Timeseries forecasting

Whether working with seasonality or stochastic processes our experts have previously created solutions to forecast time-series data for short and long term.

Our approach combines various forecasting techniques, creating an ensemble model to maximise the effectiveness of the forecasts based on your data.

We have previously delivered models for weather and climate forecasting, economic and finance forecasting creating value for the business on a daily basis.

We use our proprietary tool for backtesting, to estimate the expected future accuracy of the forecasting methods. This is vital to assess which forecasting models and their combination should be used for the best results.

Sales, churn forecasting

Retailers generate enormous amounts of data, meaning that machine learning technology quickly proves its value. Forecasting sales is a common and essential use of machine learning (ML). Sales forecasts can be used to identify benchmarks and determine incremental impacts of new initiatives, plan resources in response to expected demand, and project future budgets.

Customer retention is one of the primary KPI for companies with a subscription-based business model. Competition is tough particularly in the SaaS market where customers are free to choose from plenty of providers. One bad experience and customer may just move to the competitor resulting in customer churn.

In order to succeed at retaining customers who would otherwise abandon the business, marketers and retention experts must be able to predict in advance which customers are going to churn through churn analysis and know which marketing actions will have the greatest retention impact on each particular customer. Armed with this knowledge, a large proportion of customer churn can be eliminated.

Model productionisation

One of the biggest leaps forward that a data science team can make is to get more of its machine learning models informing real decisions continually in operational systems and apps.

First the output model of the experimentation, training phase, an analytical algorithm needs to be integrated into a business process - and this can be new to all parties involved.

This means running the model frequently (i.e. daily basis) or continously (streaming), for which it needs a stable technology and reliable integration with the production environment.

Usually the input data is changing over the time (drift) and the model needs to be recalibrated to adjust for this skew, a process should be in place for that as well.

In the context of MLOps, the model serving pipeline is part of a broader set of components that work together to manage the lifecycle of the machine learning model from experimentation, training, serving and monitoring as shown in the diagram below.