LibSat Opportunity Index now includes filtering by Context

The Opportunity Index (OI) report now includes filtering by any context question within the LibSatLibrary Customer Satisfaction Management Service survey(s) to produce results for the filtered group of respondents, i.e., those responding with the selected context option. Filtering options have always been available to views the OI for any selected start Month, by Region, by Location. The generated OI ranking indicates the greatest opportunities to improve overall customer satisfaction within that filtered group of respondents. The report also provides drill-downs to the comments provided by those respondents.

The Opportunity Index (OI) is a report that effectively shows which aspects of library service if improved would realize the greatest increase in overall customer satisfaction. The report presents a ranking from highest to lowest opportunity and provides the OI score for the library’s Overall Importance and Satisfaction as a baseline [highlighted with a   Pink Background  .] Any improvements in an aspect of service above this baseline are more likely to impact overall satisfaction, while anything below would have less impact on overall satisfaction.

The algorithm to generate the OI is based on the following formula: Importance + (Importance-Satisfaction) = Opportunity.

The formula is calculated for each question that includes the Satisfaction and Importance scaled question parts and is calculated individually for each respondent. It is important to note that (Importance - Satisfaction) can never be less than zero (0) in this algorithm, and these instance are not included in the calculation. In other words, high levels of satisfaction do not detract from importance.

The overall library OI scores are calculated by averaging the individual respondent OI scores for each question. The resulting OI scores are then ranked and displayed in the   Blue Background  . The number of respondents is shown for your reference, as well as the average Overall Importance and average Overall Satisfaction scores. Note that the OI algorithm cannot be reproduced by using the overall average Importance and Satisfaction since these scores are the average of the Importance scores and Satisfaction scores for all respondents for each question, some of which may have been discarded from the OI algorithm for the individual respondent if Importance – Satisfaction was less than 0.