Propensity Scoring

Propensity scoring solves a fundamental marketing problem: you have more leads and customers than you can meaningfully engage with, and you need to know who's most likely to respond. Traditionally, marketers relied on gut feel, basic demographics, or last-click attribution. Propensity scoring uses historical data to build a mathematical model that predicts future behavior.

Think of it like a credit score, but for customer intent. Just as banks use credit scores to decide who gets a loan, propensity models use behavioral signals—website visits, email opens, content downloads, purchase history, time since last interaction—to predict who's ready to buy. The model learns patterns from your past customers who converted and applies those patterns to your current audience.

In practice, you'll see propensity scoring show up in marketing automation platforms (HubSpot, Marketo), CDP tools (Segment, Tealium), and AI-powered email platforms (Klaviyo, Iterable). For example, an e-commerce brand might score all website visitors on their likelihood to purchase within 30 days, then automatically route high-propensity visitors to a sales team or trigger a personalized discount offer. A B2B SaaS company might score leads on their likelihood to convert to a paying customer, allowing sales to focus on the 20% of leads that account for 80% of deals.

When evaluating AI tools, propensity scoring quality depends on data quality, model recency, and transparency. Ask vendors: How often is the model retrained? What signals does it use? Can you see the score and the reasoning behind it? A poorly calibrated model wastes time chasing low-intent prospects; a good one multiplies your team's efficiency.