It is required to use analytics in both BPO and KPO companies. BPO is Business process outsourcing where the skills of the people are average to moderate for the working knowledge for a standardized routine processes. Graduates could be trained in the relevant business processes and it is training service personnel for business transactions. These transactions could be invoice processing, credit card processing, order processing, support help desk for any kind of activity, simple maintenance of the applications without involving the coding part, any kind of document processing, giving user awareness or limited technical help.
BPO are guided by Service level agreements (SLAs) and these are to be analyzed periodically to show performance improvement over time that need to optimize operations, reduce repeated tickets. Revising the SLAs require the usage of analytics techniques balancing the various factors like team size, shifts, skill levels, attrition or transition of people, tools and technologies, load variations with time and maintenance schedules of the applications, correlation studies of resolved tickets, maintainability availability and scalability of existing knowledge base, shifting trends etc., .
Whereas KPO is Knowledge process outsourcing requiring high knowledge intensive skills to work compared to BPO. It is like a exploratory research work to be done so highly qualified multi skilled persons are required involving specialized domain expertise as well as business sense. KPO processes demand advanced information search, usage of right analytics techniques depending upon the problem under study and data, interpretation and with proper judgment and decision making. Naturally, the compensation package also will be different.
Some of the examples of KPO are R&D in Pharma or biotechnology areas like new drug discovery, exploring high yield seeds, competitive research, financial modeling and equity research, data mining and database creation , specialized industry reports using the basic and advanced analytics techniques like hypothesis testing, correlation and regression, logistic regression, discriminant , cluster and factor analysis, times series analysis, design of experiments, conjoint analysis for product design, structural equation modeling etc.,
by Srilakshmi & varanasi Subrahmanyam
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