In the previous post, we discussed how data mining, a process through which meaningful data and patterns are acquired from large data sets, can benefit the charitable sector. Since the techniques used for mining meaningful data relies on computer science and coding, it is often automatic (or semi-automatic) once the algorithms are in place. Therefore, this process can be a feasible and effective tool for donor profiling in India.
The advantages of data mining for philanthropy in India:
Matching donors with donees and NGOs: A key issue that most NGOs face with funding is the availability of potential donors who share the ideologies, vision, and mission the organization operates on. With information from various social media sites and the help of a VALS-like framework, the task of matching donors with donees becomes more feasible. For example, an organization working in marine-life conservation can connect with an entrepreneur who is an environmentalist and wants to support conservation of biodiversity.
Creating clusters of donors: With information like location and psychographics, data mining can aid in providing suitable clusters or groups of individuals who can get together to create awareness for a cause, start a local project, and network for meetings and future events.
Database: With a population of 1.2 billion, it is important to maintain a database of individuals with information on their previous charitable donations, characteristics and psychographics, time of the year to approach for solicitations, etc. This also helps in tracking and monitoring trends which help policymakers, as well as researchers.
Hidden Information: Even when NGOs maintain a well-established database, there are often chances that hidden variables and their relationships are often not visible at face value. Mined data, through techniques like sequential pattern analysis, can be used to identify hidden patterns. For example, while an NGO may know that men in their fifties donate to the cause of prostate cancer more often, it may also be true that women in their fifties donate to breast cancer more.
Long-term impact: One of the key issues that charitable trusts and organizations face is the short span of funding for a particular cause. For example, instead of providing books to 100 students for a single year, it may be more impactful to donate school fees, provide resources and career counselling to just 10 students for a period of 12 years. Short-term funds may not always have the same level of impact as long-term projects with careful monitoring would.
Donor Retention: Another issue that NGOs often face is the rate of attrition of donors. This also contributes to the above-mentioned issue of short-term impact, since donors often leave before seeing a project through. Information acquired through data mining would help assess when and why donors exactly leave. Is it after a specific number of years? Is it due to lack of feedback from the NGOs? Could it be a particular form of solicitation or appeal that makes donors leave the project mid-way? Understanding when, why and which donors leave would ultimately aid in retaining them.
Solicitation Methods: As mentioned in the previous point, different solicitation methods have their advantages and disadvantages. They may appeal to certain donors, whereas they may not work for the others. Having information on which appeal attracts more donors, and more donations will aid NGOs in customizing their appeal strategy for each set of donors.
Since data mining is widely used in a commercial capacity, ethical constraints for using the tool for philanthropic goals should be achievable. Further, through proper application, data mining can help increase charitable giving and proper allocation of resources, thus aiding in poverty alleviation and overall economic and social growth of the country.