The Klaviyo Replenishment Flow That Lands Before They Run Out
Your customer's coffee runs out on its own schedule, set by how fast they use it. This flow sends the refill nudge just before empty, timed to each product's own cycle.
Replenishment runs on the customer's clock
Consumables run out on a schedule.
Three steps, timed to the product.
- From order history
Find each product's cycle
The cycle is the gap between one customer's reorders of the same product. Find it in your order history. Group orders by product. Measure the days between each customer's repeat purchases. Take the median gap across customers: that is the product's default cycle. This is ordinary spreadsheet work: one median per product. Every product gets its own number, because coffee and moisturizer run out on different clocks.
- Just before empty
Set the nudge before empty
Send the first nudge just before the predicted empty date. Operator guides such as Klaviyo's replenishment writeup and BS&Co (linked in Sources) offer the rule of thumb: a product that lasts about 30 days gets its nudge around day 25, and a 75-day cycle gets its nudge roughly 10 days before the predicted date. Treat those as starting points. Calibrate to your own reorder data.
- After the nudge, then quiet
Send one follow-up, then stop
If the first nudge gets no order, send one follow-up 3 to 5 days later (same sources). Then stop. A customer who lets the cycle pass has answered you. A third reminder will not change that answer, and it costs attention you will want later.
What Klaviyo already predicts for you
Klaviyo predicts an expected date of next order for every customer who has ordered. It gives the flow a per-customer clock with no spreadsheet work, built from that customer's own purchase pattern. A one-time buyer has no personal pattern yet, so Klaviyo predicts that customer's date from how all your customers behave (per Klaviyo's docs, linked). Where the prediction exists, use it as the flow's default clock.
Klaviyo's own documentation names this limit. In Klaviyo's words, the prediction "doesn't consider what products the customer ordered," so it cannot tell which item is running low or how long that item lasts. For a catalog with distinct replenishment cycles, Klaviyo's docs recommend per-product flows instead: a Placed Order trigger filtered to the products that share a cycle, with a time delay set to that cycle. Those per-product flows are what this page builds.
The missed date is your earliest churn signal
When the expected date passes with no order, that customer has told you something months before recency metrics would show it. Act on both reads. The customer who reorders like clockwork is your strongest subscription candidate: offer the subscription as a convenience they can cancel anytime. The customer who misses the date should not sit and wait for lapse. Route them straight to the winback ladder.
Two numbers, read per product.
On-time refill conversion
Track the share of nudges that convert to a reorder before the predicted empty date. Track the gap between orders next to it. If the median gap holds steady while refill conversion climbs, the nudge lands before empty instead of borrowing orders customers would have placed anyway. Read both monthly.
Subscription opt-ins
Watch subscription opt-ins as the secondary read. A customer who keeps reordering on cycle is a strong candidate for the subscription, offered as a convenience. If opt-ins rise while refill conversion holds, you are converting your cycle-reliable customers into subscribers without cannibalizing the refill numbers.
Cycles come from order history. Value tiers come from scored buckets. Score the list first with the RFM teardown: the flow reads the customer properties that teardown writes back. Then check the work in the segment migration view. Refills hold recency up, and the migration view is where you watch it hold, month over month.
Replenishable products on a campaign calendar
When we read a consumable brand's program cold, the most common finding is replenishable products marketed on a campaign calendar, with no flow watching the cycle. That read is part of every Blueprint: your cycles, on your data. Book the fit call if you want us to run it for you, or build the flow straight from this page. Either way, the cycles and the flow are yours to keep.
What operators ask
What about customers with only one order?
Use the product's median cycle as the fallback. A single order gives no personal cycle, so the product's rhythm stands in until a second order personalizes the clock. That fallback is the same number step one produced. Klaviyo makes the same move: for a one-time buyer, it predicts the expected date from how all your customers behave (per Klaviyo's docs).
How many reminders before it is nagging?
Two. Send the nudge just before empty and one follow-up a few days behind it, then stop. A customer who watched the cycle pass has answered, and a third refill reminder will not change the answer. From there the missed cycle belongs to the winback ladder.
Does this play work outside consumables?
Weakly. Care products, refills, and accessories have soft cycles the flow can read. Furniture does not. Test it in your own history: pull the reorder gaps per product, and if no rhythm shows, do not build this play. Spend the effort on a flow that fits how your customers actually buy.
Build the flow yourself, or bring us your reorder gaps
This page holds everything you need to build the flow in your Klaviyo this week. Or book the fit call and we read your reorder cycles as part of the Blueprint: $2,500, credited, built in your account, and yours if we ever part ways.
More from this series: the lifecycle playbook the RFM teardown segment migration the winback ladder
Sources
- Klaviyo: the replenishment flow for consumable goods brands
- BS&Co: time the replenishment flow with your data
- Klaviyo: predictive analytics and the expected-date field
- Concomitant: the RFM scoring teardown (where the scored buckets come from)
Timing rules of thumb above are third-party operator guidance. They vary by product, price point, and purchase cycle; calibrate every one to your own reorder data.