Digital marketing actions that achieve a better attraction and loyalty of users: an analytical study

Currently, the digital economy contributes decisively to an increase in competitiveness, especially as a digital transformation involves migrating to new technological models where digital marketing is a key part of growth and user loyalty strategies. Internet and Digital Marketing have become important factors in campaigns, which attract and retain Internet users. This study aims to identify the main ways in which users can be gained and retained by using Digital Marketing. The Delphi method with in-depth interviews was the methodology used in this study. The results of the research show the most important actions for achieving user recruitment and loyalty with Digital Marketing from the opinions of consulted experts. The limitations of this study are those related to the number of experts included in the study, and the number of research papers consulted in the literature review. The literature review and the results of this research are used to propose new solid research with a consolidated critical methodology. This research deals with a new approach that will optimize web technologies for the evolution of user trends, and therefore, will be of academic and professional use for marketing managers and web solution developers. The conclusions of the investigation show the key factors, discarding others that do not affect the optimization of conversions in B2C businesses such as the duration of the session and the rebound percentage. Likewise, the results of the research identify the specific actions that must be carried out to attract and retain users in B2C companies that use the Digital Marketing ecosystem on the Internet. The requirements for companies that wish to implement a model to optimize conversions using the current digital economy are also shown.info:eu-repo/semantics/publishedVersio

Using analytical CRM system to reduce churn in the telecom sector: A macine learning approach

Applied project submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2019Customers are considered to be the most valuable assets of any business, and thus their loyalty is key to profitability as they indulge in repeat purchases and attract their colleagues through word-of-mouth. In competitive markets such as telecommunications, customers have a lot of flexibility due to the variety of service providers available and the introduction of mobile number portability (MNP) thus they can easily switch services and service providers. Customer churn is, therefore, a major problem among telecommunication companies hence their quest to reduce customer churn rate and retain an existing customer. Customer relationship management systems have been used over the years to track patterns within the customer data, but this could be improved notably with the technological advances hitting the universe on a daily basis. We have moved past the age of innovations around steam engines, electricity, computers, mobile, internet to the current technology trends in artificial intelligence and big data. We are at the cusp of a new wave where enterprises have embraced the application of machine learning in streamlining different business processes. Telecom companies have the advantage of mining large customer datasets that can be leveraged on for predictive analysis using data science. This project explores the use of analytical CRM system in reducing customer churn in the telecom industry using machine learning algorithms to predict customer behavior in order to retain them. Its goal is to analyze all relevant customer data and develop focused customer retention programs. This is on the focus that if you could somehow predict in advance which customers are at risk of leaving, you could develop focused customer retention programs to reduce customer churn.Ashesi Universit

Online Store Locator: An Essential Resource for Retailers in the 21st Century

Most retailers use their websites and social media to increase their visibility, while potential customers get information about these retailers using the Internet on electronic devices. Many papers have previously studied online marketing strategies used by retailers, but little attention has been paid to determine how these companies provide information through the Internet about the location and characteristics of their stores. This paper aims to obtain evidence about the inclusion of interactive web maps on retailers’ websites to provide information about the location of their stores. With this purpose, the store locator interactive tools of specialty retailers’ websites included in the report “Global Powers of Retailing 2015” are studied in detail using different procedures, such as frequency analysis and word clouds. From the results obtained, it was concluded that most of these firms use interactive maps to provide information about their offline stores, but today some of them still use non-interactive (static) maps or text format to present this information. Moreover, some differences were observed among the search filters used in the store locator services, according to the retailer’s specialty. These results provided insight into the important role of online store locator tools on retailers’ websites

Impact of Big Data over Telecom Industry

During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currentlymore than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. Thereis a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones togenerate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected datawe use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data

A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Platformisation of Mobile Operators Business Model: A Proposition Using Design Science Approach and Grounded Theory Principles

Mobile network operators (MNOs) business models (BM) are under pressure due to their lesser capability of introducing superior values to their customers in the mobile ecosystem. However, recent research efforts in developing new BM advocate two-sided BM that encompasses the diversity of MNO’s activities and capabilities. As a result, a new multidisciplinary service-based two-sided BM needs to be developed that incorporates these activities and skills. The small body of the extant literature on the subject suggests that it may be possible to enhance MNOs' BM by combining contemporary information technology tools with managerial design principles and concepts. This study designs a two-sided mobile advertising BM to investigate the application of a big data-driven BM to transform MNOs' current one-sided BMs to two-sided ones. To accomplish this, it combines the design science research methodology (DSR), which aims to create a problem-solving artifact for real-world problems, and the grounded theory approach, which aims to develop substantive theory and increase the rigor of the design process. The initial BM was proposed using deductive and adductive reasoning from academic and grey literature. The results of this study have shown that the new BM can enhance their revenue streams and competitive edge. This study identified that successful BM should be built on established MNOs core competencies and business activities. This study showed the applicability of two-sided theory and big data-driven tools and technologies to create new superior value propositions to both advertisers and end-users and thus innovative BMs for MNOs. The paper concludes with the fundamental requirements to build a data-driven two-sided BM.

The Role of Big Data Analytics on Innovation: A Study from The Telecom Industry

Telecom companies face a fierce competition from innovation based start-up companies, particularly those that are using internet networks to offer communication services through the voice and video over internet protocol (VoIP & PVoIP) technologies. More than 10 years have passed from the time the internet and VoIP were widely used, but still, telecom companies are having a great deal of business success through offering a wide spectrum of services, products, deals, and packages to consumers using both B2C and B2B models. The future landscape of how telecom companies will evolve in the market is still not clear, particularly with the increase of aggressive competition from companies that are technology-innovative and starting to deliver new forms of ubiquitous communication technology and services. Understanding why and how telecom companies innovate in the market is very crucial in order to predict the future of this business sector. In this paper, we argue that telecom companies are utilising their capabilities that have a significantly important role in fostering innovation, namely information technology (IT) capability and knowledge management (KM) capability. IT capabilities have changed dramatically in the last few years with the introduction of intelligent systems, big data analytics, the Internet of Things and the wide use of mobile apps and sensors. It is not clear how these technologies play a role in telecom companies’ innovation and it is not clear whether IT impacts innovation directly or if KM capability has a mediation role in utilising technology to support innovation. This paper is a position paper to establish grounds for understanding how telecom companies innovate, and in particular how IT and KM capabilities influence innovation. We outline the methodology of this investigation as a qualitative study with stakeholders from multiple telecom companies and we expect at the end of the study to be able to offer a holistic view on the way these companies innovate in regard to their products and services. We aim at providing a cross case studies comparison towards a prediction of the future of the telecom business sector