6 Claude Workflows For Commerce

⏱ 5-minute read

Many of you have already tried this. You connected Shopify to Claude. Maybe Klaviyo too. You asked a real question – which products are compounding repeat? why is payback slipping? – and got something that looked like an answer. Then you looked closer: generic, and some of the numbers were wrong.

That’s not a Claude problem. It’s a data‑layer problem. It’s missing the context.

This week we're releasing our own answer to that: a RetentionX MCP for Claude, and a set of skills built on top of it.

A raw-data MCP gives the model a database. A commerce data layer gives it context.

Raw-data MCPs, including Shopify's, expose what your database exposes: orders, customers, line items. To answer "are May cohorts worse than April," Claude has to assemble the cohort from millions of rows. It can't. It samples, joins, and fills the gaps from training data. That's where the wrong numbers come from.

The same gap explains why those connections never answer the second-layer questions: which channel? which product? which discount? No raw schema exposes those dimensions pre-built.

The RetentionX MCP is not a connection to your store's database. It's a connection to a commerce intelligence layer. Cohort retention curves, follow-up rate per product, replenishment timing, LTV horizons, payback windows, segment-level P&L, brand stickiness, category quality, already computed, already structured, ready to read.

That changes two things at once.

Depth. Because every dimension is pre-computed, the second question is free. You don’t ask “what’s my retention.” You ask it by acquisition product, for Meta-acquired customers, last quarter, against baseline — in four pivots without changing tools. This is where connections, anomalies, and root causes start surfacing.

Trust. Structured answers mean zero hallucination. Claude isn’t guessing what LTV90 means or estimating follow-up rate from a sample — it’s reading a number out of a defined contract. If the layer doesn’t have it, it says so. Instead of inventing it.

Pre-Built Claude Workflows For Commerce

Below are six skills. Each one demonstrates what this layer makes possible. They're not the product, every question you bring is one query away.

rx-cohort-check

“Are this month’s customers worse than baseline?”

Retention curve overlay + LTV horizon + repeat-survival — with the exact divergence point named. You see it before revenue does.

rx-anomaly-scan

“What should I be paying attention to that I didn’t ask?”

Five findings ranked by revenue impact, each tagged silent leak / positive surprise / structural / reversible — each with a one-line next move.

rx-product-stickiness

“Which SKUs are ritual heroes — and which are replaceable?”

2×2 of brand stickiness vs product stickiness, the protect-and-scale list on the right. A merchandising audit, condensed.

rx-revenue-forecast

“Where will we land — and why?”

Bear / Base / Bull projection with named drivers and trigger signals to watch over the next 14 days. Event-aware. No linear extrapolation through peaks.

rx-segment-insights

“Which segments behave structurally differently?”

The aggregate hides them. The scan finds them — with cross-promotion callouts when two segments rotate through different catalogs.

rx-category-performance

“Which categories are Gold — and which are on Watch?”

Six-dimension tiered scorecard + loyalty×margin scatter. Two weeks of merchandising prep, in a single afternoon.

Six skills. A surface that doesn’t end at six.

The dashboard era was about making humans interpret data. The raw-data MCP era was about making models assemble data. What's next is neither: a structured commerce layer the model reads – and the operator asks.

We offer a 30-day free trial of RetentionX, including all Claude MCP functionality.

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