Enable AI Targeting on a goal

Enabling AI Targeting on a goal tells Kameleoon to train a machine learning model that calculates each visitor’s likelihood of converting for that goal. Once training completes, you can use the Likelihood to convert condition in segments to target visitors by predicted conversion probability.

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Enable AI Targeting on an existing goal

1. Click Settings > Goals.
2. Find the goal you want to update and click Edit.
3. Click Advanced settings.
4. Toggle Use this goal with AI Predictive Targeting to On.
5. Select the trigger at which AI Targeting will make probability predictions.
6. Click Save.

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Enable AI Targeting when creating a new goal

1. Click Settings > Goals.
2. Click New goal.
3. Enter the required goal details and click Next.
4. Click Advanced settings.
5. Toggle Use this goal with AI Predictive Targeting to On.
6. Click Save.

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How to choose the right trigger

A trigger is the customer action, event, or journey moment that activates a prediction. It should not be selected arbitrarily or only because the event is technically available. A good trigger represents a meaningful step in the customer journey where you have a decision to make: whether to engage, personalize, recommend, retain, incentivize, or let the user continue without intervention. Examples of meaningful triggers include:

• A user views a product page
• A user adds an item to cart
• A user abandons a checkout
• A user completes a purchase
• A user shows signs of churn risk
• A user reaches a loyalty milestone
• A user returns after a period of inactivity

In practice, triggers should be mapped to your customer journey. Most brands already define key journey stages such as discovery, consideration, purchase, onboarding, retention, loyalty, and reactivation. Propensity scores are most useful when they are generated and interpreted at these moments. A “high likelihood to convert” score is not meaningful in isolation. It becomes actionable when it is attached to a specific journey step — for example, after a product view, after cart abandonment, or before a renewal date. The same score can lead to different actions depending on the journey stage. Kameleoon groups visitors into five score buckets, each representing roughly 20% of the eligible audience:

• Very low
• Low
• Moderate
• High
• Very high

A high or very high score may indicate that the visitor only needs a light nudge, or no incentive at all. A low or very low score may indicate that the visitor needs a stronger message, a different on-site experience, a better-timed interaction, or an incentive. In some cases, it may also mean the visitor should not be targeted at all because the expected impact is too low. The right action for each score level cannot be defined universally. It should be validated through experimentation. Test which on-site experience works best for each journey stage and propensity bucket, then refine your triggers and decisioning strategy based on observed performance.

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Example: on-site personalization after cart abandonment

A retailer wants to use propensity scoring to optimize the on-site experience for visitors who abandoned their cart. The trigger should not simply be “show a personalization every time we have a score.” Instead, the trigger should be the meaningful journey moment: a visitor adds items to cart, does not complete checkout, and returns to the site within a defined period. At that point, the propensity score informs the best on-site action:

• Very high / High propensity to purchase: show a light in-page reminder of the saved cart, or no incentive at all.
• Moderate propensity to purchase: show contextual reassurance, such as social proof, product reviews, or shipping benefits.
• Low / Very low propensity to purchase: test a stronger on-site experience, such as an incentive banner, an alternative product recommendation, or a different layout for the cart page.

Over time, test these on-site experiences and learn which strategy performs best at each score bucket.

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Learning phase

After you enable AI Targeting, Kameleoon begins a learning phase before predictions are available. The model requires:

• 7 days of data
• 100,000 visits

Both conditions must be met. For example, if your site receives 100,000 visits in a single day, the model still needs a full week of data. If your site receives 50,000 visits per week, training takes two weeks. Once training completes, the model activates on day 7 (D+7) and predictions become available on day 8 (D+8), after one day of prediction data has accumulated.

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AI model status badges

Goals with AI Targeting enabled display a status badge on the Goals dashboard. Hover over an AI Learning badge to see a tooltip showing the estimated time and visitors required for AI targeting to activate.

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Next step

After the model reaches AI Active status, create a segment using the Likelihood to convert condition to target visitors by their predicted conversion probability.