Analysis of customer satisfaction survey data

Cisco Systems, Inc., conducts a customer satisfaction survey (CSAT) each year to gauge customer sentiment regarding Cisco products, technical support, partner- and Cisco-provided technical services, order fulfillment, and a number of other aspects of the companys business. The results of the analysis of this data are used for several purposes, including ascertaining the viability of new products, determining if customer support objectives are being met, setting engineering in-process and customer experience yearly metrics goals, and assessing, indirectly, the success of engineering initiatives. Analyzing this data, which includes 110,000 yearly sets of survey responses that address over 100 product and services categories, is in many respects complicated. For example, skip logic is an integral part of the survey mechanics, and forming aggregate views of customer sentiment is statistically challenging in this data environment. In this paper, we describe several of the various analysis approaches currently used, pointing out some situations where a high level of precision is not easily achieved, and some situations in which it is possible to easily end up with erroneous results. The analysis and statistical territory covered in this paper is in parts well-known and straightforward, but other parts, which we address, are susceptible to large inaccuracies and errors. We address several of these difficulties and develop reasonable solutions for two known issues, high missing value levels and high colinearity of independent variables.