Solution · Customer analytics
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.
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.
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. |
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:
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 to see which channels are actually bringing in the repeat buyers.
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.
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
Verdict
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.
Catch them while there's still time.
Lifecycle stages light up the moment your first orders sync.