Machine Learning in Product: Case Studies | Yogesh Yadav

    Machine Learning in Product: Case Studies

    Most ML in product fails not because the model is bad but because it predicts something nobody can act on. Each study here starts from the decision the model was meant to change: who to target, what to show, when to intervene.

    They cover purchase prediction, lifetime-value modelling, churn, real-time intent scoring and user segmentation — with the accuracy trade-offs and the business outcome each one moved.

    What these studies have in common

    Start from the decision, not the dataset
    Each model exists to change a specific action — who gets targeted, what gets shown, when to intervene. A prediction nobody acts on has no value however accurate it is.
    Accuracy is a trade, not a goal
    Several of these models were deliberately shipped below their achievable accuracy, because the cost of a false positive was low and the cost of delay was high.
    Segmentation before sophistication
    The largest gains came from clustering and intent scoring, not from deeper architectures. Knowing which users to treat differently usually beats predicting any one user better.

    8 Machine Learning case studies