The embodiments disclosed herein relate to dynamic segmentation of website visits.
Website personalization generally attempts to accommodate the differences between individual visitors to a website in order to make the website more relevant to each individual visitor. In particular, website personalization generally includes personalizing webpages of a website based on predetermined characteristics of a visitor. For example, when a visitor visits an online retailer website, information regarding a visitor's gender, age, and past purchasing habits may be gathered and user to alter the content of a webpage on the online retailer website in an attempt to make the content more relevant to the visitor. In this manner, website personalization attempts to focus or target webpage content to pre-gathered individual characteristics of a website visitor.
One common problem associated with website personalization involves the ineffectiveness of personalization based on visitor characteristics that are not particularly relevant to the visitor's current intentions or needs. In particular, the relevance of pre-gathered characteristics of a website visitor may decrease rapidly over time. From the example above, information regarding past purchasing habits may not be relevant to a website visitor's current intentions while visiting the same online retailer website, as the visitor may be in need of a product that is entirely unrelated to products that the visitor purchased previously on the online retailer website. Therefore, the use of past purchasing habits in the personalization of the webpages of the online retailer website would not be helpful to the user as such website personalization would tend to point the user to products that the visitors does not currently need or want. Such website personalization can be distracting and frustrating to website visitors because it fails to account for the visitors' current needs and intentions for visiting the website.
The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some embodiments described herein may be practiced.
In general, example embodiments described herein relate to dynamic segmentation of website visits. The example methods disclosed herein may be employed to track real-time behavior of a visitor to a website during a visit to the website. This tracked real-time behavior may then be the basis for assigning the visit to one of multiple segments and then personalizing the website during the visit based on the assigned segment. Unlike methods of segmentation that are visitor-based and tend to focus only on pre-gathered characteristics of a website visitor, the example methods disclosed herein are visit-based and focus instead on real-time behavior of the visitor during a particular visit. Visit-based dynamic segmentation tends to be more relevant and helpful to a website visitor that visitor-based segmentation because it accounts for the visitor's current needs and intentions during a particular visit to a website. Visit-based dynamic segmentation may also enable selective website personalization of multiple visitors exhibiting similar real-time behavior, such that the outcomes of the similar visits can be compared in order to measure the impact of the visit-based website personalization on a conversion event of the website.
In one example embodiment, a method of dynamic segmentation of web site visits includes tracking real-time behavior of a visitor on a website during a visit to the website, assigning the visit to one of multiple segments based on the tracked real-time behavior, and personalizing the website during the visit based on the assigned segment.
In another example embodiment, a method of dynamic segmentation of website visits includes tracking real-time behavior of a first visitor on a website during a visit to the website, tracking real-time behavior of a second visitor on the website during a visit to the website, determining that the first visitor's tracked real-time behavior and the second visitor's tracked real-time behavior both correspond to a particular one of multiple segments, assigning the visit of the first visitor to a test group of the corresponding segment, personalizing the website during the visit of the first visitor based on the corresponding segment, assigning the visits of the second visitor to a control group of the corresponding segment, not personalizing the website during the visit of the second visitor, and comparing the outcomes of the visit of the first visitor and the visit of the second visitor to measure the impact of the website personalization on a conversion event of the website.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, as claimed.
Example embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:
The first and second computing devices 102 and 104 may each be any computing device capable of executing a browser application and communicating over the network 108 with the webserver 106. For example, the first and second computing devices 102 and 104 may each be a physical computer such as a personal computer, a desktop computer, a laptop computer, a tablet computer, a handheld device, a multiprocessor system, a microprocessor-based or programmable consumer electronic device, a smartphone, or some combination thereof. The first and second computing devices 102 and 104 may each also be a virtual computer such as a virtual machine. The network 108 may be any wired or wireless communication network including, for example, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Wireless Application Protocol (WAP) network, a Bluetooth® network, an Internet Protocol (IP) network such as the internet, or some combination thereof.
During performance of the example methods disclosed herein, the segmentation module 120 may track real-time behavior of the first and second visitors 112 and 116 during visits to the website 110 and then assign those visits to one of multiple segments based on the tracked real-time behavior. The tracked real-time behavior of a visitor may include, for example: page(s) of the website 110 interacted with by the visitor during the visit, how long each of the page(s) has focus during the visit, a number of tabs in the browser 114 or 118 that the visitor has open during the visit, interaction between the visitor and a shopping cart of the website 110, repeat interactions with page(s) of the website 110 during the visit, or some combination thereof. The website 110 may then be personalized for one or both visits based on the assigned segments, as discussed in greater detail below. In this manner, the example methods disclosed herein can employ visit-based dynamic segmentation to make the website 110 more relevant and helpful to a website visitor because the website 110 will be personalized to account for the visitor's current needs and intentions during a particular visit to the website 110.
In addition, where the first and second visitors 112 and 116 exhibit similar real-time behavior during their respective visits, and thus their visits are assigned to the same segment, only one of the visits may include a website personalization, as discussed in greater detail below. In this manner, the example methods disclosed herein can enable selective website personalization of multiple visitors exhibiting similar real-time behavior such that the outcomes of the similar visits can be compared in order to measure the impact of the website personalization on a conversion event of the website 110. Such a conversion event may include, for example: a sale of an item to a visitor, a subscription by a visitor, a donation by a visitor, submission of personal information by a visitor, or some combination thereof.
Although only a single web server 106 is disclosed in
Having described one specific environment with respect to
The method 200 disclosed in
The method 200 may next include step 204 in which the visit to the web site is assigned to a default segment. For example, the segmentation module 120 may, at step 204, assign the visit of the first visitor 112 to the website 110 to a default segment. In at least some example embodiments, all qualified visits may at least initially be assigned to the default segment while the real-time behavior of the visitor during the visit is tracked.
The method 200 may next include one of steps 206, 208, or 210 in which the visit is assigned to a ‘low propensity to buy’ segment, a ‘needs help’ segment, or a ‘high propensity to buy segment, respectively. For example, after the real-time behavior of the visitor during the visit has been tracked, the segmentation module 120 may, at one of steps 206, 208, or 210, determine that the tracked real-time behavior corresponds to the ‘low propensity to buy’ segment, the ‘needs help’ segment, or the high propensity to buy segment, at which point the segmentation module 120 may transfer the visit from the default segment to the appropriate visit-based segment. In this example, excluding visits assigned to the ‘low propensity to buy’ segment and the high propensity to buy segment may allow the method 200 to focus on those visits for which a website personalization, such as a chat conversation, can make the difference between no conversion event, such as a sale (and/or a small dollar sale) without a chat, and a successful conversion event, such as a sale (and/or a large dollar sale) with a chat.
The method 200 may next include one of steps 212, 214, or 216 in which the visit is assigned to an N/A group, a test group, or a control group, respectively. For example, after assigning the visit to the ‘needs help’ segment, the segmentation module 120 may, at one of steps 212, 214, or 216, further determine that the tracked real-time behavior corresponds to the N/A group, the test group, or the control group, at which point the segmentation module 120 may transfer the visit from the ‘needs help’ segment to the appropriate group. As disclosed in
After the conclusion of step 212, 214, or 216, the website 110 for the visits assigned to the test group may be altered by a website personalization and the website 110 for the visits assigned to the control group may not be altered by the website personalization. In this manner, the example method 200 may enable selective website personalization for multiple visitors exhibiting similar real-time behavior such that the outcomes of the similar visits can be compared in order to measure the impact of the website personalization on a conversion event of the website 110.
The method 300 disclosed in
The method 300 may next include one of steps 304, 306, 308, or 310 in which the visit is assigned to a ‘high propensity to buy’ segment, a ‘needs help—stall’ segment, a ‘needs help—comparison’ segment, or a ‘needs help—backout’ segment, respectively. For example, after the real-time behavior of the first visitor 112 during the visit has been tracked, the segmentation module 120 may, at one of steps 304, 306, 308, or 310, determine that the tracked real-time behavior corresponds to the ‘high propensity to buy’ segment, the ‘needs help—stall’ segment, the ‘needs help—comparison’ segment, or the ‘needs help—backout’ segment, at which point the segmentation module 120 may transfer the visit from the default segment to the appropriate visit-based segment. Taking the ‘needs help—comparison’ as an example, this segment may be considered appropriate where the tracked real-time behavior includes repeat interactions with page(s) of the web site 110 during the visit, such as alternating interactions between a first page and a second page of the website 110 during the visit.
Continuing with the above example, where the visit has initially been assigned to the ‘high propensity to buy’ segment based on the tracked real-time behavior, the segmentation module 120 may later determine that the tracked real-time behavior of the first visitor 112 has changed such that the ‘needs help—backout’ segment has now become more appropriate for the visit than the initial ‘high propensity to buy’ segment. Where such a determination is made, the segmentation module 120 may transfer the visit from the ‘high propensity to buy’ segment to the ‘needs help—backout’ segment.
The method 300 may finally include step 312 in which the visit is concluded. For example, where the first visitor navigates the browser application 114 away from the website 110, closes the browser application 114, or some predetermined period of time has elapsed since the beginning of the visit to the website 110, the segmentation module 120 may, at step 312, determine that the visit has concluded. The predetermined period of time may corresponds to an attribution window in which any conversion event that occurs during the predetermined period of time will be attributed to the website personalization that occurred during the initial visit to the website 110, even if the conversion event occurs during a subsequent visit that still falls within the attribution window.
Continuing with the above example, by the conclusion of step 312 the visits assigned to the ‘needs help’ segments will generally have been altered by a website personalization to account for the visitor's current needs and intentions during a particular visit to a website. Conversely, the visits assigned to the default and ‘high propensity to buy’ segments will not be altered by the website personalization to avoid distracting and frustrating the website visitor. In this manner, the example method 300 can employ visit-based dynamic segmentation to be more relevant and helpful to the website visitor 112 because it accounts for the current needs and intentions of the visitor 112 during a particular visit to the website 110.
The method 400 disclosed in
As disclosed in
The method 500 disclosed in
As disclosed in
Accordingly, the methods 400 and 500 allow a chat conversation to be selectively offered during visits assigned to a particular segment. In this manner, the example methods 400 and 500 may enable selective chat conversations with multiple visitors exhibiting similar real-time behavior such that the outcomes of the similar visits can be compared in order to measure the impact of the chat conversations on a conversion event of the website 110.
The method 900 may include step 902 in which real-time behavior of a first visitor on a website is tracked during a visit to the website. For example, the first visitor 112 may employ the browser 114 on the first computing device 102 to visit the website 110. During the visit to the website 110, the segmentation module 120 may, at step 902, track the real-time behavior of the first visitor 112.
The method 900 may include an optional step 904 in which the type of computing device that the first visitor is employing during the visit to the website is determined. For example, the segmentation module 120 may, at optional step 904, determine the type of the first computing device 102 that is employed by the first visitor 112 to visit the website 110. This determined device type may then be employed to assign a visit to an experience prior to assigning the visit to a segment.
The method 900 may include an optional step 906 in which a personal characteristic of the first visitor is determined. For example, the segmentation module 120 may, at optional step 906, determine a personal characteristic of the first visitor 112. The personal characteristic may include, for example: past visits of the first visitor 112 to the website 110, past conversion events of the first visitor 112 on the website 110, a physical geographical location of the first visitor 112, or some combination thereof. This determined personal characteristic may then be employed to assign a visit to an experience prior to assigning the visit to a segment.
The method 900 may include a step 908 in which the visit of the first visitor is assigned to a test group of the corresponding segment. For example, the segmentation module 120 may determine, at step 908, that the tracked real-time behavior of the first visitor 112 corresponds to a particular one of multiple segments. For example, where the first visitor 112 quickly finds a product and adds the product to a shopping cart of the website 110, but then instead of purchasing the product in the shopping cart, leaves the shopping cart to continue shopping by searching for another similar product, the segmentation module 120 may determine that this this tracked real-time behavior corresponds to the ‘needs help—backout’ segment disclosed in
The method 900 may include a step 910 in which the website is personalized during the visit of the first visitor based on the corresponding segment. For example, the segmentation module 120 may, at step 910, personalize the website 110 during the visit of the first visitor 112 to the website 110 by displaying a banner on a webpage of the website 110 that invites the first visitor 112 to chat with the agent 122 of the website 110. Where the visit has been assigned to the ‘needs help—backout’ segment, the agent 122 may attempt to engage the visitor 112 in a chat to help resolve whatever concern is preventing the visitor 112 from completing the purchase of the product in the shopping cart.
The method 900 may include step 912, 914, 916, and 918, which are similar to steps 902, 904, 906, and 908, respectively, except that the visitor being tracked is a second visitor such as the second visitor 116, the computing device that is employed is a second computing device such as the second computing device 118, and the second visitor is assigned to a control group of the segment instead the test group, such as the control group of the “needs help—backout” target segment, as disclosed in
The method 900 may include a step 920 in which the website is not personalized during the visit of the second visitor based on the corresponding segment. For example, the segmentation module 120 may, at step 920, not personalize the website 110 during the visit of the second visitor 116 to the website 110 by not displaying an ‘invitation to chat’ banner on a webpage of the website 110.
The method 900 may include a step 922 in which the outcomes of the visit of the first visitor and the visit of the second visitor are compared to measure the impact of the website personalization on a conversion event of the website. For example, the segmentation module 120 may, at step 922, compare the outcomes of the visit of the first visitor 112 and the visit of the second visitor 116 to measure the impact of the chat conversation on purchases made on the website 110. These outcomes may be compared because the visit of the first visitor 112 and the visit of the second visitor 116 were both assigned to the same segment. Further, in order to be assigned to the same segment, these visits may also have been assigned to the same experience, either based on a determination that the computing device 102 employed by the first visitor 112 and the computing device 104 employed by the second visitor 116 are of the same type or based on a determination that the determined personal characteristic of the first visitor 112 and the determined personal characteristic of the second visitor 116 are of the same classification. For example, where a physical geographic location of the first visitor 116 is determined to be in the same classification as a physical geographic location of the second visitor 116 (such as both being within a predetermined geographic boundary or within a predetermined distance from one another), then the visits of the first visitor 112 and the second visitor 116 may be assigned to the same experience. By being assigned to the same experience, the visits may also later be assigned to the same segment, as disclosed in
The embodiments described herein may include the use of a special-purpose or general-purpose computer including various computer hardware or software modules or filters, as discussed in greater detail below.
Embodiments described herein may be implemented using computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media may be any available media that may be accessed by a general-purpose or special-purpose computer. By way of example, and not limitation, such computer-readable media may include non-transitory computer-readable storage media including RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other storage medium which may be used to carry or store desired program code in the form of computer-executable instructions or data structures and which may be accessed by a general-purpose computer, special-purpose computer, or virtual computer such as a virtual machine. Combinations of the above may also be included within the scope of computer-readable media.
Computer-executable instructions comprise, for example, instructions and data which cause a general-purpose computer, special-purpose computer, or virtual computer such as a virtual machine to perform a certain function or group of functions. Although the subject matter has been described in language specific to structural features and/or methodological steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or steps described above. Rather, the specific features and steps described above are disclosed as example forms of implementing the claims.
As used herein, the term “module” may refer to software objects or routines that execute on a computing system. The different modules described herein may be implemented as objects or processes that execute on a computing system (e.g., as separate threads). While the system and methods described herein are preferably implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated.
All examples and conditional language recited herein are intended for pedagogical objects to aid the reader in understanding the example embodiments and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically-recited examples and conditions.
This application claims the benefit of and priority to U.S. Provisional Application No. 61/838,800, filed Jun. 24, 2013, titled “DYNAMIC SEGMENTATION OF WEBSITE VISITORS TO MEASURE THE IMPACT OF CHAT CONVERSATIONS,” which is incorporated herein by reference in its entirety.
Number | Date | Country | |
---|---|---|---|
61838800 | Jun 2013 | US |