> Source: https://tryordinary.com/solutions/customer-analytics/

# Customer analytics for Shopify

Stop losing customers before you knew they were slipping.

See exactly when customers are due to reorder, which ones are slipping away, and who your most valuable buyers actually are. Then act on it — before the win-back window closes and they're gone for good.

[Get started →](https://app.tryordinary.com/sign-up) [See all solutions](/solutions)         The problem

## Most stores measure acquisition. Almost none measure who actually came back.

Your dashboard probably tells you how many orders came in
this week. It almost certainly doesn't tell you which of
last month's customers haven't ordered again, who's about
to slip out of the replenishment window, or which of your
new buyers actually become repeat ones. The data exists.
Most analytics tools just don't surface it.

Lifecycle stages

## Every customer in a stage you can act on today.

Defaults match a typical consumable DTC cadence; adjust
them for apparel, home goods, or longer-cycle products.
The "Due Reorder" segment — customers who bought once and
are now overdue — is the single highest-ROI window most
stores under-touch. Once a customer slides into "Lapsed,"
the math gets harder; into "Lost," harder still.

Stage |  Default threshold |  What to do |
New  |   ≤30 days since last order  |  Welcome series, cross-sell, plant the reorder seed. |
Due Reorder  |   31–60 days  |  Replenishment reminder. Highest-ROI segment most stores under-touch. |
Lapsed  |   61–90 days  |  Win-back. Small discount, soft tone. |
Lost  |   90+ days  |  Reactivation. Bigger offer or no-strings gift. |
Cohort retention

## Find out if customers actually come back, or buy once and leave.

Of every customer you've ever sold to, what fraction came
back for a second order? A third? The shape of the curve
tells you whether you have a small loyal core or a brand
people fall in love with — and which of your campaigns are
bringing in the right kind of buyer.

Worked example — a protein-powder brand:

100% 1st order     45% 2nd     30% 3rd     22% 4th     18% 5th     15% 6th
45% of customers came back for a second order. Of those,
most made a third — the curve flattens after that.
Translation: getting customers to the second purchase is
the campaign objective with the highest expected payoff.

Run the curve store-wide, by product, or per individual SKU
— useful when one hero product creates lifelong customers
and another doesn't. Pair it with  [multi-touch attribution](/solutions/attribution) to see which channels are actually bringing in the
repeat buyers.

Top spenders

## See exactly who your best customers are.

One toggle narrows your customer list to your top 20% of
spenders — the buyers carrying most of your revenue. Use
the segment for VIP outreach, referral asks, early-access
invites, or shipping-promo targeting. Sort and search work
on top of the filter, so "top 20% who bought from us in the
last 30 days" is one click away.

Offer Calculator

## Know whether that 25% off actually pays back.

Type in the discount and your costs. Ordinary uses your
store's real customer retention data — not industry
averages, not flat assumptions — to tell you whether the
promo will pay back, and after how many follow-on orders.
Green means ship it. Red means rethink.

Inputs

Product Hero SKU · $40   Discount 25%   COGS 34%   CAC $22
Verdict

First-order margin −$4.20   Expected repeat revenue $87.40   Pays back at order #2   Decision Profitable         Honest limits

## What the math floor doesn't model

- ### Cannibalization.  Some discounted customers would have bought at full price anyway.

- ### Brand equity.  Discounting too often trains customers to wait for sales.

- ### Competitor response.  A 25% discount on a low-differentiation product can trigger a race to the bottom.

The calculator gives you the math floor. The qualitative
judgment is still yours.

Go deeper

## Want LTV cut by acquisition campaign?

Lifecycle and cohort retention answer the question
store-wide. The next layer down is per-campaign:  [Google Ads cohort & LTV](/solutions/google-ads-cohort-ltv) shows you which Google campaigns acquire repeat buyers and
which acquire one-and-done tourists. Same first-party data,
sliced by where the customer came from.

## Catch them while there's still time.

Lifecycle stages light up the moment your first orders sync.

[Get started →](https://app.tryordinary.com/sign-up)