Content Marketing

The size of the Internet interaction allowed the customer to be part of the marketing of any product, product, services, or other organization. To this end, digital internal marketing promotes a customer-cantered perspective where organizations should focus on customer support and engage them in the value proposition process. An important part of digital marketing is creating value and consumer engagement in content marketing. The purpose of this paper was to provide a ‘content marketing’ test and to gain an understanding of the critical dimensions of this digital marketing strategy, with its basic strategies that companies can use online. The ideas expressed in this study have an impact on the content marketing strategy, as does the effective strategy for other disruptive marketing strategies. In the digital age, the value of digital marketing has increased from one year to the next as part of a marketing strategy developed by organizations of any kind and size. Considering that digital marketing requires the presence of content marketing, the success or failure of a company’s online communication depends largely on the quality of its content marketing. In this context, in addition to advertising high quality content marketing, digital advertisers must perform targeted analysis to tailor their content and select the appropriate method to promote it.

Post-pandemic marketing: when the peripheral becomes the core

PurposeThe purpose of this paper is to articulate the impact of COVID-19 on marketing. It will shift from “physical first” to “digital first,” and from “selling to serving” the customers. This will impact all 4 Ps of marketing, as well as branding and innovation.Design/methodology/approachIt is a conceptual paper based on literature review. The underlying construct used is transaction cost economics (TCE).FindingsUsing TCE, the paper finds that both consumers and marketers are very willing to shift to e-commerce and digital platforms which are both convenient, as well as cost-effective. Also, customer support organization will become a strategic advantage in interactive marketing.Originality/valueThis is an original paper written specifically for the special issue on the post-pandemic shock.

Understanding the costs and challenges of the digital divide through UK council services

This study investigates the issue of digital exclusion resulting from the digitisation of government and council services within the United Kingdom. An initial analysis of customer support log data from a council in a large UK city helped identify the most commonly queried services and modes of support. The main findings are based on qualitative analysis of 10 interviews, structured around the results from the log analysis, conducted with front-line staff members at the central library of the same council. The study identifies a range of issues associated with the provision of e-government services and the subsequent under-utilisation by the public, including poor design, issues with effective access and the level of digital literacy among end users. The study also proposes the concept of the ‘digital carer’, a friend or family member who is relied upon by users unable to interact with e-government services themselves. The findings of this study have implications for the way in which these services are designed and delivered and point to the need for further work that can contribute to the UK digital economy by facilitating better access to e-government services and reduce digital exclusion, especially for elderly and marginalised users.

Augmenting Customer Support with an NLP-based Receptionist

In this paper, we show how a Portuguese BERT model can be combined with structured data in order to deploy a chatbot based on a finite state machine to create a conversational AI system that helps a real-estate company to predict its client's contact motivation. The model achieves human level results in a dataset that containts 235 unbalanced labels. Then, we also show its benefits considering the business impact comparing it against classical NLP methods.

El Benchmarking como herramienta para el logro de calidad y eficiencia ante la burocratización mecanizada en los servicios de las instituciones

Los servicios prestados por las organizaciones son evaluados por los usuarios en sus procesos, el estilo de atención y la satisfacción que brindan sus resultados. Esta investigación analizó la experiencia que los usuarios han tenido con la atención que reciben en los servicios públicos, formulando una crítica constructiva a la burocratización mecanizada. El principal objetivo de la investigación fue determinar cómo afectan los procesos burocratizados a los usuarios del servicio en tiempo, costo y beneficio. La metodología seguida fue un análisis crítico deductivo correlacional, con un planteamiento heurístico, como base de cambio y solución a los problemas de la burocratización en los procesos. Esta investigación analizó la experiencia que los usuarios han tenido con la atención que reciben en los servicios públicos, a fin de identificar los indicadores para una propuesta de mejora, que contrarreste la burocratización mecanizada mediante la aplicación de la herramienta de Benchmarking. Se buscó alternativas de cambio en la gestión administrativa, ya que bien fundamentada es un conductor del mejoramiento en los trámites y de la satisfacción del usuario. Los resultados mostraron que la insatisfacción es moderadamente alta y que las instituciones públicas, en comparación con las privadas, necesitan reestructura e innovación inmediata. Palabras clave: benchmarking, burocratización mecanizada, calidad de servicio, procesos, satisfacción del cliente. ABSTRACT he services provided by the organizations are evaluated by the users in their processes, the style of attention, and the satisfaction that their results provide. This research analyzed the experience that users have had with the care they receive in public services, thus formulating a constructive criticism of mechanized bureaucratization. The main objective of the research was to determine how to control the bureaucratized processes to the users of the service in time, cost and benefit The methodology followed was a correlational deductive critical analysis, with a heuristic approach as a basis for change and solution to the problems of bureaucratization in processes. This research analyzed the experience that users have had with the care they receive in public services to identify the indicators for a proposal for mprovement, which counteracts mechanized bureaucratization through the application of the Benchmarking tool, to find alternatives for change in the administrative management, since well-founded is a driver of improvement in procedures and user satisfaction. The results obtained show that dissatisfaction is moderately high, and that public institutions compared to private ones need immediate restructuring and innovation. Keywords: Benchmarking, mechanized bureaucratization, processes quality of service, customer support.

Automating Incident Categorization in a Support Request System

With the expansion of the role of IT, organizations tend to maintain a support request system/ Issue Tracking System (ITS) to handle issues. ITSs are designed to streamline the process of customer support and keep a track of all the reported issues. Incident Management (IM) makes efforts to re-establish standard business operations reducing the impact and maintaining the quality and availability [1]. An incident also refers to a ticket. The manual categorization of raised issues infers an improper assignment of incident tickets, cause delays in the entire procedure of dispatch. The focus of this research is to automate the incident categorization (IC) and transfer to the appropriate support group forecasting the resolution for frequent issues based on the past.

Assessing the Impact of Electronic Business on the Performance of Small and Medium-Sized Businesses in Kumasi, Ghana

The study evaluated how electronic business impacted the performance of a selected small and medium-sized enterprises (SMEs) in Kumasi. A convenient sample of 50 SMEs in wholesale and retail of consumer goods in Kumasi were selected at random to be surveyed; however, only 30 responded. Structured questionnaires were used to collect quantitative data from the participants to answer the research questions. The results were statistically analyzed to draw conclusions. The results revealed that, to a large extent, the businesses used e-business methods in several ways to undertake five categories of business activities which include advertising, customer support service, marketing, payment system, and order and delivery. Several factors influenced the adoption and use of e-business methods by the businesses, but the most influencing ones were the businesses’ customers’ low use of e-business and telecommunications services not being very dependable. It was also discovered that the adoption and use of e-business made positive impact on the businesses’ performance. It is therefore recommended that more SMEs be encouraged and assisted to fully embrace the use of e-business.

MACHINE LEARNING TECHNOLOGIES IN BUSINESS BASED ON NEURAL NETWORKS

В данной статье рассматриваются алгоритмы машинного обучения, которые строят математическую модель на основе выборочных данных, известных как «обучающая выборка» (training data) для исполнения прогнозных решений без явного задания алгоритма в целях выполнения поставленной задачи. Сложные маркетинговые проблемы рассматриваются с помощью технологий машинного обучения, уделяя первостепенное внимание индивидуальной поддержке клиентов и разработке новых продуктов. Предлагаемые решения на основе интеллектуальных систем бизнес-задач, представляющих наибольшую сложность, позволят прогнозировать возможные вариации поведения клиентов. Алгоритмы машинного обучения в данном случае для реализации бизнес-проектов используются для решения проблем, для которых сложно или невозможно разработать традиционный алгоритм для эффективного выполнения задачи. Примененные технологии машинного обучения помогают систематизировать и извлекать информацию из огромного набора необработанных данных. This paper discusses machine learning algorithms that construct a mathematical model based on sample data, known as “training data,” to execute predictive decisions without explicitly specifying an algorithm in order to perform a given task. Complex marketing issues are addressed through machine learning technologies, focusing on individual customer support and new product development.The proposed solutions based on intelligent systems of business tasks, which are the most complex, will predict possible variations in customer behavior. In this case, machine learning algorithms for implementing business projects are used to solve problems for which it is difficult or impossible to develop a traditional algorithm for efficiently performing a task. Applied machine learning technologies help systematize and extract information from a huge set of raw data.