Your revenue increased 30% this year. Your customer acquisition cost increased 45%. You are running faster to stay in the same place.
This is the DTC customer economics trap: revenue growth driven entirely by new customer acquisition, with rising CAC and flat or declining retention. It works until it does not — until ad costs rise, or a platform algorithm shifts, or a competitor outbids you. The brands that build durable businesses focus on LTV: acquiring customers who buy again, and understanding which channels bring those customers rather than one-time buyers. Customer Analytics makes this visible on a continuous basis rather than in an annual cohort analysis that arrives too late to act on.
Solved by Customer Analytics — Who are your best customers? What makes them come back?
What Customer Analytics does
Customer Analytics connects your Shopify order history to a structured view of customer behaviour: who your customers are, how much they spend over time, how often they return, which channels acquired them, and which segments are growing or declining. Understanding whether your revenue growth comes from acquiring new customers or from existing customers increasing their spend is one of the most important questions for sustainable DTC growth — Customer Analytics answers it continuously, not just in quarterly reviews.
- Customer lifetime value (LTV) tracking and cohort-based LTV trends
- New vs returning customer split: acquisition vs retention revenue contribution
- Top customer identification: highest spenders, most frequent buyers, and most recently active
- Acquisition source attribution: which channels brought your best customers
- Customer purchase frequency and average inter-purchase interval
- Churn signal detection: customers who have not purchased within their expected cycle
Works with
Why teams look for a Customer Analytics solution
The real operational pain that drives people to Alpomi
Most DTC dashboards show total revenue and order count — neither reveals whether growth is sustainable
New customer acquisition costs have risen 60% over the past four years (Triple Whale, 2024) — making LTV optimisation more important than ever
A customer who purchases once and never returns contributes differently to business value than a customer who purchases three times a year
Acquisition channel attribution typically shows volume (clicks, conversions) — not the quality of customers each channel brings
What you get when you use Customer Analytics
Real outcomes from teams using this feature in production
LTV by acquisition channel
Understand which channels bring your best long-term customers, not just your cheapest first-time buyers
New vs returning revenue split
Know whether your growth is driven by acquisition or retention — and whether that is healthy for your model
Churn signals before customers are lost
Customers approaching their expected repurchase window are flagged so you can re-engage before they lapse
Cross-brand customer health comparison
LTV, retention rate, and acquisition channel quality across all brands in one view
Investment decisions grounded in LTV
Justify acquisition budget allocation with customer economics, not just revenue projections
Portfolio-level retention monitoring
Detect retention degradation at brand level before it appears in portfolio revenue totals
Before Alpomi vs After Alpomi
From pain to clarity with Customer Analytics
Before
Monthly review: revenue is up 25%. You celebrate. Two months later, a quarterly cohort analysis reveals that 60% of new customers from the last six months have not repurchased. LTV is declining. The revenue growth was fuelled by acquisition, and retention is eroding beneath it.
With Alpomi
Monthly review: Customer Analytics shows new customer revenue up 35% but returning customer revenue flat, and repeat purchase rate declining from 38% to 29%. You identify the problem in month two, not month eight. Retention email sequence is reviewed and updated.
Before
Channel planning: you allocate budget based on CPA from each channel. Meta has the lowest CPA so it gets the largest budget. A year-end review reveals Meta customers have 40% lower LTV than Google customers because they skew toward sale and discount buyers.
With Alpomi
Customer Analytics shows Google customers have a 12-month LTV of £245 vs Meta customers at £148. You shift budget toward Google for prospecting. CPA is higher but LTV justifies it. Profitability improves.
Built for your segment
See how Customer Analytics solves problems specific to your business type
For DTC brands
Your revenue is growing. Is it because you are acquiring better customers, or retaining fewer?
Revenue growth has two engines: new customer acquisition and existing customer retention. Most DTC dashboards show revenue totals. Customer Analytics shows the engine mix — so you know whether your growth is structurally healthy or dependent on a customer acquisition treadmill that breaks when ad costs rise.
- LTV trends show whether the value of your average customer is growing or declining
- New vs returning split: see the exact revenue contribution from acquisition vs retention every month
- Acquisition source data shows which channels bring high-LTV customers, not just high-volume customers
For enterprise teams
You manage five brands. Each has different LTV profiles. Do you know which brands have the best customer economics?
Enterprise brand portfolios have different customer economics by brand, region, and product category. Customer Analytics gives the portfolio team a cross-brand customer health view — so brand investment decisions are informed by customer quality, not just revenue totals.
- Cross-brand LTV comparison to identify which brands have the strongest customer economics
- Acquisition channel efficiency: which brands are acquiring high-LTV customers from paid channels
- Churn signals across the portfolio to identify brands where customer retention is weakening
Get started in three steps
Connect your Shopify store
Customer Analytics syncs your full Shopify order and customer history. Every customer's purchase timeline, spend, and acquisition source is imported automatically.
Customer segments and cohorts are built automatically
Alpomi calculates LTV, purchase frequency, retention rates, and new vs returning splits from your Shopify data. No manual segment configuration required for the core views.
Monitor customer health and act on signals
View LTV trends, acquisition channel quality, and churn signals updated daily. Drill into any segment to understand the customers driving your best and worst outcomes.
Frequently asked questions
- How is customer lifetime value calculated?
- LTV is calculated from your Shopify order history: average order value multiplied by purchase frequency, projected over a customer lifespan estimated from your cohort data. The model updates as new order data arrives.
- What does a 'churn signal' mean in Customer Analytics?
- A churn signal is raised when a customer has not purchased within 1.5x their average inter-purchase interval. For example, if a customer typically buys every 30 days and has not purchased in 45 days, they are flagged as at-risk for lapsing.
- Can I see which acquisition channel brought each customer?
- Yes — where UTM tracking or Shopify attribution data is available, Customer Analytics shows the first-touch acquisition source for each customer segment. This lets you compare LTV by channel — not just cost per acquisition.
- Does Customer Analytics work for stores with a large number of customers?
- Yes — Customer Analytics aggregates data at the cohort and segment level for stores with large customer bases. Individual customer records are available for top customers by spend.
- Is this different from Shopify's built-in customer reports?
- Shopify's customer reports show individual customer records and basic cohort data. Customer Analytics adds LTV projections, acquisition channel quality comparison, churn signal detection, and cross-platform connection to ad spend — none of which are available natively in Shopify.
Explore related features
These features work alongside Customer Analytics. See how they fit together.
Ready to see Customer Analytics in action?
Book a demo and we'll show you how Customer Analytics connects to your stack and solves your reporting and attribution challenges.
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