A guided tutor that teaches you to choose between 30, 60, or 90 days without a purchase based on each product's cycle, instead of using one number for everything. You apply it to a real product type and end up with a list of who has already passed that mark.
This tutor doesn't sort out which customers stopped buying for you. It teaches you to pick, for each product type, how many days without a purchase already deserve attention (30, 60, or 90), instead of applying the same number to everything the store sells. It asks what the real buying cycle for that product is, whether the store has each customer's last purchase date on record, and what window it uses today.
From the answer, the tutor finds where the problem sits: a missing last-purchase date, a fixed window that doesn't match the product's cycle, or a store that already picks the right number and flags who passed it. If the store picks a number without being able to explain why that product has that cycle, the tutor asks for the reasoning before moving on.
You leave the session with one task: pick a real product type, decide between 30, 60, or 90 days based on its buying cycle, and check last-purchase dates to build a list of who has already passed that mark. The tutor is upfront that there's no ready-made feature to build that list on its own yet. Sorting it out, for now, is manual, in a list or a note.
You start by answering one question at a time: what's the buying cycle for this product type, whether each customer's last-purchase date is on record, and what no-purchase window the store uses today for this product (30, 60, 90 days, or none). The tutor uses your answers to figure out if the problem is missing records or the wrong number.
It compares the logic to an oil change: change it too early and you waste good oil, change it too late and you damage the engine, and the right point depends on the car, not a fixed number of miles. The same way, a short-cycle product calls for 30 days, a medium-cycle one for 60, and a longer-cycle one for 90. If the store uses 60 days for everything, including fast-turnover products, the tutor shows why that leaves the store noticing who stopped far too late.
At the end, it asks the store to walk back the reasoning in its own words: this product has a cycle of this length, so this is the window that matters, and a smaller number would catch people who were still going to come back while a bigger one would take too long to notice. When the store returns, the tutor asks which window it picked for that product and who showed up on the list, fixing only the number or the date reading, without repeating the whole lesson.
Real examples of how this skill fits into a store’s routine to speed up decision-making and sales.
A lipstick lasts months, but a daily facial moisturizer runs out in a few weeks, and a cosmetics store still uses 90 days without a purchase as the warning sign for both. The tutor asks about each product type's buying cycle separately and teaches how to pick a different number for each one. The store starts noticing fast who stopped buying moisturizer, instead of waiting the same 90 days that only make sense for lipstick.
A bottle of supplements or vitamins lasts around a month, but the store only counts a customer as stopped after 90 days without a purchase. The tutor shows that this window is too long for the product's cycle: the customer should have come back two months ago. Adjusting to 30 days, the store notices who's gone right after the first missed cycle, instead of finding out much later.
A toy store owner wants to know who stopped buying, but has no record anywhere of each customer's last purchase date, just a few cases remembered off the top of the head. The tutor spots this first and explains that without that date written down, no window can be applied reliably. The store learns the step that comes first: recording the date of every sale before trying to build any list.
A phone and accessories store uses 30 days without a purchase as the warning sign for every customer, the same number it would use for a fast-turnover product. Since this product type has a much longer buying cycle, most of the customers flagged as stopped had no real reason to buy again yet. The tutor teaches how to stretch the window to 90 days in this case, cutting down the number of customers flagged for nothing.
The buying cycle for kids' shoes changes over time: a younger child goes up a size faster than an older one. Even so, the store uses one fixed window for every customer buying this product type. The tutor teaches that the reference cycle should match the product type, worked out realistically for that age range, instead of one number that only fits part of the cases. The store adjusts the window to reflect that cycle more accurately.
Most of a stationery store's customers only buy school supplies during a certain time of year, but the store applies 60 days without a purchase to this product type all year round. That makes customers who buy off-season, and are still within a normal cycle, show up as stopped for no reason. The tutor helps the store recognize that this product's cycle isn't constant, and that the window needs to account for that bunching before flagging anyone.
Before, you applied one fixed number of days without a purchase to every product type, from ones that last weeks to ones that last months. After choosing the right window for each cycle, the store ends up with a standard that reflects how each product is actually bought, instead of one generic number used for everything.
When the window is too short for the product's cycle, you end up marking as stopped someone whose time to buy again hasn't even arrived. Adjusting the number to the real cycle, the store cuts these false alarms and puts effort into people who genuinely should have come back by now.
When the window is too long, you only notice a customer stopped long after that customer has already forgotten your store. With the window matched to each product's cycle, the store catches that drop-off earlier, while reaching back out still makes sense.
When you try to apply the window, you may discover you have no last-purchase date recorded anywhere for a good share of your customers. This tutor exposes that gap before the store tries to build any list, and points the focus toward fixing the record first.
Free · No installation · No setup
Pick your store type, walk away with the finished result.
Learn how to chooseRetain · Keep
Use your product list or sales history to know when to reach out to each customer again, what to offer, and what message to send on WhatsApp.
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A guided tutor that helps you find out, from one real purchase, which of the three parts of the repeat-purchase process is missing: care after the sale, contact with a real reason, or a next offer at the right time. You walk away knowing which door is closed and which tutor to go to next.
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A guided tutor that teaches you to reach out after delivery, log what the customer says back, and notice when the same problem already showed up with someone else. You work through one real delivery and finish that full cycle, without relying on an automatic feature that does not exist yet.