One of the first steps towards improving student services and strategic planning is gaining a firm understanding of the student population.

If you are using basic student group definitions today, you already know the value of segmentation. However, general segments offer limited utility for improving student services or supporting strategic planning. While these general segments do provide broad differentiation, they offer little insight into the intentions, interests, needs and characteristics of the more homogeneous naturally occurring groups within these broad categories.

There is a solution. Predictive modeling can reveal profiles of candidates most likely to enroll. Armed with this information your recruitment efforts can be executed with greater precision and higher accuracy leading to an increase in recruitment of successful students.

KRI's Curriculum Planning report leverages data mining to find patterns representing successful combinations of factors, weed out insignificant factors, help reduce the problem set and add clarity to curriculum planning.

With the many programs available to improve student performance, maintaining high achievement is still a challenge for most institutions. The challenge stems from the fact that performance assistance programs can only be offered once poor performance has been identified. What is needed is the ability to identify early warnings of drops in student performance and an understanding of profiles of performance risk.

KRI's Student Performance report uses analytic modeling to find patterns and trends in student data that reveal key leading indicators of performance.

Despite the many student retention programs being executed today, student retention is still one of the most troublesome problems in higher education. The key to student retention is to understand the needs of each specific student segment, identify patterns in student defection, spot students who are at risk, and know which programs are most likely to be effective.

KRI's Student Retention report leverages data on past student dropouts to develop core profiles of retention and identify critical events that can serve as early warnings of potential risk.

The challenge faced by marketing is in knowing which course titles will be most interesting to which prospect segment. By examining the response patterns of previous campaigns, data mining can reveal patterns between response, campaign content, and respondent characteristics. Together, this information can help create more intelligent and targeted marketing of continuing education offerings as well as help define a wining set of program offerings.

KRI's Continuing Education report provides insight into the profiles of likely responders to various CE courses and identifies successful combinations of courses which can be marketed together.

What if you could spot every prospective donor and estimate their likelihood to donate and their likely donation amount. Armed with this information you would be able to target fundraising efforts and map the level of effort to the expected return.

KRI's Fundraising report uses predictive modeling to examine donation data and find patterns in giving based on donor characteristics. Allowing you to improve the effectiveness of your fundraising effort by getting more return at less cost.