Accelerating your Competitive Advantage
Specialised data analysis, statistical & econometric modelling and training services in multiple sectors tailored to your industry
Advanced econometric techniques and data analysis to uncover opportunities to accelerate your competitive advantage
Quantitative Analysis & Modelling
Data analysis with advanced statistical methods and econometric techniques. We identify causal relationships, demand patterns and hidden patterns that enhance business decisions in energy, retail, marketing, shipping, pharmaceuticals and other industries.
Development of Statistical & Econometric Models
Development of customized statistical and econometric models for forecasting, scenario analysis, risk analysis and hazard assessment. We create modelling solutions, such as for energy demand, distribution costs, sales promotion (marketing), freight effects, clinical drug acceptance and more.
Education, Training & Specialisation
Specialised training programmes in quantitative analysis, statistics and econometrics. We train teams in data analysis, modelling and evidence-based decision-making, tailored to the industry and the strategic priorities of the company.
Indicative Industry Services
Econometric models of marketing performance
Research Question
What are the factors that affect sales in product categories—specifically, the impact of pricing, advertising spend, seasonality, and competitive dynamics—to optimize marketing investment and pricing strategy.
Process
Worked closely with sales and marketing teams to collect historical sales data, advertising costs, and competitor data. Developed various econometric models (time series, panels, etc.) with the company’s participation in interpreting and validating results.
Delivery
A dynamic econometric model that quantified price elasticity of demand, advertising spend, and seasonal effects. The model showed a 15% sales regression due to lagged advertising effects—a finding that informed the reallocation of the advertising budget to increase ROI.
Econometric models for energy sector forecasts
Research Question
Forecast electricity demand in 12-24 months for capacity optimization and energy contract management—disaggregated by consumer type (residential, commercial, industrial).
Process
Retrieve monthly historical data on consumption, temperature, working days, and industrial production indicators. Collaborate with the planning unit to identify non-statistical events (e.g., exceptional conditions or policy changes). Develop alternative models (ARIMA, exponential smoothing with seasonality, VARs, and machine learning ensemble models) with regular feedback.
Delivery
A time series model that predicted energy demand by capturing seasonality, calendar effects, and unexpected changes in demand. Provide updated forecasts with confidence intervals for portfolio management.
Data processing
Research Question
Declassify personal and sensitive data while maintaining statistical accuracy for research. Allow analysis without revealing individual data that violates confidentiality.
Process
Evaluate methods with noise, data categorization, asymmetry, etc. Collaborate with relevant departments of the organization to verify data status.
Delivery
Falsified data system. Guarantee that any analysis on data does not reveal individual data. Statistical measures (mean, variance, correlations) were maintained with an error of < 2%.
Econometric model - demand forecasting model
Research Question
Forecasts of cargo demand (TEU) per route to optimize vessel chartering, scheduling, and fuel orders—and assess sensitivity to changes in rates or geopolitical turmoil.
Process
Collection of historical container data, freight rates, shipping indicators, and macroeconomic indicators. Development of linear and nonlinear econometric models (VAR, Machine Learning) with multivariate sensitivity analysis.
Delivery
Dynamic VAR model that explained the interrelationships between freight rates, macroeconomic dependencies, and cargo demand. Demand forecasting model with 90% confidence intervals for freight rate scenarios. Sensitivity analysis between freight rates and cargo. Dashboard for updating with new data and forecasting routing and fuel orders.
Advanced statistical analysis seminars with business practice
Research Question
Corporate training programme in advanced statistical techniques—including regression, time series analysis, machine learning methods—for company employees.
Process
Development of syllabus structure and learning objectives. Development of modular curriculum with theory and practice (R, Python) on everyday business questions.
Delivery
Seminar-style teaching of OLS, GLM, time series (ARIMA), machine learning (forest, boosting), and resampling methods. Practical application using statistical programs on business problems.
