Day 1

• Decision making in the environment of uncertainty
• Variety and evolution of views on business decision-making
• Emotion and rationality, the interaction between logic and intuition
• Manipulation of human memory
• Excessive self-confidence and overreaction to events
• Psychological money traps
• Psychology of inequality. (People often complain about inequality and say that because of inequality they have no chance of success in life, but it’s important to understand that inequality has always existed and won’t go away, and with the right strategy you can you can have your own rags-to-riches story)

Day 2

• Strategic interaction and game thinking
• Bellman's Principle as a way to solve problems and its use in business decision-making process
• Market competition and models of finding market equilibrium
• Strategy games, lobbying, models of non-price competition, bluffing in business
• Models of bundling goods and services as a tool for transferring power from one market segment to another, and models of price differentiation
• Business as a game. Nash Equilibrium and the concept of conflict resolution (Nash Equilibrium is a way of predicting the future based on a strategic analysis of the interaction of economic agents, calculating multi-step combinations of players and identifying stable strategies in which none of the players will want to deviate from their chosen alternatives)

Day 3

Day 1

• Modeling economic processes.
• Deterministic and stochastic business models.
• Key Performance Indicators (KPIs) of a business.
• Modeling the influence of various factors on the KPI of the business. Demand curve. Elasticity: price, relative to income, relative to prices of other substitutes and complementary products.
• Evaluation of non-price factors influencing the company's sales.
• Linear regression multifactor model.
• Finding, assessing and interpreting significant factors.
• Case solving by econometric analysis in STATA environment.
• Retail sales valuation model, sales model in the field of medical diagnostics services.
• Business valuation models based on NET SALES and EBITDA.
• Capital asset valuation models (CAPM, ART).
• Calibrating parameters of valuation models and predicting consumer behavior.
• Portfolio approaches to forecasting using a covariance matrix.
• Clustering and its effect on forecasting accuracy.
• Econometric approaches to selecting the best forecasting models.

Day 2

• Introduction to customer analytics.
• Trends in customer analytics (global experience, Ukraine).
• Customer life cycle as a basis for customer analytics.
• Segmentation model called “Leaky Bucket” (LB).
• Decomposition of revenue via segmentation. Alternative scenarios.
• Practical use of long-distance segmentation. Communication and economics.
• RFM customer segmentation. Building an RFM model.
• Using RFM segmentation. Applied value.
• Data Mining methods in customer analytics. Customer clustering.
• Monetization of customer data.
• Customer analytics as a source of additional profits.

Day 1

• Profit optimization of a company.
• Profit optimization based on distributor and retail margin.
• Marketing cost modeling and analytical models of profit maximization.
• Models of price differentiation.
• Models of optimal bundling of goods.
• Basic theoretical and game economic concepts. Nash Equilibrium. The main patterns of interaction of economic agents in the market.
• Case study of competitive strategic interaction.
• Principles of building economic and financial analytical models.
• Finding Nash Equilibrium in dynamic economic games.
• Forecasting market equilibrium.
• Irrationality and emotionality in making economic decisions.

Day 2

• Analytical models of competition.
• Models of competition in production volumes (Cournot model).
• The model of competition in market leadership (Stackelberg model).
• Multi-tributive decision-making model to increase sales efficiency in a competitive environment.
• Definition of attributes and their perception by consumers.
• Optimal redistribution of marketing resources to increase integrated performance of the company's products.
• Decision Tree method as a tool of strategic planning.
• Strategic economic modeling. Winning strategies and inverse induction method.
• Bluff in models of market competition. Predatory pricing.
• Economic models with network effects.
• Models of competition with information asymmetry: signaling, adverse selection, moral hazard.
• Analytics of social choice models.
• Module summary.

Day 1

• Time-based value of money. Basic formulas of financial mathematics.
• Discounted cash flow method (DCF).
• Assessment of shares and bonds via DCF.
• The method of equivalent cash flows for making optimal financial decisions and carrying out debt restructuring.
• Personal financial planning.
• The main criteria for investment decisions: NPV and IRR.
• Case study
• Additional investment criteria of PP, DPP, PI, MIRR, NPV-BE.
• Portfolio approach to determining the best ways to invest.

Day 2

• Risk degrees and analysis of their measurement: volatility and beta.
• Sensitivity analysis and spider chart to determine the risks of an investment project.
• Scenario analysis and Monte Carlo simulation analysis in the risk analysis of an investment project.
• Coverage Ratio (CR) to determine the probability of project failure.
• Non-parametric assessment of NPV and IRR distributions of an investment project.
• Value at risk (VaR) methodology as a tool for integrated risk management and the company's investment project portfolio.
• Use of option contracts to increase the efficiency of business management.
• Optimal models of financial risk management of the company using the example of the agro-industrial complex.
• An integrated approach to company risk management.