Investigate the impact of drivers such as macroeconomic factors, seasonality, trends, weather data, distribution channels, media investment, etc. on the sales. This makes it possible to better plan and predict various changes to see what will have the biggest impact.


We support predicting future sales based on given input data, picking what variables can be changed and optimizing those to maximize sales, and comparing various scenarios to see how they compare to each other.

Customer churn is the percentage of customer that stops using a service within a specific time frame. Given that there is usually a cost of acquiring new customers, it is vital to minimize churn. There are a lot of factors that impact churn such as brand loyalty, price, customer satisfaction, etc. By building a model that analyzes customer churn, it is possible to understand what impacts it and how much. When one knows that we make it possible to simulate various different strategies to mine customer churn and pick the best one.

New customers are always nice to have, but what drives new customers may be different from what minimizes customer churn as it comes down to establishing a new relationship not maintaining one. That is why it is key to be able to decompose the various drives of new customers in order to focus on how to acquire new customers while at the same time minimizing Customer Acquisition Cos(CAC)

In demand forecasting, one aims to forecast future customer demand over a defined period using historical data and other information such as seasonality, trends, macroeconomic factors, etc.


By knowing the demand for products one can decrease inventory costs by reducing or eliminating redundant and obsolete inventory and at the same time reduce the inventory carrying costs. Without demand forecasting one is at risk of making poor decisions that may increase inventory holding costs, poor supply chain management, and not having enough inventory all negatively affecting customer satisfaction and profitability.