What’s the difference between explicit and implicit data?

Explicit data is what subscribers tell you directly. They select preferences in a preference center, fill in their job title at signup, respond to a survey, or indicate content interests when they opt in. It's clean and unambiguous. You know exactly what they said because they said it. The limitation: collection friction. People don't always fill things out, and what they say doesn't always match how they actually behave.

Implicit data is what you infer from behavior. They clicked on product category X three times, they're probably interested in X. They haven't opened in 60 days, engagement is declining. They buy every Black Friday. They're price-sensitive. Implicit data gathers automatically, scales easily, and reflects real behavior rather than claimed preferences. The limitation: inference can be wrong, and you can't always distinguish signal from coincidence with small sample sizes.

The most effective segmentation uses both. Explicit data gives you structure and declared intent. Implicit data fills the gaps and updates as behavior changes. If someone says they want weekly emails but consistently ignores them and hasn't clicked in 4 months, implicit behavioral data is telling you something the explicit preference isn't.

A preference center is how most programs collect explicit data systematically. It's worth building if your ESP supports it. Subscribers who set their own preferences have lower unsubscribe rates and higher engagement. But pair it with behavioral monitoring so you're not relying on stated preferences that have gone stale.

If you're just getting started, implicit behavioral data (opens, clicks, purchase history) is already in your ESP and is the faster starting point for building useful segments.