BigCommerce Analytics Guide for Store Owners in 2026

What BigCommerce analytics includes

BigCommerce includes ecommerce analytics reports inside the platform. Based on BigCommerce documentation and support materials, the core reporting areas include:

• Store overview
• Real-time activity
• Orders
• Customers
• Marketing
• Carts
• Abandoned carts
• In-store search
• Merchandising and product performance
• Purchase funnel behavior

BigCommerce has also published guidance around using ecommerce analytics for average order value, cart abandonment, marketing ROI, customer behavior, and conversion funnels.¹

That means BigCommerce analytics can answer common questions:

• How much revenue did the store generate?
• Which products sold?
• How many carts were abandoned?
• Which channels drove traffic and orders?
• What did customers search for on the site?
• How did conversion change over time?

This is a strong starting point. It is not a complete operating system.

Where BigCommerce analytics is strongest

Store health reporting

BigCommerce gives store owners a clear view of store performance. Revenue, orders, traffic, average order value, conversion rate, and cart behavior are all available without building a custom dashboard.

For a smaller store, this is often the right amount of reporting. You can see whether the business is up or down and whether conversion or cart abandonment changed.

Cart and abandonment analysis

Cart reporting is one of the most useful native BigCommerce analytics areas. Abandoned cart data helps teams see where shoppers drop off before purchase. BigCommerce’s own ecommerce guidance treats cart abandonment as one of the central metrics for improving revenue.²

Use this data to answer:

• Are carts being abandoned more often than usual?
• Which products are frequently added but not purchased?
• Did a shipping change hurt checkout completion?
• Are discount campaigns creating low-intent carts?

Product and merchandising reporting

Product reporting helps store owners identify winners, slow movers, and items that need merchandising work. This is especially useful when you have a larger catalog and cannot inspect every product manually.

The practical questions are simple:

• Which products drive revenue?
• Which products get views but do not convert?
• Which products have declining demand?
• Which categories deserve more promotion?

Marketing visibility

BigCommerce marketing reports help connect traffic and campaigns to store outcomes. This is helpful for store owners who need a quick read on campaign performance without opening every ad platform.

The limitation is attribution depth. A campaign can produce revenue and still be bad for the business if it brings one-time buyers, heavy discount users, or customers who refund later.

Where BigCommerce analytics falls short

1. Refunds and returns need more attention

Most ecommerce dashboards focus on sales first. Store owners care about sales, but refunds can erase a good-looking week.

A useful BigCommerce analytics setup should track:

• Refund rate by product
• Refund rate by campaign
• Refund rate by customer segment
• Refund amount as a percentage of gross sales
• Products with unusual return patterns

The important insight is rarely “refunds increased.” It is more specific: “Three refunds came from the same product variant in 24 hours.” That is operational. Someone can act on it.

2. Native reports do not connect every source

A BigCommerce store rarely runs on BigCommerce alone. The full picture often lives across:

• BigCommerce orders
• GA4 behavior data
• Meta Ads spend
• Google Ads spend
• Klaviyo campaigns
• Stripe or PayPal payments
• Shipping and fulfillment systems
• Support tickets

BigCommerce can show store data. It cannot always explain how external spend, customer quality, refunds, and repeat purchases connect.

That connection is where better decisions happen.

Example:

Customers from your spring campaign placed 42 orders, but their refund rate was 19% versus 7% overall.

That is not a generic report. That is a decision.

3. Dashboards wait for someone to inspect them

This is the main problem with most BigCommerce analytics setups.

The data exists. The charts exist. But the store owner has to log in, scan the reports, spot the pattern, ask the follow-up question, and decide whether it matters.

That works when the business is small. It breaks when there are more products, channels, discounts, and customer segments than one person can mentally track.

4. Date-based reports miss launch and campaign context

Store owners do not always think in calendar ranges. They think in events:

• The week we launched the new product
• The first weekend after the discount email
• The period after we changed shipping prices
• The campaign that brought a spike in new customers

A strong analytics stack should make it easy to connect performance to business events, not just dates.

Best BigCommerce analytics tools

BigCommerce Ecommerce Analytics

Best for: native store reporting

BigCommerce Ecommerce Analytics should be the first layer. It is close to the source data, included with the platform, and covers the basics most stores need.

Use it for:

• Revenue and order reporting
• Cart and abandoned cart tracking
• Product and merchandising analysis
• Store overview reporting
• Quick checks on day-to-day performance

Add another tool when you need cross-channel reporting, deeper customer analysis, or proactive insight detection.

Google Analytics 4

Best for: traffic, acquisition, and site behavior

GA4 is the standard second layer. It helps answer where visitors came from, what pages they viewed, which events they triggered, and how traffic sources convert.

Use GA4 for:

• Channel performance
• Landing page conversion
• Funnel analysis
• Paid and organic traffic reporting
• Ecommerce events

GA4 is not enough for product-level operations, refunds, or contribution margin. It is a traffic and behavior tool first.

Glew

Best for: multi-channel commerce intelligence

Glew supports BigCommerce and connects commerce data with tools like Google Analytics, Facebook, Instagram, Amazon, Klaviyo, and Mailchimp.³

It is a good fit for stores that need mature reporting across customers, products, inventory, and marketing.

Use Glew if:

• You have multiple channels
• You need customer segmentation
• You want deeper product and inventory reporting
• You have someone who will use a full BI-style tool

The tradeoff is weight. Smaller stores may not need a commerce data cloud.

Reaktion

Best for: profit tracking and marketing performance

Reaktion positions its BigCommerce app around profit tracking, analytics, product reporting, ad tracking, and customer lifetime value.⁴

This is useful if the main question is not gross revenue, but profit after advertising and product costs.

Use Reaktion if:

• You care about profit by product
• You need ad tracking tied to orders
• You want LTV visibility
• You are trying to improve marketing efficiency

Putler

Best for: sales and customer reporting across ecommerce and payment sources

Putler supports BigCommerce reporting and positions itself around real-time reports, customer insights, and ecommerce analytics without requiring developer work.⁵

Use Putler if:

• You want a simpler reporting layer
• You use multiple payment sources
• You need sales, customer, and product reports in one place

Zoho Analytics

Best for: custom dashboards and business intelligence

Zoho Analytics offers a BigCommerce connector for reporting and dashboards.⁶ It can be a practical choice if your team already uses Zoho or wants a more traditional BI workflow.

Use Zoho Analytics if:

• You need custom dashboards
• You have multiple business data sources
• You are comfortable designing reports
• You want scheduled exports or stakeholder dashboards

AgencyAnalytics

Best for: agency client reporting

AgencyAnalytics offers BigCommerce reporting integrations and scheduled client reports.⁷

Use it if you are an agency reporting to ecommerce clients. If you operate the store yourself, you may want something more action-oriented.

AI analytics layer

Best for: finding changes across store, traffic, ad, and payment data

An AI analytics layer is useful when the question crosses systems. BigCommerce can show the order. GA4 can show the visit. An ad platform can show the campaign. The useful answer often needs all three.

Use this layer when you want to answer questions like:

• Which products are quietly getting worse?
• Did last week’s campaign attract customers who refund more often?
• Which day looked normal at the revenue level but had abnormal order quality?
• What changed since yesterday that I should care about?

Noomaro fits here. It is best treated as an analysis layer above BigCommerce and acquisition data, not a replacement for native BigCommerce reporting.

Recommended BigCommerce analytics stack by stage

Early store

Use:

• BigCommerce Ecommerce Analytics
• GA4
• A simple weekly KPI review

Track:

• Revenue
• Orders
• Conversion rate
• AOV
• Cart abandonment
• Top products

At this stage, do not overbuild reporting. The goal is to understand the business rhythm.

Growing store

Use:

• BigCommerce Ecommerce Analytics
• GA4
• A reporting tool like Putler, Reaktion, or Glew
• An AI analytics layer for cross-source questions

Track:

• Refund rate
• Repeat purchase rate
• Revenue per visitor
• Campaign quality
• Product-level margin
• Customer cohorts

This is where hidden problems start to cost real money.

Mature store

Use:

• BigCommerce Ecommerce Analytics
• GA4 or warehouse-exported behavioral data
• BI tool or commerce intelligence platform
• Attribution and profit tracking
• AI analytics layer for fast questions and exception detection

Track:

• LTV by channel
• Contribution margin
• Cohort retention
• Refund rate by campaign
• Inventory risk
• Product quality issues
• Paid acquisition payback

At this stage, the job is not to collect more reports. It is to reduce the time between a change happening and the team acting on it.

Example campaign quality workflow in BigCommerce

Here is a concrete workflow that native reports rarely answer by themselves.

A store launches a Meta campaign for a new product. BigCommerce shows 64 orders and $4,900 in gross revenue. GA4 shows paid social traffic converting above the site average. The ad platform reports a healthy cost per purchase.

That looks good until refunds are included.

A better BigCommerce analytics workflow checks:

1. BigCommerce orders for the campaign period.
2. Product and variant refund rate for those orders.
3. GA4 landing pages and source data for the same period.
4. UTM-tagged campaign names from paid social.
5. Net revenue after refunds.
6. Repeat purchases from that customer cohort two to four weeks later.

The decision is different if the campaign brought 64 orders with a 4% refund rate versus 64 orders with a 19% refund rate. The first campaign may be scalable. The second may be acquiring the wrong customers or setting the wrong expectation on the landing page.

That is the reporting gap BigCommerce store owners should care about: not more charts, but cleaner answers about customer quality.

BigCommerce analytics checklist

Use this checklist to decide whether your current setup is enough.

You need a better analytics layer if you cannot answer these in less than two minutes:

• Which product had the biggest unexpected change this week?
• Which campaign brought the highest quality customers?
• Which products are responsible for most refunds?
• Did AOV increase because customers bought more, or because product mix changed?
• Are repeat customers growing or flattening?
• Which customer segment is most profitable after discounts and refunds?
• What changed yesterday that deserves attention today?

If those questions require exports, spreadsheets, or a Slack thread with your agency, your reporting stack is not doing enough.

BigCommerce analytics versus ecommerce reporting tools

BigCommerce analytics is the native reporting layer. Ecommerce reporting tools sit above it.

Native BigCommerce analytics is best for:

• Order trends
• Store overview
• Product reporting
• Cart behavior
• Platform-level reporting

Ecommerce reporting tools are better for:

• Cross-channel performance
• Profit reporting
• Customer cohorts
• Ad spend analysis
• Refund and return patterns
• AI-generated insights
• Stakeholder reports

Most growing stores need both.

Bottom line

BigCommerce analytics gives you a solid foundation. Keep it. Use it as the source-level view of what happened in the store.

Then add tools based on the questions you cannot answer quickly:

• GA4 for acquisition and behavior
• Glew or Zoho for BI-style reporting
• Reaktion for profit and ad performance
• AgencyAnalytics for client reports
• An AI analytics layer for change detection and follow-up questions

The best BigCommerce analytics setup is not the one with the most charts. It is the one that helps you catch the refund spike, campaign quality problem, or product decline before it becomes next month’s explanation.

Sources