3 Churn Signals You Can Catch in a Spreadsheet

An inactivity rule can fire weeks after the useful retention window closes. Better churn signals appear in a customer’s order history: purchase intervals stretch, category breadth narrows, baskets contract.

Catch those changes with an order export and spreadsheet. Use the thresholds below as starting heuristics, not universal truths. Run them for 4–6 weeks, inspect false positives by category, then adjust. The goal is an actionable weekly list, not a prediction score.

Churn Signals Should Measure Change, Not Inactivity

A 90-day inactivity rule treats a weekly buyer and a twice-yearly gift buyer as equivalent. They are not. One may be lost by day 30; the other may be behaving normally at day 120.

Build personal baselines for customers with at least three completed orders. Compare their latest 2–3 orders with the prior 3–5 where history permits. Three orders provide a usable minimum, not a reliable law; sparse histories deserve lower confidence.

Your export needs only customer ID, order date, order value, units, and product category. Group products into 5–12 categories that represent distinct customer needs. Excessive merchandising detail creates fake breadth: shampoo and conditioner probably belong together, while haircare and supplements do not.

The trap: scoring every customer against one storewide average. Personal change matters more. Cohort medians remain useful as a fallback for new repeat buyers, but separate replenishment, seasonal, subscription, and gift-heavy customers before applying them.

Use Three Spreadsheet Churn Signals

These three rules cover timing, dependence, and spend. None should trigger an automatic discount alone. Use them as diagnostic flags, then contact customers according to the behavior that changed.

• Purchase interval: calculate the median of the previous 3–5 gaps between orders. Start flagging when days since the last order reach 1.5 times that baseline. Minimum useful history: three intervals. Exclude known seasonal buyers. Test a replenishment reminder or reorder link within 3–7 days of the flag.
• Category narrowing: compare categories bought across the latest 2–3 orders with the earlier 3–5. Start flagging customers who previously repeated purchases across at least three categories but now buy from one. Exclude gifts, trials, and categories bought only once. Test recommendations tied to a genuinely dropped need.
• Basket contraction: compare median units or order value across the latest 2–3 purchases with the earlier 3–5. Start with a 20% decline threshold. Exclude returns, stockouts, split shipments, and unusually large promotional orders. Test bundles, saved products, or one direct question about what changed.

The interval threshold deserves particular care. For a customer who normally orders every 20 days, 1.5 times baseline means a flag around day 30. For a 45-day buyer, it means roughly day 68. Review results after 4–6 weeks; high-frequency categories may need 1.3 times baseline, while volatile categories may need 1.7 or 2 times.

Use the median rather than the average. One delayed holiday purchase can distort an average for months. In the sheet, divide days since last order by baseline interval, then filter ratios at or above your test threshold every Monday.

For basket contraction, units often outperform revenue when inflation, discounts, or price changes distort order value. Revenue may work better when product mix matters more than item count. Track both if clean data already exists; do not postpone the first review to rebuild your catalog.

The trap: treating one lower order as a verdict. A customer may split an order, use fewer discounted items, or buy a cheaper refill. Require contraction across 2–3 purchases, or pair it with another signal before intervening.

Combine Two Flags Before Spending Margin

Act when two of the three signals fire together. This starting rule suppresses obvious noise while identifying customers whose relationships are weakening across more than one dimension.

Match the message to the evidence. A stretched interval calls for timing help: replenishment, saved favorites, or a low-friction reorder path. Category narrowing calls for relevant discovery. Basket contraction calls for a service, assortment, delivery, or value diagnosis.

Do not lead with 20–25% off. A coupon cannot fix missing stock, inconvenient delivery, declining product quality, or a need that disappeared. It can also train an otherwise healthy customer to wait for the next offer.

Keep the first process manual. Size the weekly list so one person can review it in a single sitting once exclusions are visible — 25–50 flagged customers is a sensible place to start. Check recent support contacts, returns, subscriptions, stockouts, and campaign history before sending anything.

The trap: automating the first draft of the rules. Bad thresholds produce more messages, not more retention. Keep human review for 4–6 weeks; automate only after most flags produce plausible cases and clear treatments.

Calibrate Churn Signals Against Outcomes

Measure flagged customers for the next 30–45 days. Record whether they purchase, how quickly they return, whether their interval moves toward baseline, and whether category breadth or basket size recovers.

Where volume permits, withhold outreach from 5–10% of flagged customers. This is a practical starting range: large enough to expose whether treatment beats natural recovery, small enough to keep the cost of withholding outreach tolerable. Low-volume businesses can compare successive flagged cohorts, though seasonality makes that evidence weaker.

Review thresholds by category after 4–6 weeks. If many flagged customers return without intervention, raise the threshold or add an exclusion. If known repeat buyers disappear before being flagged, lower it. Keep separate settings only where behavior differs materially; dozens of micro-rules become impossible to operate.

Track three operating numbers: flagged customers, contacted customers, and incremental repeat purchases. Also record discount cost. A campaign that lifts orders while giving margin to customers who would have returned anyway is not a retention win.

The classic failure: waiting for a predictive model while obvious behavioral deterioration sits unused in the order export. Three calibrated rules can run this week. A model earns its place later, once message volume, customer value, and treatment complexity exceed what a weekly review can manage.

Early detection supports cheaper interventions than late-stage recovery. Once a customer has crossed the inactivity threshold, use the sequencing in Win-Back Emails That Actually Win: A Lifecycle Playbook; before then, let combined churn signals trigger the smaller, more relevant action.