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Models of excellence make it possible to measure the quality of services an organisation provides. It offers the possibility to correct an organization’s strategy in order to adjust it better to the expectations and needs of its clients.

Level I Objetives

Evaluate the clients’ rational and emotional level of satisfaction with the key factors of quality and their specific aspects.

Identify and validate the key factors that define quality of service. Estimate their relative contribution to clients “health” and isolate the true symptoms that might affect it.

Define a guide of conduct on basis of action priorities and estimate the impact that improvements will have on the clients’ health.

Level II Objetives

Identify key performance indicators (KPIs) to measure the degree to which strategy address the needs and expectations of clients on the long term.

Define procedures on how to establish objective goals in KPI monitoring.

Develop a model to monitor the clients’ degree of satisfaction, loyalty and engagement. This model is based on evaluating the difference between real performance and clients’ perception of it. Also a system of alarms that allow for swift intervention is defined. Integrate the resulting model it into the model of performance excellence.

Design and implement an automated monitoring tool that permits continuous KPI tracking: Questionnaire design and printing, sampling and weighting methods, scheduling, systems of supervision and control, data analysis as well as generation and dissemination of reports to different stakeholders.

Employed Techniques

Creating a model of excellence requires a previous qualitative and desk research phase. Frequently also a thorough revision of the tools that have been used so far to measure service quality as well as analysing complaints and drop-out statistics are advisable. Statistically speaking the most commonly used techniques are Factor Analysis, Linear or Logistic Regression, treatment of Outliers, Dispersion Analysis, Correlations, Hierarchical Cluster Analysis, Non-Hierarchical Cluster Analysis, CHAID (Chi Squared Automatic Interaction Detector) and Discriminant Analysis

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