Study of blood donation campaign communication methods and attributes of donors: A data analytics approach

ABSTRACT

Blood cannot be produced artificially. This life-saving liquid is the most precious thing a human being can donate for another human being to save life. The world is even realizing this now more due to the outbreak of the novel coronavirus COVID-19 pandemic as antibodies through plasma donation for the treatment of COVID-19 patients being used to save lives. Blood organizations use different methods to communicate with potential blood donors. This study analyzes different communication methods and their effect on blood donors. The study compares different ways of communicating with blood donors. The study also analyzes different attributes of the actual donors like age, gender, ethnicity, frequency of donation, donation type, etc. The study finds that sms (text messages) has the highest turn out rate followed by email. The overall turn out rate is about 1.4–1.6%. Some other major findings of the study include: most donors donate blood within 7-day period after they receive communications, average age of donors is 40 years, more male donors donate blood than females, mobile vehicles are popular to blood donors than established centers, etc. This study presents and discusses the results of the analysis in detail as well as provides recommendations.

KEYWORDS:

• Data analysis
• blood donation
• blood donor
• campaign
• communication effect

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Muhammed Miah

Muhammed Miah is an associate professor of Business Information Systems at the Tennessee State University. Miah has a PhD in Computer Science, MBA in Computer Information Systems, MS in Computer & Information Science, and BS in Civil Engineering. He has many years of experience in teaching both graduate and undergraduate levels. His main teaching areas include data analytics, database systems, data mining, decision support systems, etc. Miah’s research areas include data analytics, data mining, information retrieval, educational technology, technology changes, health care informatics, etc.