Chargeback Cohort Analysis: Compare Orders at the Same Age

Recent orders have had less time to generate a chargeback than older orders. If you compare their observed outcomes without allowing for that difference, the newest group can appear safer simply because it is younger. Build an equal-age cohort table before drawing conclusions about improvements in order quality.

The table should follow original purchases and measure what was observed within a defined period after each one. This is an internal research view. It does not replace the provider's monitoring calculation or predict that no further cases can occur after the chosen window.

Define the purchase group and observation clock

Choose a stable purchase event, such as the original order creation time for a clearly defined population of completed purchases. Document any exclusions and keep them consistent across cohorts. If your business question requires captured transactions instead, use that population deliberately and name it accurately.

Record the original event time, the cohort month, the linked dispute event time and the data cutoff. Use a consistent timezone and age calculation. Do not replace the original purchase date with a later order edit or the date a case was imported into your reporting system.

For an order-based measure, decide whether the outcome is “at least one dispute observed by this age.” Deduplicate multiple linked cases accordingly. If you also need a case count, show it as a separate measure because one order can contribute differently to those two questions.

Shopify's chargeback-process guide explains the broader dispute lifecycle. Your observation windows are analytical choices within that lifecycle, not universal filing deadlines.

Mark observations that have not matured

Suppose the hypothetical data cutoff is 6 September 2026. All June orders have reached an age of 60 days, but the full July cohort has not. The August cohort has not even reached 30 days for every order. A blank immature cell is more honest than treating cases not yet observable as zero.

The following fictional table measures distinct orders with a dispute within the stated age window:

For this table, a monthly cell is shown only after every order in that cohort has reached the target age. This simple rule avoids comparing a complete month with a selective subset of its earliest orders. A more advanced partial-cohort method can be useful, but it needs its own explicit eligibility and weighting approach.

Do not fill the unavailable cells with the cases observed so far and then format them like final equal-age values. That makes the table look complete while changing the measurement between rows. Use a distinct label and explain the next date at which the cell can be evaluated.

Read only the comparisons the table supports

The hypothetical June and July cohorts can be compared at 30 days: six versus eight disputed orders out of equally sized populations. The table does not yet support a 60-day comparison between those months. It also does not prove that July is intrinsically worse; product mix, campaign promises and sampling variation may still matter.

Imagine that an unadjusted snapshot shows 12 observed disputed orders from June, nine from July and two from August. Calling August the safest month would confuse time at risk with performance. The equal-age table makes the missing observation time visible before that conclusion is repeated in a meeting.

Keep the event being counted consistent. If the measure concerns dispute initiation, do not later remove a case because its outcome changed. A separate outcome table can follow wins, losses or other finalized states, with its own definition and maturity treatment. Combining incidence and outcome into one shifting count makes historical comparisons difficult to interpret.

Shopify's monitoring documentation describes its official account-health view. Keep that provider reporting alongside the cohort analysis and label the distinction clearly. Your chosen order-age window is not a new definition of the provider's monitoring ratio.

Make the analysis reproducible

Save the cohort definition, cutoff, age-window rule and relationship used to connect disputes to purchases. Preserve unmatched cases in a visible exception count. An apparently low cohort incidence is not reliable if a changing share of cases cannot be linked to their original orders.

Use a review checklist before sharing the table: original purchase dates are stable; populations use the same inclusion rule; disputed orders are deduplicated; unavailable observations are labeled; currencies are not mixed in any accompanying value measure; and the analysis cutoff is printed beside the results.

When new data arrives, extend the same table rather than silently replacing the earlier snapshot. This allows the team to see how immature observations developed and whether earlier interpretations were justified. Correct genuine source errors with a note explaining what changed.

The useful output is a supported investigation decision. An equal-age difference may justify reviewing a product cohort, a fulfillment change or a campaign promise. It should not become a confident causal claim before those alternatives have been examined. Comparing orders at the same age removes one major distortion and gives that next investigation a firmer starting point.

Review your current dispute status in Lower Chargeback while you build the cohort analysis separately.

Review your disputes

Related reading in this collection:

• How to Calculate Your Shopify Chargeback Rate: Count and Value Explained
• Which Products Drive Disputes? A Fair SKU-Level Analysis
• Are International Orders Riskier? Audit the Comparison First