Targeted model customisation is particularly relevant in the areas of artificial intelligence, big data, smart data and digital transformation. The aim here is to modify digital models - such as forecasting models or software models - so that they fulfil a specific purpose even better.
Imagine a company uses software that predicts how much of a product will be sold over the next few months. If the purchasing behaviour of customers suddenly changes, for example due to a price promotion, the old model may deliver inaccurate results. With targeted model adjustment, the model is now specifically adapted to the new conditions - for example, by using new data or changing certain calculations. This ensures that the forecast remains useful and reliable.
The advantage of targeted model customisation: models are kept up to date quickly and effectively without having to start from scratch. This saves time and costs. With this method, companies remain flexible and competitive because they can react quickly to changes.
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