Recommendation

Published July 27, 2026

Recommendation is a Bio Block that selects the first owner-approved result whose rule matches the visitor. If no rule matches, it displays the required fallback recommendation.

How matching works

Rules run from top to bottom. The first match wins. A visitor can match against:

• A Path Chooser value, answer, or custom variable
• Any audience tag in a configured list
• A link parameter and value

Every Recommendation block has exactly one fallback. This guarantees that visitors always receive a useful next step, even when no rule matches or decision data is unavailable.

Add a Recommendation

1. Open the Bio Funnel builder, choose Add block, and select Recommendation under Discovery.
2. Add recommendation variants in priority order and configure the signal each should match.
3. Add a title, description, optional image, and a transparent Why this fits explanation.
4. Choose whether the CTA starts the Flow, routes to an Intent Path or step, opens a Content Page or URL, or reveals another Bio Block.
5. Configure the useful fallback recommendation visitors should see when no rule matches.
6. Use the Bio Funnel preview conditions or a Path Chooser to confirm both matching and fallback results.

Recommendation actions

Each result can:

• Start the Flow
• Start at an Intent Path
• Open the Flow at a selected step
• Open a Content Page
• Open an external URL
• Reveal another Bio Block, including Booking, Form, or PDF / Resource

Analytics

The Block performance table reports Recommendation views, interactions, and click-through rate. Result chips identify which recommendation received a CTA click.

Before publishing

• Order rules from most specific to most general.
• Confirm exactly one fallback is configured.
• Give every result complete copy and a valid CTA destination.
• Add meaningful alternative text to images.
• Keep the Why this fits explanation accurate and understandable.
• Test both matching and fallback behavior.

Current scope

Recommendation is deterministic: owners control every eligible result, condition, claim, and destination. Future Recommendation Engine enhancements can add multi-factor scoring, ranked alternatives, reusable catalogs, returning-visitor context, experiments, and AI-assisted setup without changing this reliable foundation.