Which data mining task is most appropriate for the following business question?: "What is the probability that a customer will revisit our store within the next two months? (e.g. 32%)"

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BlackTom AI · Accepted Answer

We start by identifying what the business question is asking for: a numeric estimate of the likelihood (a probability) that a customer will revisit within a time window. This points toward a prediction task where the target is a numeric value.
Option 1: Similarity matching. This technique focuses on finding items that are similar to a given instance, typically used for case-based reasoning or recommendation; it does not directly produce a probability of visit, so it is not the best fit for predicting a numerical probability.
Option 2: Clustering. Clustering groups customers into segments based on similarity without predicting a numeric outcome for individual customers. It’s useful for segmentation but not for estimating the probability of a specific event for a particular customer, so it’s not appropriate here.
Option 3: Classification. Classification assigns discrete labels (e.g., yes/no, churn/no churn). While some classifiers can output probabilities for class membership, the typical end goal of classification is a categorical outcome rather than a numeric probability value like 32%. If you needed a probability, you'd often use regression or calibrated probability outputs from classifiers, but as stated, the primary task is a numeric probability, not a class label.
Option 4: Regression. Regression models predict a continuous numeric target, such as a probability or expected value, making it the most appropriate choice for estimating the two-month revisit probability (e.g., 32%). This aligns with standard predictive analytics where the target variable is a numeric measure like a probability, rate, or amount.

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Which data mining task is most appropriate for the following business question?: "What is the probability that a customer will revisit our store within the next two months? (e.g. 32%)"

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