Next-Purchase Propensity predicts when a specific member is entering a buy decision, so the reorder nudge arrives while the rack is empty instead of on a fixed calendar date. It combines consumption cadence, depletion timing, and category affinity, and it works because triggered emails click near 5% versus roughly 1.5-2% for batch campaigns.
Consider what a calendar release actually assumes. It treats a few thousand members as if they share a single buying rhythm, and it picks a single Tuesday to interrupt them all at once. A handful will be ready to buy. Most will not. And a quiet, expensive slice will have run empty weeks ago and already refilled the rack somewhere else, in the exact gap your quarterly schedule left open.
That gap is where reorder revenue leaks. Not to a better wine or a lower price, but to a competitor whose message happened to arrive closer to the moment the member needed it. Timing, not quality, decided the sale. And timing is a signal you already have, sitting unused in your order history.
This is worth sitting with, because the usual response to soft reorder numbers is a discount, and a discount is the wrong tool for a timing problem. A member who ran out three weeks ago and already refilled does not need ten percent off; they needed to hear from you three weeks ago. A member who is fully stocked does not become more likely to buy because you shaved the price; you have simply trained your best buyers to wait for the markdown. When the real constraint is timing, price promotion spends margin to solve a problem it cannot reach. Predicting the window costs nothing but attention to data you already own, and it protects the price you worked to hold.
The Next-Purchase Window
Next-Purchase Propensity is the discipline of predicting when a specific member is entering a buy decision, and reaching them inside that window rather than on a broadcast date. Three signals carry it, and you collect all three today.
Consumption cadence. Every repeat buyer has an interval, the typical spacing between their orders, and it varies by format and price tier. A member who buys a case of everyday wine every eight weeks and a few reserve bottles twice a year has two distinct clocks. Cadence is the baseline: it tells you roughly how long a member’s purchase lasts them before the next one.
Depletion timing. Cadence only matters relative to where the member is inside it right now. A member two weeks past a purchase is not in a window; a member who is a week short of their usual reorder interval is. Depletion timing is the live position: it converts a static average into a this-week signal about who is approaching a decision and who just made one.
Category affinity. The third signal keeps the timing relevant. It reads what a member actually repurchases, the varietals, formats, and tiers that recur in their history, rather than what they clicked once and never bought. Affinity ensures the well-timed message is also the right message: the member entering a window for their regular Pinot hears about Pinot, not a blanket release of everything. It also protects you from the most common false signal in DTC, the browse that never becomes a buy. A member who clicked a reserve tier once but has only ever purchased everyday bottles is telling you where their curiosity is, not where their wallet is, and affinity keeps you from mistaking the first for the second.
Combined, these three answer a question a calendar can never answer: which members are in a buy window this week, and for what? That list is small, specific, and actionable, and it changes every week as members move through their intervals. Notice that all three signals are backward-looking and already in your possession. You are not buying new data or guessing at intent from a survey. You are reading the purchase history you already store and letting it tell you what it plainly knows: roughly when each member tends to buy, where they are in that rhythm now, and what they reliably reach for.
What Timing Produces
The counterintuitive part is that this is not more marketing. It is frequently less. A propensity-timed program often sends fewer total messages than a calendar program, because it stops interrupting members who are nowhere near a decision and concentrates on the ones who are.
The lift shows up in the numbers the platforms already publish. Triggered, behavior-based emails, the kind that fire when a member enters a buy window, typically earn click rates around 5 percent, against roughly 1.5 to 2 percent for one-off batch campaigns (Klaviyo, Email Benchmarks 2024; GetResponse, 2024). The gain comes from relevance and timing, not volume: the same offer, aimed at the moment it is useful, converts at a materially higher rate than the same offer broadcast to everyone on a fixed date. And because frequency holds or drops, you spend down less of your permission asset to get the lift.
There is a retention effect underneath the revenue one. A member who consistently hears from you right when they need to reorder learns that your messages are worth opening. A member who consistently hears from you two weeks late learns the opposite. Over a year, timing quietly trains your open rate in one direction or the other, and your open rate is the foundation every other campaign stands on. A well-timed program is not just selling more reorders; it is protecting the deliverability and attention that make the next release land at all.
The objection here is usually “we already send plenty of email.” That is exactly the point. Propensity timing is not permission to send more; it is a rule for sending the same volume to better-chosen recipients. In most programs it lets you retire a portion of the blanket calendar sends, the ones going to members nowhere near a decision, and reinvest that frequency into the members who are. The member with a full rack stops hearing from you about reorders they don’t need, which is its own form of respect, and the member about to run dry hears from you first. You are not adding to the noise; you are moving it to where it reads as service.
Putting It Into Practice
- For your top repeat buyers, calculate the median interval between their orders. That single number is a usable cadence baseline; you do not need a model to start.
- Flag members who are currently near or past that interval without a reorder. That is your live buy-window list this week.
- Match each flagged member to their most-repurchased category, and send the release or reorder nudge for that category, not a blanket announcement.
- Hold your calendar release as the control for one cycle, and compare conversion on the propensity-timed send against it. Let the numbers, not the theory, earn the change.
Two cautions keep this honest. First, cadence is a starting estimate, not a guarantee. A member’s interval shifts with seasons, gifting, and life, so treat the window as a reason to reach out, not a certainty to over-message against; if a flagged member doesn’t buy, that is information, not a failure. Second, resist the urge to widen the net. The value of a propensity list is its narrowness: twenty-five members genuinely near a decision will outperform a thousand-member blast, and the moment you loosen the criteria to feel productive, you are back to the calendar with extra steps. Precision is the product, and the discipline is holding the list small even when a bigger one feels busier.
This Week’s Action
Pull the 25 members who last ordered closest to one full cadence-interval ago. They are, statistically, your most likely buyers this week. Send them a category-matched message now and tag it. When you compare its conversion to your last calendar send, the difference is the size of the timing leak you have been paying for all year. Do this for three cycles before you judge it: one send can flatter or disappoint on luck, but three will show you whether the window is real. It almost always is, because you are no longer guessing when your members buy; you are reading the record of when they already have.
P.S. The fastest version of this needs no new tools: one saved segment of “members past their median reorder interval,” refreshed weekly, matched to category. It is the same custom-segment work you already do by hand for releases, pointed at timing instead of a date. The members it surfaces are the ones a competitor is otherwise happy to reach first.
