Examples of analytics services include analyzing the impact of TV advertising on customer acquisition or identifying and removing duplicate entries in a prospect database to increase accuracy. The latter involves the use of algorithms to split American and foreign names into their phonetic components and find matching names in a database. Eliminating duplicate names enables more efficiency in up selling and cross-selling campaigns, customer-complaint resolution and obtaining a 360-degree view of a customer.
Data-analytics firms face some stiff competition from captive operations, which have moved many of their analytics offshore. General Electric has 1,000 employees working on analytics in India, American Express has 350 and HSBC has 200 working in India. Some onshore companies, such as Capital One Financial have ingrained analytics into every one of their business processes, so the company is always clued in to what products are selling and to whom, and which products are profitable, so that it can adjust its marketing strategy accordingly.
Large financial institutions have thousands of employees working on analytics. As their numbers grow, the case for outsourcing and offshoring becomes stronger because of the labor arbitrage and the abundance of talent. “A typical project will entail about a dozen people, most of them highly skilled, who will be dedicated to the project for six months or more,” says Lalit Wangikar, VP, Inductis, India operations. Under those circumstances, offshoring will be an attractive alternative to fielding an internal project team.
Working Directly with End Users
In one of the models of KPO delivery, the service provider works directly with the end user — a financial institution, media company or a manufacturer — and functions as a professional-services firm, performing onsite data gathering, interviews, problem definition and solution.
Inductis, an Indian service provider that specializes in analytics, employs this model, which it calls staff augmentation because it involves embedding consultants within the client’s organization to solve problems such as customer-life-cycle management, product development, business re-engineering and financial management.
A typical consulting team will include an analytics lead, with 3-5 years experience leading data-intensive analytics projects; a modeler, with 2-3 years experience in developing statistical-analysis methods; and a programmer analyst, with two years experience in data loading, quality checking, ad-hoc analysis and report generation.
The work that this team will perform includes hosting, managing and creating an integrated view of the client’s customer data; generating samples based on ad-hoc or streamlined survey requirements and performing complex data analysis; and model validation and scoring.
