You have five brands. Three are growing revenue. Are their customer economics healthy or are they burning through acquisition budget?
Revenue growth can mask deteriorating customer economics. A brand growing 40% year-on-year through aggressive new customer acquisition but with declining LTV and poor retention is building a fragile business. Enterprise CMOs need cross-brand customer health data — not just revenue totals — to allocate investment correctly. Customer Analytics provides portfolio-level customer economics that can be reviewed at the aggregate level or drilled into any brand.
Why For enterprise look for this solution
The real operational pain we solve
Problem
Portfolio-level customer health analysis requires manual data extraction from each brand's Shopify instance — a quarterly process at best
Problem
Different brands attract different customer profiles with different LTV characteristics — a portfolio view is required to compare them
Problem
Acquisition channel efficiency varies significantly by brand — what works for one brand's customer profile may not work for another's
Problem
Retention rates declining at the brand level are invisible in portfolio-level revenue reporting until the problem is significant
How Customer Analytics helps
Customer Analytics for enterprise provides cross-brand customer data from all connected Shopify stores in one view. The portfolio team sees comparative LTV, retention rates, and acquisition channel quality across all brands. Brand managers drill into their own customer data. The result is a continuous, portfolio-level customer health picture available to every stakeholder without analyst overhead.
Before Alpomi vs After Alpomi
From pain to clarity with Customer Analytics
Before
Annual portfolio review: each brand presents customer metrics separately. Brand A shows a strong repeat purchase rate. Brand B shows strong revenue growth. Brand C shows nothing because they do not track customer cohorts. No cross-brand comparison is possible.
With Alpomi
Annual portfolio review: Customer Analytics shows all three brands side by side. Brand A: strong LTV and 42% repeat rate. Brand B: revenue growth driven by first-time buyers with 18% repeat rate — a retention problem. Brand C: mature cohort with slowing new acquisition. Three different strategies recommended.
Before
Investment decision: two brands are requesting budget for a new customer acquisition push. Finance asks for LTV data to justify the investment. Neither brand can produce LTV by acquisition channel in less than two weeks.
With Alpomi
Investment decision: Customer Analytics shows Brand D's Google customers have a 24-month LTV of £320 and Brand E's Meta customers are £165. Budget is approved for Brand D's Google acquisition campaign. Brand E is directed to focus on improving retention before scaling acquisition.
What you get when you use Customer Analytics
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
Want the full technical breakdown?
See how Customer Analytics worksRelated features for you
These features work alongside Customer Analytics for for enterprise.
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