Use machine learning to score leads | Census

Lead scoring is a process used by businesses to prioritize their leads based on their potential to convert into sales. It involves using predictive models to evaluate each lead's characteristics and assign a score that determines its priority in the sales pipeline. The goal of lead scoring is to identify high-quality leads that are most likely to become customers, allowing sales teams to focus on those opportunities first. Effective lead scoring requires high-quality data, including information about past conversions, lead behavior, and customer demographics. Lead scoring models can be based on simple rules or more complex machine learning algorithms, but all require careful consideration of factors such as data quality, target leakage, and selection bias. By using lead scoring effectively, businesses can improve their sales productivity, increase revenue, and better understand their customers' needs.

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