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Rapid growth in the number of measures available to describe customer-organization relationships is presenting 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 thi...
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... set. A brief discussion is also provided before we con- clude and outline potential future work. The aggregation and presentation of KPI information within CRM or call center systems broadly falls into the class of Business Intelligence (BI) [3] software. User Interfaces design for BI software has historically been less than intelligent but has in recent years slowly embraced intelligent features in order to improve usability for less technical users. This work has been industry and development led rather than research led, and initially focused on the integration of multiple interfaces and the embedding of context-sensitive information displays alongside business process interfaces. The development of alerts based on business measures moving beyond some acceptable bounds continue to be considered an important aspect of intelligence in BI software, but such alerts are typically hard coded event triggers [7]. More recently active development in BI interfaces has began to move to- wards the incorporation of more intelligent techniques such as the incorporation of improved visualization methods, speech based communication, and multi-touch and multi-modal in- terfaces[1]. While these developments represent progress, BI interfaces generally remain bloated and often require a user to navigate through many pages of content to find important information. Adaptive Graphical User Interfaces (see for example [5] for an overview) self-modify to provide users with the most appropriate interface for their needs, and are thus of particular relevance to us. There are a number of different types of adaptations possible. These include: (a) device adaptation where an application adapts and conforms to the parameters of the display; (b) presentation adaptation where visual set- tings are for example auto modified to accommodate users with eyesight limitations; to (c) content adaptation wherein displayed options available to a user vary based on a model of the user’s previous interactions with the system. The dynam- ically selected collection of programme menu options available in Windows XP through Windows 7 is a classic example of such content based selection. Within the broad CRM and BI domain, Singh recently examined the role of the adaptive user interface in Enterprise Resource Planning systems [13]. While Singh’s analysis was comprehensive, it did however stay focused on issues far removed from our question of information selection for adaptive prompting and focused instead on issues such as the partial activation of menus based on usage context. Underlying technologies in the recommender domain (see e.g., [12]) such as collaborative filtering provides a useful foundation for information filtering and provision to business users in the enterprise software domain. For example, per- sonalization and adaptation have been used extensively in the CRM domain for the identification of customized services or products that can be offered to the customer [4]. Indeed one of the great applications of data science in the business domain has been the targeting of products and special offers to specific customers. For our purposes however, the output of recommender systems in the classical sense is inappropriate for KPI selection. In our case the the selection of appropriate output is linked to the intrinsic properties of current data rather than the specific preferences of an individual customer or service agent. The selection of appropriate KPIs for display is therefore closer from a modeling perspective to the content selection process as used in the natural language or text generation communities [11], i.e., our task is to select the most salient of items to present to the user. In order to investigate alternative strategies for the selection of key information to be provided to service agents, we have developed a prototype KPI recommendation application. Running alongside a traditional CRM solution, the prototype application provides company agents with the most appropriate information generated at run-time and customized to each specific customer and case. An essential part of the design of the system is that for a given company or even division, the recommendation strategies can be tailored for the given environment. While we provide specific details on the selection strategies later in the paper, it is worth noting at this point that we have adopted a Man- aged Selection Strategy . By this we mean that strategies are not learned based on feedback from individual Contact Center Agents or indeed customers. Instead we have adopted a semi-automatic learning system that can be used by IT personnel and Contact Center management to bootstrap and su- pervise the assistance provided. Our primary reason for doing so was due to feedback from industry partners who indicated that gathering accurate feedback from Contact Center Agents is rarely feasible in a high throughput environment. Figure 1 outlines the training and usage models for the enterprise assistance system. During training the assistance recommender is customized by Enterprise Systems or IT personnel through the use of a training application which augments the models used in the assistance system. Once trained, or partially trained, the assistance recommender can then be integrated alongside CRM software to provide key insights from individual customer ...
Citations
... 2. 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). ...
In the dynamic domain of web and mobile application development, the imperative to continuously evolve and enhance user experience is paramount. The EDIT UX Framework offers a robust, systematic approach to redesign, aimed at significantly enhancing user engagement, accessibility, and business performance. This framework is delineated into four pivotal stages: (1) Evaluation, which establishes a solid analytical foundation by synthesizing metrics analysis, heuristic evaluations, accessibility assessments, and user insights; (2) Design, where ideation and prototyping are driven by user-centric insights, fostering innovative solutions; (3) Iteration, a phase dedicated to refining designs through iterative user feedback and rigorous testing, with an unwavering focus on inclusivity and accessibility; and (4) Transformation, which transitions the refined product into the market, emphasizing continuous evaluation and iterative enhancements post-launch. By integrating principles of user-centered design, data-driven decision-making, and comprehensive accessibility, the EDIT UX Framework empowers design teams to create digital experiences that not only meet but exceed user expectations, ensuring a product’s resilience and adaptability in an ever-evolving digital landscape.
... 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. ...
Universities are developing a large number of Open Learning projects that must be subjectto quality evaluation. However, these projects have some special characteristics that make theusual quality models not respond to all their requirements. A fundamental part in a quality modelis a visual representation of the results (a dashboard) that can facilitate decision making. In thispaper, we propose a complete model for evaluating the quality of Open Learning courses and thedesign of a dashboard to represent its results. The quality model is hierarchical, with four levelsof abstraction: components, elements, attributes and indicators. An interesting contribution is thedefinition of the standards in the form of fulfillment levels, that are easier to interpret and allow usinga color code to build a heat map that serves as a dashboard. It is a regular nonagon, divided intosectors and concentric rings, in which each color intensity represents the fulfillment level reachedby each abstraction level. The resulting diagram is a compact and visually powerful representation,which allows the identification of the strengths and weaknesses of the Open Learning course. A casestudy of an Ecuadorian university is also presented to complete the description and draw newconclusions.
... 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], [58], [60], [62], [63] The distribution of articles classified by the CRM functional solution is shown in Table IV. Among the nine CRM functional solutions, direct marketing (10 out of 51 articles, 20 %) is the most common CRM functional solution for which data analytics is used to support decision making. ...
... Among the seven data mining techniques, clustering (7 out of 51 articles, 14 %) is the most common data mining technique for which data analytics is used to support decision making. [18], [29], [45], [53], [59], [63], [50], [47] Customer Segmentation 6 12 % [18], [27], [40], [46], [55], [67] loyalty programme 9 18 % [21], [24], [28], [35], [38], [42], [48], [58], [60] Direct marketing 10 20 % [34], [37], [44], [49] , [52], [57], [61], [65], [ [20], [22], [23], [32], [36], [38] [35], [42], [51], [55], [59] Full list of reviewed publications with classification is available at https://drive.google.com/open?id=0Bwp9RlyV--pwcFg1dC1kSzlMNG8 VI. ...
Nowadays, the data scarcity problem has been supplanted by the data deluge problem. Marketers and Customer Relationship Management (CRM) specialists have access to rich data on consumer behaviour. The current challenge is effective utilisation of these data in CRM processes and selection of appropriate data analytics techniques. Data analytics techniques help find hidden patterns in data. The present paper explores the characteristics of data analytics as the integrated tool in CRM for sales managers. The paper aims at analysing some of the different analytics methods and tools which can be used for continuous improvement of CRM processes. A systematic literature has been conducted to achieve this goal. The results of the review highlight the most frequently considered CRM processes in the context of data analytics.
This chapter addresses the market launch and sales of smart services. It opens with an introduction of the new challenges that the market launch of smart services creates for companies. Then follows the discussion of a four-phase approach to the market launch of smart services. Subsequently, successful practices are presented for this approach along eight design fields of the market launch.
In Recent days, the challenges in Business applications are more in analyzing the complexities of day to day activities. Key execution pointer (KPIs) are business estimations used by corporate bosses and various directors to follow and examine factors thought about noteworthy to the achievement of a Business. Reasonable KPIs base on the business cycles and limits that senior organization sees as commonly huge for assessing progress toward meeting imperative destinations and execution targets. KPIs differ from relationship to affiliation subject to business needs. One of the key display pointers for an open association will likely be its stock expense, while a KPI for a covertly held startup may be the amount of new customers incorporated each quarter. Surely, even direct adversaries in an industry are likely going to screen different plans of KPIs specially designed to their individual business procedures and the leader’s strategies for thinking.
