Nonprofit donor data analytics

Goal: Develop actionable recommendations using raw data existing in company databases

Over the past four decades there has been systematic automation of business and customer information using computer technology. This automation has helped companies revolutionize delivery of services at a reduced cost. An un-intended benefit of the revolution is that a lot of data about customer behavior is stored in the databases. Most companies do not tap into this rich information that is already within their
possession.

eMpulse Solution
eMpulse offers data analytics solutions that are best in class. This is
achieved by applying state of the art mathematical and statistical methods.
Database analytics conducted by eMpulse include;
Data mining
Data modeling
Customer segmentation
Conjoint analysis
Other advanced data analytics

Data mining is an exploratory process in which a skilled analyst mines
the data to observe patterns that are not observed without rigorous
mathematical analysis. Data models provide objective predictors for
customer behavior. Customer segmentation will allow us to refine the
marketing efforts that are customized by type of customers and their
preferences.
For database analysis, data is typically manipulated using database
engines like Oracle, SQL, Access or equivalent. Then statistical analysis
is conducted using statistical packages like SPSS, Minitab or SAS.

eMpulse Facts
One of the leading Market Research Agencies
Established in 2007, growing rapidly by delivering high quality
research
http://www.empulseglobal.com/


Case Study

Situation
Wayne State University is a very successful research university based out of Michigan, USA. The university already had a very strong alumni following and engagement process. The office of alumni relations wanted to understand the alumni needs in a much rigorous manner so that it could enhance the engagement even further. Wayne owns and operates an alumni management database that contains data of the past two decades for the 250,000 alumni. eMpulse was engaged to analyze the data to provide insights on Alumni.

Research Conducted
The raw data was transferred over using a CSV format which was then
transferred over to an SQL database. Data cleaning was performed to
ensure that no logical inconsistencies existed in the database. Hypothesis
was developed for various business scenarios. Mathematical and
statistical tests to prove or disprove these hypotheses were developed.
Analysis was conducted using different tools and recommendations
were developed based on the analysis.

Results
Actionable recommendations were provided to the alumni association
leadership who summarized the finding and presented it at the Alumni
Association Board meeting. The recommendations were debated,
prioritized and acted upon by the university, which has resulted in an
improved engagement quality with the Alumni. This in-turn is expected
to increase the revenue due to increased donations in the long term.

The eMpulse Difference
Advanced Analytics Capability
eMpulse analysts have extensive experience in dealing with complex data analytics situations. Most database analytics needs high level of technical abilities along with an eye for detail. eMpulse analysts are your partner for your complex business problems.

Business Savvy Solutions
eMpulse provides solutions that are practical in nature. Years of experience of management in
senior leadership positions at large and small corporations have helped us develop a unique
perspective on how change gets executed at corporations.

Innovative Approach
Successful analysts need to be very innovative in data analytics because of the unstructured nature of data residing in the customer’s database. A combination of theoretically sound methods coupled with research execution savvy gives eMpulse an edge over the competition

Strategic impact
Most companies have a lot of raw data about their customers. Actionable insights are hidden in the raw data. Mining the raw data to extract these insights will provide great business benefits.
Technical challenges Typically data in its raw format is not conducive to analytics. Experienced analysts will have to prepare the data using database refinement skills to prepare it for statistical analysis.

Business value
Database analysis is conducted on information aready existing within the companies' IT systems. Research analysts explore to understand customer behavior patterns and other characteristics.
Success strategies eMpulse follows a proprietary analytics thought process. Data is ystematically refined and database prepared for analysis. Then a series of business hypothesis is developed which are statistically analyzed to develop answers. Business aumen is used to provide ationable recommendation based on data