Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/759
Title: DETERMINANTS OF DATE ANALYTICS MODELS TO IMPROVE CUSTOMER ENGAGEMENT
Authors: Trenevska blagoeva, Kalina
Josimovski, Saso
Mijoska belshoska, Marina 
Jovevski, Dimitar 
Issue Date: Mar-2018
Publisher: INSTITUTE OF KNOWLEDGE MANAGEMENT SKOPJE, MACEDONIA
Journal: KNOWLEDGE – International Journal
Conference: Promoted in Vrnjacka Banja, Serbia 16-18.03.2018
Abstract: Abstract: Organizations that turn data into insights are gaining competitive advantage through improved connections with consumers. Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, aided by specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. Our findings demonstrate a significant increase in the number of organizations that are using analytics to gain a competitive advantage and innovate — a key component of this shift is more effective use of analytics to improve customer engagement. Data and analytics allow organizations to use intelligence from feedback to tailor offerings that improve customer satisfaction. Several factors appear to be at work, including the use of a wide range of data sources, well-developed core analytics capabilities, and integration of artificial intelligence (AI) and the internet of things (IoT) into processes. Companies that have businesses as their main customers (business-to-business, or B2B) are gaining the most benefits from this shift, in part because they are able to share data with customers in a way that directly strengthens their relationship. Data analytics initiatives support a wide variety of business uses. For example, banks and credit card companies analyze withdrawal and spending patterns to prevent fraud and identity theft. E-commerce companies and marketing services providers do clickstream analysis to identify website visitors who are more likely to buy a particular product or service based on navigation and page-viewing patterns. Mobile network operators examine customer data to forecast churn so they can take steps to prevent defections to business rivals; to boost customer relationship management efforts, they and other companies also engage in CRM analytics to segment customers for marketing campaigns and equip call center workers with up-to-date information about callers. We assigned respondents to one of three categories based on their relative level of sophistication in adopting analytics: the Analytically Challenged organizations display limited analytical capabilities; Analytical Practitioners largely use analytics to track and support performance indicators; and Analytical Innovators incorporate analytics into virtually every aspect of their strategic decision making, including gleaning data from a variety of sources such as direct measurement and sensors, industry data, and third parties. One of the clear differences between Analytical Innovators and the other maturity groups is their ability to successfully use data and analytics to deepen customer engagement along several key dimensions. The most analytically mature organizations are twice as likely to report strong customer engagement compared with the least analytically mature organizations. According to this interpretation, Analytical Innovators’ heightened awareness of customer and competitor behavior leads to a greater appreciation of the risks of customer loss as a result of their data-driven customer intelligence and engagement. To determine the relative analytics proficiency of an organization, we calculated the Analytics Core Index, based on the organization’s core analytics capabilities in (1) ingesting data (capturing, aggregating, and integrating data); (2) analyzing (descriptive analytics, predictive analytics, and prescriptive analytics); and (3) applying insights (disseminating data insights and incorporating insights into automated processes).
URI: http://hdl.handle.net/20.500.12188/759
ISSN: 2545-4439
Appears in Collections:Faculty of Economics: Journal Articles / Статии во научни списанија

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