Customer Retention & LTV Analytics for a Shopify Beauty Brand
A fast-growing Shopify beauty brand had a loyal customer base but no clarity on who their best customers were, why they returned, or which marketing channels were actually driving repeat buyers. We built a full customer intelligence layer that turned scattered data into targeted retention and growth decisions.
Strong Revenue. No Idea Who Was Actually Buying.
A New York-based Shopify beauty brand had been growing consistently for two years — driven by strong Instagram presence and Klaviyo email marketing. Revenue looked healthy. But when the founders dug deeper, they realised they had no structured view of customer behaviour. They couldn't answer basic questions: Which customers come back? Which channels bring the highest-value buyers? Which products drive retention vs one-time purchases?
Their data sat across Shopify, Klaviyo, Meta Ads, and Google Analytics — but none of it was connected. Marketing decisions were being made by gut feel, and repeat purchase rates were declining without a clear explanation why.
Four Retention Problems Quietly Costing Growth
No customer segmentation
All customers were treated the same in marketing — high-value loyalists and one-time buyers received identical messaging and offers.
Unknown LTV by channel
They had no way to know whether customers from Meta Ads, organic search, or email had different lifetime values — so ad budget allocation was guesswork.
Declining repeat purchase rate
Repeat purchases were trending down but there was no cohort data to understand when customers were churning or what triggered them to come back.
No product-level retention data
It was unclear which products drove repeat buyers versus one-time purchases — making product development and bundling decisions uninformed.
A Complete Customer Intelligence Layer Across All Data
We connected all data sources and built four interconnected dashboards that gave the brand full visibility over who their customers were, how they behaved, and what drove them to buy again.
RFM Customer Segmentation
- Recency, Frequency, Monetary scoring
- VIP, loyal, at-risk and lost segments
- Segment-specific revenue contribution
- Personalised campaign triggers per segment
LTV & Retention Dashboard
- Customer LTV by acquisition channel
- Cohort retention curves (30/60/90 day)
- Repeat purchase rate by product category
- Churn risk identification
Marketing Attribution
- Revenue by acquisition channel
- LTV vs CAC ratio per channel
- Email campaign performance by segment
- Best-performing audience cohorts
Product Retention Analytics
- Products that drive repeat buyers
- First purchase to second purchase paths
- Bundle opportunity identification
- Return rate impact on LTV
From Guesswork to Targeted Growth
Within 10 weeks the brand had a clear picture of their customer base — and used that clarity to run targeted campaigns, reallocate ad budget, and improve retention significantly.
"We always knew some customers were more valuable than others — we just had no way to find them or talk to them differently. Now we know exactly who our best customers are, where they came from, and what keeps them coming back. The RFM segmentation alone changed how we run every campaign."
Do You Know Who Your Best Customers Really Are?
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