Patent Application
20130325530
Today, many businesses are implementing customer relationship management (CRM) programs for managing their interactions with clients, customers, and sales prospects. Generally, CRM programs involve the use of technology to organize, automate, and synchronize sales and marketing activities for businesses. For example, Hewlett-Packard Company offers an Academic Purchase Program (HPA), which is essentially a CRM program for customers associated with academic institutions including current or former students, parents of students, and educators. In this context, knowing or, at least, anticipating when a customer will make a purchase or “convert”, would immensely increase the effectiveness of any marketing campaign.
The features and advantages of the inventions as well as additional features and advantages thereof will be more clearly understood hereinafter as a result of a detailed description of particular embodiments of the invention when taken in conjunction with the following drawings in which:
The following discussion is directed to various embodiments. Although one or more of these embodiments may be discussed in detail, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be an example of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment. Furthermore, as used herein, the designators “A”, “B” and “N” particularly with respect to the reference numerals in the drawings, indicate that a number of the particular feature an designated can be included with examples of the present disclosure. The designators can represent the same or different numbers of the particular features.
The figures herein follow a numbering convention in which the first digit or digits correspond to the drawing figure number and the remaining digits identify an element or component in the drawing. Similar elements or components between different figures may be identified by the user of similar digits. For example, 143 may reference element “43” in
Timing is critical in establishing an effective marketing campaign. Some examples of often asked questions include (a) time from registration to conversion, (b) time of conversion to higher value segment (e.g., from first purchase to second purchase), (c) time from campaign contact to purchase, etc. Such questions naturally lend themselves to the field of “survival analysis”; where one models the time from origin to event. A problem arises in that only a small fraction of the target audience experiences the event of interest (i.e., converts, makes first purchase, or makes second purchase). Predicting and anticipating when and whether a customer will make a purchase post registration immensely increases the effectiveness of any marketing campaign to target registrants.
The field of “survival analysis”; involves modeling the time from origin (registration date) to event (purchase date). Marketing data has suggested that only a limited proportion of the registrants go on to convert while many customers become inactive, thus lending traditional survival analyses techniques futile. Prior solutions for estimating customer conversion in the future involves modeling a binary response—“subject converts in the next k months.” For these models, however, every different k involves building and validating a separate model. Furthermore, information on subjects likely to convert in the (k+1)th time frame is not captured in such models. As such, there is a need in the art for a scalable algorithm which solves this common yet critical problem of predicting customer conversion while also being easily executable.
Embodiments of the present invention help to model the propensity of an event of interest along with the timing of said event of interest utilizing both a multivariable predictor space and a finite time horizon. One example embodiment incorporates time as a continuous variable, thus aminating the inherent discrete nature of the existing approaches to such problems. Moreover, the yielded results are meaningful and offer an intuitive and easily implementable targeting framework.
Referring now in more detail to the drawings in which like numerals identify corresponding parts throughout the views.
Probability of conversion=1/(1+exp{−(α+α1*Q1+α2*Q2+α3*Q3)})
Where Qx equals an attribute of the customer or customer profile, and αx equals an estimated weight for a particular customer/profile attribute or average conversion propensity. The table below includes an example of attributes and weights that may be considered in computing the probability or propensity for conversion:
Additionally, the probability of conversion may also take into account the registrant's current tier/status with the organization, age, sex, employment and marital status, etc. According to one example embodiment, the values (QX) may be flagged as a 1 by the processing system if the condition/attribute is satisfied, or as 0 if the condition/attribute is not satisfied. Based on the probability of conversion, the system will divide the registrant group 206 into at least two disparate groups: a registrant group likely to convert 207 and a registrant group less likely to convert 209. The division of the registrant group 206 may be based on the probability of conversion exceeding a threshold value. For example, if the probability of conversion for an individual customer is greater than fifty percent, then that particular customer or registrant will be flagged as one likely to convert and placed within high conversion propensity group 207. Conversely, those registrants identified as having a probability of conversion less than fifty percent as belonging to the low conversion propensity group 209. According to one example, the system may then determine the time it will take each customer (e.g., 207a-207c) within the high conversion propensity group 207 to convert or reach the event of interest. The time of conversion for a particular registrant may be generated from the survival model using the following formula:
Time of conversion=σ*log(log (2))+(α+α1*P1+α2*P2+α3*P3)
Where Px equals an attribute of the customer and a, equals an estimated weight for a particular customer attribute or average conversion propensity (α). The parameter σ is a Weibull parameter which gets estimated from the data. The table below includes an example of attributes and weights that may be considered in computing the timing for conversion:
The timing for conversion may also take into account the registrant's current tier/status with the organization, employment and marital status, etc. As in the computation for the propensity to convert, here the values (PX) may be flagged as a 1 by the processing system if the condition/attribute is satisfied, or as 0 if the condition/attribute is not satisfied. Given a time of conversion for each identified customer, the system may further divide the high conversion propensity group 207 into registrant(s) that are converting within a first time frame and those converting within a second time frame (though multiple time frames may be used). As seen in the example of
Embodiments of the present invention provide a novel targeting framework for predicting time from registration to conversion for customers. Moreover, many advantages are afforded by the system and method in accordance with embodiments of the present invention. For example, example embodiments dually models the propensity of a customer to convert as well as the time a customer takes to convert utilizing both a multivariate variable space and a finite time horizon. Thus, embodiments of the present invention may be used to aid in solving common marketing problems like predicting the time taken from first purchase to second purchase or the time from campaign contact to purchase, etc.
Furthermore, while the invention has been described with respect to exemplary embodiments, one skilled in the art will recognize that numerous modifications are possible. For example, although exemplary embodiments depict purchase of a notebook computer as the conversion event, the invention is not limited thereto. For example, the conversion event may involve software download or other electronic devices, apparel, cloud or similar services, or any product or service offered by a vendor. Thus, although the invention has been described with respect to exemplary embodiments, it will be appreciated that the invention is intended to cover all modifications and equivalents within the scope of the following claims.
