Exploring customer specific KPI selection strategies for an adaptive time critical user interface

Abstract

Rapid growth in the number of measures available to describe customer-organization relationships has presented a serious challenge for Business Intelligence (BI) interface developers as they attempt to provide business users with key customer information without requiring users to painstakingly sift through many interface windows and layers. In this paper we introduce a prototype Intelligent User Interface that we have deployed to partially address this issue. The interface builds on machine learning techniques to construct a ranking model of Key Performance Indicators (KPIs) that are used to select and present the most important customer metrics that can be made available to business users in time critical environments. We provide an overview of the prototype application, the underlying models used for KPI selection, and a comparative evaluation of machine learning and closed form solutions to the ranking and selection problems. Results show that the machine learning based method outperformed the closed form solution with a 66.5% accuracy rate on multilabel attribution in comparison to 54.1% for the closed form solution.

The EDIT UX Framework: A User-Centered Approach to Effective Product Redesign

“…Impact Measurement: Post-launch, the focus shifts to a dual assessment of the product's impact, encompassing both business performance and user satisfaction. By monitoring key performance indicators (KPIs) and user feedback, the framework gains actionable insights into the product's reception and areas for improvement, validating the EDIT UX Framework's commitment to user-centricity (Keck & Ross, 2014;Palmer, 2002).…”

Data Analytics in CRM Processes: A Literature Review

“…Among the four CRM dimensions, customer development (19 out of 51 articles, 37.3 %) is the most common dimension for which data analytics is used to support decision making. [18], [27], [40], [46] , [47] , [50], [55], [67] Customer Attraction 16 31 % [19], [20], [29], [34], [37], [44], [45], [49], [52], [53], [57], [59], [61], [65], [66], [68] Customer Retention 7 14 % [17], [21], [24], [26], [28], [35], [64] Customer Development 19 37 % [3], [22], [23], [25], [30], [31], [32], [33], [36], [38], [42], [43], [48], [51], [56], …”

Dashboard for Evaluating the Quality of Open Learning Courses

“…The selection of the KPIs must meet a number of constraints that we have already discussed: they must be directly related to the organization's goals, they must focus on few key metrics, they must consider the state of the organization and be adapted to the business model and features. An interesting work is that of Keck and Ross [42], that have investigated solutions to the selection of KPIs through the use of machine learning techniques in the particular case of a call center. In this context of dynamism they have consider the problem as one of multi-label classification where the most relevant KPIs are labeled and selected later.…”