Analytical approach to digital channel performance optimization of mobile money transactions in emerging markets

Nowadays, Big Data and Artificial Intelligence (AI) play an important role in different functional areas of marketing. Starting from this assumption, the main objective of this theoretical paper is to better understand the relationship between Big Data, AI, and customer journey mapping. For this purpose, the authors revised the extant literature on the impact of Big Data and AI on marketing practices to illustrate how such data analytics tools can increase the marketing performance and reduce the complexity of the pattern of consumer activity. The results of this research offer some interesting ideas for marketing managers. The proposed Big Data and AI framework to explore and manage the customer journey illustrates how the combined use of Big Data and AI analytics tools can offer effective support to decision-making systems and reduce the risk of bad marketing decision. Specifically, the authors suggest ten main areas of application of Big Data and AI technologies concerning the customer journey mapping. Each one supports a specific task, such as (1) customer profiling; (2) promotion strategy; (3) client acquisition; (4) ad targeting; (5) demand forecasting; (6) pricing strategy; (7) purchase history; (8) predictive analytics; (9) monitor consumer sentiments; and (10) customer relationship management (CRM) activities.

The widespread use of information and communication technologies and subsequent transformations have led to the formation of a digital economy (DE). The European Union, as an international organization, has become the subject of building such an economy, striving to bring member countries closer in the field of digitalization.
The aim of this paper is to compare the DE development parameters of the EU countries based on cluster analysis and determine the most significant of them to solve the problems of bridging the digital divide between countries. For clustering, a feature DE vector of 20 indicators was created and the k-means algorithm and the Euclidean distance metric were used. For classification, the decision tree method was applied.
Three clusters of EU countries were identified by the level of DE development (leaders, followers and outsiders), which allowed assessing their positions relative to each other. Key parameters that determine countries’ positions in the general rating are identified. A parameter chart is generated to control the establishment of DE in the EU countries, which, in addition to key parameters, includes maximum, minimum and harmonic mean values of these parameters by cluster. This characterizes the landscape of DE development in the EU countries, assesses the digital divide and is the basis for decision-making in the area of bridging this divide.

The study of various aspects of social economy is stipulated by the fact that the focus of any economic system is the human being as the main object and the result of economic activity. The purpose of this paper is to cluster of social economies of the countries throughout the world with distinguishing the models of social economy for transition economies under globalization conditions.
The results of research represent four clusters of social economy that prove validity of classification of 4 classic models of social economy: liberal, Scandinavian, corporatist, and Mediterranean. While the most developed countries have effective models of social economy, there is still no clear concept of social development for transition economies. This paper deals with social economy clustering of different countries with the view to determinate the place of transition economies in social metrics of global economy.
Our study is limited to the number of countries – 40 countries of the world, mainly European, and timeframes – 2015 and 2016.
The obtained results could be taken into account by governments when developing and implementing new social policy for transition economies considering the experience of countries with classical social models. The authors propose the main practical tools for transition social model.
It is proposed to distinguish one more model of social economy – the transition model, typical for transition economies that implement social reforms and has some common features.