finsilabs/customer-analytics

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 |

Related Skills

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