The weather service now measure things with such precision that we can determine flat (rain) vs. elongated (ice) droplets, etc. A new project on data science competition site Kaggle focuses on predicting how much rain actually hits the ground from a series observations on the way down. The objective of the contest is to estimate the likely outcomes from an hour's worth of data. Such models have implications for agriculture, highway safety, and other resource allocation projects.
|Weather now measured in two-dimensions|
Marketing is often faced with a similar problem: how to align out of store activity, digital or otherwise, with in-store sales. Like rain, sales vary with time and space so the analogy is apt.
Recently Yicheng Song and colleagues at Boston University have been working on identifying common path-to-purchase analyses that combine multiple touch points and discrete sales outcomes. Their approach can be summarized as follows:
- There are patterns to the path to purchase (clusters of consumers emerge along common lines)
- The path and outcome varies by initial stimulus (catalog, email > online, offline)
- Paths traverse on and offline steps (variance is a matter of degree, not one or the other)
Maybe there's something more to be learned from the rain.