# What is rule-based personalization?

> Rule-based personalization is personalization by hand-written conditions: a person writes if-then rules that decide what each visitor sees. It is transparent, fast to set up, and easy to measure, which is why it is still the most common form, but it never learns and it does not scale to every case.

_Last updated 2026-11-01. From The Personalization Guide, https://personalization.guide_

Rule-based personalization is personalization by hand-written conditions: a person writes an if-then
rule, and the system changes what a visitor sees whenever the rule matches. A returning visitor with
more than one hundred dollars in the cart sees a free-shipping banner; a first-time visitor from a paid
ad sees a different landing section. No model is involved. The logic is whatever a human decided in
advance.

## How it works

A rule pairs a condition with an action. The condition reads signals the system already knows: segment
membership, cart value, the referring source, device, the current page, the time, or consent state.
The action changes a surface: show a banner, swap a content block, route to an offer, or reorder a
short list. Rules are usually grouped and given a priority order, so that when two rules could fire at
once, a defined winner takes the slot rather than both colliding.

Rules almost always act on [segments](/library/comparisons). Segmentation groups people by shared
attributes or behavior; a rule then decides what a given group sees. The two are often spoken of as one
thing, but they are separate: segmentation produces the group, and the rule produces the change.

## What it is good at

Rules have real strengths, which is why they remain the most common form of personalization in email,
on-site, and business-to-business programs.

- **Transparent.** You can read a rule and know exactly why a visitor saw what they saw. Nothing is
  inferred behind the scenes.
- **Fast to set up.** A marketer or product manager can write a rule without training data or a model.
- **Easy to measure.** A rule is a clean variant, so it drops straight into an A/B test or a holdout.
  Whether it helped is a question [measurement](/measurement) can answer directly.

For a small number of high-value cases, a handful of rules is often the right tool, and reaching for a
model would be overkill.

## Where it breaks down

The weakness is built into the method: someone has to anticipate every case in advance. As a program
grows, rules multiply, and they start to conflict in ways that are hard to see. A rule written for one
campaign quietly overrides another. Conditions go stale when the product or the audience changes, and
nothing flags it. Above all, a rule never learns from outcomes: it does exactly what it was told,
whether or not that still works. Rules are a good default and a poor ceiling.

## When to move beyond rules

Two signals suggest it is time to add a model. The first is scale: when the number of cases exceeds
what a person can maintain, hand-written logic becomes a liability rather than an asset. The second is
the wish to learn: when you want the system to improve from behavior rather than from a person's guess,
a learned method fits better. From there the options open up, to
[collaborative filtering](/library/techniques/collaborative-filtering), propensity and uplift models,
and [bandits](/library/techniques/contextual-bandits).

In practice the move is rarely all-or-nothing. The most common mature pattern is a model wrapped in a
few rules: the model makes the choice, and the rules enforce the things a business will not leave to a
model, such as inventory limits, legal constraints, or a brand rule about what must never appear next
to what. Rules do not disappear; they stop being the whole system.