ENROLLMENT SCORING SYSTEM

Methods, systems, and apparatuses, including computer programs encoded on computer-readable media, for enrollment analysis including receiving historical data related to an entity and a first set of applicants. A model is generated based on the historical data. The model includes a set of factors. First data between the entity and a second plurality of potential applicants is received. A first factor score for each factor for each of the second plurality of potential applicants is generated based on the model. Second data between the entity and the applicants is received. The second data reflects interactions that occur after the first data. A second factor score for each factor is generated for each potential applicant based on the model. A priority range for each factor is received. A subset of the second plurality of potential applicants is identified based a second factor score that is within the priority range.

BACKGROUND

Schools, Universities, and colleges spend large amounts of money and time trying to convince students to attend their institution. Typically, admission departments hire admission specialists and recruiters to management the enrollment process. Typical duties of such a person include reaching out to potential students, inviting students to programs, and helping students complete enrollment tasks.

With hundreds if not thousands of potential students, admission departments have a large task in managing the enrollment process for each and every potential student. In addition, some students are more likely to attend a particular institution than other students. Determining who to contact, when to contact, and what type of contact are questions that admission departments face. Current procedures are based on manual note keeping, simple funnel stage triggers and instincts.

A system that provided up to date information to admission faculty while also providing insights into a particular student's behavioral and communication patterns throughout the enrollment process would be useful.

DETAILED DESCRIPTION

Various disclosed implementations provide insights into the students currently in an enrollment pipeline for an educational institution. Specifically, as potential students interact with admissions staff or other elements of the institution such as athletic coaches, faculty or campus tours, the likelihood that the student enrolls in an institution change. In addition, taking certain actions based on the various behavioral patterns displayed by the potential student to interface with the student may also alter the chance that the student attends. Various implementations, provide a model and analysis of data indicating what actions have been taken by students, what actions should be taken, and when those actions can be taken. In addition, institutions are able to visualize all of the students within its enrollment pipeline along with number of students likely to enroll in the institution. Today institutions rely on historical numbers and past experiences to drive the enrollment process. Various implementations provide tools that both help guide actions taken by enrollment staff, focus actions on students that are most likely to be influenced to attend an institution, and provide real-time insights into the enrollment. In addition, various implementations may be used to eliminate personal bias that may creep into the selection process.

FIG.1is a diagram of a system100for building an enrollment model130in accordance with an illustrative embodiment. In an example, historical data110is used by a model builder120to train the model130. The historical data110includes at least communications between students and enrollment staff. In addition, the historical data110includes an indication if a particular student enrolled in the institution. Accordingly, the historical data110at a minimum provides past communications between the institution and students along with if a particular student enrolled in the institution. The historical data110may also include other information, such as if students completed various enroll tasks. Enrollment tasks may include items such as receiving a deposit, completing financial aid forms, receiving housing contracts, receiving immunization records, attending an event, etc. The model builder120may also information related to a student that is not based on communication. For example, a student's age may be used to build the model130.

The model130may be used to score a student's likelihood to enroll in the institution. Initially, the model130may include a large number of features. Features may be considered the raw input into the model130. In various implementations, the features are used to generate factors. A factor may be a specific feature. A factor may also be derived from factors or other data. Various implementations reduced the number of factors while having the model output remain accurate with respect to the sigmoid yield curve. In an example, an applicant score yield rate curve may be used to reduce the number of factors. As described in greater detail below, the applicants score yield rate curve is used to reduce the number of relevant features and factors that are used to score potential students.

In one example, the completed model130includes a small number of factors. Each factor has a score associated with the factor. In some implementations, the factor is a binary. Binary factors may be one of two scores. In various implementations, a binary factor has a score of zero or a factor value. Factors may be weighted differently. For example, factor values may be different for different factors. The difference in factor values results in the factors having different weights.

In some implementations, the model builder120may reduce the number of factors of the model130. In some implementations, the model builder120removes a factor and then applies the model with the factor removed to the historical data. In some examples, the result of the applied model is compared to the yield curve. If the results are such that the model still accurately predicts the yield curve the removed factor can be removed from the model130. In other implementations, multiple factors are removed at once rather than a single factor. The model with the factors removed may then be used to process the data to create a yield curve that can be compared to the actual yield curve. In some examples, factors may be reviewed and removed due to where they fall within the timing of the traditional enrollment funnel, as well as their overall statistical influence in the enrollment decision. In some examples, the number of factors for the model130number between 10 and 20.

In addition to removing factors, the factor value can be changed. In this process, the model builder120is able to test different weights of factors. In some examples, the historical data110may be used to change the factor value. For example, if a particular factor is present in a large amount of the enrolled students, the model builder120may increase the factor value for that factor. In some implementations, the factor value is selected for a small number of possible values. For example, the factor value may be 5, 10, or 15.

Some factors may have logic used to calculate relevant data in determining the score of the factor for a student. For example, the number of events attended, the length of time since the last contact, etc., may all be determined from data. Another example calculated factor is email engagement rate. The email engagement rate may consider the time period in which communications were sent and received. In other examples, the email engagement rate may be a simple ratio of emails sent to the student to emails received from the student. Another example a calculated factor is an interaction count. This factor can consider the number of interactions with a particular student. The interactions maybe limited to particular types of interactions, such as phone calls, event attendance, emails, etc. The interactions count though may be the sum of all interactions with a student. The interactions factor may also consider time and be limited to the number of interactions within a particular time period. The model builder120may also determine that multiple interaction factors may be used. For example, both a total interactions count and a recent interactions count factors may be used in a particular model130.

Other factors may be determined once and do not change. For example, the time between receiving an application and an admission decision may be a factor. If the time is less than 30 days, the factor may be set to one value and if greater than 30 days, then another value. Once the factor is determined, this factor does not change for a particular student.

FIG.2is a diagram of a system for using an enrollment model in accordance with an illustrative embodiment. In various implementations, the model130is used on data related to a current enrollment process. For example, the data may be related to a particular enrollment year. Previous data210, is data that the model130has seen or processed sometime in the past. New data212is data related to the current enrollment process that has yet to be seen or processed by the model130. Using the previous data210and the new data212, the model is able to determine and use changes between the data. In some examples, the model130processes the new data212on a daily basis. Once the new data212is processed, the new data212may become part of the previous data210.

The model130uses the previous data210and the new data212to rank and score students that are part of the enrollment process. In some examples, the previous data210may be data derived from the processed new data212. For example, the dates and times a student responded may be derived from the new data212. These dates and times may be stored in the previous data210, but the actual communications may not be. In other examples, the previous data210may store past communications or a combination of past communications and derived data.

The model130uses the new data212to determine a score for each of the model's factors. A scoring and ranking220engine may be used to calculate an enrollment score for each student. For example, the enrollment score may be the summation of the various factors scores. Students may then be ranked based on their enrollment score. In one example, students may be bucketed into various different buckets. For example, an institution may realize that students have a score that reaches 45 have a roughly 40% chance of enrolling. In addition, an enrollment score of 60 may result in an increase to 70% chance of enrolling. Accordingly, students can be bucketed into different buckets based on a probability of attending. The application score yield rate curve inFIG.3may be used to create the various buckets and score.

Using the different buckets or applicant score yield rate curves, the scoring and ranking220engine may determine changes in a student's enrollment score based on the new data212. For example, a factor may take into account the number of recent interactions. A student that in the past had a high number of interactions, may have had less recent interactions. Accordingly, the interaction factor may change based on the new data212. For example, the recent interactions factor may be reduced to zero from a previous score of 10. In this example, the scoring and ranking220engine may identify and flag this student as needing an interaction. An admission staff may then be alerted to this student and can make the required reach out.

Factors may also be grouped together. For example, factors may be considered intent factors. Intent factors indicate that the user is taking actions that indicate they intend to enroll in the institution. For example, submission of admission data, immunization records, receipt of deposit, tec. Other groups may include engagement factors. Engagement factors may indicate the interactions a student has had with admissions. For example, communications received, event attended, etc., may be grouped together as engagement factors.

FIG.3is an applicant score yield rate chart300in accordance with an illustrative embodiment. The x-axis represents the cumulative score of the various factors. The y-axis is the likelihood that a student enrolls in the institution. For example, if a student's factor score is 65, there is a roughly 80% chance the student will enroll in the institution.

The score yield rate curve302can be generated by submitting the historical data110into the completed model130. As the historical data110has an indication of the actual decision to enroll for each student, the score yield rate curve302can be compared with actual enrollment rate numbers. Any differences between the actual enrolle rate number and the score yield rate curve302may be used to update factor values or to add/reduce the factors of the model.

The score yield rate curve302may also be generated by submitting new data to the complete model130. In this example, the model130is used to generate a score yield rate curve302for the students in the current enrollment pipeline.

FIG.4is a flow diagram illustrating a process for identifying potential applicants in an illustrative embodiment. The process400can be implemented on a computing device. In one implementation, the process400is encoded on a computer-readable medium that contains instructions that, when executed by a computing device, cause the computing device to perform operations of process400.

At410, historical communication data is received. The historical communication data includes communications between an entity, such as a learning institution, and a first group of applicants. The historical communication data is from a previous enrollment cycle where the enrollment status of the applicants is known. Accordingly, the historical communication data includes communications from students that enrolled in the institution and from students that did not enroll in the institution.

At420, a model based on the historical communication data is generated. The model includes a number of factors. Each of the factors has corresponding score value that indicates a likelihood that an applicant enrolls in the institution. For example, factors may include an email engagement rate, interaction metric, amount of time between receipt of application and acceptance, etc.

In some examples, the factors may be determined from a larger set of possible factors. For example, an initial factor score value for each factor may be determined. A historical cumulative score for each of the first set of potential applicants. The historical cumulative score for an applicant may be compared to if the applicant actually enrolled in the institution. If the historical cumulative score accurately predicted the enrollment decision for a large majority of the first set of potential applicants, the initial factor score values may be used as the factor score values. In addition, removing a factor and calculating the cumulative scores may be done to determine if the factor can be removed. If the historical cumulative scores have some error, the model may be updated by changing the initial factor score values and recalculating the historical cumulative scores using the model. If the model with the changed initial factor score values proves to be more accurate then the changed initial factor score values may be used in the model. In some examples, the factor scores are binary values. For example, a binary factor score may be zero or some other value, such as 1, 5, 10, 15, etc.

In some examples, the factors may be grouped into a plurality of categories. The category that a factor belongs to may be used to influence or determine the factor's score value. In some examples, identifying as factor the applicant submitting a deposit for enrollment might fall into a specific factor category called “intent”. In some examples, each category has a range of total possible points. Accordingly, when updating the factor's initial score values, the category's cumulative score can be used in guiding changes. For example, if a category has a total of 40 points, the factors in that category may be changed but only such that the total points in the category remains at40. In addition, factors may be removed from the model based on the categories. For example, some implementations allow only a certain number of factors for one or more categories. As a specific example, an implementation may require between 3 and 5 factors within a category, such as engagement. This limit may be used to determine which and how many factors may be removed from a category.

At430, a first set of communication data between the institution and a second group of potential applicants is received. The applicants in the second group are currently in the process of deciding whether to enroll in the institution. In some examples, an applicant in the second group may also be in the first group, but did not enroll in the institution.

At440, for each applicant in the second group, a first factor score for each factor is generated. The factor scores are generated based on the first set of communication data, the model, and the score value corresponding to the particular factor. For example, the first set of communication data may be input into the model. In other examples, the first set of communication data is processed and model inputs are derived from the first set of communication data. In some examples, each factor can be one of two values. For example, a factor can be scored a zero of a value of 5. In other examples, a factor score may be one of multiple numbers within a range of numbers. For example, the factor score may be any number between −10 and 10.

At450, a second set of communication data between the institution of the second group of applicants is received. The second set of communication data is different that the first set of communication data. For example, the second set of communication data may be communication data that was sent or received after the communications in the first set.

At460, for each applicant in the second group, a second factor score for each factor is generated. Accordingly, each applicant in the second group has a set of factor scores based on the first set of communication data and then a set of factor scores based on the second set of communication data. The second factor scores may be based on a combination of the first and second sets of communication data. The second factor scores may be generated in a similar manner as the first factor scores but with the addition of the second communication data.

A cumulative score may be generated for each applicant based on the first factor scores. Once the factor scores are updated with the second set of communication data, a updated cumulative score may be generated for each applicant based on the second factor scores. In some examples, changes in factor scores may be determine by comparing corresponding first factor scores and second factor scores. Applicants with a list of factors that have changed can be determined. The list of applicants may be provided along with an indication of the factor and how much that factor has changed.

At470, a priority range for one or more of the factors for each applicant. The priority range may be based on the first and second sets of communication data. The priority range may also be based on historical data. For example, the time after an application has been submitted may have a priority range. For example, the priority range may be 45, 60, 65, etc. days after submission. As another example, the email engagement rate priority range may be less than 1 or greater than 3. These values may be calculated based on the first and second communication data. For example, the values may be set as a standard deviation below and above the average email engagement rate. Historical data or a combination of all of the data may be used as well.

At480, a subset of applicants is identified based on data associated with a specific second factor score that is within the priority range of that factor. This subset of applicants represents an applicant that has at least one factor that has fallen outside a preferred range of values. In other words, each applicant in the identified subset of applicants has at least one factor score that falls within the priority range. At490, the specific second factor is identified for each applicant. Identifying the specific factor whose score falls within the priority range provides an indication of possible follow up actions that can be taken by enrollment staff. For example, if the age to acceptance is getting close then admissions can be checked. In this example, the age to acceptance factor score value may go from10to0on day 70 after receipt of an application. If acceptance has not been communicated by day 70, then these 10 factor points are lost and cannot be regained for the particular applicant. Accordingly, a priority range of greater than 60 days can be used to trigger a priority alert. Such an alert can indicate that the age to acceptance is now at 60 days and that the institution has 10 days to communicate acceptance in order to keep the10relevant points. The enrollment staff can decide on the action to take on a case by case basis.

The subset of potential applicants with a factor score within a priority range may be visualized as a list of applicants that may require some further action. Changes in the applicant's total score may also be determined and visualized. If the subset of applicants is each assigned to a specific enrollment counselor, a counselor may be only be shown their respective applicants. Such a list may act as a to-do list for each counselor, removing the need for the counselors to determine which applicants to reach out to each day. Using the subset helps or eliminates unintentional bias that may be introduced by the counselors.

FIG.5is a block diagram of a computer system in accordance with an illustrative implementation. The computing system500can be used to implement the web server, search service, advertisement service, etc., and includes a bus505or other communication component for communicating information and a processor510or processing circuit coupled to the bus505for processing information. The computing system500can also include one or more processors510or processing circuits coupled to the bus for processing information. The computing system500also includes main memory515, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus505for storing information, and instructions to be executed by the processor510. Main memory515can also be used for storing position information, temporary variables, or other intermediate information during execution of instructions by the processor510. The computing system500may further include a read only memory (ROM)510or other static storage device coupled to the bus505for storing static information and instructions for the processor510. A storage device525, such as a solid state device, magnetic disk or optical disk, is coupled to the bus505for persistently storing information and instructions.

The computing system500may be coupled via the bus505to a display535, such as a liquid crystal display, or active matrix display, for displaying information to a user. An input device530, such as a keyboard including alphanumeric and other keys, may be coupled to the bus505for communicating information and command selections to the processor510. In another implementation, the input device530has a touch screen display535. The input device530can include a cursor control, such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor510and for controlling cursor movement on the display535

Thus, particular implementations of the invention have been described. Other implementations are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results.