Consumer Goods
Indexed performance data for Consumer Goods brands on Shopify: conversion rate, AOV, free shipping behavior, and shipping revenue, tracked against a consistent baseline month over month.
Part of the PDQ Checkout Benchmarks: 130M+ checkout sessions across 500+ Shopify merchants, indexed to June 2024 = 1.0x.
Consumer Goods Checkout Performance Index: March 2026
An index of 1.15x means that metric is 15% above baseline. 0.92x means it's 8% below. We publish relative change rather than absolute numbers because absolute rates vary too much by merchant size and category to be meaningful as cross-merchant benchmarks.
March 2026: Consumer Goods Checkout Insights
Three signals worth acting on this month
Written for Consumer Goods operators. Every observation connects to a decision you can make this week.
Consumer Goods ARPC just hit 1.80x baseline, the highest reading of any vertical in this dataset
The February 2026 ARPC reading for Consumer Goods is not a rounding error. At 1.80x baseline, it's the single highest monthly reading across all six verticals tracked in this dataset, and it follows a January reading of 1.56x that was itself a multi-month high. The vertical has produced two consecutive months of ARPC performance that no other category has matched at any point in the series.
Understanding what's behind it requires understanding how Consumer Goods differs from other verticals in this dataset. Consumer Goods buyers are often purchasing higher-ticket, lower-frequency items: home goods, appliances, outdoor equipment, hardware, personal care devices. The consideration cycle is longer, the purchase is more deliberate, and when the buyer finally arrives at checkout, they've already done the work of convincing themselves. That dynamic produces high ARPC when demand conditions are right, and right now they are.
The AOV series confirms it. Consumer Goods AOV came in at 1.64x baseline in February, also the highest reading in the dataset for this vertical. Buyers are not just arriving more often. They're spending more per transaction than at any point since this data series began. For operators who have been holding back on checkout investment because the category felt too volatile, the last two months are a signal worth acting on.
What to do: When ARPC and AOV are both at series highs, the primary checkout risk is friction that interrupts a high-intent buyer at the worst moment. Audit your checkout for anything that creates unexpected hesitation: surprise shipping costs, unclear delivery timelines, or warranty and return terms that aren't visible before the payment step. A buyer spending $200 or more has a lower tolerance for checkout ambiguity than a buyer spending $30.
Shipping revenue is elevated and structurally persistent, but the free shipping rate tells a more complicated story
Consumer Goods shipping revenue has been running well above baseline for the entire dataset, and February 2026 at 2.15x is near the top of its historical range. That's not surprising given AOV: when buyers are spending more per transaction, the absolute shipping cost they'll tolerate scales with it. A buyer purchasing a $250 piece of home equipment has a different shipping cost frame than a buyer purchasing a $40 candle.
What's more interesting is the free shipping rate, which has been persistently low across this entire series. February 2026 came in at 0.71x baseline, meaning Consumer Goods brands are offering free shipping at a meaningfully lower rate than the all-industry average. That's not necessarily wrong for the category. Heavy, bulky, or high-value items carry real shipping costs, and blanket free shipping economics don't always work. But the gap between high ARPC and low free shipping rate is worth examining at the brand level.
The risk is a two-tier buyer experience: buyers spending above a threshold who expect free shipping as a function of their order size, and buyers just below that threshold who encounter a shipping fee that feels disproportionate to what they're spending. If your free shipping threshold is set too high relative to where most orders cluster, you're charging shipping on a large share of buyers who are already spending significantly above baseline.
What to do: Map your order distribution against your free shipping threshold. If more than 40% of orders fall below the threshold and are paying shipping, model what a lower threshold would do to shipping margin versus conversion. In Consumer Goods, the shipping fee is often the last piece of friction before a high-intent buyer completes or abandons. Getting that threshold right has an outsized impact on the orders that matter most.
Consumer Goods conversion has been the strongest and most consistent in the dataset. February's 1.09x continues a remarkable run
Consumer Goods has been the top-performing vertical on conversion rate for most of this dataset. The series has stayed above 1.0x baseline for all but one month since tracking began, and February 2026 at 1.09x continues a run of outperformance that now spans nearly two years.
The explanation connects to the buyer type. Consumer Goods purchases are high-consideration and high-commitment. The buyer who arrives at a Consumer Goods checkout has typically spent more time in the research and comparison phase than buyers in any other vertical tracked here. By the time they're entering payment information, they've already made the decision. Checkout is confirmation, not persuasion.
That dynamic means Consumer Goods conversion holds up well even when other signals are volatile. But it also means that when Consumer Goods conversion does drop, the cause is almost always something that breaks trust at the transaction moment rather than something earlier in the funnel. Unexpected shipping costs on a large order, a delivery window that's vague or long, or return terms that feel punitive on a high-ticket item are the conversion killers in this category. The consideration arc is long enough that buyers will come back if they trust the brand. But they won't complete the transaction under uncertainty.
What to do: If your conversion rate is below the vertical's 1.09x benchmark, the audit starts at the highest-friction points for high-ticket purchases: delivery date specificity, return and warranty terms visibility, and shipping cost predictability. Run a checkout session recording sample focused specifically on orders above your AOV and watch where hesitation appears. In Consumer Goods, the fix is almost never a redesign. It's a single piece of missing information at the wrong moment.
How does your Consumer Goods store's checkout compare?
Checkout Index tells you where your store sits inside this vertical: personalized Health Score, shipping signal analysis, and a revenue impact estimate based on your actual checkout behavior.
Archive
Monthly archive: Health & Wellness
Every monthly dispatch, indexed and preserved. Use the archive to track how Consumer Goods checkout behavior has shifted over time, to validate whether seasonal patterns in your own data match the vertical.
March 2026 {{latest}}
ARPC hits 1.80x, highest reading of any vertical in the dataset; AOV reaches 1.64x series high; conversion holds above 1.09x baseline.
ARPC and AOV sustain multi-month highs; shipping revenue near top of historical range at 2.15x; free shipping rate remains structurally below baseline at 0.71x.
Data begins June 2024 (baseline). Earlier dispatches available on request.
Methodology
About this dataset
The Consumer Goods dataset within the PDQ Checkout Benchmarks draws from aggregated, anonymized session data across consumer goods-categorized merchants on Shopify's platform. Merchants are classified using Shopify's standard industry taxonomy and must meet a minimum session threshold for inclusion. The Consumer Goods cohort spans home goods, appliances, outdoor and sporting equipment, personal care devices, hardware, and general household products.
All figures are indexed to June 2024 = 1.0x. Figures exclude bot traffic, draft orders, and point-of-sale transactions. Data refreshes monthly, typically in the first week, reflecting the prior month's activity. Absolute conversion rates are not published; all metrics represent relative indexed change against the baseline cohort.
To compare your store's actual performance against this vertical, use Checkout Index.
