What is the average conversion rate on BigCommerce stores?

BigCommerce stores achieve an average conversion rate of 2.5% as of 2025, according to platform performance data (Coalition Technologies). This figure exceeds typical ecommerce conversion rates, which usually hover between 1% and 2% across the industry. However, this baseline varies significantly by industry vertical, device type, and traffic source, making segment-level analysis essential for meaningful benchmarking.

The 2.5% figure represents a notable improvement for BigCommerce merchants, reflecting both platform enhancements and merchant sophistication in conversion optimization practices. Understanding where your store fits within this spectrum—and more importantly, why certain segments perform differently—forms the foundation for systematic improvement.

Numbers at a glance

• 2.5% — BigCommerce platform average conversion rate (2025)
• 1-2% — Typical ecommerce industry conversion rate range
• 2.2% — Mobile conversion rate for BigCommerce stores
• 63% — Mobile traffic share to BigCommerce stores
• 26% — Year-over-year GMV increase during Cyber Week 2024
• 130,000+ — Active BigCommerce merchants worldwide

Why “Average Conversion Rate” matters

Conversion rate serves as your store’s efficiency metric—the percentage of visitors who complete a purchase during their session. Unlike traffic volume or total revenue, conversion rate reveals how well your site transforms browsers into buyers, making it a critical diagnostic tool for ecommerce performance.

Conversion rate vs. other revenue KPIs

While revenue and average order value (AOV) capture the financial outcome of your efforts, conversion rate measures the effectiveness of your customer experience. A store generating $100,000 monthly with a 1.5% conversion rate faces different challenges than one earning the same revenue at 3.5%—the latter likely has superior product-market fit, user experience, or traffic quality.

Conversion rate also correlates directly with customer acquisition cost (CAC) efficiency. Higher-converting stores can afford more aggressive marketing spend per visitor, creating a competitive advantage in paid advertising channels.

Benchmarking pitfalls (industry, price point, traffic mix)

Raw conversion rate comparisons often mislead without proper context. Different industries naturally convert at different rates based on purchase complexity, price points, and customer decision-making cycles.

Similarly, traffic source composition dramatically affects baseline expectations. Stores driving 60% of traffic from branded search queries naturally convert higher than those relying heavily on cold social media advertising or display remarketing.

The benchmark spectrum for BigCommerce merchants

BigCommerce’s 2.5% average conversion rate advantage stems from several platform-specific factors: streamlined checkout flows, mobile-optimized themes, and integrated payment processing that reduces friction points common on other platforms.

Platform-wide median baseline

Current data from Coalition Technologies indicates BigCommerce stores achieve an average conversion rate of 2.5%, representing a significant advantage over the general ecommerce industry average of 1-2%. This performance gap reflects the platform’s focus on conversion optimization features and merchant-friendly tools.

The distribution isn’t uniform across all stores—performance varies based on implementation quality, industry vertical, and optimization efforts. This spread suggests significant opportunities exist for underperforming segments.

How vertical & AOV shift the baseline

While BigCommerce-specific industry breakdowns aren’t available in current research, general ecommerce data from Speed Commerce shows significant variation by vertical:

Higher-converting categories typically include:

• Personal care products: 6.8%
• Food & beverages: 4.9%
• Electronics & home appliances: 3.6%

Lower-converting categories typically include:

• Retail (fashion, jewelry, shoes): 1.9%
• Cars & automobile parts: 2.1%
• Home decor: 1.4%

These patterns likely apply to BigCommerce stores as well, though specific platform data isn’t currently available.

Device & channel-specific averages

BigCommerce data shows mobile traffic dominates at 63% of total visits, but mobile conversion rates average around 2.2%—slightly lower than the overall platform average. This gap represents the largest optimization opportunity for most merchants.

Desktop traffic, while smaller in volume, typically converts at higher rates due to improved usability and user intent. However, businesses must continue optimizing mobile experiences as mobile commerce continues growing.

How to calculate your actual conversion rate in BigCommerce

Accurate conversion tracking forms the foundation of meaningful optimization efforts. BigCommerce provides multiple reporting interfaces, each with specific use cases and limitations.

Where to pull the metric (Store Overview, Google Analytics, custom reports)

The Store Overview report in your BigCommerce admin panel offers the most straightforward conversion rate calculation. Navigate to Analytics > Store Overview to access near real-time data updated every 60 seconds.

This report divides total orders by total sessions over your selected timeframe, providing a clean platform-native metric. The Store Overview report includes comprehensive views of your entire business across all key metrics, purchase funnel analysis, abandoned carts, top products, and sales by marketing channels.

Google Analytics integration offers more sophisticated analysis capabilities. Set up Enhanced Ecommerce tracking through BigCommerce’s Data Solutions to capture detailed funnel metrics, traffic source attribution, and behavioral flow analysis.

For advanced users, custom reporting through BigCommerce’s API enables precise metric definitions and automated dashboard creation. This approach works best for stores requiring specific attribution models or complex segmentation rules.

Recommended segment filters for cleaner insight

Segment your conversion analysis across these key dimensions:

Device type: Desktop, mobile, tablet performance often varies significantly, requiring separate optimization strategies.

Traffic source: Organic, paid, email, social, and direct traffic convert at dramatically different rates and deserve individual attention.

New vs. returning visitors: First-time visitors typically convert at lower rates than returning customers, making this segmentation crucial for acquisition strategy.

Geographic region: International traffic may convert differently due to shipping costs, payment method availability, or cultural factors.

Time-based segments: Weekday vs. weekend, seasonal patterns, and promotional periods all influence baseline conversion expectations.

Common reporting mistakes to avoid

Mixing timeframes: Comparing conversion rates across different seasonal periods without accounting for natural fluctuations leads to incorrect conclusions.

Ignoring statistical significance: Small traffic volumes produce unreliable conversion rate calculations. Require minimum sample sizes of 1,000 sessions before drawing optimization conclusions.

Conflating correlation with causation: Higher conversion rates during promotional periods don’t necessarily indicate that discounting improves baseline performance.

Overlooking mobile attribution: Cross-device customer journeys mean mobile sessions may contribute to desktop conversions, understating mobile’s true impact.

7 factors that drive conversion up or down

Systematic conversion improvement requires understanding the primary levers affecting purchase decisions. These factors operate independently but often compound when optimized together.

Site performance & page speed

Page load speed directly correlates with conversion rates across all device types. Stores loading in under 2 seconds typically convert 35-50% higher than those requiring 4+ seconds.

Core Web Vitals checkpoints

Monitor these Google-defined performance metrics:

• Largest Contentful Paint (LCP): Target under 2.5 seconds
• First Input Delay (FID): Target under 100 milliseconds
• Cumulative Layout Shift (CLS): Target under 0.1

BigCommerce’s built-in CDN and image optimization help most stores achieve acceptable Core Web Vitals scores, but custom code additions and third-party apps can degrade performance over time.

Mobile UX friction

With 63% of BigCommerce traffic coming from mobile devices but conversion rates averaging 2.2%, mobile optimization represents the largest opportunity for most merchants.

Common friction points include:

• Checkout complexity: Multi-step checkout flows that work on desktop often overwhelm mobile users
• Form field optimization: Auto-fill compatibility, appropriate input types, and minimal required fields reduce mobile abandonment
• Touch target sizing: Buttons and links smaller than 44px create frustration and accidental taps

Checkout flow & payment options

Checkout abandonment affects the majority of ecommerce carts, making this optimization area particularly impactful. Key improvement areas include:

Guest checkout availability: Forcing account creation reduces conversion for first-time customers.

Payment method variety: Offering PayPal, Apple Pay, Google Pay, and buy-now-pay-later options increases conversion by capturing different customer preferences.

Shipping cost transparency: Unexpected shipping charges cause significant cart abandonment. Display shipping costs early in the funnel or offer free shipping thresholds.

Merchandising & product page persuasion

Product page conversion rates vary dramatically based on information architecture and persuasion elements:

High-quality imagery: Multiple product angles, zoom functionality, and lifestyle shots increase conversion significantly.

Social proof integration: Customer reviews, ratings, and user-generated content build trust and reduce purchase hesitation.

Scarcity and urgency messaging: Limited inventory notifications and time-sensitive offers can boost conversion when used authentically.

Trust & security signals

Security concerns prevent a significant percentage of potential customers from completing purchases. Address these through:

SSL certificate display: Ensure the padlock icon appears prominently in the address bar.

Trust badges: Display security certifications, payment processor logos, and money-back guarantees near checkout buttons.

Professional design: Clean, modern design aesthetics signal legitimacy and reduce abandonment rates.

Offer strategy (shipping, pricing, promotions)

Pricing and promotional strategies significantly impact conversion rates:

Free shipping thresholds: Setting thresholds 20-30% above average order value encourages larger purchases while maintaining profitability.

Promotional timing: Limited-time offers create urgency but can train customers to wait for discounts if overused.

Bundle pricing: Product bundles often convert higher than individual items by increasing perceived value.

Traffic quality & intent alignment

High-converting stores attract visitors with strong purchase intent through:

Targeted advertising: Specific product ads convert better than generic brand awareness campaigns.

SEO optimization: Ranking for commercial keywords brings higher-intent traffic than informational queries.

Email segmentation: Personalized email campaigns based on browsing behavior and purchase history drive qualified traffic.

Benchmark-busting framework: diagnose → prioritise → optimise

Systematic conversion improvement follows a structured approach that maximizes impact while minimizing wasted effort.

Step 1 – Diagnose under-performing segments (device/channel)

Begin optimization efforts by identifying your biggest conversion gaps:

Device analysis: Compare mobile, desktop, and tablet conversion rates. Significant gaps indicate optimization opportunities.

Channel performance: Evaluate conversion rates by traffic source. Underperforming channels may require landing page customization or audience refinement.

Funnel analysis: Track visitor progression from product page to checkout completion. Identify the largest drop-off points for focused improvement efforts.

Geographic segmentation: International traffic often converts differently due to shipping costs, payment methods, or cultural factors.

Step 2 – Score quick-win vs. effort matrix

Prioritize optimization projects using a simple scoring framework:

High Impact, Low Effort:

• Checkout flow simplification
• Payment method additions
• Trust badge implementation
• Mobile button sizing fixes

High Impact, High Effort:

• Complete mobile redesign
• Advanced personalization implementation
• Custom checkout development
• Comprehensive A/B testing program

Focus initial efforts on high-impact, low-effort improvements to build momentum and demonstrate ROI before tackling complex projects.

Step 3 – Build experimentation backlog (A/B testing cadence)

Sustainable conversion improvement requires systematic testing:

Test prioritization: Focus on high-traffic pages and elements affecting the largest visitor segments.

Statistical rigor: Run tests until reaching 95% confidence levels with adequate sample sizes.

Documentation: Record all test results, including failed experiments, to build institutional knowledge.

Testing calendar: Plan 2-4 concurrent tests monthly, allowing sufficient time for statistical significance.

Step 4 – Track, learn, iterate in 90-day sprints

Organize optimization efforts into quarterly cycles:

Month 1: Implement quick wins and launch major tests
Month 2: Monitor test results and implement winning variations
Month 3: Analyze overall impact and plan next quarter’s priorities

This cadence provides sufficient time for meaningful results while maintaining optimization momentum.

Frequently asked questions

What counts as a conversion in BigCommerce?

BigCommerce defines conversion as completed orders divided by total sessions. This includes all payment methods and excludes refunded orders. The platform tracks conversions when customers reach the order confirmation page, regardless of payment processing delays.

How do you find conversion rate in BigCommerce analytics?

Navigate to Analytics > Store Overview in your BigCommerce admin panel to view your conversion rate. The report updates in near real-time (within 60 seconds) and shows conversions as a percentage of total sessions. You can segment by date ranges, compare periods, and export data for deeper analysis.

Should I worry about bounce rate or conversion first?

Focus on conversion rate optimization first. High bounce rates often indicate traffic quality issues rather than site problems. A store with 70% bounce rate converting 4% of remaining visitors outperforms one with 40% bounce rate converting 1.5%.

What affects BigCommerce conversion rate the most?

Mobile optimization has the biggest impact, since 63% of BigCommerce traffic comes from mobile devices but converts at lower rates. Other major factors include page load speed, checkout complexity, payment method variety, and traffic source quality. Site performance and mobile UX typically offer the highest ROI for optimization efforts.

Does theme choice impact conversion on BigCommerce?

Yes, theme choice significantly impacts conversion rates. Mobile-optimized themes with streamlined checkout flows, fast loading times, and clear call-to-action buttons typically convert 20-40% higher than outdated designs. BigCommerce’s built-in themes are optimized for conversion, but customizations can either help or hurt performance.

How often should I re-benchmark my store?

Review conversion benchmarks quarterly, accounting for seasonal variations and promotional periods. Monthly monitoring helps identify trends, but avoid making optimization decisions based on short-term fluctuations. Always compare similar time periods (e.g., Q4 2024 vs Q4 2023) for accurate analysis.

Why is mobile conversion lower than desktop on BigCommerce?

Mobile conversion rates average 2.2% compared to higher desktop rates due to smaller screens, touch interface challenges, and checkout complexity. Common issues include difficult form filling, slow loading times, and multi-step checkout processes that work better on desktop. Mobile-first design and simplified checkout flows can close this gap significantly.

How can I A/B test conversion improvements on BigCommerce?

Use BigCommerce’s built-in A/B testing capabilities or integrate third-party tools like Optimizely or VWO. Test one element at a time (headlines, buttons, checkout flow) with sufficient traffic for statistical significance. Run tests for at least 2-4 weeks or until you reach 95% confidence levels before implementing changes.

Sources & references

• Coalition Technologies — BigCommerce platform statistics including 2.5% conversion rate and mobile traffic data
• Speed Commerce — Industry-specific ecommerce conversion rate benchmarks and device performance data
• Chargeflow — BigCommerce merchant statistics and Cyber Week performance data
• BigCommerce Support — Official documentation on Store Overview report and analytics tracking methodology