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.
Why For e-commerce look for this solution
The real operational pain we solve
Problem
Most DTC dashboards show total revenue and order count — neither reveals whether growth is sustainable
Problem
New customer acquisition costs have risen 60% over the past four years (Triple Whale, 2024) — making LTV optimisation more important than ever
Problem
A customer who purchases once and never returns contributes differently to business value than a customer who purchases three times a year
Problem
Acquisition channel attribution typically shows volume (clicks, conversions) — not the quality of customers each channel brings
How Customer Analytics helps
Customer Analytics tracks LTV, purchase frequency, acquisition source, and retention signals from your Shopify data. You see the new vs returning split every month, your top customers by spend and frequency, and which acquisition channels are bringing high-LTV customers rather than one-time buyers. Churn signals flag customers who have not purchased within their expected cycle so you can act before they are lost.
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.
What you get when you use Customer Analytics
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
Want the full technical breakdown?
See how Customer Analytics worksRelated features for you
These features work alongside Customer Analytics for for e-commerce.
Ready to fix this for your business?
Book a demo and we'll show you how Customer Analytics solves these exact problems for for e-commerce.