The Value of Predicting Bad Things

30 Oct

Foreknowledge of bad things is useful because it gives us an opportunity to a. prevent it, and b. plan for it.

Let’s refine our intuitions with a couple of concrete examples.

Many companies work super hard to predict customer ‘churn’—which customer is not going to use a product over a specific period (which can be the entire lifetime). If you know who is going to churn in advance, you can: a. work to prevent it, b. make better investment decisions based on expected cash flow, and c. make better resource allocation decisions.

Users “churn” because they don’t think the product is worth the price, which may be because a) they haven’t figured out a way to use the product optimally, b) a better product has come on the horizon, or c) their circumstances have changed. You can deal with this by sweetening the deal. You can prevent users from abandoning your product by offering them discounts. (It is useful to experiment to learn about the precise demand elasticity at various predicted levels of churn.) You can also give discounts is the form of offering some premium features free. Among people who don’t use the product much, you can run campaigns to help people use the product more effectively.

If you can predict cash-flow, you can optimally trade-off risk so that you always have cash at hand to pay your obligations. Churn can also help you with resource allocation. It can mean that you need to temporarily hire more customer success managers. Or it can mean that you need to lay off some people.

The second example is from patient care. If you could predict reasonably that someone will be seriously sick in a year’s time (and you can in many cases), you can use it to prioritize patient care, and again plan investment (if you were an insurance company) and resources (if you were a health services company).

Lastly, as is obvious, the earlier you can learn, the better you can plan. But generally, you need to trade-off between noise in prediction and headstart—things further away are harder to predict. The noise-headstart trade-off is something that should be done thoughtfully and amended based on data.

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