This is a schematic diagram of the framework of a user operation efficiency determination device provided according to an embodiment of the present disclosure;

[0081] Figure 8 This is a block diagram of an electronic device used to implement a method for determining user operation efficiency according to an embodiment of the present disclosure. Detailed Implementation

[0082] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0083] First, let me explain the terms used in this application:

[0084] GroupBy, literally meaning "grouping according to certain rules," divides a dataset into smaller regions based on specific rules, and then processes the data within those smaller regions.

[0085] In existing technologies, objectively measuring user operational efficiency on application platforms only considers data from the single dimension of user dwell time, which has limited accuracy and makes it difficult to reflect the customer's subjective operational experience. Furthermore, the perspective for measuring user operational efficiency is not close enough to the user platform's business, and lacks a detailed breakdown of each material level and scenario dimension involved in the interaction process, as well as each marketing objective.

[0086] Therefore, the above reasons ultimately lead to a divergence between "objective data indicators and subjective customer experience," which in turn distorts the effectiveness of measuring user operation efficiency, makes it impossible to discover problems on the platform in a timely manner, and fails to provide a data-driven foundation for improving the user operation experience.

[0087] To address the aforementioned issues, this disclosure provides a method, apparatus, device, and storage medium for determining user operation efficiency, applicable to the field of Internet technology, particularly in the field of software front-end development technology. It proposes a scientific and multi-dimensional operational efficiency measurement system to measure and continuously observe the efficiency of user / customer interaction behavior on a target platform.

[0088] Figure 1 This is a flowchart of a method for determining user operation efficiency according to embodiments of the present disclosure, such as... Figure 1 As shown, this disclosure provides a method for determining user operation efficiency, including the following steps:

[0089] S101, acquire user interaction behavior data on the human-computer interaction interface provided by the target platform.

[0090] S102, determine the interaction flow corresponding to the above-mentioned interaction behavior data, as well as the material level and marketing objectives associated with the above-mentioned interaction flow.

[0091] S103, determine the operational efficiency index of the above material level and the operational efficiency index corresponding to the above marketing objectives based on the above interactive behavior data.

[0092] Optionally, the aforementioned target platform may be, but is not limited to, a B2B platform for managing merchants, such as an advertising platform. The human-computer interaction interface provided by the target platform can be either a human-computer interaction interface for an application (APP) or a human-computer interaction interface for a web page.

[0093] Optionally, in one example, the above-mentioned interaction behavior data includes: the operation duration and the number of operations of the interaction behavior.

[0094] This disclosed embodiment differs from traditional efficiency measurement schemes by not simply equating the determined user operation efficiency with the operation duration of the interaction, but rather fully considering the actual operational actions of the interaction to determine the number of operations. This allows for a more scientific and realistic quantification of user operation efficiency when using the B-end platform, thereby driving optimization of the user experience and constructing a sustainable operation efficiency measurement system.

[0095] In addition to measuring the operation duration of interactive behaviors, this embodiment of the disclosure also adds the measurement of the number of operations of interactive behaviors such as mouse clicks and keyboard input, providing more valuable data indicators and enabling a more comprehensive and objective evaluation of the user's operational efficiency in the human-computer interaction interface.

[0096] As an optional implementation, when collecting the number of interactive actions, for mouse clicks, it can automatically distinguish whether the click operation occurred in a valid area (for example, clicks in a blank area without content will be counted separately). At the same time, a throttling mechanism is added to take into account that if there are rapid and continuous clicks in a short period of time (multiple clicks within 1 second), only one count will be performed in order to remove duplicates.

[0097] Optionally, the target platform can be a B2B platform for merchant management, such as an advertising platform. Since the business models of advertising platforms are quite diverse, there are multiple levels of materials under a customer account. These material levels may include, but are not limited to, campaigns, ad groups, creatives, keywords, etc., and the operation interface for each process is different.

[0098] This publicly available solution allows for differentiated settings of various metrics across all material levels within a customer's account, facilitating subsequent filtering of specific material levels. It should be noted that the aforementioned material levels can be configured or modified depending on the target platform.

[0099] Optionally, because advertising platforms have precise definitions and distinctions for marketing objectives, the form elements / options under different marketing objectives may differ. This allows the advertising platform to find the best product features to achieve the user's objective, saving user time and generating better ads. For example, marketing objectives may include, but are not limited to, website links, app promotion, product catalogs, e-commerce stores, local promotion, etc. Using this disclosed solution allows for differentiated settings of marketing objectives, facilitating subsequent filtering for specific marketing objectives.

[0100] Furthermore, in measuring user operation efficiency, this embodiment of the disclosure considers each material level and each marketing objective involved in the interaction process, providing a richer dimensional perspective. It not only focuses on which material level the user is currently in the interaction process but also further subdivides the scenario based on specific business characteristics. For example, in the case of advertising, it collects information such as which marketing objective the current interaction process belongs to and how many materials it affects, providing more perspectives and thus more closely aligning with the assessment of B-end platform business.

[0101] Therefore, this disclosure provides a more scientific and customer-centric operational efficiency measurement system construction scheme, which can be used to measure and continuously observe the efficiency of user / customer behavior within the site, so as to promptly identify problems on the website page and provide an enabling foundation for improving the user experience.

[0102] According to the technology disclosed herein, user interaction behavior data on the human-computer interaction interface provided by the target platform is obtained. The interaction behavior data includes: operation duration and number of operations; determining the interaction process corresponding to the interaction behavior data, as well as the material level and marketing objective associated with the interaction process; and determining the operation efficiency index of the material level and the operation efficiency index corresponding to the marketing objective based on the interaction behavior data.

[0103] This embodiment of the disclosure acquires user interaction behavior data on the human-computer interaction interface, including not only the operation duration but also the number of operations, which accurately quantifies the user's operational efficiency. Then, based on this interaction behavior data, the user's interaction flow on the human-computer interaction interface is determined, such as creating or editing a flow, as well as the material level and marketing objectives associated with the interaction flow. By determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objectives, the user's operational efficiency on the target platform's human-computer interaction interface is quantified. Furthermore, the operational efficiency indices of the material level and the marketing objectives can be used as data support to enable optimization of the user's operational experience on the target platform's human-computer interaction interface.

[0104] In one example, Figure 2 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of this disclosure, such as... Figure 2 As shown, the user's interaction behavior data on the human-computer interaction interface provided by the target platform is obtained, including:

[0105] S201, Obtain the user's interaction behavior on the human-computer interaction interface, wherein the interaction behavior includes: mouse click behavior and keyboard input behavior.

[0106] S202, obtain the operation duration and number of operations of the above-mentioned interactive behavior at the end of the above-mentioned interactive behavior.

[0107] In one optional embodiment of this disclosure, when the background server detects that a user has entered an interactive process, for example, when a user creates or edits a business process on the human-computer interaction interface, the server obtains the user's interactive behavior on the human-computer interaction interface, such as mouse click behavior and keyboard input behavior.

[0108] Subsequently, when the aforementioned interactive behavior ends, for example, when the user clicks the "New Completed", "Edit Completed", or "Cancel Exit" button on the human-computer interaction interface, and it is determined that the user has left the current interactive process, a data upload request carrying various interactive behavior indicators is sent to the backend server, uploading the operation duration and number of operations of the aforementioned interactive behavior at the determined end of the interactive behavior to the backend server.

[0109] Optionally, in this embodiment of the disclosure, the operation duration of the above-mentioned interactive behavior is the time difference between leaving the interactive process and entering the interactive process.

[0110] In one optional embodiment, the number of mouse clicks is counted only when the mouse clicks within a valid area (non-blank area), i.e., when the mouse clicks on a meaningful form element, such as text input, drop-down selection, checkbox selection, radio button selection, etc. Furthermore, in this embodiment, to avoid duplicate counting due to accidental operation, multiple mouse clicks within a preset time period (e.g., within 1 second) are counted as only one operation.

[0111] In one optional embodiment, the number of mouse click actions is the number of clicks that occur in any area of ​​the interactive interface (including blank and non-blank areas), and each click action is counted as a mouse click action.

[0112] In another optional embodiment, the number of keyboard input operations is the number of times the user presses the keyboard on the human-computer interaction interface. It should be noted that the number of keyboard input operations is independent of the speed at which the keyboard is pressed. For example, if the user presses the four keys a, b, c, and d on the keyboard at different speeds, the number of keyboard input operations for each operation is counted as 4.

[0113] It should be noted that the solution provided in this disclosure requires no adjustments or changes to the front-end human-computer interaction interface. Compared with existing solutions that only consider the duration of user stay on a specific human-computer interaction page—a single dimension of interactive behavior data—the interactive behavior data collected in this disclosure includes both the operation duration and the number of operations. The interactive behavior indicators involved are more multi-dimensional and valuable. Therefore, based on the interactive behavior data determined in this disclosure, user operation efficiency can be quantified more accurately.

[0114] In one example, Figure 3 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of this disclosure, such as... Figure 3 As shown, the number of operations for obtaining the above interactive behavior includes:

[0115] S301, obtain the number of clicks corresponding to the above mouse click behavior and the number of inputs corresponding to the keyboard input behavior.

[0116] S302, determine the normalization coefficients for normalizing the above keyboard input behavior.

[0117] S303, the number of operations for the above-mentioned interactive behavior is calculated based on the above-mentioned number of clicks, the above-mentioned normalization coefficient, and the above-mentioned number of inputs.

[0118] In one optional embodiment, the number of interactive actions in this disclosure includes the number of clicks corresponding to mouse clicks and the number of inputs corresponding to keyboard inputs. Furthermore, in order to filter out certain extreme cases to avoid fluctuations, such as a user leaving within 1 second after entering a business process, or a user staying in a business process for a long time (e.g., 1 hour) without performing any actions, this disclosure, after obtaining the number of clicks corresponding to mouse clicks and the number of inputs corresponding to keyboard inputs, can fit a normalization coefficient for normalizing the keyboard inputs, and then calculate the number of interactive actions = number of clicks + normalization coefficient * number of inputs.

[0119] Through the above embodiments, this disclosure can calculate the number of operations of interactive behaviors more accurately by using the number of clicks corresponding to mouse click behavior and the number of inputs corresponding to keyboard input behavior, and after determining the normalization coefficient for normalization processing of keyboard input behavior, filtering out certain extreme cases to avoid data fluctuations.

[0120] As an optional embodiment, Figure 4 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of this disclosure, such as... Figure 4 As shown, the normalization coefficients for normalizing the above keyboard input behavior are determined, including:

[0121] S401, obtain the historical click count and historical input count of the above human-computer interaction interface within the historical time period.

[0122] S402, set the weight values ​​corresponding to the above-mentioned historical click count and the above-mentioned historical input count respectively.

[0123] S403, based on the above-mentioned historical click counts and historical input counts, and the weight values ​​corresponding to the above-mentioned historical click counts and historical input counts respectively, the above-mentioned normalization coefficient is calculated by fitting.

[0124] In this embodiment of the disclosure, firstly, relatively stable interactive behavior data within a historical time period can be obtained, such as the historical click count and historical input count of the aforementioned human-computer interaction interface, to obtain a baseline, which is a necessary condition for fitting the normalization coefficient.

[0125] Secondly, for a certain business process, all historical clicks within that historical time period are denoted as SumA, and historical inputs are denoted as SumB. The importance of the two is made equal, that is, neither factor should dominate to the point of overpowering the other. For example, if the weights of the historical clicks and historical inputs are fitted with a weight of 1:1, then the normalization coefficient of the business process = SumB / SumA.

[0126] Through the above embodiments, by determining the normalization coefficient for normalizing keyboard input behavior, certain extreme cases can be filtered out to avoid data fluctuations, thereby more accurately calculating the number of operations for interactive behavior.

[0127] Furthermore, in the embodiments of this disclosure, during the subsequent calculation of the number of interactive operations, the normalization coefficient can be fine-tuned based on website function iterations and user feedback on operation efficiency.

[0128] An optional embodiment involves determining the interaction flow corresponding to the aforementioned interactive behavior data, as well as the material hierarchy and marketing objectives associated with the aforementioned interaction flow, including:

[0129] S501, determine the data collection scenario corresponding to the above-mentioned interactive behavior data.

[0130] S502, Based on the above data collection scenario, determine the above interaction process, as well as the material level and marketing objectives associated with the above interaction process.

[0131] In this embodiment of the disclosure, when the backend server first obtains the user's interaction behavior data, it needs to determine the corresponding interaction process. For example, the user creates or edits a business process on the human-computer interaction interface.

[0132] First, we can obtain the data collection scenarios corresponding to the user's interactive behavior on the human-computer interaction interface, such as advertising scenarios or advertising effect acceptance scenarios.

[0133] Next, determine the interaction flow within the defined data collection scenario. For example, create a new interaction flow or edit an existing one within an advertising scenario, along with the associated material hierarchy and marketing objectives. For instance, in an advertising scenario, determine which marketing objective the current interaction flow belongs to, how many materials it affects, etc., to provide more analytical perspectives on operational efficiency.

[0134] As an optional embodiment, Figure 5 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of this disclosure, such as... Figure 5As shown, the operational efficiency index of the above material level is determined based on the aforementioned interaction behavior data, including:

[0135] S601, Obtain the quantity of materials created or edited in the interaction process corresponding to the above-mentioned interaction behavior data.

[0136] S602, determine the overall operation efficiency index corresponding to the above interaction process based on the above interaction behavior data.

[0137] S603, based on the above total operating efficiency index and the above material quantity, calculate the average operating efficiency index of a single material in the above material level.

[0138] Optionally, the total operation efficiency index corresponding to the above interaction process = (operation time of the interaction behavior, number of operations of the interaction behavior).

[0139] In this embodiment of the disclosure, since user operations may be performed in batches, taking "keywords" as the material level as an example, hundreds of thousands of "keywords" can be created in a single interaction process. Therefore, this disclosure also counts the number of materials created or edited in the interaction process, so as to calculate the average operation efficiency index per material based on the total operation efficiency index and the number of materials in the subsequent interaction process. For example, the average operation efficiency index = the total operation efficiency index corresponding to the interaction process / the number of materials created or edited in the interaction process.

[0140] There is also an alternative embodiment, Figure 6 This is a flowchart of an optional method for determining user operation efficiency provided according to an embodiment of this disclosure, such as... Figure 6 As shown, the operational efficiency index corresponding to the above marketing objectives is determined based on the aforementioned interaction behavior data, including:

[0141] S701, Determine the overall operation efficiency index corresponding to the above interaction process based on the above interaction behavior data.

[0142] S702, based on the above total operational efficiency index and the above marketing objectives, calculate the operational efficiency index corresponding to the above marketing objectives.

[0143] Optionally, the overall operational efficiency index corresponding to the above interaction process = (operation time of the interaction behavior, number of operations of the interaction behavior). The operational efficiency index corresponding to the above marketing goal = overall operational efficiency index GroupBy marketing goal.

[0144] Through the above embodiments, the user's interaction flow on the human-computer interaction interface is determined based on the interactive behavior data, such as creating or editing a new flow, as well as the material level and marketing objectives associated with the interaction flow. By determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objectives, that is, by subdividing and processing each material level scenario dimension and each marketing objective involved in the interaction flow, the user's operational efficiency on the target platform's human-computer interaction interface is quantified. Furthermore, the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objectives can be used as data support to enable the optimization of the user's operational experience on the target platform's human-computer interaction interface.

[0145] Another optional embodiment, after determining the interaction flow corresponding to the aforementioned interaction behavior data, and the material hierarchy and marketing objectives associated with the aforementioned interaction flow, further includes:

[0146] S801, determine whether the above interaction process has been successfully completed.

[0147] S802, if the above interaction process is not successfully completed, the user's interaction sinking data on the above human-computer interaction interface is determined based on the above interaction behavior data, so as to optimize the above human-computer interaction interface based on the above interaction sinking data.

[0148] S803, if the above interaction process is successfully completed, then execute the method steps to determine the operation efficiency index of the above material level and the operation efficiency index corresponding to the above marketing objective based on the above interaction behavior data.

[0149] In one optional embodiment, as shown in Table 1 below, in addition to basic data such as "operation duration, number of mouse clicks, and number of keyboard inputs", the above-mentioned interactive behavior data may also include, but is not limited to, the following additional fields: marketing objectives, whether the interactive process was successfully completed (new creation completed / editing completed) or abandoned midway (the user clicked the cancel button when the process was not successfully completed), the material level (plan / unit / creative / keyword) involved in the interactive process, and the quantity of materials.

[0150] Table 1

[0151]

[0152] In this way, by determining whether the interaction process was successfully completed and discussing different cases, we can not only analyze the operational efficiency of the interaction processes that users successfully completed in various specific scenarios, but also know the interaction processes that users failed to complete. For example, when a user edits an interaction process but abandons it halfway, how much sunk cost did they incur? Specifically, based on the above interaction behavior data, we can determine the user's interaction sunk data on the above human-computer interaction interface, such as the duration of dwell time, the number of clicks, and so on.

[0153] In this embodiment of the disclosure, for the case where a user clicks to enter a business process but ultimately abandons it, it is assumed that the user's operation efficiency is not as expected in such cases, that is, it is believed that the human-computer interaction interface has room for optimization. Therefore, the human-computer interaction interface can be optimized based on the above-mentioned interaction sinking data.

[0154] In this embodiment of the disclosure, there is also an optional embodiment in which, after determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the aforementioned interactive behavior data, the method further includes:

[0155] S901 provides a visual representation of the operational efficiency index for the aforementioned material levels, as well as the operational efficiency index corresponding to the aforementioned marketing objectives.

[0156] S902, based on the operational efficiency index of the above material level and the operational efficiency index corresponding to the above marketing objectives, optimize the human-computer interaction interface of the above target platform.

[0157] In this embodiment of the disclosure, after determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the aforementioned interactive behavior data, the determined operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective can be visualized and displayed to the software developers on the display interface, so that the software developers can promptly view the content that can be optimized. Subsequently, the backend server can perform corresponding optimization operations on the human-computer interaction interface of the target platform based on the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective. Alternatively, the software developers can also manually perform optimization operations on the human-computer interaction interface of the target platform.

[0158] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0159] According to embodiments of this disclosure, Figure 7 This is a schematic diagram of the framework of a user operation efficiency determination device provided according to an embodiment of the present disclosure, as shown below. Figure 7 As shown, this disclosure also provides a user operation efficiency determination device 700, which includes:

[0160] The acquisition unit 701 is used to acquire the user's interaction behavior data on the human-computer interaction interface provided by the target platform, wherein the aforementioned interaction behavior data includes: the operation duration and the number of operations of the interaction behavior.

[0161] The first determining unit 702 is used to determine the interaction process corresponding to the above-mentioned interaction behavior data, as well as the material level and marketing objectives associated with the above-mentioned interaction process.

[0162] The second determining unit 703 is used to determine the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the interactive behavior data.

[0163] This embodiment of the disclosure acquires user interaction behavior data on the human-computer interaction interface, including not only the operation duration but also the number of operations, which accurately quantifies the user's operational efficiency. Then, based on this interaction behavior data, the user's interaction flow on the human-computer interaction interface is determined, such as creating or editing a flow, as well as the material level and marketing objectives associated with the interaction flow. By determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objectives, the user's operational efficiency on the target platform's human-computer interaction interface is quantified. Furthermore, the operational efficiency indices of the material level and the marketing objectives can be used as data support to enable optimization of the user's operational experience on the target platform's human-computer interaction interface.

[0164] According to one or more optional embodiments of this disclosure, the above-mentioned acquisition unit includes:

[0165] The first acquisition module is used to acquire the user's interactive behavior on the human-computer interaction interface, wherein the interactive behavior includes mouse click behavior and keyboard input behavior.

[0166] The second acquisition module is used to acquire the operation duration and number of operations of the above-mentioned interactive behavior at the end of the above-mentioned interactive behavior.

[0167] According to one or more optional embodiments of this disclosure, the second acquisition module is further configured to: acquire the number of clicks corresponding to the mouse click behavior and the number of inputs corresponding to the keyboard input behavior; determine a normalization coefficient for normalizing the keyboard input behavior; and calculate the number of operations of the interactive behavior based on the number of clicks, the normalization coefficient, and the number of inputs.

[0168] According to one or more optional embodiments of this disclosure, the second acquisition module is further configured to: acquire the historical click count and historical input count of the human-computer interaction interface within a historical time period; set weight values ​​corresponding to the historical click count and the historical input count respectively; and calculate the normalization coefficient based on the historical click count and the historical input count, as well as the weight values ​​corresponding to the historical click count and the historical input count respectively.

[0169] According to one or more optional embodiments of this disclosure, the first determining unit described above includes:

[0170] The first determining module is used to determine the data collection scenario corresponding to the above-mentioned interactive behavior data;

[0171] The second determining module is used to determine the above-mentioned interaction process, as well as the material level and marketing objectives associated with the above-mentioned interaction process, based on the above-mentioned data collection scenario.

[0172] According to one or more optional embodiments of this disclosure, the second determining unit described above includes:

[0173] The third acquisition module is used to acquire the quantity of newly created or edited materials in the interaction process corresponding to the above-mentioned interaction behavior data;

[0174] The third determining module is used to determine the total operational efficiency index corresponding to the above-mentioned interactive process based on the above-mentioned interactive behavior data.

[0175] The first calculation module is used to calculate the average operating efficiency index of a single material in the above material level based on the above total operating efficiency index and the above material quantity.

[0176] According to one or more optional embodiments of this disclosure, determining the operational efficiency index corresponding to the marketing objective based on the aforementioned interactive behavior data includes:

[0177] The fourth determining module is used to determine the overall operation efficiency index corresponding to the above interaction process based on the above interaction behavior data.

[0178] The fifth determining module is used to calculate the operational efficiency index corresponding to the marketing objectives based on the overall operational efficiency index and the marketing objectives.

[0179] According to one or more optional embodiments of this disclosure, the above-described apparatus further includes:

[0180] The third determining unit is used to determine whether the above interaction process has been successfully completed.

[0181] The fourth determining unit is used to determine the user's interaction sinking data on the human-computer interaction interface based on the interaction behavior data if the above interaction process is not successfully completed, so as to optimize the human-computer interaction interface based on the interaction sinking data.

[0182] If the above interaction process is successfully completed, the method steps for determining the operational efficiency index of the above material level and the operational efficiency index corresponding to the above marketing objectives based on the above interaction behavior data will be executed.

[0183] According to one or more optional embodiments of this disclosure, the above apparatus further includes:

[0184] The display unit is used to visually display the operational efficiency index of the above-mentioned material levels, as well as the operational efficiency index corresponding to the above-mentioned marketing objectives;

[0185] The optimization unit is used to optimize the human-computer interaction interface of the target platform based on the operational efficiency index of the above-mentioned material level and the operational efficiency index corresponding to the above-mentioned marketing objectives.

[0186] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0187] According to embodiments of the present disclosure, the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method according to any one of the above claims.

[0188] According to embodiments of the present disclosure, a computer program product is provided, comprising: a computer program stored in a readable storage medium, at least one processor of an electronic device being able to read the computer program from the readable storage medium, and the at least one processor executing the computer program causing the electronic device to perform the scheme provided in any of the above embodiments.

[0189] According to embodiments of this disclosure, this disclosure also provides an electronic device. Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0190] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0191] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0192] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the method for determining user operation efficiency. For example, in some embodiments, the method for determining user operation efficiency may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the method for determining user operation efficiency described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the method for determining user operation efficiency by any other suitable means (e.g., by means of firmware).

[0193] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0194] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0195] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0196] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0197] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0198] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0199] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0200] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for determining user operation efficiency, the method comprising: Acquire user interaction behavior data on the human-computer interaction interface provided by the target platform, wherein the interaction behavior data includes: operation duration and number of operations; Determine the interaction flow corresponding to the interaction behavior data, as well as the material level and marketing objectives associated with the interaction flow; The operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective are determined based on the interactive behavior data, including: Obtain the number of newly created or edited materials in the interaction process corresponding to the interactive behavior data; The overall operational efficiency index corresponding to the interaction process is determined based on the interaction behavior data. Based on the total operating efficiency index and the quantity of materials, the average operating efficiency index of a single material in the material hierarchy is calculated. Based on the overall operational efficiency index and the marketing objective, the operational efficiency index corresponding to the marketing objective is calculated.

2. The method according to claim 1, wherein, Acquire user interaction data on the human-computer interaction interface provided by the target platform, including: The user's interactive behavior on the human-computer interaction interface is acquired, wherein the interactive behavior includes: mouse click behavior and keyboard input behavior; The operation duration and number of operations of the interaction behavior are obtained when the interaction behavior ends.

3. The method according to claim 2, wherein, The number of operations for obtaining the interaction behavior includes: Obtain the number of clicks corresponding to the mouse click behavior and the number of inputs corresponding to the keyboard input behavior; Determine the normalization coefficients for normalizing the keyboard input behavior; The number of operations for the interactive behavior is calculated based on the number of clicks, the normalization coefficient, and the number of inputs.

4. The method according to claim 3, wherein, Determining the normalization coefficients for normalizing the keyboard input behavior includes: Obtain the historical click count and historical input count of the human-computer interaction interface within a historical time period; Set weight values ​​for the historical click count and the historical input count respectively; The normalization coefficient is calculated by fitting the historical click count, the historical input count, and the weight values ​​corresponding to the historical click count and the historical input count.

5. The method according to any one of claims 1 to 4, wherein, Determine the interaction flow corresponding to the interaction behavior data, as well as the material hierarchy and marketing objectives associated with the interaction flow, including: Determine the data collection scenario corresponding to the interactive behavior data; The interaction process, as well as the material hierarchy and marketing objectives associated with the interaction process, are determined based on the data collection scenario.

6. The method according to any one of claims 1 to 4, wherein, After determining the interaction flow corresponding to the interaction behavior data, and the material hierarchy and marketing objectives associated with the interaction flow, the method further includes: Determine whether the interaction process was successfully completed; If the interaction process is not completed successfully, the user's interaction sinking data on the human-computer interaction interface is determined based on the interaction behavior data, so as to optimize the human-computer interaction interface based on the interaction sinking data. If the interaction process is successfully completed, the method steps of determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the interaction behavior data are executed.

7. The method according to any one of claims 1 to 4, wherein, After determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the interaction behavior data, the method further includes: The operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective are visualized. Based on the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective, the human-computer interaction interface of the target platform is optimized.

8. A device for determining user operation efficiency, the device comprising: The acquisition unit is used to acquire user interaction behavior data on the human-computer interaction interface provided by the target platform, wherein the interaction behavior data includes: operation duration and number of operations of the interaction behavior; The first determining unit is used to determine the interaction flow corresponding to the interaction behavior data, as well as the material level and marketing objective associated with the interaction flow; The second determining unit is used to determine the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the interactive behavior data. The second determining unit is specifically used to obtain the quantity of newly created or edited materials in the interaction process corresponding to the interaction behavior data; The overall operational efficiency index corresponding to the interaction process is determined based on the interaction behavior data. Based on the total operating efficiency index and the quantity of materials, the average operating efficiency index of a single material in the material hierarchy is calculated. Based on the overall operational efficiency index and the marketing objective, the operational efficiency index corresponding to the marketing objective is calculated.

9. The apparatus according to claim 8, wherein, The acquisition unit includes: The first acquisition module is used to acquire the user's interactive behavior on the human-computer interaction interface, wherein the interactive behavior includes: mouse click behavior and keyboard input behavior; The second acquisition module is used to acquire the operation duration and number of operations of the interaction behavior determined when the interaction behavior ends.

10. The apparatus according to claim 9, wherein, The second acquisition module is further configured to: acquire the number of clicks corresponding to the mouse click behavior and the number of inputs corresponding to the keyboard input behavior; and determine a normalization coefficient for normalizing the keyboard input behavior. The number of operations for the interactive behavior is calculated based on the number of clicks, the normalization coefficient, and the number of inputs.

11. The apparatus according to claim 10, wherein, The second acquisition module is further configured to: acquire the historical click count and historical input count of the human-computer interaction interface within a historical time period; and set weight values ​​corresponding to the historical click count and the historical input count respectively; The normalization coefficient is calculated by fitting the historical click count, the historical input count, and the weight values ​​corresponding to the historical click count and the historical input count.

12. The apparatus according to any one of claims 8 to 11, wherein, The first determining unit includes: The first determining module is used to determine the data collection scenario corresponding to the interactive behavior data; The second determining module is used to determine the interaction process, as well as the material level and marketing objectives associated with the interaction process, based on the data collection scenario.

13. The apparatus according to any one of claims 8 to 11, wherein, The device further includes: The third determining unit is used to determine whether the interaction process has been successfully completed. The fourth determining unit is used to determine the user's interaction sinking data on the human-computer interaction interface based on the interaction behavior data if the interaction process is not successfully completed, so as to optimize the human-computer interaction interface based on the interaction sinking data. If the interaction process is successfully completed, the method steps of determining the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective based on the interaction behavior data are executed.

14. The apparatus according to any one of claims 8 to 11, wherein, The device further includes: The display unit is used to visually display the operational efficiency index of the material level and the operational efficiency index corresponding to the marketing objective; The optimization unit is used to optimize the human-computer interaction interface of the target platform based on the operation efficiency index of the material level and the operation efficiency index corresponding to the marketing objective.

15. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

17. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-7.

Images