---
type: blog
title: "Ecommerce Report: Market Update and Store Performance Template"
description: "See the latest official U.S. ecommerce figures, then build a store report that connects traffic, sales, refunds, products, sources, and funnel loss."
date: "2026-08-31"
lastModified: "2026-08-31"
tags: ["Ecommerce Reporting", "Store Analytics", "Business Operations"]
featured: false
readTime: "9 min read"
authors: "Runner AI Team"
thumbnail: "https://storage.googleapis.com/runner-blog/blog/ecommerce-report/cover"
thumbnailAlt: "An ecommerce operator comparing a market report with store traffic, sales, refund, product, source, and funnel report cards"
seo:
  title: "E Commerce Report: Q2 2026 Data and Store Template"
  description: "Use this e commerce report guide for the latest U.S. market figures and a practical template covering traffic, sales, refunds, sources, and funnels."
---

An ecommerce report can describe an entire market or explain what happened inside one store. The first uses industry data to show sales and consumer trends; the second connects a merchant's traffic, sales, refunds, products, sources, and funnel. Runner Analytics supports that store-level review with consistent 24-hour, 7-day, and 30-day reporting windows.

> **Key takeaways**
>
> - Market reports answer how ecommerce is changing; store reports answer what happened in your business.
> - The latest official U.S. release estimated $340.2 billion in seasonally adjusted retail ecommerce sales for Q2 2026.
> - A useful store report puts sessions, GMV, net sales, refunds, products, sources, and funnel stages in one view.
> - Define the time range, timezone, metrics, and missing-data rules before comparing results.
> - A funnel shows where recorded progress stopped. It does not prove why a shopper left.

## What does an ecommerce report mean?

The phrase usually refers to one of two documents. An **industry ecommerce report** summarizes a market, region, category, payment method, or shopper trend. A **store performance report** summarizes one merchant's recorded activity so an operator can decide what to inspect or change next.

| Report type | Main audience | Typical data | Useful decision |
| --- | --- | --- | --- |
| Market or industry report | Executives, analysts, investors, and market-entry teams | Government estimates, surveys, forecasts, benchmarks, and category trends | Whether a market or trend deserves deeper research |
| Internal store report | Merchant, operator, marketer, or ecommerce manager | Sessions, orders, GMV, net sales, refunds, products, sources, and funnel events | What changed in the store and what to investigate next |

These reports complement each other, but they are not interchangeable. Market growth does not prove that one store should grow at the same rate. A store's conversion drop does not establish an industry trend. Start by naming the decision and choosing the report that owns the relevant data.

If your immediate need is a deeper introduction to revenue, funnels, and customer groups, use the separate guide to [store analytics, funnels, and cohorts](./store-analytics-revenue-funnels-cohorts). This article focuses on assembling a repeatable report rather than surveying every analytics method.

## Latest official U.S. ecommerce report in brief

The U.S. Census Bureau's [Quarterly Retail E-Commerce Sales Report](https://www.census.gov/retail/ecommerce.html), released August 18, 2026, is the current official primary source for a concise U.S. market snapshot.

| Q2 2026 measure | Official estimate |
| --- | ---: |
| Seasonally adjusted retail ecommerce sales | $340.2 billion |
| Change from Q1 2026 | +3.8% (+/-0.4%) |
| Change from Q2 2025 | +12.2% (+/-0.9%) |
| Ecommerce share of total retail sales | 17.1% |

The Census Bureau says these estimates are adjusted for seasonal variation but not for price changes. Its unadjusted estimates are different, so do not combine an adjusted growth rate with an unadjusted sales total. The release also provides spreadsheets, time series, supplemental tables, and explanatory material for readers who need to inspect the methodology.

Commercial market reports may organize their analysis by country, logistics, payments, category, or retailer benchmark. They can help frame a strategic question, but their populations and methods differ. Read the date, geography, sample, definitions, and limitations before comparing one headline with another.

## What an internal ecommerce report should answer

A store report should connect each metric to a checkable decision. It should let an operator answer seven questions without switching definitions halfway through:

1. **Did traffic change?** Compare sessions under one date range and timezone.
2. **Did commercial output move with traffic?** Read orders, GMV, net sales, and conversion together.
3. **How much completed activity was adjusted later?** Keep refunds visible rather than hiding them behind a top-line number.
4. **Which products contributed to the result?** Separate product contribution from assumptions about margin or inventory risk.
5. **Which recorded sources contributed?** Leave missing source data unattributed instead of guessing.
6. **Where did recorded shopper progress stop?** Compare adjacent funnel stages from traffic through purchase.
7. **What needs verification next?** Turn observations into named checks, owners, and follow-up dates.

The word **sessions** also needs a definition. Google Analytics defines a session as a period during which a user interacts with a site or app, and calculates session counts by estimating the number of unique session IDs ([Google Analytics, "About Analytics sessions"](https://support.google.com/analytics/answer/9191807?hl=en)). Your commerce platform may use a different event model. When two systems disagree, align date ranges, timezones, filters, identity rules, and metric definitions before deciding that either system is wrong.

## Ecommerce report template

Use this structure for a weekly operating review, a monthly summary, or an incident follow-up. The columns matter more than the presentation software.

| Report block | Include | Interpretation rule |
| --- | --- | --- |
| Report identity | Store, date range, timezone, comparison period, generated date | Never compare periods that use different boundaries |
| Traffic | Sessions and trend | More traffic does not automatically mean better traffic |
| Commerce | Orders, GMV, net sales, conversion rate | Keep GMV and net sales distinct |
| Adjustments | Refund count, refund value, and refund rate when available | Review post-purchase outcomes separately |
| Products | Leading products under the available sales measure | Product contribution is not profit margin |
| Sources | Sessions and sales by recorded source | A blank source remains unknown |
| Funnel | Traffic, add to cart, checkout, and purchase | Compare adjacent stages and record the largest loss |
| Decisions | Observation, hypothesis, owner, next check, due date | Separate measured facts from explanations |

### Keep GMV and net sales separate

GMV describes gross merchandise value in the selected period. Net sales reflects the adjustments included by the reporting system. Their relationship is not a universal equation across platforms. Taxes, shipping, discounts, returns, cancellations, and report timing may be treated differently.

Do not write `GMV - refunds = net sales` unless the system's definitions establish that identity. Put both measures in the report, link or copy their definitions, and explain material differences with underlying order data.

### Keep refunds visible

A sales increase can look healthier than it is when refunds arrive later or cluster around one product. Show refunds beside commerce totals, then inspect the affected orders and reporting periods. A sudden refund-rate move can also be unstable when the number of orders is small, so include the denominator before escalating the result.

For the operational distinction between payment and fulfillment state, see the guide to [order-level refunds](./order-level-refund).

### Treat source data as evidence, not attribution proof

A source breakdown can show where recorded sessions or sales were assigned. It does not by itself prove that one touchpoint caused the purchase or that another channel had no influence. The guide to [marketing attribution models](./marketing-attribution-channels-creatives-lift) explains why channel credit and incremental lift are separate questions.

## How to build the report in Runner Analytics

In Runner Analytics, operators can assemble this report from a published store's recorded activity. The current [Runner Analytics overview](https://www.runnerai.com/docs/en/guides/analytics-and-seo/analytics-overview) documents the prerequisites: the plan must include storefront data, the store must be published, and shopper, order, refund, and funnel events must exist before their sections can show meaningful results.

1. Open the store project and select **Analytics**.
2. Choose **Last 24 hours**, **Last 7 days**, or **Last 30 days**. Keep that range fixed while comparing sections.
3. Read **Sessions**, **GMV**, **Net Sales**, and **Conversion Rate** together.
4. Check product and source sections to see where the recorded result came from.
5. Compare **Traffic**, **Add to Cart**, **Checkout**, and **Purchase** to find the largest adjacent-stage loss.
6. Review cart abandonment and refunds separately from the acquisition view.
7. If **Share & export** is available in your Analytics view, use it for a manual artifact. This staged workbench control downloads supported aggregate sections as CSV files in one ZIP, with a manifest that identifies unavailable sections. **Print / save PDF** opens the browser print flow.
8. Add observations, hypotheses, owners, and follow-up checks outside the metric totals.

The guide to [metrics and filters](https://www.runnerai.com/docs/en/guides/analytics-and-seo/metrics-filters) explains the available windows and why a partial current day is not directly comparable with a complete past day. The guide to [traffic and conversion](https://www.runnerai.com/docs/en/guides/analytics-and-seo/traffic-and-conversion) covers source gaps, funnel stages, abandonment, refunds, and cross-service discrepancies.

Use accounting or warehouse systems for accounting-grade reconciliation, ad-platform cost, and custom fiscal calendars. Use Runner to review storefront traffic, commerce, products, sources, and funnel evidence together. The [AI ecommerce analytics feature](https://www.runnerai.com/features/ai-ecommerce-analytics) shows how that evidence can lead to a bounded merchandising or storefront review rather than an unattended change.

## How often should you review an ecommerce report?

Match the reporting window to the decision and the amount of data. Frequency is not a quality score.

| Window | Useful for | Main risk |
| --- | --- | --- |
| Last 24 hours | Launch checks, active promotions, incidents, and tracking verification | Partial days and small samples can move sharply |
| Last 7 days | A recurring operating review with recent traffic and orders | Day-of-week mix can distort comparisons |
| Last 30 days | Broader trends and lower-volume stores | Recent changes can be hidden by the longer period |

Use comparable prior periods when the question requires a trend, and annotate launches, promotions, outages, price changes, or tracking changes that alter interpretation. For longer analysis, preserve each period's definitions. Where **Share & export** is available, export its supported aggregate sections or use the browser print flow instead of merging incompatible totals.

The right cadence leaves enough time to act without turning ordinary variation into constant intervention. A low-volume store may learn more from a 30-day review than from a daily alert. A launch may justify a 24-hour check because the decision is whether the purchase path and tracking work at all.

## Turn the report into action without inventing a cause

The report should narrow the next investigation, not pretend to finish it. Use an evidence ladder:

1. **Observation:** Checkout completion fell in the selected period.
2. **Comparison:** Sessions and the recorded source mix stayed broadly stable.
3. **Hypothesis:** Something in the checkout journey may have changed.
4. **Verification:** Reproduce the journey and inspect shipping, payment, validation, device behavior, and relevant errors.
5. **Action:** Propose one bounded change with an owner.
6. **Follow-up:** Review the same metric and journey under comparable conditions.

This language keeps the report honest. A funnel identifies where events stopped; it cannot see every shopper's motive. If the largest loss is around the cart or checkout, use the [ecommerce shopping-cart abandonment workflow](https://www.runnerai.com/features/ecommerce-shopping-cart-abandonment) to document the affected journey, store facts, hypothesis, and verification steps. If evidence points to a broader page problem, use a bounded [ecommerce website audit](https://www.runnerai.com/features/ecommerce-website-audit) rather than changing unrelated sections.

## Frequently asked questions

### What is an ecommerce report?

An ecommerce report is either an industry document about a market or an internal performance document about one store. Industry reports use estimates, surveys, forecasts, and benchmarks. Store reports use recorded traffic, orders, sales, refunds, product, source, and funnel data to support operating decisions.

### What should an ecommerce performance report include?

Include the store, date range, timezone, comparison period, sessions, orders, GMV, net sales, conversion rate, refunds, product contribution, recorded sources, and funnel progression. Add metric definitions and a decision log so readers can distinguish observed results from hypotheses.

### What is the best reporting tool for ecommerce?

There is no universal best tool. Evaluate store integration, metric definitions, product and source breakdowns, funnel visibility, refund handling, exports, access controls, and the reporting window you need. Also decide whether the job requires accounting-grade reconciliation, cross-platform business intelligence, or a store-operations view.

### What is the difference between GMV and net sales?

GMV is the gross merchandise value measured by the reporting system. Net sales is sales after the adjustments that system includes. Because platforms can treat discounts, refunds, taxes, shipping, cancellations, and timing differently, use the exact definitions in your tool instead of assuming one universal formula.

### Can a funnel report explain why shoppers abandoned checkout?

No. A funnel shows how many recorded sessions reached each stage and where progression stopped. It helps identify the journey to inspect, but diagnosis needs supporting evidence such as a safe storefront walkthrough, payment and shipping checks, device testing, errors, and shopper research.

## Build the next report around a decision

Start with one question, one store, and one fixed period. Open [Runner Analytics](https://www.runnerai.com/docs/en/guides/analytics-and-seo/analytics-overview), compare sessions with GMV and net sales, keep refunds visible, then trace products, sources, and funnel loss.

After identifying one result that needs investigation, bring the selected period, affected metric or funnel stage, relevant product or source data, store constraints, and one acceptance check into Runner. Ask for one reviewable storefront change, inspect the preview, and publish only after verifying it against the original report. Where a manual export is available, preserve its supported sections as the review artifact.

## Sources

- U.S. Census Bureau, ["Quarterly Retail E-Commerce Sales Report"](https://www.census.gov/retail/ecommerce.html), released August 18, 2026. The estimates include stated sampling uncertainty and are seasonally adjusted but not adjusted for price changes.
- Google Analytics Help, ["About Analytics sessions"](https://support.google.com/analytics/answer/9191807?hl=en), retrieved August 31, 2026.