customer-analytics

name	customer-analytics
description	Analyze customer behavior with RFM scoring, purchase frequency tracking, churn prediction, and cohort analysis to improve retention strategy
category	data-analytics
risk	safe
source	curated
date_added	2026-03-12
tags	["customer-analytics","rfm","churn-prediction","purchase-frequency","cohort","retention","customer-data","segmentation"]
triggers	["customer analytics","rfm scoring","churn prediction","purchase frequency analysis","customer retention analytics","customer behavior analysis","customer data analysis"]
tools	["claude-code","cursor","gemini-cli","copilot","codex-cli","kiro","opencode"]
platforms	["shopify","woocommerce","bigcommerce","custom"]
difficulty	advanced

Customer Analytics

Overview

Customer analytics transforms raw order data into actionable insights about purchase patterns, lifecycle stages, and churn risk. The core analyses — RFM scoring, cohort retention, purchase frequency, and churn prediction — reveal which customers are loyal, which are at risk, and which channels produce the best long-term customers.

This skill guides you through running these analyses using your platform's built-in tools and dedicated analytics apps, with data warehouse approaches for stores that need deeper segmentation.

When to Use This Skill

• When the marketing team needs data-driven segments beyond simple demographic filters
• When calculating at-risk customer counts for quarterly business reviews
• When measuring the impact of loyalty programs on purchase frequency
• When identifying the acquisition channels that produce the highest-LTV customers
• When preparing customer health dashboards for account management or VIP programs
• When building cohort retention analysis to understand customer lifetime value trends

Core Instructions

Step 1: Choose the right tool for your platform

Platform	Recommended Tool	What It Provides
Shopify	Klaviyo + Shopify's built-in customer segments	RFM-style segments, purchase frequency, CLV prediction, cohort reports
Shopify (advanced)	Lifetimely or Triple Whale	True cohort LTV, CLV by acquisition channel, retention curves
WooCommerce	Metorik	Customer segmentation, RFM analysis, cohort retention, churn identification
WooCommerce (email)	Klaviyo for WooCommerce	Behavioral segments + automated flows based on customer lifecycle stage
BigCommerce	Klaviyo for BigCommerce + Glew.io	Glew provides cohort analysis and CLV tracking natively for BigCommerce
All platforms (data-first)	Export to Google Looker Studio + BigQuery via Fivetran	Full SQL-based analysis; required for advanced RFM and cohort modeling

Step 2: Set up customer segmentation on your platform

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Shopify

Using Shopify's built-in customer segments (all plans):

1. Go to Customers → Segments
2. Shopify provides pre-built segments including:
   • Abandoned checkout in the last 30 days
   • Customers who have purchased more than X times
   • Customers who haven't purchased in 90 days (at-risk segment)
   • High-spend customers (based on total spend threshold)
3. Create custom segments using the query editor with filters like:
   • number_of_orders >= 3 (loyal customers)
   • days_since_last_order > 90 (churn risk)
   • total_spent > 500 (high-value)
4. Export segments to CSV or sync directly to Klaviyo for email campaigns

Using Klaviyo for RFM segmentation on Shopify:

1. Install Klaviyo from the Shopify App Store
2. Klaviyo automatically syncs all historical and new Shopify order data
3. Go to Segments → Create Segment and build RFM-style segments using:
   • Recency: "Has placed an order in the last X days"
   • Frequency: "Number of orders is greater than X"
   • Monetary: "Total amount spent is greater than $X"
4. Pre-built segment examples:
   • Champions: Ordered in last 30 days + 3+ orders + $200+ lifetime spend
   • At Risk: No order in 90–180 days + previously placed 2+ orders
   • Lost: No order in 180+ days
5. Use these segments to trigger flows in Klaviyo: win-back campaigns for at-risk, VIP rewards for champions

Using Lifetimely for cohort LTV on Shopify:

1. Install Lifetimely from the Shopify App Store
2. Go to Lifetimely → Cohorts to see a month-by-month retention matrix: what percentage of customers from each acquisition cohort are still buying at months 1, 3, 6, 12
3. Go to Lifetimely → Channels to compare 12-month LTV by acquisition source (Google, Meta, organic, email)
4. Go to Lifetimely → Customer Segments to see RFM distribution and predicted CLV per customer

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WooCommerce

Using Metorik:

1. Connect Metorik to your WooCommerce store via API
2. Go to Metorik → Customers to browse all customers with filters:
   • Last order date (identify churned/at-risk)
   • Total spent (identify high-value)
   • Order count (identify one-time vs. repeat buyers)
3. Go to Metorik → Reports → Customer Cohorts to see retention by monthly acquisition cohort
4. Go to Metorik → Segments to create saved customer segments (equivalent to RFM groups); export segments as CSVs for Klaviyo or Mailchimp

Using Klaviyo for WooCommerce:

1. Install the Klaviyo for WooCommerce plugin
2. Klaviyo syncs WooCommerce customers and orders, enabling the same RFM segments described for Shopify above
3. Go to Klaviyo → Analytics → Cohort Analysis to see retention curves and predicted CLV by acquisition date

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BigCommerce

1. BigCommerce Customer Groups: Go to Customers → Customer Groups — create groups based on purchase history, spend thresholds, and geographic criteria
2. Install Glew.io from the BigCommerce App Marketplace — provides cohort retention analysis, RFM scoring, and CLV by acquisition channel
3. Install Klaviyo for BigCommerce for behavioral email segmentation and lifecycle automation

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Step 3: Analyze purchase frequency and retention

Purchase frequency distribution (using any platform's export):

Export your customer order data to a CSV or Google Sheet and calculate:

• What % of customers have placed exactly 1 order?
• What % have placed 2–3 orders?
• What % have placed 4+ orders?

Industry benchmarks:

• Typical DTC brand: 60–70% of customers are one-time buyers
• Healthy subscription or consumable brand: 40–50% of customers reorder within 90 days
• Your second-purchase rate (% of first-time buyers who place a second order within 90 days) is the single most important leading indicator of long-term CLV

Cohort retention analysis:

A cohort retention grid shows what percentage of customers acquired in month X are still buying at months 1, 3, 6, 12:

• Shopify + Lifetimely: Available natively in the Cohorts view
• Klaviyo: Available under Analytics → Cohort Analysis
• Metorik: Available under Reports → Cohorts
• Manual (any platform): Export all orders to Google Sheets; create a pivot table with acquisition month as rows and "months since first order" as columns

What good retention looks like:

Months After First Order	Minimum Viable	Healthy	Excellent
Month 1 (second purchase rate)	15%	25%	40%+
Month 3 retention	10%	20%	35%+
Month 12 retention	5%	15%	30%+

Step 4: Identify at-risk customers and act

At-risk customer identification:

The simplest at-risk definition: customers who previously ordered multiple times but have not ordered in longer than their typical interval.

• Shopify Segments: number_of_orders > 1 AND days_since_last_order > 90
• Klaviyo: Create a "Winback" segment: "Has placed more than 1 order" AND "Has not placed an order in the last 90 days"
• Metorik: Use the "Customers at risk of churning" pre-built filter

Action by segment:

Segment	Recommended Action
Champions (recent, frequent, high-spend)	Invite to VIP program; early access to new products
Loyal but cooling (frequent but not recent)	Targeted win-back email with personalized product recommendations
At risk (inactive > 90 days, multiple prior orders)	Win-back sequence: reminder → small incentive → final offer
One-time buyers	Second purchase campaign; show complementary products
Lost (inactive > 180 days)	Low-cost re-engagement attempt; if no response, suppress to reduce email costs

Best Practices

• Run RFM scoring monthly — customer order history changes continuously; stale segments lead to wrong targeting
• Track second-purchase rate as a leading indicator — the conversion from one-time to repeat buyer is the highest-leverage retention metric; it predicts CLV far in advance of any LTV model
• Segment CLV by acquisition channel — customers from organic search, paid social, and email/SMS referrals often have dramatically different LTVs; measure them separately to inform budget allocation
• Build alerts for segment migration — when the "at risk" segment grows week over week, it signals a retention problem needing immediate action; set up alerts in Klaviyo or Lifetimely
• Flag seasonal buyers separately — customers who only buy in Q4 should not be marked as churned in Q2; apply a seasonal buyer tag before running churn analysis

Common Pitfalls

Problem	Solution
RFM segments shift dramatically after a sale event	Use a rolling 90-day window for scoring; recent sales spikes should not permanently elevate scores for customers who only responded to a discount
Acquisition channel CLV analysis not accounting for multi-touch	Use first-touch attribution for CLV by channel — the channel that introduced the customer, not the channel that converted the last order
Cohort retention shows 0% after month 6	Check whether the query or export is filtering out cohorts that do not have 6 months of data yet; exclude cohorts acquired in the last 6 months from long-term retention views
Customer count in segments does not match email list size	Some customers in your store may not be subscribed to email; segment by customer (order-based) separately from email list (consent-based)
Win-back campaigns going to customers who bought recently	Ensure segment filters are current — sync order data before running segment exports; stale data causes emails to go to wrong contacts

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