Quality & Satisfaction Research Institute
Driving Product & Service Excellence
The Expanded Model follows the logic behind the e-business model by adding two satisfaction drivers to fully measure the quality of a product as well as the service quality offered by the same business. This model has proven to exceed traditional customer feedback analysis and simple customer satisfaction metrics. The cause-and-effect framework shows the impact of individual elements of the customers experience to learn how to increase satisfaction with the correct element to positively impact your business’s bottom line accurately and effectively.
The Customer Satisfaction index of Puerto Rico is a powerful multi-equation econometric system derived from ACSI model developed at the University of Michigan’s Ross School of Business. The CSIPR standard model is proprietary patented cause–and-effect equation with variables that exemplified the drivers of satisfaction on the left side (Customer Expectation, perceived quality , and perceived value) , satisfaction (CSIPR) in the center, and outcome of satisfaction on the right side ( customer complaints and customer loyalty), incorporating customer retention and price tolerance to maximize the capability to measure satisfaction precisely and accurately, and in a way that is operational , to predict future financial performance with an increase in satisfaction.
The Index is constructed by multi-variable aggregates each creating a latent variable composed of several questions that are weighted in the model. The questionarie measure what affects satisfaction as well as the effects of satisfaction. The scores are reported on a 0 to 100 value scale. The standard model methodology quantifies the strength of each variable relationship on the left to those were the arrow points on the right. These arrows are defined as “impacts.” The model is self-weighting to use the full advantage of scientific rigor to explain customer satisfaction CSIPR on customer loyalty and therefore retention. An examination of the latente variables and impacts directions , users can determine what drivers of satisfaction , if improved, effects most on customer loyalty.