Insights & news
While there is an infinite number of use cases, we present below compilation of interesting use cases structured into eight major categories applicable to a wide variety of industries. They have been selected based on the areas with the greatest impact on businesses and the activities within them that advanced analytics can optimize.
Clariba SEIDOR's act.in|extensions for Corporater help customers go beyond the capabilities of relying solely on the out-of-the-box Corporater BMP software. With our custom-built extension for forecasting, customers can visualize and forecast data more accurately using machine learning models in an easy-to-use format with stunning visuals right from within Corporater BMP.
Optimizing the customer experience with better resource allocation is often a critical objective for businesses or governmental agencies. The following case study concerns the Emergency Services Contact Center of a large European metropolitan city. Their objective was to accurately predict the volume of inbound calls to be able to improve agent rostering and ensure optimal response times for callers in serious need of assistance.
Prediction models are trending globally, and are now more widely used than ever before. If you’re looking to take advantage of this technology to better understand your data, don’t worry, our experts are right here to support you, whether you are an existing SAP HANA customer or you are planning to move to SAP HANA soon.
These days “machine learning” is a common phrase across a wide range of industries. The need to manage, analyse and report on large scale datasets is no longer restricted to tech companies - sectors from retail to manufacturing to healthcare rely on data scientists to make sense of the huge volumes of data gathered in the execution of their business, and attempt to use it to plan for the future, optimise processes and predict risks and opportunities.
Feature Engineering is a crucial step in the process of building a Machine Learning pipeline, as the features will be used by the algorithm as predictors.
Therefore, it’s advisable to prioritise building and optimizing our features to make sure that we start with a robust data model - which will result in our machine learning model achieving good results.
