We need to talk about the mess we’re making with Large Language Models. Lately, it seems every project I touch is trying to “automate engagement” by letting an LLM loose on their customer lifecycle. The emails sound human, the chat is polite, but the logic is often a complete train wreck. For some reason, the standard advice has become focused on stylistic fluency, and it’s killing performance because AI customer journeys require structural metrics to actually work.
I’ve spent 14 years debugging broken checkout flows and race conditions. I can tell you right now: a customer doesn’t care how “empathetic” an AI sounds if it sends a “first-time buyer” coupon two days after they’ve already made their third purchase. That’s not a tone problem; it’s a structural failure. If you aren’t measuring how your AI moves a user through a defined taxonomy, you’re just generating noise.
Why Standard LLM Metrics Fail Your Business
In the dev world, we love our benchmarks. But when it comes to customer journeys, metrics like Perplexity or BLEU are essentially useless. Perplexity measures how “surprised” a model is by a token, and BLEU compares text to a reference. However, real-world customer journeys don’t have a single “correct” reference text. They are dynamic, iterative, and co-created.
Consequently, relying on “LLM-as-a-judge” often leads to a bottleneck where the AI just critiques the prose instead of the progression. Specifically, we need a deterministic way to evaluate if the journey is actually going somewhere. That’s where the CDP framework comes in.
The CDP Framework: Continuity, Deepening, and Progression
To fix these broken flows, we have to map journey messages onto a taxonomic tree—stages like motivation, purchase, delivery, and loyalty. Once you have that structure, you can calculate three core metrics:
- Continuity: Does message B actually follow the context of message A? Or are you jumping from “How to use your product” back to “Here is a sales pitch”?
- Deepening: Is the content getting more specific? If the user is at the “Ownership” stage, are they getting detailed maintenance advice, or just generic “Thanks for buying” fluff?
- Progression: Is the user moving forward? If they’ve already signed the paperwork, message sequences should move to delivery logistics, not circle back to test drive bookings.
Furthermore, these aren’t just abstract concepts. In my recent WordPress AI dev updates, I’ve been looking at how we can use semantic embeddings to calculate these scores deterministically.
The Technical Gotcha: Mapping the Taxonomy
The “hack” here isn’t a complex prompt; it’s a structural mapping. You embed your journey stages (anchors) and your generated messages into the same vector space. Then, you use cosine similarity to find the closest node. If your sequence path looks like a frantic zigzag across your taxonomy tree, your model is hallucinating the journey logic.
<?php
/**
* A simple conceptual check for journey progression in WordPress.
* This is how you might store taxonomy-mapped stages as metadata.
*/
function bbioon_check_journey_progression( $user_id, $new_message_stage_id ) {
$current_stage = get_user_meta( $user_id, 'bbioon_current_journey_stage', true );
// Progression check: Stage IDs should generally increment
if ( (int) $new_message_stage_id < (int) $current_stage ) {
// We are backtracking. This might be a structural failure.
error_log( 'Potential journey regression for User: ' . $user_id );
return false;
}
update_user_meta( $user_id, 'bbioon_current_journey_stage', $new_message_stage_id );
return true;
}I’ve seen legacy codebases where these rules were hard-coded in messy if/else chains. Moving to LLMs doesn’t mean you throw the rules away; it means you use the LLM to generate the content and a structural metric to validate it before you ship it to production.
Look, if this AI Customer Journeys Require Structural Metrics stuff is eating up your dev hours, let me handle it. I’ve been wrestling with WordPress since the 4.x days.
The Senior Dev Takeaway
Stop obsessing over how “vibrant” your AI’s tone is. If your underlying structure is a mess, the most poetic AI in the world won’t save your conversion rate. Therefore, start building taxonomic references for your content. Use tools like OpenAI Evals or deterministic CDP scoring to ensure your automation actually aligns with business logic. Refactor your evaluation pipeline today, or prepare to debug a lot of unhappy customers tomorrow.
