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What is the average ecommerce return rate?电商平均退货率是多少？
The latest public U.S. benchmark identified as of September 15, 2026 is an estimated 19.3% of online sales returned in 2025. The figure comes from the National Retail Federation and Happy Returns’ 2025 Retail Returns Landscape. It is a survey-based estimate from large U.S. merchants—not a 2026 observed rate, transaction census, universal target, or category-specific benchmark.
截至 2026 年 9 月 15 日，本页识别到的最新美国公开基准是：2025 年线上销售预计有 19.3% 被退回。该数字来自美国零售联合会与 Happy Returns 的《2025 Retail Returns Landscape》。它是大型美国商家的调查估计值，并非 2026 年实测值、全量交易统计、通用目标或某一品类基准。
This article is for ecommerce operators, finance leaders, merchandisers, and analysts who need a planning reference or want to judge whether their own rate is unusual. It covers the U.S. market benchmark, its source and sample, why published answers differ, and a like-for-like comparison method. It does not declare one “healthy” rate across product categories.
本文面向需要规划参考或判断自身退货率是否异常的电商运营、财务、商品与分析团队。内容涵盖美国市场基准、来源与样本、公开答案为何不同，以及同口径比较方法；本文不会把一个数字定义为所有品类通用的“健康值”。
Where the 19.3% ecommerce return-rate estimate comes from19.3% 电商退货率估计来自哪里
The National Retail Federation’s October 15, 2025 release says NRF and Happy Returns conducted two U.S. surveys in summer 2025. The consumer survey included 2,006 people who had returned at least one online purchase in the prior 12 months. The merchant survey included 358 ecommerce professionals working for large U.S. merchants with more than $500 million in revenue across multiple verticals.
美国零售联合会 2025 年 10 月 15 日发布的信息显示，NRF 与 Happy Returns 在 2025 年夏季开展了两项美国调查。消费者调查包含 2,006 名在过去 12 个月至少退过一次网购商品的人；商家调查包含 358 名来自多个行业、任职于年收入超过 5 亿美元的大型美国商家的电商专业人士。
The published report frames the merchant question as the percentage of sales types expected to be returned. That wording matters: 19.3% is a retailer estimate about online sales, not a count created by pooling every order line from every U.S. merchant. The large-merchant sample also means a small direct-to-consumer brand should treat it as market context rather than its default target.
公开报告中的商家问题询问各类销售预计有多少比例会被退回。这个措辞很重要：19.3% 是零售商对线上销售的估计，并不是汇总全美国所有商家每一条订单行得到的统计。由于样本来自大型商家，小型 DTC 品牌应把它视为市场背景，而不是默认目标。
Read the 2025 benchmark numbers without mixing them正确阅读 2025 年基准数字
| Figure数字 | Scope范围 | Evidence type证据类型 | Correct use正确用途 |
|---|---|---|---|
| 19.3% | U.S. online sales, 20252025 年美国线上销售 | Merchant survey estimate商家调查估计 | Headline ecommerce context电商整体背景 |
| 15.8% | All U.S. retail sales, 20252025 年美国全部零售销售 | Merchant survey estimate商家调查估计 | All-channel retail comparison全渠道零售对照 |
| $849.9B | Total U.S. retail returns, 20252025 年美国零售退货总额 | Industry projection行业预测 | Market-size context, not a store KPI市场规模背景，不是商店 KPI |
| 17% | Holiday sales expected returned, 20252025 年节日销售预计退回 | Merchant expectation商家预期 | Seasonal planning only仅用于季节规划 |
| 21% higher | Online versus overall rates in a separate October 2024 NRF study另一项 NRF 2024 年 10 月调查中线上相对整体的差距 | Relative difference, not a 21% rate相对差异，不是 21% 退货率 | Channel-direction context渠道方向参考 |
These values answer different questions. The online rate cannot be subtracted from the all-retail rate and interpreted as a causal channel effect. The dollar total cannot be divided by your unit sales. The “21% higher” statement is a relative comparison from another study, not an absolute online return rate of 21%.
这些数值回答不同问题。不能把线上退货率减去全零售退货率后解释为渠道的因果影响；也不能把行业退货总额除以自己的商品件数。“高 21%”来自另一项调查，是相对差异，不表示线上退货率就是 21%。
Why average ecommerce return rates range from 15% to 30%为什么电商平均退货率会出现 15%–30% 的范围
The disagreement is often methodological rather than factual. Some pages quote retailer survey expectations; others divide aggregate returned merchandise value by estimated online revenue; others summarize selected client portfolios or high-return categories. A percentage can be mathematically correct for its dataset and still be unsuitable for your comparison.
这些差异通常来自方法，而不一定是谁算错了。有的页面引用零售商调查预期，有的用退回商品总金额除以线上收入估计，还有的汇总特定客户组合或高退货品类。一个百分比在自己的数据集中可能计算正确，却仍不适合与您的业务比较。
| Difference差异来源 | Possible versions可能口径 | Risk when mixed混用风险 |
|---|---|---|
| Denominator分母 | Orders, units, gross sales value, net sales value订单、件数、总销售额、净销售额 | Same store produces different rates同一商店会得到不同退货率 |
| Return event退货事件 | Requested, authorized, shipped, received, refunded申请、批准、寄回、收货、退款 | Open or cancelled requests inflate or shift timing未完成或取消申请会放大数值或改变时间 |
| Population样本总体 | Large retailers, DTC brands, one platform, selected clients大型零售商、DTC 品牌、单一平台、特定客户 | Merchant size and mix change the result商家规模与结构改变结果 |
| Scope范围 | U.S., global, one category, all categories, holiday only美国、全球、单品类、全品类、仅节日 | Local policy and product mix disappear本地政策与商品结构被掩盖 |
| Time basis时间基准 | Return month, sale cohort, rolling window, annual estimate退货月、销售群组、滚动窗口、年度估计 | Recent sales may not have matured近期销售可能尚未成熟 |
For example, Capital One Shopping’s secondary analysis reports 24.5% of 2024 online sales revenue returned using aggregate values. That is a different year and construction from NRF’s 2025 merchant estimate. This page keeps the NRF figure as the headline because its publisher, sample, question, and limitations are directly disclosed; it records the 24.5% figure only to explain the SERP conflict.
例如，Capital One Shopping 的二次分析根据汇总金额计算出 2024 年线上销售收入有 24.5% 被退回。它与 NRF 的 2025 年商家估计在年份和构造方法上都不同。本页把 NRF 数字作为主答案，是因为其发布方、样本、问题和限制均有直接披露；24.5% 只用于解释搜索结果为何冲突。
Match the benchmark to your return-rate formula让基准与自己的退货率公式一致
Before comparing any average, name your own metric. A unit return rate measures physically returned units divided by eligible fulfilled units. An order return rate measures orders with at least one physical return divided by eligible fulfilled orders. A value return rate measures returned merchandise value divided by eligible merchandise sales value. The three support different decisions.
比较任何平均值前，先明确自己的指标。件数退货率是实体退回件数除以符合口径的履约件数；订单退货率是至少包含一件实体退货的订单除以符合口径的履约订单；金额退货率是退回商品金额除以符合口径的商品销售额。三者支持不同决策。
Shopify’s current sales-report documentation defines returned quantity as units physically returned and returned quantity rate as returned quantity divided by ordered quantity. It distinguishes physical returns from broader sales reversals, which may include refunds, cancellations, or order edits. Use the full return-rate calculation guide to define your numerator, denominator, exclusions, and sales-cohort timing before benchmarking.
Shopify 当前销售报告文档把 returned quantity 定义为实际退回件数，把 returned quantity rate 定义为退回件数除以订购件数，并区分实体退货与可能包含退款、取消或订单编辑的广义销售冲回。比较基准前，应使用完整的退货率计算指南定义分子、分母、排除项与销售群组时间。
Historical U.S. retail return rates are not an ecommerce series美国零售历史退货率并不是电商时间序列
The 2025 NRF report also reproduces annual return rates for all U.S. retail. They provide context for how the broader market estimate has changed, but they should not be labeled “average ecommerce return rate.” The report explicitly notes that its 2023 study used a different method and is not directly comparable.
NRF 2025 报告还列出了美国全零售年度退货率，可用于理解整体市场估计如何变化，但不能标成“电商平均退货率”。报告明确指出，2023 年研究采用不同方法，不能直接比较。
| Year年份 | All-retail annual return rate全零售年度退货率 | Reported total return value报告退货总额 | Comparability note可比性说明 |
|---|---|---|---|
| 2019 | 8.1% | $309B | All retail全零售 |
| 2020 | 10.6% | $428B | All retail全零售 |
| 2021 | 16.6% | $760.8B | All retail全零售 |
| 2022 | 16.5% | $816.8B | All retail全零售 |
| 2023 | — | — | Different method; omitted by the report方法不同；报告未列可比值 |
| 2024 | 16.9% | $890B | All retail全零售 |
| 2025 | 15.8% | $849.9B | All retail estimate全零售估计 |
Do not infer a smooth ecommerce trend from this table. The channel scope is broader, the 2023 break is material, and a change in estimated industry return value reflects both the rate and the sales base. For an operational trend, compare your own mature, consistently defined ecommerce cohorts and use the external series only as context.
不要从这张表推导平滑的电商趋势。它的渠道范围更广，2023 年存在方法断点，而且行业退货总额变化同时受退货率和销售基数影响。运营趋势应比较自己成熟且定义一致的电商销售群组，外部序列只作为背景。
Use a ten-field test before calling a benchmark comparable使用十字段测试判断基准是否可比
- Year and observation period. Record when sales occurred, when returns were observed, and whether the figure is an estimate, projection, or completed measurement.年份与观察期间。记录销售发生时间、退货观察时间，以及数字属于估计、预测还是完成后的实测。
- Market and merchant population. Match country, retailer size, business model, platform mix, and customer population.市场与商家总体。匹配国家、零售商规模、商业模式、平台结构与客户总体。
- Channel and category. Separate owned-site, marketplace, and store purchases; compare the same product category and taxonomy depth.渠道与品类。区分自营站、平台与门店购买，并比较同一商品品类和分类层级。
- Numerator and event stage. Match requested, authorized, shipped, received, or refunded returns, and state how exchanges and returnless refunds are treated.分子与事件阶段。匹配申请、批准、寄回、收货或退款阶段，并说明换货与无退货退款的处理。
- Denominator and unit. Match orders, units, or sales value, along with discounts, tax, shipping, currency, cancellations, and test orders.分母与单位。匹配订单、件数或销售金额，并统一折扣、税费、运费、币种、取消与测试订单。
- Cohort maturity and return window. Ensure both rates give sales enough time to generate returns under comparable policies.群组成熟度与退货窗口。确保两个比率都让销售在可比较政策下有足够时间产生退货。
Decision rule: if any material field differs or is unknown, label the external rate “directional context,” not “comparable benchmark.” This protects the analysis from false precision.
决策规则：如果任何重要字段不同或未知，应把外部数字标为“方向性背景”，而不是“可比基准”，避免制造虚假精确。
Worked example: why 14.5% may not beat a 19.3% average示例：为什么 14.5% 不一定优于 19.3% 平均值
Synthetic store example: these store values were created only to demonstrate the comparison test. They are not customer results.模拟商店示例：这些商店数值仅用于演示比较测试，不是客户结果。
Change versus matched prior cohort = 14.5% − 12.8% = +1.7 percentage points
Relative increase = 1.7 ÷ 12.8 × 100 = 13.3%
The store rate is numerically lower than 19.3%, but the comparison is blocked: the store uses received units from a mature DTC cohort, while the NRF number is a large-merchant estimate expressed as a share of online sales. Category mix, merchant size, denominator, event stage, and sampling differ. The matched internal comparison—14.5% versus 12.8% using the same definition—shows deterioration and deserves investigation even though the store is below the headline market figure.
商店数字表面上低于 19.3%，但这项比较应被阻止：商店使用成熟 DTC 群组的实际收货件数，而 NRF 数字是大型商家以线上销售占比表达的估计。两者的品类结构、商家规模、分母、事件阶段与抽样方式均不同。使用相同定义的内部匹配比较——14.5% 对 12.8%——显示表现变差，因此即使低于市场头条数字，也值得调查。
The CSV records source, market, sample, merchant size, channel, category, numerator, denominator, event stage, cohort basis, rate, evidence type, and comparability decision.
CSV 用于记录来源、市场、样本、商家规模、渠道、品类、分子、分母、事件阶段、群组基准、比率、证据类型与可比性结论。
Download CSV worksheet下载 CSV 审核表 ↓Average return rate by category needs separate evidence按品类平均退货率需要独立证据
A blended online average hides product mix. Fit-sensitive apparel, fragile home goods, technical electronics, and restricted personal-care products face different customer expectations and return eligibility. Category tables found in search frequently reuse old or secondary numbers without preserving region, year, taxonomy, denominator, or sample.
线上综合平均值会掩盖商品结构。对尺码敏感的服装、易损家居、技术型电子产品和退货受限的个护商品，面临不同的客户预期和退货资格。搜索结果中的品类表经常重复旧数据或二手数字，却没有保留地区、年份、分类体系、分母和样本。
Use the dedicated return-rate-by-product-category guide to calculate category rates from your own data. If an external category benchmark is used, require its publication date, market, taxonomy depth, metric definition, sample, and method. Do not copy a range merely because it appears on several pages; repeated citation can trace back to one unsupported source.
使用独立的按产品品类计算退货率指南从自己的数据计算品类比率。若使用外部品类基准，必须要求其提供发布日期、市场、分类层级、指标定义、样本与方法。不要因为某个区间在多个页面出现就直接采用；重复引用可能都追溯到同一个缺乏支持的来源。
Interpret your store’s return rate with impact and causes结合影响与原因解读商店退货率
| Situation情况 | Interpretation解读 | Next analysis下一步分析 |
|---|---|---|
| Above matched benchmark and rising高于匹配基准且继续上升 | Strong investigation priority高调查优先级 | Category, SKU, reason, supplier, channel, policy品类、SKU、原因、供应商、渠道、政策 |
| Below external average but rising internally低于外部平均值但内部上升 | Do not declare success不能宣布成功 | Matched cohorts and product mix匹配群组与商品结构 |
| High rate, low volume比率高但销量低 | Uncertain or limited total impact不确定或总体影响有限 | Raw counts, longer window, margin exposure原始数量、更长窗口、利润风险 |
| Stable rate, rising cost比率稳定但成本上升 | Operational economics changed运营经济性发生变化 | Shipping, handling, markdown, disposition运费、处理、折价与处置 |
| Rate falls after policy restriction收紧政策后退货率下降 | May reflect suppression, not better product fit可能是抑制退货，而非商品更匹配 | Conversion, contacts, complaints, repeat purchase转化、咨询、投诉与复购 |
Pair the rate with returned units, returned merchandise value, contribution margin, repeat purchase, support contacts, and the full cost of returns. Then inspect return-reason patterns and product-level concentration. A lower percentage is not automatically a better customer or financial outcome.
应把退货率与退回件数、退回商品金额、贡献利润、复购、客服咨询以及完整退货成本一起查看，再分析退货原因模式和商品集中度。百分比下降并不自动意味着客户或财务结果更好。
Avoid six mistakes when using ecommerce return benchmarks使用电商退货基准时避免六个错误
- Calling 19.3% a 2026 rate: it is a 2025 estimate that remains the latest public U.S. benchmark identified on this page’s observation date.把 19.3% 称为 2026 年退货率：它是 2025 年估计，只是在本页观察日期仍是最新识别到的美国公开基准。
- Dropping the sample: the merchant respondents worked for large U.S. businesses above the stated revenue threshold.省略样本：商家受访者来自超过报告收入门槛的大型美国企业。
- Mixing orders, units, and dollars: percentages with different denominators cannot be ranked as the same KPI.混合订单、件数与金额：分母不同的百分比不能作为同一 KPI 排序。
- Ignoring category mix: a blended market rate is not a product-category target.忽略品类结构：综合市场退货率不是商品品类目标。
- Comparing incomplete cohorts: new sales have had less time to produce a return and can look artificially healthy.比较未成熟群组：近期销售产生退货的时间更短，可能显得虚假健康。
- Optimizing only the rate: restrictive policies can reduce recorded returns while harming conversion, loyalty, or support workload.只优化退货率：限制性政策可能降低记录到的退货，同时伤害转化、忠诚度或客服负担。
Turn the benchmark question into a store-level diagnosis把基准问题转化为商店级诊断
Prepare order-line, return-line, category, value, channel, market, and return-reason fields before opening Return Compass. Use the resulting analysis to compare mature internal cohorts and locate loss concentration. Treat external averages as context unless the worksheet confirms comparable definitions.
打开逆向罗盘前，准备订单行、退货行、品类、金额、渠道、市场与退货原因字段。使用分析结果比较成熟内部群组并定位损失集中点；除非审核表确认定义可比，否则外部平均值只能作为背景。
Open Return Compass打开逆向罗盘 →Average ecommerce return rate FAQ电商平均退货率常见问题
It depends on category, denominator, market, channel, policy, season, and cohort maturity. Compare 20% only with a rate using the same scope, then examine volume, margin, and reasons.这取决于品类、分母、市场、渠道、政策、季节与群组成熟度。只有范围一致时才能比较 20%，之后还需查看销量、利润与原因。
There is no universal good rate. Use your own mature historical cohort and a matched category benchmark, then protect contribution margin, conversion, complaints, and repeat purchase.不存在通用良好值。应使用自身成熟历史群组和匹配的品类基准，同时保护贡献利润、转化、投诉与复购表现。
That figure appears in secondary analysis of aggregate 2024 online-return value and online sales revenue. It uses a different year and construction from the 2025 NRF merchant estimate, so the two should not be merged.该数字出现在基于 2024 年线上退货总额与线上销售收入的二次汇总分析中，与 NRF 2025 商家估计使用不同年份和构造方法，因此不能合并。
Only as directional U.S. market context. The merchant sample covered ecommerce professionals at businesses above $500 million in revenue, so smaller brands should prioritize their own consistent cohorts and category mix.只能作为美国市场方向性背景。商家样本来自收入超过 5 亿美元企业的电商专业人士，因此小品牌应优先使用自己的统一群组和品类结构。
Review it quarterly and after a new primary U.S. returns study appears. Preserve the source year instead of renaming an older estimate as the current-year rate.应每季度检查，并在新的美国一手退货研究发布后立即复核。必须保留来源年份，不能把旧估计改名为当年退货率。
Sources, evidence labels, and limitations来源、证据标签与限制
- National Retail Federation and Happy Returns: 2025 Retail Returns Landscape — primary report for 19.3%, 15.8%, $849.9 billion, the historical table, merchant-question wording, and the 2023 comparability warning.美国零售联合会与 Happy Returns：2025 Retail Returns Landscape——19.3%、15.8%、8499 亿美元、历史表、商家问题措辞和 2023 年可比性警告的一手报告。
- National Retail Federation: Consumers Expected to Return Nearly $850 Billion in Merchandise in 2025 — primary release for the two survey populations and large-merchant threshold.美国零售联合会：Consumers Expected to Return Nearly $850 Billion in Merchandise in 2025——两项调查总体和大型商家门槛的一手发布。
- National Retail Federation and Happy Returns: 2024 Consumer Returns in the Retail Industry — prior-year context and separate-study statement about online versus annual rates.美国零售联合会与 Happy Returns：2024 Consumer Returns in the Retail Industry——上一年度背景，以及另一项调查中线上与年度退货率的相对关系。
- Shopify Help Center: Sales reports — official definitions for physically returned quantity and returned quantity rate.Shopify 帮助中心：销售报告——实体退回件数和退回件数率的官方定义。
- Capital One Shopping: Retail Return Rate Statistics — secondary aggregate analysis used only to explain the 24.5% SERP figure, not as the headline benchmark.Capital One Shopping：零售退货率统计——仅用于解释搜索结果中的 24.5%，不作为主基准的二次汇总分析。
Evidence statement: external figures retain their source year, scope, and evidence type. The worked store data is synthetic. No customer result, universal healthy rate, causal claim, guaranteed reduction, or InfiniSynapse product-effect claim is made. The public benchmark search was frozen on September 15, 2026; a named retail-data reviewer must verify the sources and wording before publication.证据声明：外部数字保留来源年份、范围与证据类型；商店示例为模拟数据。本文不声称客户结果、通用健康值、因果结论、保证降低退货率或 InfiniSynapse 产品效果。公开基准搜索冻结于 2026 年 9 月 15 日；发布前需由具名零售数据审核人核验来源与措辞。
Use 19.3% as dated U.S. context, not a universal target把 19.3% 作为注明年份的美国背景，而非通用目标
The best-supported headline answer is that NRF and Happy Returns estimated 19.3% of U.S. online sales would be returned in 2025. Preserve the words “estimated,” “U.S.,” “online sales,” and “2025.” Before comparing your store, align market, merchant population, channel, category, return event, denominator, return window, and cohort maturity. If those fields do not match, prioritize your own consistent historical baseline and use the external average only as directional context.
证据支持最充分的主答案是：NRF 与 Happy Returns 估计，2025 年美国线上销售有 19.3% 会被退回。引用时必须保留“估计”“美国”“线上销售”和“2025 年”。比较自己的商店前，应对齐市场、商家总体、渠道、品类、退货事件、分母、退货窗口与群组成熟度；若这些字段不一致，应优先使用自身定义一致的历史基线，外部平均值只作为方向性背景。