Join us online on Thursday 26th November for our next session with dunnhumby, featuring Marco Brambilla, Research Data Science Specialist, as he explores how data scientists can add greater detail to forecasts without rebuilding forecasting systems from scratch.
In retail analytics, forecasts are produced at scale. Senior stakeholders often want them broken down further by shopper type, demographic group, or other business dimensions. How can data scientists provide this extra detail? Should they build more granular models, rely on historical averages, or use a different approach?
Photography faces a similar challenge. Optical zooms capture genuine detail at a cost. Digital zooms are cheaper but lose detail. Modern AI-enhanced zooms can create sharper, more detailed images without more expensive hardware.
Can forecasting be approached in the same way?
In this session, we use the analogy of optical zooms, digital zooms, and AI-enhanced zooms to explore three approaches to adding detail to forecasts. You will leave with a practical framework for understanding when simple methods are sufficient, when additional modelling is needed, and how greater forecast detail can be achieved without rebuilding forecasting systems from scratch.
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