Analytics vs. Screening

I am often asked to compare and contrast analytics and screening. Analytics generally refers to statistical data analysis tools such as data mining, segment profiling, modeling, etc. Screening generally refers to data purchasing to enhance a file with wealth or biographical data.

Screening and analytics answer different questions. If you were to ask, "who has the means to give a major gift?" you would need to acquire wealth data either through screening or prospect research. If you were to ask, "who is likely to give a major gift to us?" you would need to use analytics or surveys. However, surveys are limited by response rates.

Analytics may be used to determine which records to screen. Screening acquires external assets and biographical information to address the ability to give. However, not all asset data is public, only some records will receive ratings, and the data is often best used for major gift identification.

Analytics incorporates internal data and external data to address the likelihood of giving. It is a science of probability. All records receive a score, and all departments can benefit from analytics. Some of my clients had me build models predicting giving to specific colleges or units like the library or the law school. Organizations with in-house data mining/analytics programs frequently build models for specific constituency groups.

Screening:

• Pros: Actual wealth data, helps inform capacity to give, can bring efficiency to research
• Cons: Limited to information in public databases, not everyone gets rated, people can hide from these sources, cost of outsourcing

Analytics

• Pros: Every record can be scored, can bring efficiency to research, builds understanding of the database, effectively done in-house
• Cons: provides probability data rather than actual data, requires investment in skill development or should be outsourced, can be difficult to explain to non-technical staff