2023. No. 1 Vol.17

      Customer retention is one of the most important tasks of a business, and it is extremely important to allocate retention resources according to the potential profitability of the customer. Most often the problem of predicting customer churn is solved based on the RFM (Recency, Frequency, Monetary) model. This paper proposes a way to extend the RFM model with estimates of the probability of changes in customer behavior. Based on an analysis of data relating to 33 918 clients of a large Russian retailer for 2019–2020, it is shown that there are recurring patterns of change in their behavior over a single year. Information about these patterns is used to calculate the necessary probability estimates. Incorporating these data into a predictive model based on logistic regression increases prediction accuracy by more than 10% on the metrics AUC and geometric mean. It is also shown that this approach has limitations related to the disruption of behavioral patterns by external shocks, such as the lockdown due to the COVID-19 pandemic in April 2020. The paper also proposes a way to identify these shocks, making it possible to forecast degradation in the predictive ability of the model.

      The situation of a trade war between Russia and Western countries is unprecedented in recent history, both in terms of the scale of the restrictions being introduced and because of their mutually dangerous nature, as a result of which the entire world economic system is experiencing difficulties. An urgent task is to develop an economic policy for Russia that will allow for a quick reorientation to Eastern markets and the use of new growth drivers. Evaluation of the effectiveness of the measures taken should be carried out using modern tools, one of which is agent-based economic models. Since Russia is not considered as a key player in the models of international trade relations developed in a number of countries, in order to assess the sanctions imposed against it, it was necessary to develop a new tool – an agent-based model of trade wars between Russia, the USA, China and the European Union. The purpose of the study presented in this article is to assess the need of the Russian economy for additional investments in various industries for large-scale import substitution of products till now supplied from unfriendly countries. To achieve this, the agent-based model reproduces the sectoral structure of the considered economies of the countries and trade relations among them that existed before the start of the special military operation, compiles scenarios of possible sanctions, and simulates the corresponding changes in international trade relations. As part of the scenario calculations, three series of experiments were carried out. In the first series, for each scenario the expected dynamics of Russia’s GDP in 2022 was estimated in the context of organizing import substitution programs in key industries, and the cost of these programs was calculated. In the second series, the dependence of GDP dynamics on the volume of investments was studied. The third series simulated the dynamics of trade relations for the period up to 2025 for two investment policy options in each scenario. The results of the experiments also show that the impact of investments on the economy is stronger, the more severe the sanctions are, and under these conditions, the implementation of investment programs can accelerate economic recovery on average by 0.5% of GDP per year.

      This paper investigates the impact of external shocks on the spread of digital technologies. Using the example of the COVID-19 pandemic, we identify and describe four patterns that reflect the uneven response of different digital technologies to external conditions undergoing transformation. The patterns differ in both the magnitude of the pandemic’s impact and the timing of the resulting effects. Video conferencing, business continuity and telemedicine services showed a dramatic increase in demand at the beginning of COVID-19 and a gradual decline in the later stages. A more moderate response in the early weeks of the pandemic is typical of e-commerce and online entertainment. Delayed effects are seen in digital logistics services and digital currencies, which reacted much later than other technologies. Finally, a slow decline in significance after the pandemic began has been observed for biometrics and cybersecurity technologies. Similar patterns may describe the transformation of the spread of digital technologies not only under the influence of COVID-19, but also in the face of dramatic economic and social changes of other origins.

      Accounting ensures the collection and systematization of documented information about the facts of the economic life of enterprises and organizations. The information collected is systematized and formalized in various forms of reporting. One of the key forms of reporting is the balance sheet. The balance sheet is based on the principle of double entry, according to which each change in the financial resources of the organization is reflected in at least two accounts related assets and liabilities. Thus, the condition of the balance of the volumes of the generalized values of assets and liabilities is realized. The control element of the balance sheet is the equality of the values of assets and liabilities. However, this control element does not allow us to identify the systemic difference (diversity) of balance sheets with equality of distributed funds. Namely, the equality condition is integral in nature and its fulfillment is not related to the specific nature of item-by-item distributions, since, at a given size of the total cost of the balance sheet, the condition can be fulfilled by various options for the distribution of financial resources by assets and liabilities. Therefore, within the framework of this article, an attempt has been made to introduce a new control element of the balance sheet, taking into account the uneven distribution of financial resources by assets and liabilities of credit and financial organizations.

      This paper compares the driving factors of changes in energy intensity in both net energy exporting and importing countries using a DEA-Malmquist (Data Envelopment Analysis) and panel GMM (Generalized Method of Moments) methods over the period of 2000–2021. The findings show that technological progress has played a significant role in reducing of energy intensity in both groups. Moreover, we use the DEA method to decompose the Malmquist total factor productivity (TFP) into its components including technical change (TC), efficiency change (EC), pure efficiency change (PEC) and scale efficiency change (SEC). The results show that in energy exporting countries, the effects of each of these TFP components on energy intensity are negative but relatively weak, while the effects of these components on reducing energy intensity in importing countries is considerable. Specifically, the estimated coefficient of the pure efficiency component in reducing energy intensity in very remarkable, which shows the high importance of the efficiency components of TFP in energy management. Next, we investigate what is the main driver of technological progress in both the energy exporting and importing countries. The findings imply that in net energy exporting countries trade openness is a dominant factor to improve productivity, while in net energy importing countries, internal R&D is the dominant factor for improving technological efficiency.