Patent Publication Number: US-2016225021-A1

Title: Method and system for advertisement retargeting based on predictive user intent patterns

Description:
TECHNICAL FIELD 
     The present disclosure relates to the field of on-line advertising. More specifically, the present disclosure relates to a method, computer program product and system for advertisement retargeting based on predictive user intent patterns. 
     BACKGROUND 
     The usage of websites to make dedicated web content available to a large public is now prevalent, in relation with the widespread usage of fixed Internet access and mobile Internet access. In particular, e-commerce has become a major component of the economy, in a plurality of business areas such as for example travel agencies, on-line banking, consumer electronics and multimedia retail sales, etc. Websites in relation to professional services and administration are now also widely used to reach prospects and users. 
     However, the average e-commerce website conversion rate is generally a little more than 2% (according to studies). In other words, nearly all of the people who visit an e-commerce website for the first time leave without some form of desired action. Retargeting is a technique for driving customers to return to a previously visited website. Retargeted customers are four times more likely to convert than new customers who have never been exposed to a company brand (according to studies). 
     Behavioral data collection is a known technique for optimizing the selection of an advertisement for retargeting a potential customer to a website. Behavioral data related to a previous visit of the website by the potential customer are used to better understand the intent of the customer, in order to select the most effective retargeting advertisement. However, the collected behavioral data are not always representative of the real intent of the potential customer when visiting the website. For instance, it seems intuitive to assume through behavioral data collection that a visitor who visited the cart of an e-commerce website has an intent to purchase. However, studies have shown that 56% of visitors who visit the cart do not intend to purchase. 
     Furthermore, web surveys have shown that 67% of visitors who have a stated intent to purchase (as expressed in a response to a question in a web survey related to a website) do not even make it to the cart of the website. This means that a retargeting campaign leveraging behavioral data related to the cart as a trigger is neglecting the majority of visitors who intend to purchase, missing a huge conversion opportunity. 
     There is therefore a need for a new method, computer program product and system for advertisement retargeting based on predictive user intent patterns. 
     SUMMARY 
     According to a first aspect, the present disclosure provides a method for advertisement retargeting based on predictive user intent patterns. The method comprises collecting behavioral data from a plurality of user devices. The behavioral data are representative of a series of actions performed by a user of each of the plurality of user devices while visiting a website. The method comprises collecting survey participation data from at least some of the plurality of user devices. The survey participation data correspond to survey information received from the users of the at least some of the plurality of user devices in relation to the visiting of the website. The method comprises determining an intent of the users of the at least some of the plurality of user devices in relation to the visiting of the website, based on the survey participation data. The method comprises analyzing the intent of the users and the related behavioral data to generate the predictive user intent patterns. The method comprises collecting current behavioral data from a current user device. The current behavioral data are representative of a series of actions performed by a user of the current user device while visiting a current website. The method comprises determining an intent of the user of the current user device in relation to the visiting of the current website based on the current behavioral data and the predictive user intent patterns. The method comprises selecting a retargeting advertisement directed to the current website for the current user device based at least on the determined intent of the user of the current device. 
     According to a second aspect, the present disclosure provides a computer program product comprising instructions deliverable via an electronically-readable media, such as storage media and communication links. The instructions comprised in the computer program product, when executed by a processing unit of a user device, provide for advertisement retargeting based on a determined user intent. More specifically, the instructions provide for collecting behavioral data representative of a series of actions performed by a user of the user device while visiting a website. The instructions provide for transmitting the collected behavioral data to a survey server. The survey server is capable of determining an intent of the user of the user device in relation to the visiting of the website based on the collected behavioral data and predictive user intent patterns. The instructions provide for receiving the determined intent of the user of the user device from the survey server. The instructions provide for transmitting the determined intent to an advertising server. The instructions provide for receiving a retargeting advertisement directed to the website from the advertising server. The retargeting advertisement is selected at least based on the determined intent. 
     According to a third aspect, the present disclosure provides a system for advertisement retargeting based on predictive user intent patterns. The system comprises a survey server and an advertisement server. The survey server comprises a communication interface for exchanging data with user devices. The survey server comprises memory for storing the predictive user intent patterns. The survey server comprises a processing unit for collecting behavioral data from a plurality of user devices. The behavioral data are representative of a series of actions performed by a user of each of the plurality of user devices while visiting a website. The processing unit also collects survey participation data from at least some of the plurality of user devices. The survey participation data correspond to survey information received from the users of the at least some of the plurality of user devices in relation to the visiting of the website. The processing unit further determines an intent of the users of the at least some of the plurality of user devices in relation to the visiting of the website, based on the survey participation data. The processing unit analyzes the intent of the users and the related behavioral data to generate the predictive user intent patterns. The processing unit also collects current behavioral data from a current user device. The current behavioral data are representative of a series of actions performed by a user of the current user device while visiting a current website. The processing unit determines an intent of the user of the current user device in relation to the visiting of the current website based on the current behavioral data and the predictive user intent patterns. The processing unit further transmits the determined intent to the current user device. The advertisement server comprises a communication interface for exchanging data with user devices. The survey server comprises a processing unit for receiving the determined intent from the current user device. The processing unit further selects a retargeting advertisement directed to the current website for the current user device based at least on the determined intent. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the disclosure will be described by way of example only with reference to the accompanying drawings, in which: 
         FIG. 1  illustrates a system for advertisement retargeting based on predictive user intent patterns; 
         FIGS. 2A and 2B  illustrate a method for advertisement retargeting based on predictive user intent patterns; 
         FIG. 3  illustrates an example of a web survey for collecting a user intent in relation to a visit of a website; and 
         FIG. 4  illustrates audience segments based at least on intents of users. 
     
    
    
     DETAILED DESCRIPTION 
     The foregoing and other features will become more apparent upon reading of the following non-restrictive description of illustrative embodiments thereof, given by way of example only with reference to the accompanying drawings. Like numerals represent like features on the various drawings. 
     Various aspects of the present disclosure generally address one or more of the problems related to the optimization of advertisement retargeting, using behavioral data and survey participation data. 
     The following terminology is used throughout the present disclosure:
         Web survey: A web survey aims at collecting user feedback related to a visit of a website by a user. The term survey is used in a generic manner, and may include surveys, questionnaires, comment cards, etc.   Behavioral data: Data representative of a series of actions performed by a user while visiting a website. Behavioral data include visited web pages, time spent on the visited web pages, specific interactions with the visited web pages, etc. The behavioral data are generally collected from the user device by an analytic server, which further processes the data collected from a plurality of user devices visiting the web site.   Advertisement retargeting: Retargeting is a form of online advertising for keeping a brand in front of visitors, after they leave a website related to the brand, and are visiting other websites. Retargeting is generally implemented as a cookie-based technology that uses a script (e.g. Javascript code) to anonymously follow an audience all over the Web. Every time a new visitor visits a particular website, the script generates an anonymous browser cookie. Later, when the cookied visitor browses the Web, the cookie allows a retargeting provider to know when to serve advertisements, ensuring that advertisements related to the particular website (or particular brand related to the particular web site) are only served to people who have previously visited the particular site. Behavioral retargeting is a form of retargeting that leverages collected behavioral data related to the visited particular website to improve the retargeting process. The advertisement served to a specific prospect is personalized based on the behavioral data previously collected from the specific prospect.       

     Referring now concurrently to  FIGS. 1, 2A and 2B , a system and a method for advertisement retargeting based on predictive user intent patterns are represented. The system comprises a survey server  200  and an advertisement server  300 . At least some of the steps of the method  400  are performed by the survey server  200  and the advertisement server  300 . 
     The survey server  200  comprises a processing unit  210 , having one or more processors (not represented in  FIG. 1  for simplification purposes) capable of executing instructions of computer program(s). Each processor may further have one or several cores. The survey server  200  also comprises memory  220  for storing instructions of the computer program(s) executed by the processing unit  210 , data generated by the execution of the computer program(s), data received via a communication interface  230  of the survey server  200 , etc. The survey server  200  may comprise several types of memories, including volatile memory, non-volatile memory, etc. The survey server  200  further comprises the communication interface  230  (e.g. Wi-Fi interface, Ethernet interface, etc.). The communication interface  230  is used for exchanging data with other entities, such as a user device  100 . 
     The survey server  200  exchange data with the other entities through communication links, generally referred to as the Internet  10  for simplification purposes. Such communication links may include wired (e.g. a fixed broadband network) and wireless communication links (e.g. a cellular network or a Wi-Fi network). 
     The survey server  200  may further comprise a display (e.g. a regular screen or a tactile screen) for displaying data generated by the processing unit  210 , and a user interface (e.g. a mouse, a keyboard, a trackpad, a touchscreen, etc.) for allowing a user to interact with the survey server  200 . The display and the user interface are not represented in  FIG. 1  for simplification purposes. 
     The user device  100  may consist of a computer, a laptop, a mobile device (e.g. smartphone, tablet, etc.), an Internet connected television, etc. The user device  100  is capable of retrieving web content from a web server  20  over the Internet  10 , and displaying the retrieved web content to a user of the user device  100  via a web browser. The user device  100  comprises a processing unit  110 , having one or more processors (not represented in  FIG. 1  for simplification purposes) capable of executing instructions of computer program(s) (e.g. the web browser). Each processor may further have one or several cores. The user device  100  also comprises memory  120  for storing instructions of the computer program(s) executed by the processing unit  110 , data generated by the execution of the computer program(s), data received via a communication interface  130  of the user device  100 , etc. The user device  100  may comprise several types of memories, including volatile memory, non-volatile memory, etc. The user device  100  further comprises the communication interface  130  (e.g. cellular interface, Wi-Fi interface, Ethernet interface, etc.). The communication interface is used for exchanging data over the Internet  10  with other entities, such as the web server  20 , the survey server  200 , and an advertisement server  300 . 
     The user device  100  further comprises a display  140  (e.g. a regular screen or a tactile screen) for displaying data generated by the processing unit  210 , web content retrieved from the web server  20 , etc. The user device  100  also comprises a user interface  150  (e.g. a mouse, a keyboard, a trackpad, a touchscreen, etc.) for allowing a user to interact with the user device  100  (e.g. interactions of the user with the displayed web content). 
     The web server  20  generally consists of a dedicated computer with high processing capabilities, capable of hosting one or a plurality of websites. The web server  20  comprises a processing unit, memory, and a communication interface (e.g. Ethernet interface, Wi-Fi interface, etc.) for delivering web content of a hosted website to the user device  100 . The components of the web server  20  are not represented in  FIG. 1  for simplification purposes. 
     Although a single user device  100  is represented in  FIG. 1 , a plurality of user devices  100  exchange data with the web server  20  in relation to a visit of a particular website (hosted by the web server  20 ) by the plurality of user devices  100 . 
     The advertisement server  300  comprises a processing unit  310 , having one or more processors (not represented in  FIG. 1  for simplification purposes) capable of executing instructions of computer program(s). Each processor may further have one or several cores. The advertisement server  300  also comprises memory  320  for storing instructions of the computer program(s) executed by the processing unit  310 , data generated by the execution of the computer program(s), data received via a communication interface  330  of the advertisement server  300 , etc. The advertisement server  300  may comprise several types of memories, including volatile memory, non-volatile memory, etc. The advertisement server  300  further comprises the communication interface  330  (e.g. Wi-Fi interface, Ethernet interface, etc.). The communication interface  330  is used for exchanging data over the Internet  10  with other entities, such as the user device  100 . As is well known in the art, the advertisement server  300  interacts with the user device  100  over the Internet  10 , for delivering advertisement(s) (e.g. a banner, a video, etc.) to the user device  100 , while the user of the user device  100  is visiting a website hosted by the web server  20 . The advertisements are displayed on the display  140  along with a web content of the visited web site. 
     The advertisement server  300  may further comprise a display (e.g. a regular screen or a tactile screen) for displaying data generated by the processing unit  310 , and a user interface (e.g. a mouse, a keyboard, a trackpad, a touchscreen, etc.) for allowing a user to interact with the advertisement server  300 . The display and the user interface are not represented in  FIG. 1  for simplification purposes. 
     Referring now particularly to  FIGS. 2A and 2B , the steps of the method  400  will be described. The method  400  comprises two phases: a learning phase for generating predictive user intent patterns, and an operational phase for using the generated predictive user intent patterns. 
     Learning Phase (FIG.  2   a ) 
     At step  405 , web content corresponding to a website is transmitted by the web server  20  to a user device  100  over the Internet  30 . The website (e.g. http://www.ecommerce.com) is hosted by the web server  20  and visited by a user of the user device  100 . The interactions between the user device  100  and the web server  20  for exchanging the web content are well known in the art. The web content is sent via the communication interface (not represented in  FIG. 1 ) of the web server  20  and received via the communication interface  130  of the user device  100 . 
     The web content may include text, image(s), video(s), icon(s), etc. The web content is displayed on the display  140  of the user device  100  by the browser executed by the processing unit  110  of the user device  100 . The step of displaying the web content on the display  140  is not represented in  FIG. 2A  for simplification purposes. During a browsing session of the web site, a sequence of web pages of the website containing the web content is displayed on the display  140 . The user of the user device  100  interacts with the web content of the webpages through the user interface  150  of the user device  100 . 
     At steps  410  and  411 , behavioral data are respectively collected by the processing unit  110  of the user device  100 , and transmitted by the processing unit  110  from the user device  100  to the survey server  200 . The behavioral data are representative of a series of actions performed by the user of the user device  100  while visiting the website. The behavioral data are sent via the communication interface  130  of the user device  100  and received via the communication interface  230  of the survey server  200 . The type of behavioral data which can be collected is well known in the art of web analytics, and examples of such behavioral data will be provided later in the description. 
     In an alternative embodiment, the web server  20  performs the collection of the behavioral data, and the transmission of the behavioral data to the survey server  200  over the Internet  10 . In still another alternative embodiment, the behavioral data are partially collected by the user device  100  and partially collected by the web server  20 , before transmission to the survey server  200 . In yet another alternative embodiment, at least some of the behavioral data (collected by the user device  100  or the web server  20 ) are transmitted to a third party server (e.g. an analytic server not represented in  FIG. 1 ), where they are processed for purposes specific to the third party server. The behavioral data are further transmitted from the third party server to the survey server  200 , where they are processed according to the method  400 . These alternative embodiments have not been represented in the Figures for simplification purposes. 
     A plurality of user devices  100  visit the website and generate corresponding behavioral data. The processing unit  210  of the survey server  200  collects the behavioral data from the plurality of user devices, for further processing at step  425  of the method  400 . 
     The behavioral data are received via the communication interface  230  of the survey server  200  and stored in the memory  220  for later use. Furthermore, the behavioral data of a specific user device  100  may be received in several bundles, and aggregated in the memory  220  using a unique identifier of the specific user device  100  (e.g. a unique session identifier or unique device identifier). 
     The processing unit  210  of the survey server  200  may also filter the collected behavioral data, and discard some of them based on pre-determined criteria. The criteria may include at least one of the following: incomplete data, erroneous data, irrelevant data, etc. 
     The user of the user device  100  also participates to a web survey related to the visit of the website, and provides survey information by participating to the web survey. 
     At steps  415  and  416 , survey participation data are respectively collected by the processing unit  110  of the user device  100 , and transmitted by the processing unit  110  from the user device  100  to the survey server  200 . The survey participation data correspond to the survey information provided by the user. The survey participation data are sent via the communication interface  130  of the user device  100  and received via the communication interface  230  of the survey server  200 . 
     An example of survey participation data comprises responses to a survey questionnaire related to the visited website, and includes at least one of the following: free-form text, ratings, selection of one or more elements among proposed alternatives, ordering of proposed elements, etc. An invitation to participate to the web survey may be prompted to the user of the user device  100  during the visit of the website, voluntarily triggered by the user of the user device  100  (e.g. through the selection of a survey icon), communicated to the user of the user device  100  in a delayed manner (e.g. through an email), etc. 
     Users of several user devices  100  participate to the web survey related to the website, and the several user devices  100  generate corresponding survey participation data. The processing unit  210  of the survey server  200  collects the survey participation data from the several user devices, for further processing at steps  420  and  425  of the method  400 . 
     The survey participation data are received via the communication interface  230  of the survey server  200  and stored in the memory  220  for later use. Furthermore, the survey participation data of a specific user device  100  may be received in several bundles, and aggregated in the memory  220  using a unique identifier of the specific user device  100  (e.g. a unique session identifier or unique device identifier). 
     The processing unit  210  of the survey server  200  may also filter the collected survey participation data, and discard some of them based on pre-determined criteria. The criteria may include at least one of the following: incomplete data, erroneous data, irrelevant data, etc. 
     For a specific user device  100  for which behavioral data are collected, survey participation data may or may not be collected. For instance, if the user of the specific user device  100  is not invited to participate to the web survey, no survey participation data are collected. Similarly, if the user of the specific user device  100  is invited to participate to the web survey, but refuses to participate, no survey participation data are collected. Thus, the survey server  200  collects the behavioral data from a plurality of user devices  100 , and collects the survey participation from at least some of the plurality of user devices  100 . 
     At step  420 , the processing unit  210  of the survey server  200  determines an intent of the users of the at least some of the plurality of user devices  100  in relation to the visiting of the website, based on the collected survey participation data. 
       FIG. 3  illustrates an example of a web survey comprising a question for determining the intent of the users in relation to the visit of the website. A Graphical User Interface  500  of the browser executed by the processing unit  110  of the user device  100  displays web content related to the visited website (e.g. http://www.ecommerce.com) on the display  140  of the user device  100 . A GUI  550  for allowing the user of the user device  100  to provide the survey information is also displayed on the display  140 . For example, the GUI  550  consists in an overlay popup window partially covering a browsing window  520  containing the displayed web content (e.g. web page home_hardware). 
     A survey content displayed in the overlay popup window  550  comprises a closed-ended question  551  related to the intent of the user, and a selection widget  552  comprising four selectable items (information, purchase, support, other) corresponding to an intent of the user. 
     The interactions of the user with the GUI  550  (e.g. selection of one of the four items of the selection widget  552 ) generate survey participation data representative of the intent of the user for visiting the website. The survey participation data may comprise a value selected among pre-defined values (e.g. 1 for information, 2 for purchase, 3 for support, 4 for other) corresponding to the user intent metric. 
     In the embodiment illustrated in  FIG. 3 , upon reception of the survey participation data, the survey server  200  directly extracts the intent of the user from the survey participation data. In an alternative embodiment, the web survey does not include a question directly related to the intent of the user. Consequently, the intent of the user is inferred from the survey participation data, rather than being directly extracted from the survey participation data. For this purpose, at least some of the survey participation data are processed by the processing unit  210  of the survey server  200 , to determine the intent of the user. This processing for determining the intent of the user is out of the scope of the present disclosure, but is well known in the art of analyzing survey participation data. 
     At step  425 , the processing unit  210  of the survey server  200  analyzes the intent of the users and the related behavioral data to generate predictive user intent patterns. 
     As mentioned previously, a unique session identifier is used by the survey server  200  and a specific user device  100  for uniquely identifying the specific user device  100  when transmitting the behavioral data at step  411  and the survey participation data at step  416 . This unique session identifier is used to associate the user intent determined at step  420  with the corresponding behavioral data for the specific user device  100 . The unique session identifier can be generated by the survey server  200  (e.g. generation of a unique random number) and transmitted to the specific user device  100  before step  410 . The unique session identifier can also be generated by the specific user device  100  (e.g. based on a unique characteristic of the specific user device  100 ). The unique session identifier can be stored in a cookie at the specific user device  100 . Alternatively, a unique device identifier of the specific user device  100  (e.g. a Media Access Control (MAC) address, an International Mobile Station Equipment Identity (IMEI), an International Mobile Subscriber Identity (IMSI), etc.) can be used in place of (or complementarity to) the unique session identifier. 
     Step  425  is performed when a sufficient amount of intent of users and corresponding behavioral data have been collected from the user devices  100 . Correlations between the intent of users and the corresponding behavioral data are inferred by the processing unit  210  of the survey server  200  through analysis of these data, and the predictive user intent patterns are generated based on these correlations. Based on the predictive user intent patterns, having only behavioral data for a particular user device  100 , a corresponding intent of the user of the particular user device  100  for visiting the web site can be determined. 
     Techniques for the determination of correlations between two sets of data, and the generation of predictive patterns based on the correlations, is well known in the art of data analysis, and is out of the scope of the present disclosure. For instance, statistical and/or artificial intelligence (e.g. machine learning) techniques can be used for this purpose. Additionally, the generation of predictive patterns based on collected behavioral data and collected survey participation data is further described in U.S. application Ser. No. 14/288,347, the disclosure of which is incorporated herein in its entirety. 
     At step  450 , the processing unit  210  of the survey server  200  stores the generated predictive user intent patterns in the memory  220 , for use in the operational phase. 
     Operational Phase (FIG.  2   b ) 
     During the operational phase, current user devices  100  visit a current website, and the predictive user intent patterns generated at step  425  and stored at step  450  are used to determine an intent of the users of the current user devices  100  in relation to the visiting the current web site. 
     The current website is generally the same as the website referred to in the learning phase. Thus, the predictive user intent patterns are generated when a sufficient number of user devices have been visiting the website for completing the collection of behavioral data at step  411  and survey participation data at step  416 . Afterwards, the generated predictive user intent patterns are used for current user devices  100  visiting the website. 
     Alternatively, the current website is different from the website referred to in the learning phase, but their content is sufficiently related so that the user intent patterns generated for the website of the learning phase can be used for the current website. For example, the website of the learning phase and the current website may belong to the same industry (e.g. automotive, travel agencies, etc.), and respectively correspond to two different brands of a same company (e.g. two brands of cars from the same auto manufacturer). 
     At step  455 , web content corresponding to the current website is transmitted by the web server  20  to a current user device  100  over the Internet  30 . The current website is hosted by the web server  20  and visited by a user of the current user device  100 . The current website may also be hosted by another web server. This step is similar to step  405 . 
     The web content is displayed on the display  140  of the current user device  100  by the browser executed by the processing unit  110  of the current user device  100 . The step of displaying the web content on the display  140  is not represented in  FIG. 2B  for simplification purposes. During a browsing session of the current web site, a sequence of web pages of the current website containing the web content is displayed on the display  140 . The user of the current user device  100  interacts with the web content of the webpages through the user interface  150  of the current user device  100 . 
     At steps  460  and  461 , current behavioral data are respectively collected by the processing unit  110  of the current user device  100 , and transmitted by the processing unit  110  from the current user device  100  to the survey server  200 . The current behavioral data are representative of a series of actions performed by the user of the current user device  100  while visiting the current website. The current behavioral data are sent via the communication interface  130  of the current user device  100  and received via the communication interface  230  of the survey server  200 . Steps  460  and  461  are similar to steps  410  and  411 . 
     As mentioned previously for the behavioral data collected for the learning phase, at least some of the current behavioral data may be collected by a third party server (e.g. the web server  20 ) and/or transmitted to an intermediate third party server (e.g. an analytic server), before transmission to the survey server  200 . Ultimately, the processing unit  210  of the survey server  200  collects the current behavioral data from the current user device  100 , for further processing at step  465  of the method  400 . 
     The current behavioral data are received via the communication interface  230  of the survey server  200 , and may be stored in the memory  220 . For instance, the current behavioral data of the current user device  100  may be received in several bundles, and aggregated in the memory  220  using a unique identifier of the current user device  100  (e.g. a unique session identifier or unique device identifier). 
     The processing unit  210  of the survey server  200  may also filter the collected current behavioral data, and discard some of them based on pre-determined criteria. The criteria may include at least one of the following: incomplete data, erroneous data, irrelevant data, etc. In particular, if some of the collected current behavioral data do not correspond to the type of behavioral data collected at steps  410  and  411  for the learning phase, they are discarded. The current behavioral data need to be of the same type/same scope as the behavioral data collected for the learning phase in order to obtain a relevant result at step  465 . 
     At step  465 , the processing unit  210  of the survey server  200  determines an intent of the user of the current user device  100  in relation to the visiting of the current website, based on the current behavioral data (collected at steps  460  and  461 ) and the predictive user intent patterns (generated at step  425  and stored at step  450 ). Step  465  leverages the learning phase, by using the predictive user intent patterns to guess the intent of the user for having visited the current website, without resorting to the collection of survey participation data for this purpose. 
     At step  470 , the processing unit  210  of the survey server  200  transmits (via its communication interface, not represented in  FIG. 1 ) the determined user intent to the current user device  100  over the Internet  10 . The determined user intent is received by the processing unit  110  of the current device  100  via its communication interface  130 . The determined user intent can be stored in memory  120  for future use, or can be processed immediately by the processing unit  110 . 
     At step  475 , the processing unit  110  of the current user device  100  transmits (via its communication interface  130 ) the determined user intent to the advertisement server  300  over the Internet  10 . The determined user intent is received by the processing unit  310  of the advertisement server  300  via its communication interface (not represented in  FIG. 1 ). The determined user intent can be stored in memory  320  for future use, or can be processed immediately by the processing unit  310 . 
     At step  480 , the processing unit  310  of the advertisement server  300  selects a retargeting advertisement directed to the current website for the current user device  100 , based at least on the determined user intent transmitted at step  475 . The advertisement server  300  may only take into consideration the determined user intent for selecting the retargeting advertisement directed to the current website. Alternatively, the advertisement server  300  takes into consideration the determined user intent in combination with other parameter(s) for selecting the retargeting advertisement directed to the current website. The retargeting advertisement being directed to the current website means that the purpose of the retargeting advertising is to influence the user of the current user device  100  to visit the current website again. 
     At step  485 , the processing unit  310  of the advertisement server  300  transmits (via its communication interface, not represented in  FIG. 1 ) the selected retargeting advertisement to the current user device  100  over the Internet  10 . The selected retargeting advertisement is received by the processing unit  110  of the current device  100  via its communication interface  130 . 
     At step  490 , the processing unit  110  of the current user device  100  displays the selected retargeting advertisement on the display  140 . The selected retargeting advertisement may consist of a banner, a video, a picture, etc. The selected retargeting advertisement is displayed when the user of the current user device  100  is visiting another website, and the displayed retargeting advertisement contains content directed to the current website, for driving the user to visit the current website again. For instance, by clicking on a displayed content of the retargeting advertisement, the web browser of the current user device  100  is redirected to the current website. 
     The execution of steps  475  and  480  depend on a specific implementation of the interactions between the current user device  100  and the advertisement server  300 . For instance, the determined user intent received at step  470  by the current user device  100  may be stored in a cookie, along with an identifier of the current website (e.g. its URL). When the user of the current user device  100  visits another website, a script related to the advertisement server  300  is executed by the browser of the current user device  100 , sending a request for an advertisement to the advertisement server  300 . This request corresponds to step  475 , and contains the determined user intent and the identifier of the corresponding website for which the user intent was determined. The request may contain a plurality of identifiers of websites previously visited by the user of the current user device  100 , at least one of them having a corresponding user intent. The advertisement server  300  generally uses a biding algorithm for selecting one among the previously visited websites as candidate for advertisement retargeting (this step is not represented in  FIG. 400 , since it is well known in the art of retargeted advertisement). If the selected previously visited website is a website for which a user intent has been determined at step  465 , and transmitted at steps  470  and  475 , the advertisement server  300  further uses the determined user intent to select a particular retargeting advertisement directed to the selected previously visited website (at step  480 ). Taking into consideration the determined user intent allows for a selection of a particular retargeting advertisement more prone to driving the user to visit the selected previously visited website again. 
     Alternatively or complementarily, the selection by the advertisement server  300  of a candidate for advertisement retargeting takes into consideration a plurality of pre-defined websites, each having a particular biding level which may be adjusted in real time. As mentioned previously, when the candidate website for advertisement retargeting is selected among the plurality of pre-defined websites, if a corresponding user intent for the candidate website is available, is it used at step  480  for selecting a particular retargeting advertisement more prone to driving the user to visit the selected candidate website again. The selection of the particular retargeting advertisement based on the determined intent will be detailed later in the description, in relation to  FIG. 4 . 
     The determined user intent received by the current user device  100  at step  470 , during the visit of the current website, may be transmitted to the advertisement server  300  (step  475 ) immediately (along with an identifier of the corresponding current web site). The determined intent (along with the identifier of the current website) is stored in the memory  320  of the advertisement server  300 . The determined user intent is used later when the current user device  100  visits another website, and requests the advertisement server  300  to select a retargeting advertisement. Alternatively, the determined user intent is stored in the memory  120  (e.g. via a cookie) of the current user device  100  (along with an identifier of the corresponding current web site). When the current user device  100  visits another website, and requests the advertisement server  300  to select a retargeting advertisement, the determined user intent is transmitted to the advertisement server  300  (step  475 ), along with the identifier of the corresponding web site. 
     Although the learning phase and the operational phase have been represented sequentially in  FIGS. 2A and 2B  for simplification purposes, they may also occur simultaneously. For instance, the learning phase may be performed solely until satisfying user intent patterns have been generated at step  425  of the method  400 . For example, the generated user intent patterns are satisfying if they allow to determine a user intent at step  465  of the method  400  with a pre-defined level of accuracy (e.g. 95% of the predicted user intents are accurate). Then, the operational phase is performed, but the learning phase can still be performed simultaneously to improve/update the user intent patterns generated at step  425  of the method  400 . 
     In a particular aspect, the user intent for visiting a website comprises at least one of the following: information, purchase and support. The user intent being information corresponds to a user visiting the website for obtaining information about a product, a service, etc. presented on the website. The user intent being purchase corresponds to a user visiting the website for purchasing a product, a service, etc. available through the website. The user intent being support corresponds to a user visiting the website for obtaining support via the website for a product or service previously purchased by the user. 
     Other types of user intent may be determined at steps  420  and  465  of the method  400 , such as for example: a purpose of visit, a purchase horizon, a purchase stage, a channel of choice (e.g. online versus offline), an intent of travel (e.g. business versus leisure), etc. The present method  400  can be applied to a variety of websites, and for each particular website, a list of relevant user intents can be determined based on the specificities of the particular website. The list of relevant user intents can be submitted to a visitor of the particular website via a survey, as illustrated in  FIG. 3 , to collect survey participation data comprising the user intent at step  415  of the method  400 . 
     In another particular aspect, the behavioral data collected at steps  410  and  460  of the method  400  comprise at least one of the following: a time spent on a web page, a scrolling activity on a web page, a backtracking activity on a web page, an action firing activity on a web page, a comment card filing activity, an exit activity on a web page, and a hit activity on a web page. The web page is a web page of the website for step  410  (learning phase) and a web page of the current website for step  460  (operational phase). As mentioned previously, the website for the learning phase and the current website (for the operational phase) are generally the same, but may be different. 
     The time spent on a web page is a duration which can be measured in seconds. The scrolling activity on a web page can be measured by the number of times the user of the user device  100  has scrolled the web page either horizontally or vertically (the action of scrolling a web page is well known in the art). The backtracking activity on a web page can be measured by the number of times the user of the user device  100  has come back to the web page from another web page of the web site during a pre-defined interval of time. The action firing activity on a web page can be measured by the number of times the user of the user device  100  has performed a specific action among a plurality of pre-defined actions (e.g. clicking on a download button, accessing a cart, etc.). The plurality of pre-defined actions depends on the design and function of the web page. The comment card filing activity can be measured by the number of times the user of the user device  100  has filed a comment card. In a particular embodiment, only comment card(s) associated to the web page may be taken into consideration. In another embodiment, comment card(s) associated to the entire website are taken into consideration. The exit activity on a web page can be measured by an occurrence of the user of the user device  100  exiting the website from the web page. The hit activity on a web page can be measured by a number of occurrences of the user of the user device  100  accessing the web page. 
     In still another particular aspect, the method  400  comprises determining a bid level based at least on the determined intent of the user of the current device  100 . The determination of the bid level can be performed by the processing unit  310  of the advertisement server  300 , for example at step  480  of the method  400 . The determination of the bid level can also be performed by the processing unit  110  of the current user device  100 , for example between steps  470  and  475  of the method  400  (the bid level is then transmitted to the advertisement server  300  at step  475 , along with the determined intent). The bid level determines a price that a brand owner is ready to pay for having a retargeting advertisement related to its brand served to the current user device  100  by the survey server  300 . The survey server  300  generally implements an auction process, to take into consideration the bid levels offered by the brands in the selection of which retargeting advertisement (corresponding to a particular brand) to serve. 
       FIG. 4  illustrates examples of the determination of bid levels based on determined user intent. If the determined intent is purchase, the bid level has the highest value since a conversion of the user is the most likely to happen. Decreasing values for the bid level are associated respectively with the determined user intent being information, support and other; since the probably of converting the user decreases accordingly. 
     In yet another particular aspect, the selection of the retargeting advertisement directed to the current website for the current user device  100  at step  480  of the method  400  also takes into consideration complementary behavioral data collected from the current user device  100 . The complementary behavioral data consist in behavioral data collected by the advertisement server  300  for performing standard behavioral retargeting based on collected behavioral data. The complementary behavioral data may at least partially overlap with the current behavioral data collected at step  460 , or may be totally different from them. The advertisement server  300  may determine a candidate user intent based on the complementary behavioral data, and refine/correct the candidate user intent based on the determined user intent transmitted at step  475 . Then, step  480  of the method  400  is based on the refined/corrected candidate user intent. 
     In another particular aspect, for the learning phase, the behavioral data collected from the plurality of user devices  100  (steps  410  and  411  of the method  400 ) and the survey participation data collected from at least some of the plurality of user devices  100  (steps  415  and  416  of the method  400 ) correspond to a plurality of websites visited by the users of the user devices  100 . For example, the plurality of websites belong to the same industry (e.g. automotive, travel agencies, etc.), and respectively correspond to several brands of a same company (e.g. several brands of cars from the same auto manufacturer). Thus, the mechanism (e.g. statistical and/or artificial intelligence method) for determining a user intent based on current behavioral data and user intent patterns is trained (step  425  of the method  400 ) with data from the plurality of websites. The user intent patterns can then be used at step  465  of the method  400  for current behavioral data collected from at least one current website. 
     In still another particular aspect, the method  400  comprises generating audience segments based at least on the intents of the users. The audience segments may be generated by the processing unit  310  of the advertisement server  300  and stored in its memory  320 . Alternatively, the audience segments are generated by a third party entity, transmitted to the advertisement server  300 , and stored in its memory  320 .  FIG. 4  illustrates four audience segments ( 101 ,  102 ,  103  and  104 ) respectively corresponding to the following user intents: purchase, information, support and other. Identifiers of the audience segments (e.g.  101 ,  102 ,  103  and  104 ) can be used for identifying the audience segments when exchanging data between the advertisement server  300  and other entities, such as the current user device  100 . 
     The selection at step  480  of a retargeting advertisement directed to the current website for the current user device  100  is based on the user of the current user device  100  belonging to a specific audience segment among the generated audience segments (e.g.  101 ,  102 ,  103  and  104 ). For instance, the objective of the retargeting advertisement for segment  101  (purchase intent) is to increase conversion. Consequently, the retargeting advertisement may consist of special offers, promotions, coupons, etc. The objective of the retargeting advertisement for segment  102  (information intent) is to perform an effective lead nurturing. Consequently, the retargeting advertisement may be directed to product awareness, product specifications, product options, etc. The objective of the retargeting advertisement for segment  103  (support intent) is to increase customer retention. Consequently, the retargeting advertisement may be directed to support topics, community knowledge, etc. The objective of the retargeting advertisement for segment  104  (other intent) is to address users for whom no specific intent has been determined. Consequently, the retargeting advertisement may consist of brand building, etc. 
     The present disclosure also relates to a computer program product. Instructions of a computer program implement steps of the method  400  when executed by the processing unit  110  of the user device  100 . The instructions are comprised in the computer program product (e.g. memory  120 ), and provide for advertisement retargeting based on a determined user intent, when executed by the processing unit  110 . The instructions comprised in the computer program product are deliverable via an electronically-readable media, such as a storage media (e.g. a USB key or a CD-ROM) or communication links (e.g. via the Internet  10  through the communication interface  130  of the user device  100 ). 
     The instructions comprised in the computer program product more specifically implement steps of the method  400  illustrated in  FIG. 2B  and corresponding to the aforementioned operational phase. The instructions are executed by the processing unit  110  of the aforementioned current user device  100 . 
     The execution of the instructions provides for collecting behavioral data representative of a series of actions performed by a user of the current user device  100  while visiting a website (step  460 ). 
     The execution of the instructions provides for transmitting the collected behavioral data to the survey server  200  (step  461 ), via the communication interface  130  over the Internet  10 . 
     The execution of the instructions provides for receiving a determined intent of the user of the current user device  100  from the survey server  200  (step  470 ), via the communication interface  130  over the Internet  10 . The intent of the user has been determined based on the collected behavioral data and the predictive user intent patterns by the survey server  200 . 
     The execution of the instructions provides for transmitting the determined intent to the advertising server  300  (step  475 ), via the communication interface  130  over the Internet  10 . 
     The execution of the instructions provides for receiving a retargeting advertisement directed to the website from the advertising server  300  (step  485 ), via the communication interface  130  over the Internet  10 . The retargeting advertisement has been selected at least based on the determined intent by the advertisement server  300 . 
     The execution of the instructions provides for displaying the retargeting advertisement on the display  140  of the current user device  100  while visiting another website (step  490 ). 
     Although the present disclosure has been described hereinabove by way of non-restrictive, illustrative embodiments thereof, these embodiments may be modified at will within the scope of the appended claims without departing from the spirit and nature of the present disclosure.