The Journey Scoring System helps merchants understand:

The system converts journey adoption and feature usage into a normalized store score out of 100.

This score acts as an operational maturity indicator for a merchant’s automation ecosystem.

BIK supports 300+ journey types with advanced capabilities — AI-powered templates, AI Assistant blocks, smart retry logic, multi-channel orchestration, anonymous user recovery, and discount-based conversion optimization.
But in practice, most stores only scratch the surface.

Three patterns kept showing up:
​1. The basics-only trap Most merchants set up a handful of standard journeys — abandoned cart, welcome series — and stop there. The advanced capabilities that drive meaningfully higher recovery and conversion rates (AI blocks, multi-channel fallbacks, smart retries) stay untouched. Not because they wouldn't help, but because there's no clear signal telling merchants they're leaving money on the table.

​2. No way to know what "good" looks like Without a benchmark, merchants have no frame of reference for whether their automation setup is mature or minimal. A store running 3 basic journeys and a store running 15 optimised journeys with AI and fallbacks both just see "journeys are live." There's no feedback loop that says "here's what you're missing and here's what it could be worth."

​3. Feature discovery is passive Advanced journey capabilities exist in the platform, but surfacing them depends on merchants exploring on their own or happening to read the right documentation. There's no structured, prioritised path that says "based on your current setup, this is the highest-impact thing to enable next."
Journey Scoring closes these gaps by giving every store a clear, quantified view of how well their automation setup uses the platform's full potential — and what to do next to improve it.

The Journey Scoring System enables merchants to:

The scoring framework creates a unified automation health layer across all stores.

Key Concepts

The score is influenced by:

Whether a merchant has configured important automation journeys.

Examples:

Whether advanced monetization features are enabled.

Examples:

The scoring system is intentionally non-linear.

Not all journeys contribute equally to revenue.

For example:

Therefore, the scoring model prioritizes:

Each journey has a predefined base score.

Examples:

Higher-impact journeys receive larger base scores.

Features add additional score multipliers on top of the base journey score.

Example feature multipliers:

A journey’s total score is calculated as:

Example:

If:

Then:

13×0.7=9.113 times 0.7 = 9.113×0.7=9.1

Feature contribution = 9.1 points.

The store score is normalized across all applicable journeys.

Formula:

This ensures:

Feature score:

13×0.7=9.113 times 0.7 = 9.113×0.7=9.1

Total:

13+6.5+5.2+6.5=31.2

After AI Agent Added

31.2+9.1=40.3

Each trigger has a theoretical maximum.

Example:

Total possible store score:

416.5

Formula:

Store score = 36%

Formula:

Store score = 38%

Although the AI Agent added:

+9.1 points

The total ecosystem denominator is very large:

416.5

Therefore, a single feature improvement may move the overall store score by only a few percentage points.

This is expected behavior.

The scoring system rewards:

rather than isolated feature additions.

The scoring engine also powers recommendations.

Recommendations are generated when:

Examples:

The system:

This creates a continuous optimization loop.

The merchant-facing dashboard displays:

The UI helps merchants progressively improve automation maturity.

The Journey Score is an indicative operational maturity metric.

It is based on:

However:

The scoring system should be interpreted as:

A recommendation and optimization framework — not a guaranteed revenue prediction engine.

For further assistance or to raise feature requests related to journey score, please contact [email protected]