1. Fundraising Analytics to identify potential prospects using SAS 12.1
Ramcharan Kakarla
MS in MIS and SAS and OSU Data Mining Certificate, Oklahoma State University, Stillwater, OK 74078
Data has also been collected externally from the US census at a county level. External factors were considered in the following ratios: Health Behaviors 30%, Clinical Care 20%, Social and Economic Environment 40%, Physical Environment 10%. Different external data fields were combined to form a single external data set at a county level. Internal and External Data sets are merged to create the final dataset. The final dataset contained 221 variables with 247,000 records.
Results
Abstract
Fundraising is an important activity for any foundation or charitable trust. It plays a pivotal role in development of the university propelling the growth and research. In fact it is often noted that the best universities around the world have deep roots in the history of Alumni giving. Solicitation involves cost; therefore it is important to keep a check on the dollar amount raised. The primary objective of the project is to distinguish the characteristics of donors from non-donors in the alumni using SAS Enterprise Miner This enables the university foundation to minimize the solicitations count and raise maximum money.
The predictive model estimates the likelihood of donation based on different inputs such as Age, Marital Status, Number of education records, Alumni pairs, degrees, scholarships, awards, events and so on related to the donor characteristics. The data for the project has been obtained from XYZ University Foundation Alumni database and enhanced with external data resulting in 221 variables and 247,000 records. Models such as Regression, Neural Networks, LARS, Decision Trees and Ensemble models have been built to predict the likelihood of donation. Age, Marital Status and Attendance at university events emerged as significant variables in the analysis.
It is found that donation patterns vary widely across age groups based on marital status. Age, Martial Status and attendance to events conducted by XYZ university were found to be the most influencing factors in distinguishing donors from non donors for Annual Giving. Single are least likely to give. Widowed and divorced are most likely to make a donation starting from their late 30s. More Results
Methodology
Conclusions
SAS 12.1 was used to analyze the data. The modeling approach followed for the project is SEMMA (Sample, Explore, Modify, Model and Assess). The data was portioned into two stratified samples (Training, Validation). Exploration is used for detecting trends and refining the data.
The main objective of the project was to discover the factors that influence the annual giving patterns. The facts discovered through the process not only give insights to the strategic management but also provide a decision making process backed up by the data. SAS Enterprise miner is a very powerful tool that helped in finding these patterns given the amount of data thrown in with larger number of variables
The variables are modified and transformed to adjust skewness and kurtosis values. Complete data for all the fields was available for only 10% of the data. Tree based Imputation methods were used to make necessary imputations. Directly related variables were rejected as part of analysis. Models ranging from decision trees, neural networks, logistic regressions, Dmine regressions were applied as part of the analysis.
References
Birkholz, Joshua Fundraising Analytics: Using Data to Guide Strategy. Hoboken, New Jersey : John Wiley & Sons, Inc., 2008
Sargeant, Adrian. Marketing management for nonprofit organizations. Oxford: Oxford University Press, 1999
Baker, Michael John, and Susan J. Hart, eds. The marketing book. Routledge, 2008.
Sun, Xiaogeng, Sharon C. Hoffman, and Marilyn L. Grady. "A multivariate causal model of alumni giving: Implications for alumni fundraisers." International Journal of Educational Advancement 7.4 (2007):
Background & Data Preparation
Data Exploration & Model Building
The project is focused on annual giving prospects of XYZ foundation. Annual Giving concentrates on donor acquisition. It inculcates the habit of donating on a regular basis. The main objective is to broaden the base of constituents in this area by building sustained relationships. XYZ foundation wants to make use of in-house data structure to identify the influencing factors for annual giving.
Data has been obtained from XYZ University foundation Alumni database. Several tables were joined together before making up the final internal dataset. Data was spread across 9 tables with multiple instances and transaction histories. The information is flattened out using various transformations, recoding and binning of the variables. Few data fields had a substantial amount of missing values. The internal data encompassed details such as date of birth, gender, marital status, student activities, graduation year, solicitations, education details and other demographic details.
Initial Data Exploration was done to identify the relations between the target and inputs. StatExplore was useful in identifying the usefulness of variables against the targets. Highly correlated input variables were removed. Clustering node was useful in identifying and picking up the important variables from the initial list of variables. After reducing the input space, the filtered set of variables were used to make the predictions about the responses. Tree models in general were more robust and predicted the responses well in this case. Dmine regression was found to be the best model based on Validation Misclassification rate.
Acknowledgement
I would like to thank Dr. Goutam Chakraborty for his generous support and supervision.
Contact
Your Comments and Questions are valued and encouraged.
Ramcharan Kakarla, Oklahoma State University, OK

2. Fundraising Analytics to identify potential prospects using SAS 12.1
Ramcharan Kakarla
MS in MIS and SAS and OSU Data Mining Certificate, Oklahoma State University, Stillwater, OK 74078
Results
Married are likely to make donations around age groups of 45 and above. It is also found that if the constituent is a volunteer there is a greater chance of donation
The chances of donation are likely to go up if the constituent has attended an event conducted in the past 6 to 12 months
2 in every 3 are likely to make a donation if the constituent and spouse are from the university (Alumni Pair)
The chances of donations are directly related to the number of educational records of the constituent
Couples of same gender have a high likelihood of giving
Scholarship is not a strong distinguisher among the constituents who donate and those who don’t
Direct Mail Solicitations are found to be most influencing followed by solicitations statistically
Counties with better median incomes have a better prospect base
As percent of smokers increase in a county the chances of donation are likely to go down
The best months in terms of donation are December, January and May