Personalization

The right message for every visitor. With proof it works.

Target audiences by behavior, traffic source, and device, author the experience in the same visual editor as your tests, and let a built-in holdback prove the lift in conversions and revenue. Most personalization tools can’t answer whether it worked. This one can.

  • Rules, not code
  • Built-in holdback measurement
  • Targeting runs in the visitor’s browser
How it works

From audience to proven lift

1

Define the audience

Plain-language rules: pages visited, new vs returning, session count, traffic source (first-touch and current), device, past conversions, even membership in an A/B variant.

2

Author the experience

The same click-to-edit visual editor as A/B testing. Change the hero, swap sections, or drop in dynamic values like {{first_name}}.

3

Launch with a holdback

A slice of the matched audience (10% by default, adjustable) keeps the original page. That control group is what makes the result provable.

4

Read the lift

Personalized vs holdback: conversions and revenue per visitor, significance tested. You’ll know if the personalization earns its keep, or doesn’t.

What you get

Personalization that behaves like an experiment

Because under the hood, it is one: same identity, same statistics, same honesty.

Audiences from real behavior

Visit history, session count, new vs returning, UTM and referrer (first-touch and current), device, and past goal conversions. Compose rules in a visual builder.

The holdback proves it

Every personalization measures itself against its own control group, a capability usually reserved for enterprise tools. Lift or no lift, you get a number, not a feeling.

Dynamic values (merge tags)

"Hey {{first_name}}" from a URL parameter, a cookie, or your Google Tag Manager data layer. Every tag carries a required fallback, so copy never renders broken.

Same editor, no second tool

Personalized experiences are authored exactly like A/B variants: click, edit, launch. Nothing new to learn, no separate subscription.

On-device targeting

The behavioral profile lives in the visitor’s browser and rules evaluate locally in milliseconds. No server round-trip, no flicker, no profile database.

Plays well with your tests

Run an A/B test and a personalization on the same page. Target popups at the holdback group. One visitor identity ties it all together.

Private by design

Personalization without a server-side profile

Most personalization platforms build a copy of your visitors on their servers. OptiWolf doesn’t: the behavioral profile that drives targeting stays in the visitor’s own browser, and only outcomes (exposure and conversion events) ever reach us. Dynamic values resolve on the page and are never transmitted. It’s a simpler privacy story for your legal team, and a better deal for your visitors.

  • Behavioral data never leaves the visitor’s browser
  • Dynamic values render locally and are never sent to OptiWolf
  • Measurement stays server-enforced, so the proof is still trustworthy
Good to know

Asked before starting

What can I personalize?

Anything the visual editor can touch: headlines, images, buttons, whole sections, plus custom CSS or JavaScript for the edge cases.

How many segments can I target?

One audience per experience, by design; it keeps the measurement clean. Ship one experience per segment and they apply in a stable order.

Do history rules work on a first visit?

Source, device, and UTM rules work immediately. History rules ("visited pricing twice") need a prior visit by definition; the profile starts building from the first pageview.

Better together

One suite, one visitor identity

Every pillar rides the same snippet and the same measurement spine, so they compound: target popups by test variant, personalize for popup subscribers, and read it all in one place.

Greet your visitors like you know them.

Because you do: their source, their history, their device. Personalize with proof, from the free plan up.