Key learning areas:

The Data Science & Analytics simulation covers analytical decision making on topics including product life cycle management, product mix management, market segmentation, positioning, distribution channel investments, advertising budget allocation, after sales services, pricing, sales forecasting, marketing research, competitor analysis, research and development, revenue and margin management and overall profitability.

Key techniques:

The analytical techniques which can be applied while using the simulation includes Classification techniques - cluster analysis/k-means/x-means/K-NN, Factor analysis – Principal Component Analysis, Regression – Linear & Discriminant, Time Series Analysis, Conjoint Analysis, Marketing Mix Modelling, Optimisation & Goal Seek.

Key tools:

SPSS, RapidMiner, R, Python, and Excel can be used by the participants to obtain insights from the data set. The decisions then can be made in simulation which will simulate the environment and produce the results.

Key success factors:

The essence of Cesim Data Science & Analytics is to mirror a rapidly changing marketplace that is driven by short product life cycles and innovation; where constant attention to data-driven decision making, core competencies, timely product development, segmentation, positioning, and marketing communications are the keys to success.

Expected outcome:

As an outcome of the simulation game, participants will fully comprehend the different parts of the data-driven decision-making process, their relationship with each other, and their impact on the company’s overall results. In addition, participants will gain invaluable experience in teamwork and problem solving.