IPerceptions Satisfaction Index (iPSI)
The iPerceptions Satisfaction Index (iPSI) is a leading international indicator of online customer satisfaction. Spanning a broad array of industries, including transactional e-commerce, automotive, hospitality, government, and B2B, iPSI scores have been computed for millions of actual website visitors. Results are made available in real-time and individual site results can be compared to broad industry benchmarks. The information can then be deployed as a critical component of a business intelligence platform.
History of the iPSI
The iPSI had its genesis in the work of the late Dr. Max Garfinkle, who in 1988 devised an innovative perceptual framework for measuring organizational efficiency, which he termed the HaLo Map (Higher performing, Agile, Learning Organization)1. Dr. Garkinkle’s approach contains a 2 x 2 matrix formed at the intersection of two strategic axes: time and focus of attention. The four strategic challenge areas for an organization concatenate around this intersection, with a fifth area, one that serves as the means of implementation of the other four, also present.
Methodology
In 1999, Dr. Garfinkle’s research was adapted to an online setting, and the perceptual framework underpinning the iPSI, which analyzes the totality of the web experience, was born. The iPSI is based on a proprietary two-stage web-based sampling method, which solicits ratings for the most salient Attributes of the online experience. The scoring for these Attributes is done on a 0 to 10 point scale to incorporate an appropriate range of feeling.
The Attribute question stem is identical from question to question, giving a sense of uniformity and comfort to the sampling process and ensuring that consent and dissent in the respondent base are expressed naturally and not forced. Visitors are asked to evaluate each aspect of the site compared to their best online experience. Each Attribute of the online experience is mapped back to the appropriate higher-order Dimension.
The four formative Dimensions of the web experience subsumed into the iPSI are as follows:
Navigation – Is the site loading properly? Is it easy to navigate?
Content – Is the information relevant and accurate? Is it appropriately detailed and concise?
Interactivity – Do users get the feeling that the site responds to their needs?
Motivation – Do visitors feel fulfilled after their visit? Does the site save them time and/or money?
The one summative Dimension of the web experience that composes part of an iPSI score is:
Adoption – How loyal are a website’s visitors? Are they becoming advocates of the site?
All five Dimensions are compiled into the iPSI and weighted equally. Linear regression analysis is brought to bear on the Attribute scores to draw out which ones have the most statistically forcible impact on overall site satisfaction. Further, using complex tree-growing algorithms developed by Dr. Antonio Ciampi and known as RECPAM (Recursive Partition and Amalgamation)2, the iPSI score can also be understood as a function of the most unusual visitor segments onsite.
The iPSI and web analytics
Unlike clickstream and usability data, which are the core elements of many behavioral web analytics platforms, the iPSI functions as a measurement of a user’s attitudinal response to a website experience. Thus, the iPSI can legitimately be said to represent the true voice of the online customer, in the context of conscious deliberation. The iPSI is a fully digital metric and it does not suffer from any of the portability issues that have plagued traditional market research metrics transposed into an online setting. Indeed, it can be argued that the iPSI is the one customer satisfaction metric that is fully Web 2.0 compatible.
The iPSI is one component of a much broader web analytics program known as voice of the customer (VoC) research. While much of the practical application of this research involves data mining to unearth key ROI predictors, VoC research is also very much centered on placing the customer at the epicenter of all strategic decision making.
References
1. Max Garfinkle, Higher-Performing Organizations (Montreal: manuscript, 1999).
2. Antonio Ciampi, “Classification and discrimination: the RECPAM approach.” In Proceedings in Computational Statistics (COMPSTAT 94), eds. R. Dutter and W. Grossmann (Heidelberg, Germany: Physica-Verlag, 1994), 105-152.
See also
- Customer Relationship Management
- Loyalty business model
- Market Research
- Web analytics