WO2022018505A1 - Procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site web - Google Patents

Procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site web Download PDF

Info

Publication number
WO2022018505A1
WO2022018505A1 PCT/IB2020/057036 IB2020057036W WO2022018505A1 WO 2022018505 A1 WO2022018505 A1 WO 2022018505A1 IB 2020057036 W IB2020057036 W IB 2020057036W WO 2022018505 A1 WO2022018505 A1 WO 2022018505A1
Authority
WO
WIPO (PCT)
Prior art keywords
user experience
website
interaction
user
interaction pattern
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2020/057036
Other languages
English (en)
Inventor
Ryad ZENINE
Vincent COLOMBET
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ContentSquare SAS
Original Assignee
ContentSquare SAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ContentSquare SAS filed Critical ContentSquare SAS
Priority to PCT/IB2020/057036 priority Critical patent/WO2022018505A1/fr
Publication of WO2022018505A1 publication Critical patent/WO2022018505A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • the disclosure generally relates to tracking visitors of a website and, more specifically, to a system and method for near real-time communication of user behavior on a website.
  • Website visitors usually make their way through the website until either the user performs a desired action, such as an online purchase, or the user leaves the website without performing such an action.
  • Performing a desired action is referred to as a “conversion,” and the goal of many websites owners and operators is to maximize the number of conversions from the visitors of their website.
  • Some known solutions introduce ways to track and monitor users’ journeys within websites and thereafter recommend the next optimal action based on detected events in order to provide an enhanced user experience.
  • Other known solutions allow for automatic determination of the best experience for a given visitor based on the user’s profile.
  • Another solution enables creation of custom segments in the website based on rules (e.g., searched term, articles viewed, items that were added to cart, etc.) to trigger content personalization on a certain webpage.
  • the abovementioned solutions fail to communicate in real-time or near-real time to the website owner enriched and actionable information regarding the user’s behavior on a website. As such, websites cannot be properly modified or optimized to enhance the user experience in real-time or near real-time.
  • Certain embodiments disclosed herein include a method for near-real-time communicating negative user experience of users interacting with a website.
  • the method comprises identifying at least one interaction pattern by analyzing an in- page interaction information of a user interacting with at least one page of the website; computing a user experience score for each of the at least one identified interaction pattern; generating an alert indicating that the user experience score determined for a respective identified interaction pattern demonstrates a negative user experience; and sending the alert immediately upon identifying the interaction pattern demonstrating a negative user experience.
  • Certain embodiments disclosed herein also include a non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising: identifying at least one interaction pattern by analyzing an in-page interaction information of a user interacting with at least one page of the website; computing a user experience score for each of the at least one identified interaction pattern; generating an alert indicating that the user experience score determined for a respective identified interaction pattern demonstrates a negative user experience; and sending the alert immediately upon identifying the interaction pattern demonstrating a negative user experience.
  • certain embodiments disclosed herein include a system for near-real-time communicating negative user experience of users interacting with a website.
  • the system comprises a processing circuitry; and a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to: identify at least one interaction pattern by analyzing an in-page interaction information of a user interacting with at least one page of the website; compute a user experience score for each of the at least one identified interaction pattern; generate an alert indicating that the user experience score determined for a respective identified interaction pattern demonstrates a negative user experience; and send the alert immediately upon identifying the interaction pattern demonstrating a negative user experience.
  • Figure 1 shows a network diagram utilized to describe the various embodiments of the invention.
  • Figure 2 is a flowchart illustrating a method for communicating negative user experiences of users interacting with a website, according to one embodiment.
  • Figure 3 is a flowchart illustrating a method for computing a user experience score, according to one embodiment.
  • Figure 4 is a flowchart illustrating a method for generating alerts with respect to interaction patterns of a user in an online retail platform, according to one embodiment.
  • Figure 5 shows a screenshot of an example web page and an example dashboard displaying the results of the method according to an embodiment.
  • Figure 6 is a block diagram of an analytic server utilized to process and analyze interaction information of browsing activity according to an embodiment.
  • Fig. 1 shows an example diagram of a network system 100 utilized to describe the various embodiments.
  • the network system 100 includes one or more user devices, 110-1 through 110-N (hereinafter, “user device” 110 or “user devices” 110), at least one web server 120, and an analytic sever 130, all connected to a network 140.
  • the analytic sever 130 may be adapted to gather, process, and analyze interaction information (e.g. user activity information) related to the engagement of users of user devices 110 interacting with a website hosted by the web server 120.
  • the interaction information may be received from one or more user devices 110.
  • such websites include an e- commerce website, i.e., any website that offers goods, services, or both.
  • the network 140 provides interconnectivity between the various components of the system.
  • the network 140 may be, but is not limited to, a wireless, cellular or wired network, a local area network (LAN), a wide area network (WAN), a metro area network (MAN), the Internet, the worldwide web (WWW), similar networks, and any combination thereof.
  • the network 140 may be a full- physical network, including exclusively physical hardware, a fully-virtual network, including only simulated or otherwise virtualized components, or a hybrid physical- virtual network, including both physical and virtualized components.
  • the network 140 may be configured to encrypt data, both at rest and in motion, and to transmit encrypted, unencrypted, or partially-encrypted data.
  • the network 140 may be configured to connect with the various components of the system 100 via any combination of wired and wireless means.
  • a user device 110 may be any computing device allowing a user to at least download webpages (or simply “pages”), display webpages, and interact with webpages.
  • a user device 110 may be, but is not limited to, a smart phone, a tablet computer, a personal computer, a laptop computer, a netbook computer, an electronic reader, and the like.
  • a user device may be installed with a web browser 112, such as Safari®, Firefox®, Internet Explorer®, Chrome®, and the like.
  • a processor of the user device 110 runs an operating system that may include iOS®, Android®, Unix®, Windows®, and the like.
  • the browser 112 may include any mobile application executable over the user device 110, an extension of the browser 112, or both.
  • Such an application is typically downloaded from a central repository (not shown) which may be, as examples and without limitation, AppStore® by Apple Computers®, Google® Play®, and the like.
  • the web server 120 can host at least one website accessed through a browser 112.
  • the website may be, for example, an e-commerce website, an online retail platform, a social media platform, and the like.
  • the web server 120 is further configured to execute an application, such as a web application, a mobile application (app), a cloud application, and the like.
  • the information rendered by such applications is displayed over the browser 112.
  • the disclosed embodiments allow for detection of, in real-time, interaction patterns of users (e.g., visitors) of both websites and applications, and improvement of user experience by providing near real-time notifications related to the detected interaction patterns.
  • the various disclosed embodiments may also be utilized to generate other analytics data that is based on the user’s journey through Flypertext Markup Language (HTML) scripts of either the web pages of a website displayed over a browser (e.g., the browser 112) or the content displayed over a mobile application.
  • HTML Flypertext Markup Language
  • the browser 112 can render display content retrieved from a plurality of servers, including web servers, application servers, or both.
  • Any webpage visited by a user device 110 and provided by the web server 120 includes a tracking tag.
  • the tracking tag when interpreted by a browser 112, executes a piece of code (e.g., a script) configured to track an interaction of a user with the web page. This includes a URL visited, any element in the page clicked by, or hovered over by, the user, the time spent on a certain web page, and the like.
  • the analytic sever 130 is a system configured to execute instructions, organize information, and otherwise process data.
  • the analytic sever 130 may be configured to execute the methods described hereinbelow, other, like, methods, and any combination thereof.
  • the analytic sever 130 may include various processing, memory, networking, and other components allowing the analytic sever 130 to execute instructions and provide data processing.
  • the analytic sever 130 may be implemented as physical hardware, as software virtualizing physical hardware, or as a combination of physical and virtualized components.
  • the analytic sever 130 is configured to receive browsing activity from any user device (e.g., the user device 110).
  • the browsing activity includes interaction information of the user with any tracked webpage of the website.
  • the interaction information of the browsing activity may include a mouse movement, scrolling of a web page, resizing of a browser window, click events, keyboard use with any element of a webpage, and the like.
  • the interaction information may further include URL, referrer, load time, IP address, browser type, screen resolution, page views, and the like.
  • the analytic sever 130 is configured to extract one or more predetermined interaction events from the interaction information gathered.
  • An interaction event may be, for example, a hover with the mouse cursor over an element (e.g., a virtual button, a link), a click on an element, a tap on an element (e.g., tapping on a specific virtual button), and the like. It should be noted that additional interaction events may be previously elected and, therefore, extracted from the received interaction information without departing from the scope of the disclosed embodiments.
  • the analytic sever 130 is configured to select (e.g., from a database) a corresponding website configuration (e.g., of several webpages) in which the user is currently browsing.
  • the website configuration may indicate different zones of each web page of the website, and uniform resource locators (URLs) of each webpage of the website.
  • the website configuration can be included in a file, a data structure, and the like.
  • the zones in the webpages can be identified using a process discussed in greater detail in U.S. Patent Application 16/915,190, herein after the ⁇ 90 Application, assigned to the common assignee and hereby incorporated by reference.
  • the analytic sever 130 is configured to detect the zones in a webpage and not retrieve such information.
  • the analytic sever 130 is configured to apply a designated algorithm, such as a zone attribution algorithm, to the interaction information, the interaction events and the configuration file of the website.
  • a designated algorithm such as a zone attribution algorithm
  • the output of the zone attribution algorithm allows for determination, with respect to each detected interaction event, a webpage (i.e., a URL) in which the event occurred and a zone in the web page in which the event occurred.
  • the determination may be performed in near-real-time.
  • interaction information of browsing activity of a user device e.g. the user device 110 that browses through a certain website is received at the server 130.
  • the interaction information includes, among other features, an interaction event indicating that the user, interacting with the user device, has just clicked six times on the same button (e.g., “10” shoe size button) within five seconds.
  • the interaction information when analyzed by the analytic sever 130, allows for enrichment of the interaction information and, therefore, facilitates determination of the specific webpage in which the interaction event occurred and the zone (e.g., “select size” zone) in which the interaction event occurred.
  • the analysis may include selecting, based on the interaction information, the corresponding website configuration in which the user is currently browsing.
  • the analytic sever 130 may be configured to determine in which page(s) and zone(s) the interaction event has occurred.
  • the analytic sever 130 may be configured to generate a user experience score representing the user interaction pattern, while browsing through a certain website.
  • the user experience score is computed in real-time based on the user interaction pattern and user experience factor.
  • Such interaction patterns may be determined based on one or more interaction events that occur while the user browses through the website. It should be noted that the user experience score may be constantly updated.
  • User experience factors are predefined interactions which indicate a user’s difficulty or ease in operating or navigating a webpage or site.
  • the factors may be indicative of a negative or positive interaction of a user with a website.
  • the website may be an e-commerce website
  • user experience factors may include data reflecting or describing one or more events which distract the user or otherwise hinder the user from reaching one or more predefined objectives or goals.
  • User experience factors may include, without limitation, multiple-field and multiple-button interactions, which may be identified where a user clicks a given field or button more than a predefined number of times, and rage clicks, which may be identified where a user clicks a given element more than a predefined number of times in a period shorter than a predefined time threshold.
  • user experience factors may include, without limitation, loading times, which may be identified where a page takes longer than a predetermined time threshold to load, “disappointment clicks,” which may be identified where a user’s click on a first page loads a second page, and where the user returns to the first page in a time below a predetermined time threshold, and multiple page visits, which may be identified where a user returns to the same page multiple times during a session.
  • user experience factors may include additional factors like those described above, as well as any combination of those factors described above and the various, like, factors.
  • the user experience score may be for example, a number such as 1.0, 2.7, 4.0, 7.3, etc.
  • a low number e.g., 1.0
  • a high number e.g., 7.3
  • the user experience score may be relatively low (e.g., 1).
  • the interaction information indicates that the user is hovering with the mouse cursor over the “women” menu, which includes eight subsections, for more than 25 seconds.
  • the relatively long hover may indicate an interaction event that may increase the user experience score (e.g., to 4.3).
  • the user experience score may be updated accordingly and, therefore, may represent a new and higher, than the previous score, user experience score of 8.0. It should be noted that the user experience score may be computed based on several metrics such as the interaction event type, the interaction event zone, the web page in which the event occurred, and the like.
  • the analytic sever 130 may be configured to generate and send an alert (in near real-time) to a website owner server 160 that is associated with the website in which the user browses.
  • the alert may indicate the URL of the web page or web pages in which interaction event (or events) occurred, the zone or zones in which the interaction event (or events) occurred, the interaction event type, a time pointer at which each interaction event occurred, and so on.
  • the website owner server 160 may execute one or more actions. For example, and without limitation, an action may include sending, by the website owner server 160, an electronic message for display on the web browser 112. Such an electronic message may include a suggestion to assist the user with the specific problem the user has encountered.
  • the analytic sever 130 may send the alert (e.g., one or more code lines) to the browser 112. Then, upon receiving the alert, a predetermined action may be automatically executed by the user device 110. That is, one or more predetermined code lines, such as those designed by the website owner, may be used for solving one or more possible problems. For example, the alert may indicate that the “add to cart” button does not respond after two attempts. According to the same example, a predetermined code line may address this specific problem by automatically generating and displaying an electronic message on the browser 112 inviting the user to input the product code in order to overcome the technical problem.
  • the alert e.g., one or more code lines
  • the alert may indicate that the “add to cart” button does not respond after two attempts.
  • a predetermined code line may address this specific problem by automatically generating and displaying an electronic message on the browser 112 inviting the user to input the product code in order to overcome the technical problem.
  • the analytic sever 130 may be configured to adjust and prioritize the alerts based on, for example, data that was gathered with respect to the user.
  • data may include for example, the interaction information of the user when interacting with one or more webpages and historical data of the user.
  • the interaction information may indicate, for example, the current status of the electronic shopping cart, the number of pages the user has visited, and so on.
  • the historical data of the user may include, for example, the historical purchases made by the user, average amounts spent on each visit, and so on.
  • the historical data may be extracted from, for example, a database (e.g., the database 150).
  • the analytic sever 130 may be configured to analyze the data that was gathered with respect to the user (e.g., the interaction information of the user when interacting with one or more webpages and historical data of the user) in order to adjust and prioritize the alerts based.
  • the alert may also be associated with a relatively high priority score.
  • a priority score may be a number from “1” to “5” indicating which alert should be executed first, where, for example, an alert that is associated with a priority score of “1 ” should be executed after an alert that is associated with a priority score of “4”.
  • an alert may be sent to the website owner.
  • the alert may also be associated with a relatively low priority score.
  • the user experience score crosses a predetermined threshold value after a new user was unable to click the “menu” button, an alert may be sent to the website owner.
  • the alert may also be associated with a relatively low priority score.
  • FIG. 2 shows an example flowchart 200 illustrating a method for communicating negative user experiences of users interacting with a website.
  • the method is performed by the analytic sever 130.
  • interaction information of in-page browsing activity of a user browser is received.
  • the interaction information of the in-page browsing activity may include a mouse movement, scrolling of web page, resizing of browser window, click events, keyboard use with any element of a webpage, and the like.
  • the interaction information may further include URL, referrer, load time, IP address, browser type, screen resolution, page views, and the like.
  • interaction events are extracted from the interaction information.
  • An interaction event may be for example, a hover with the mouse cursor over an element (e.g., a virtual button, a link), a click on an element, a tap on an element (e.g., tapping on a specific virtual button), and the like. That is, not every interaction of the user with the website webpage is considered an event.
  • one or more zones in the webpage being analyzed are detected or otherwise retrieved from website configuration associated with the website selected.
  • the website configuration may indicate different zones of each web page of the website, and a uniform resource locator (URL) of each web page of the website.
  • the selection of the website configuration may be achieved based on at least a portion of the interaction information indicating at a URL of a web page in which the user is currently browsing.
  • the detection of zones in a webpage is performed by analyzing the DOM structure and elements and further discussed in the above-referenced ⁇ 90 Application.
  • interaction patterns are determined based on the extracted events and the zones of the webpage. This is performed in part to determine which zones have been accessed and the events performed on the zones. For example, a user’s clicks on a product image would be considered as an interaction pattern, wherein the “click” is an event and the “image” is a zone.
  • a user experience score representing the interaction pattern is computed.
  • the user experience score is computed in real-time based on the interaction patterns.
  • a user experience score is computed for each identified interaction pattern. It should be noted that the user experience score may be constantly updated. The process for computing the user experience score and determining its sentiment (positive or negative) is further discussed in Fig. 3.
  • Fig. 3 shows an example flowchart S250 illustrating a method for computing a user experience score, according to one embodiment. In an embodiment the method is performed by the analytic sever 130.
  • the user behavioral data may be collected in real-time or near real-time and may be indicative of actions made by the user while browsing through a website.
  • user behavioral data may be determined based on one or more interaction events that occur while the user browses through the website.
  • predefined user experience factors are extracted.
  • User experience factors are predefined interactions which indicate a user’s difficulty or ease in operating or navigating a webpage or site. That is, the predefined user experience factors may be indicative of a negative or positive interaction of a user with a website, as further described hereinabove with respect to Fig. 1 .
  • a user experience score is determined.
  • the user experience score provides an indication of the user experience in real-time or near real-time while the user is browsing through the website.
  • Fig. 4 shows an example flowchart S260 illustrating a method for generating alerts with respect to interaction patterns of a user in a website according to one embodiment.
  • the method is performed by the analytic sever 130.
  • a current user experience score (that is constantly generated and updated in near real-time) is constantly tracked and monitored.
  • user experience scores represent the user interaction pattern while browsing through a particular website.
  • the user experience score is computed in real-time based on the user behavior and friction patterns the user has experienced.
  • the user experience score may be determined based on one or more interaction events that occur while the user browses through the website.
  • each interaction event may be indicative of the URL of a web page or web pages in which interaction event (or events) occurred, the web page zone or zones in which the interaction event (or events) occurred, the interaction event type, a time pointer at which each interaction event occurred, and so on. It should be noted that the user experience score may be constantly updated.
  • the predetermined threshold value may be a number that is used for distinguishing between cases in which the user experience is positive and cases in which the user experience is negative. Thus, when the user experience score is determined to be relatively high (i.e., above the predetermined threshold value) it means that the user encountered some issues while browsing through the website. That is, in this example, a high user experience score means a negative user experience.
  • an alert is generated to notify the website owner that a user is experiencing one or more difficulties.
  • the alert may indicate the URL of a web page or web pages in which interaction event (or events) occurred, the webpage’s zone or zones in which the interaction event (or events) occurred, the interaction event type, a time pointer at which each interaction event occurred, and the like.
  • the alert is sent over a network (e.g., the network 140 of Fig. 1) to, for example, a predefined server that is associated with the owner of the tracked website (e.g., the website owner server 160). It should be noted that the alert may be sent in near-real-time (e.g., within milliseconds) from the when the current user experience score is determined to be above the predetermined threshold value.
  • a predefined server to which the alert was sent may execute one or more actions to solve the issues or problems the user has encountered.
  • the alert may be sent to, for example, the abovementioned predefined server using an application programming interface (API).
  • API application programming interface
  • a modified code (HTML) code is generated and sent to the server hosting the website.
  • the modified code is provided to improve a webpage in which a negative user experience has been encountered.
  • the code may fix an issue with an “unclickable” image.
  • the modified code is downloaded to a browser, a user will be able to click the image, so that an action would be triggered.
  • an alert may include any form of an electronic notification, such as, a message, a report, and the like.
  • the electronic notification may be communicated in a form of an email, a pop-up widow, a signal, a text message, and the like.
  • FIG. 5 shows an example screenshot 500 of an example webpage and an example dashboard displaying the results of the method according to an embodiment.
  • the screenshot 500 includes an image 510 of the web page that is being analyzed.
  • a list 520 of all interaction information and interaction events is presented.
  • An interaction event 530 indicates that the “add to cart” virtual button 540 was clicked multiple times in a short time.
  • a user experience score 550 indicates the user behavior and user experience factors the user has experienced. As noted above, the user experience factors may be determined based on one or more interaction events that occur while the user browses through the website.
  • Fig. 6 is an example block diagram of the analytic sever 130 designed according to an embodiment.
  • the analytic sever 130 includes a processing circuitry 610 coupled to a memory 620, a storage 630, and a network interface 640. In an embodiment, the components of the analytic sever 130 may be communicatively connected via a bus 650.
  • the processing circuitry 610 may be realized as one or more hardware logic components and circuits.
  • illustrative types of hardware logic components include field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), Application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), graphics processing units (GPUs), tensor processing units (TPUs), general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), and the like, or any other hardware logic components that can perform calculations or other manipulations of information.
  • FPGAs field programmable gate arrays
  • ASICs application-specific integrated circuits
  • ASSPs Application-specific standard products
  • SOCs system-on-a-chip systems
  • GPUs graphics processing units
  • TPUs tensor processing units
  • DSPs digital signal processors
  • the memory 620 may be volatile (e.g., random access memory, etc.), non volatile (e.g., read only memory, flash memory, etc.), or a combination thereof.
  • software for implementing one or more embodiments disclosed herein may be stored in the storage 630.
  • the memory 620 is configured to store such software.
  • Software shall be construed broadly to mean any type of instructions, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise. Instructions may include code (e.g., in source code format, binary code format, executable code format, or any other suitable format of code). The instructions, when executed by the processing circuitry 610, cause the processing circuitry 610 to perform the various processes described herein.
  • the server 130 by means of the circuity 610, and the instructions can perform a process for near-real-time communication of negative user experiences of users interacting with a website.
  • the server 130 is configured to identify at least one interaction pattern by analyzing an in-page interaction information of a user interacting with at least one page of the website, to compute a user experience score for each of the at least one identified interaction pattern, to generate an alert indicating that the user experience score determined for a respective identified interaction pattern demonstrates a negative user experience, and to send the alert immediately upon identifying the interaction pattern demonstrating a negative user experience.
  • the server 130 is further configured to send the alert to a predefined server, where the alert indicates at least the identified interaction pattern demonstrating the negative user experience and a webpage where the interaction pattern is identified.
  • the server 130 is further configured to generate a piece of code for fixing the webpage where the interaction pattern is identified and to send the piece of code to a web server hosting the website, thereby improving the user experience for subsequent visits to the webpage.
  • the server 130 is further configured to receive browsing activity gathered from user devices visiting the website, where the browsing activity includes at least interaction information of a user with at least one webpage of the website and extracting interaction events from received interaction information.
  • the server 130 is further configured to analyze the in-page interaction information by detecting at least one zone included in a webpage being analyzed and identifying the at least one interaction pattern based on the at least one detected zone and the extracted events.
  • the server 130 is further configured to compare the user experience score to a predefined threshold and to analyze the interaction pattern to demonstrate a negative user experience, when the score is above the predefined threshold.
  • the server 130 is further configured to compute a user experience score by determining, based on the interaction information, a set of user experience factors and computing the user experience score based on the set of user experience factors.
  • the storage 630 may be magnetic storage, optical storage, and the like, and may be realized, for example, as flash memory or other memory technology, compact disk-read only memory (CD-ROM), Digital Versatile Disks (DVDs), or any other medium which can be used to store the desired information.
  • flash memory compact disk-read only memory
  • DVDs Digital Versatile Disks
  • the network interface 640 allows the analytic sever 130 to communicate with the various components, devices, and systems described herein for near-real time communicating negative user experience of users interacting with a website.
  • the various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof.
  • the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices.
  • the application program may be uploaded to, and executed by, a machine comprising any suitable architecture.
  • the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces.
  • the computer platform may also include an operating system and microinstruction code.
  • the various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such a computer or processor is explicitly shown.
  • various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit.
  • a non-transitory computer readable medium is any computer-readable medium except for a transitory propagating signal.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

L'invention concerne un système et un procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site Web. Le procédé comprend les étapes suivantes : identifier au moins un motif d'interaction en analysant des informations d'interaction dans la page d'un utilisateur interagissant avec au moins une page du site Web ; calculer un score d'expérience d'utilisateur pour chacun du ou des motifs d'interaction identifiés ; produire une alerte indiquant que le score d'expérience d'utilisateur déterminé pour un motif d'interaction identifié respectif démontre une expérience d'utilisateur négative ; et envoyer l'alerte immédiatement lors de l'identification du motif d'interaction démontrant une expérience d'utilisateur négative.
PCT/IB2020/057036 2020-07-24 2020-07-24 Procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site web Ceased WO2022018505A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/IB2020/057036 WO2022018505A1 (fr) 2020-07-24 2020-07-24 Procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site web

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/IB2020/057036 WO2022018505A1 (fr) 2020-07-24 2020-07-24 Procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site web

Publications (1)

Publication Number Publication Date
WO2022018505A1 true WO2022018505A1 (fr) 2022-01-27

Family

ID=71895056

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2020/057036 Ceased WO2022018505A1 (fr) 2020-07-24 2020-07-24 Procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site web

Country Status (1)

Country Link
WO (1) WO2022018505A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12488244B1 (en) 2024-09-11 2025-12-02 Uva Ip, Llc Apparatus and method for data generation for user engagement

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130104030A1 (en) * 2011-04-20 2013-04-25 IBT - Internet Business Technologies Methods and systems for real-time web tracking and marketing
US20140344217A1 (en) * 2012-11-27 2014-11-20 Gubagoo, Inc. Systems and methods for online website lead generation service
US20190260818A1 (en) * 2018-02-20 2019-08-22 Quantum Metric, Inc. Techniques for identifying issues related to digital interactions on websites

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130104030A1 (en) * 2011-04-20 2013-04-25 IBT - Internet Business Technologies Methods and systems for real-time web tracking and marketing
US20140344217A1 (en) * 2012-11-27 2014-11-20 Gubagoo, Inc. Systems and methods for online website lead generation service
US20190260818A1 (en) * 2018-02-20 2019-08-22 Quantum Metric, Inc. Techniques for identifying issues related to digital interactions on websites

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12488244B1 (en) 2024-09-11 2025-12-02 Uva Ip, Llc Apparatus and method for data generation for user engagement

Similar Documents

Publication Publication Date Title
US12307496B2 (en) Automated extraction of data from web pages
US11799746B2 (en) Method for near-real-time communicating negative user experience of users interacting with a website
US20200193487A1 (en) System and method to measure effectiveness and consumption of editorial content
CA2917048C (fr) Procede et appareil pour determiner un comportement de navigation d'utilisateur
US20130275862A1 (en) System and Method for Providing a Product-Based Chat Session
US11836666B2 (en) Identifying script errors in an online retail platform and quantifying such errors
US20220035888A1 (en) Journeying user activities through a website
US9088620B2 (en) System and method of recommending actions based on social capital of users in a social network
US11727428B2 (en) Automated testing of multiple on-line coupons
WO2015164360A1 (fr) Système et procédé de suivi d'implication d'utilisateurs avec des annonces publicitaires en ligne
CN110674404A (zh) 链接信息生成方法、装置、系统、存储介质及电子设备
US20240396957A1 (en) Systems and methods for decentralized detection of software platforms operating on website pages
US12346362B2 (en) Mapping webpages to page groups
US11947573B2 (en) Determining zone identification reliability
WO2022018505A1 (fr) Procédé de communication en temps quasi réel d'expérience d'utilisateur négative d'utilisateurs interagissant avec un site web
EP4466615A1 (fr) Scores de frustration pour flux, vues de page, pages web, sessions, et sites web
US12190343B2 (en) Passive user-generated coupon submission
US12038998B1 (en) Identifying webpage elements based on HTML attributes and selectors
US20230196418A1 (en) Providing purchase intent predictions using session data for targeting users
WO2022003403A1 (fr) Identification d'erreurs de script dans une plateforme de vente au détail en ligne et quantification de telles erreurs
US11681771B1 (en) Determining conditions for a set of webpages
WO2023209640A1 (fr) Détermination de types de zone d'une page web

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20749965

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20749965

Country of ref document: EP

Kind code of ref document: A1