Predictive analytics is about what will happen.
Based on past examples and similar situations, an analyst can build models to forecast the current trajectory, or to guess at what impact a change will make. Whether simple or complex techniques are used, predictive models generally attempt to answer either “how much” (e.g. predicted sales) or “which one” (e.g. predicted responder vs. non-response).
Once we have accurate guesses about what might happen, we can then decide whether to take an action on the entity of analysis by applying the drivers to a new set of customers or entities. A related concept is prescriptive analytics, which extends predictive analytics by helping us systematically answer “what-ifs” when we have a variety of input levers to pull and dynamic constraints to satisfy on a given prediction.
- A retail analyst decides to replace a poorly performing brand with a new item on the shelf. They gather the data that shows what products launched the best, and build a model to forecast the sales of a new item. Alternatively, the analyst might give more shelf space to a high performing brand. A model is built to relate sales performance to shelf square footage.
- A marketing leader decides to revamp acquisitions, due to a recent miss on targets. Their team gathers the profile of who responds most frequently, and also builds a model to predict who has the highest lifetime value. These two factors in conjunction serve as a targeting model.
Einblick’s powerful AutoML engine helps increase both accuracy and accessibility of models. Einblick has proven to be more accurate in 86% of scenarios than other commercially available AutoML engines, but only requires users to input criteria by answering simple English questions. The ability to quickly create, apply, and iterate on models leads to better decision making outcomes. Learn more today.