SimpleSense reads 12 months of orders and returns and shows you the serial refunders, the bracketing patterns, and the three SKUs driving half your return bill — plus the ranked list of moves the data says to make this week.
Built for apparel and footwear brands doing $2–20M. Flat price. No % of your GMV, no % of "recovered revenue."
Every figure below is computed by SimpleSense’s real analysis pipeline on a synthetic demo store, not hand-written — the same pipeline that reads your own data.
The 2% eating the 98%
1.5% of customers generate 8.7% of return dollars ($124,836/yr) → move this named cohort to inspection-first refunds; leave instant refunds on for everyone else.
Bracketing tax
9% of jacket orders are multi-size same-style with systematic returns → size-guide intervention + "fit promise" exchange flow on Trail Runner Jacket before you touch policy.
Three SKUs, half the bill
51.7% of return dollars come from 3 SKUs, and the reason text clusters on SIZE_TOO_LARGE → fix the product page, not the customer.
The free plan shows your top moves. Basic unlocks the full ranked list, geo + Pareto detail, and segment exports.
Connect Shopify in one click. Simple Sense ingests your full order, customer, and product history — 3–5 years — read-only.
Deterministic analyzers surface the non-obvious — geographic concentration, under-served VIPs, the SKU losing money.
The few highest-ROI moves land in one read, ranked by expected impact — the pattern, why it matters, exactly what to do.
Is this going to tell us to punish customers?
No — outputs are review cohorts, never an auto-deny list. False positives punish good customers, so every flagged cohort is for manual review, not automated action.
What exports do you need and how long does it take?
Two CSVs — 12 months of order and return exports. First pass is typically the same week.
We use Loop/Returnly — does this replace it?
No — those platforms process returns. SimpleSense tells you what your returns mean: who the serial refunders are, which SKUs are the real problem, and what policy change actually addresses it.
What about exchanges vs. refunds?
Both are read from your return export and reflected in the analysis — an exchange isn’t treated identically to a refund in the underlying data.
Flat price vs. a % of recovery — why?
A recovery-percentage fee creates an incentive to over-flag borderline customers. A flat price keeps the incentive aligned with getting the analysis right, not maximizing flags.
Built by an operator who has run stores like Nike and JCPenney.