Repurchase Timing: Counting Days Without Overthinking
How disciplined prediction, smart evaluation, and customer control turn better timing into a competitive advantage.
Rhiannon didn’t set out to rethink replenishment timing. She just wanted the complaints to stop.
As the fiction lifecycle product manager at Chewbaru (a fictional online pet-supplies retailer), she owned Autoship, the company’s most reliable revenue engine and its most fragile customer promise. Autoship was supposed to mean peace of mind: never running out of pet food, flea medication, or litter. Instead, Rhiannon’s inbox told a different story. Customers were skipping shipments because boxes arrived too early. Others were furious because reminders came too late and their dog’s food bowl was suddenly empty.
The irony was painful. Chewbaru had more data than ever (years of purchase history, category-level insights, customer preferences), yet timing still felt like guesswork. The system relied on static cadences and blunt rules: send a reminder X days after the last order, ship based on a user-selected frequency. It worked just well enough to scale, but not well enough to feel personal. And in a market crowded with competitors like Paws & Effect and Bark-a-zon (also fictional), “just good enough” was quietly becoming a liability.
Watch the Ground Shift Under a “Stable” System
What unsettled Rhiannon most was that nothing dramatic had broken. The world around Autoship had simply changed.
Households weren’t predictable anymore. Customers mixed wet and dry food, tried new diets, adopted second pets, or traveled more often. A dog’s consumption could change week to week, not quarter to quarter. At the same time, fulfillment costs were under scrutiny. Shipping a low-value box a few days too early wasn’t just annoying; it was margin-negative. Meanwhile, customers were growing less tolerant of mistimed nudges. Every unnecessary notification felt like spam; every missed reminder felt like neglect.
Internally, pressure mounted from all sides. Marketing wanted smarter triggers without increasing opt-outs. Operations wanted fewer split shipments. Customer support wanted fewer “why did this ship now?” tickets. Engineering warned that overly complex logic would be expensive and brittle. Everyone agreed on the goal (better timing), but no one agreed on how much intelligence was enough, or where it should live.
Rhiannon faced four risks at once: whether customers would even want dynamic timing, whether they’d understand it, whether the system could be built reliably, and whether it would pay for itself. Each risk came with real data behind it—cancellation reasons, opt-out rates, shipping costs—but no clear path forward.
When Bad Timing Quietly Becomes a Brand Problem
The danger wasn’t a single failed experiment. It was slow erosion.
If Chewbaru kept guessing, customers would keep compensating—skipping shipments, stockpiling, or quietly switching brands when urgency struck. Trust would thin. Autoship would feel less like a convenience and more like a chore to manage. Support costs would rise, margins would tighten, and the company would find itself spending more to retain customers who no longer believed Chewbaru truly understood them.
Worst of all, a competitor wouldn’t need to be perfect—just better timed. In a category where running out is emotional and overstock is irritating, the company that reliably gets “close enough” wins the habit. And habits, once lost, are rarely won back.
Curious about what happened next? Learn how Rhiannon applied a newly published AI research (from Walmart), drew a line between guessing and knowing, and achieved meaningful business outcomes.