Most ecommerce teams know LTV matters, but they calculate it once a quarter in a spreadsheet that breaks every time definitions change.
When your data lives in Shopify (orders), Mailchimp (email engagement), and Facebook Ads (acquisition), the math is straightforward—if you can connect them reliably.
Why LTV calculations drift (and how to fix it)
The problem isn’t the formula. It’s that the inputs change constantly: Order attribution shifts as you try to determine which ad actually created the customer. Email engagement varies as customers open, click, and buy at different rates. Repeat purchase windows change as you experiment with 30 days, 90 days, or 12 months. Most critically, channel definitions evolve as you refine what counts as organic versus paid, or first-touch versus last-touch.
When these live in separate tools, every calculation requires manual exports and assumptions.
The 3 LTV views that actually drive decisions
1. Acquisition LTV by channel
Acquisition LTV by channel shows which channels create customers who buy again, revealing where to invest acquisition budget. It calculates payback period by channel, showing how quickly you recover acquisition costs. Most importantly, it helps you decide where to invest next month based on actual customer value, not just acquisition volume.
2. Cohort LTV over time
Cohort LTV over time reveals whether newer customers are worth more or less than older ones, showing if your product is improving. It tracks whether your product is improving retention by comparing cohorts over time. Most critically, it shows trends by acquisition month, helping you understand how product changes affect customer value.
3. Segment LTV (email engagement)
Segment LTV by email engagement shows whether engaged email subscribers have higher LTV, validating email marketing investment. It identifies which email campaigns drive repeat purchases, helping you optimize email content. Most importantly, it helps you decide whether to invest more in email or ads based on which drives higher customer value.
What to build first (week 1)
Start with a simple, reliable calculation:
- Customer acquisition (from Facebook Ads, tagged by campaign)
- First purchase (from Shopify, linked to acquisition)
- Repeat purchases (from Shopify, within 12 months)
- Email engagement (from Mailchimp, linked to customer)
Once you have this data connected, calculate LTV as first purchase plus repeat purchases divided by customer count. Calculate payback period as CAC divided by first purchase average to see how quickly you recover acquisition costs. Finally, calculate repeat rate as customers with two or more orders divided by total customers to measure retention.
Why spreadsheets break at scale
Spreadsheets work for one-time analysis but fail when you need daily updates as new orders, new customers, and new engagement data arrives. They can’t handle historical comparisons like LTV by cohort month that require tracking data over time. They struggle with segmentation by channel, product category, or email segment that requires complex filtering. Most critically, they lack reliability because formulas break whenever data structure changes, requiring constant manual fixes.
With our managed data platform, LTV calculations update automatically as new data arrives, so you can make decisions based on current numbers—not last quarter’s spreadsheet.
The hidden cost of manual LTV
When LTV is calculated quarterly, you’re making today’s decisions with old data. By the time you realize a channel isn’t working, you’ve already spent the budget.
Automated LTV means you can pause underperforming channels faster because you see results daily rather than quarterly. You can double down on winners sooner by identifying high-value channels quickly. Most importantly, you can test new channels with confidence knowing you’ll see results fast enough to make timely decisions.