What's a good conversion rate for e-commerce?

Why "good" is relative

No universal "good" conversion rate exists. Fashion store at 1.8% might outperform category average while electronics store at 1.8% underperforms. Store age matters—new stores converting 1.2% are performing well, established stores converting 1.2% have problems. Traffic source composition matters—store with 80% organic traffic expects higher conversion than store with 80% social traffic. "Good" depends on context, not arbitrary numbers.

Asking "is 2.3% good?" misses the point. Better questions: "Am I improving?" and "What's possible given my traffic mix and business model?" Conversion rate exists within ecosystem of average order value, traffic quality, and customer acquisition cost. 1.8% conversion with $95 AOV and $25 CAC generates healthier business than 2.8% conversion with $45 AOV and $40 CAC. Conversion rate alone doesn't define success.

Industry averages and why they mislead

Global e-commerce averages

Overall e-commerce conversion rate globally: 1.5-2.5%. This number aggregates millions of stores across all categories, traffic sources, and business models. Comparing your store to this average is like comparing your store to "average of all businesses"—technically data but practically useless. Your store operates in specific category with specific traffic patterns requiring specific comparison points.

Fashion and apparel: 1.5-2.2% typical. Beauty and personal care: 2-3%. Food and beverage: 2.5-3.5%. Consumer electronics: 1.2-2%. Home and furniture: 1-2%. Automotive parts: 1.5-2.5%. Health and wellness: 2-3%. These ranges span wide because even within categories, massive variation exists based on price point, brand recognition, and traffic quality.

Why averages hide reality

Category averages combine $20 fast fashion retailers with $200 designer boutiques, both showing in "fashion" category despite entirely different customer behavior. Mixing them creates meaningless average. $20 impulse purchases convert 3-4%, $200 considered purchases convert 1.5-2%—average shows 2.5% suggesting both are "average" when actually one excels and one underperforms for their price point.

Store age dramatically affects conversion. New stores (first 6 months): 0.8-1.5% conversion typical as brand awareness builds. Established stores (2+ years): 2-3.5% conversion common from reputation and returning customers. Age-adjusted comparison matters more than overall category average. New store at 1.3% outperforms its cohort despite trailing category average of 2.1%.

What actually determines "good"

Your traffic source mix

Organic search traffic: 2.5-4% conversion typical because visitors searched with intent. Paid search: 2-3.5% conversion from targeted keywords. Social media: 0.5-1.5% conversion from interruption-based discovery. Email: 3-5% conversion due to existing relationship. Direct traffic: 2.5-4% conversion from brand familiarity. Store with 60% organic traffic should convert higher overall than store with 60% social traffic—comparing raw rates between them ignores fundamental traffic quality differences.

Calculate weighted expected conversion based on your mix. Example: 40% organic at 3% expected + 30% paid at 2.5% expected + 20% email at 4% expected + 10% social at 1% expected = (0.40 × 3%) + (0.30 × 2.5%) + (0.20 × 4%) + (0.10 × 1%) = 1.2% + 0.75% + 0.8% + 0.1% = 2.85% expected overall conversion. If actual conversion is 2.7%, you're performing near expectation despite being below some category averages.

Your average order value

Lower AOV products convert higher. Stores with $30-50 AOV: 2.5-4% conversion common because low purchase risk. Stores with $150-300 AOV: 1.5-2.5% conversion typical due to consideration time. Stores with $500+ AOV: 0.8-1.5% conversion normal for high-involvement purchases. Jewelry store converting 1.2% at $350 AOV is performing excellently, while accessories store converting 1.2% at $35 AOV has serious problems.

Revenue per session matters more than conversion rate isolated. Store A: 2.8% conversion, $55 AOV = $1.54 revenue per session. Store B: 1.9% conversion, $120 AOV = $2.28 revenue per session. Store B generates 48% more revenue per visitor despite 32% lower conversion rate. "Good" conversion rate must be evaluated alongside AOV—optimizing one while ignoring other creates incomplete strategy.

Your business model

First-time-only traffic (no brand awareness, cold traffic): 0.8-1.5% conversion typical. Mixed traffic (some brand recognition): 1.8-2.8% conversion expected. Returning customer-heavy traffic: 3-5% conversion common. Subscription services: 1-2% conversion for initial signup (converting to subscriber harder than one-time purchase). B2B stores: 0.5-1.5% conversion normal due to longer sales cycles and multiple decision-makers.

Marketplace sellers versus independent stores see different rates. Amazon/Etsy sellers benefit from marketplace trust: 2-5% conversion common. Independent store selling same products without marketplace halo: 1.5-2.5% conversion typical. Platform provides built-in trust, simplified checkout, existing customer accounts—all improving conversion independent of product or pricing. Comparing marketplace conversion to independent store conversion ignores massive structural advantages.

Better than industry benchmarks: Your own baseline

Track your personal best

Your best month's conversion rate provides more useful target than industry average. Last 12 months data: January 1.8%, February 1.6%, March 2.1%, April 2.3%, May 2.0%, June 1.9%, July 1.7%, August 1.9%, September 2.2%, October 2.4%, November 2.8%, December 2.6%. Your demonstrated peak: 2.8% (November). Personal best proves achievable—you've done it before under certain conditions. Goal becomes replicating those conditions consistently.

Analyze what drove peak performance. November 2.8% conversion: what was different? Holiday shopping intent? Specific product mix? Promotional calendar? Traffic source composition? Understanding your peak months reveals what's possible for YOUR store with YOUR traffic—more actionable than knowing theoretical industry maximum you've never achieved.

Improvement trajectory matters more than absolute rate

Store improving from 1.3% to 1.5% to 1.7% over three quarters demonstrates healthy optimization even if trailing category average of 2.1%. Trajectory indicates you're identifying and fixing problems systematically. Store stuck at 2.3% for six quarters despite being "above average" has stagnation problem—optimization efforts aren't working despite seemingly strong absolute performance.

Set quarterly improvement targets: current 1.8%, target 2% next quarter (0.2 percentage point gain, 11% improvement). Achievable incremental progress compounds. Four quarters of 10% improvement each: 1.8% → 2.0% → 2.2% → 2.4% → 2.6% = 44% cumulative improvement. Focusing on trajectory creates continuous improvement culture versus hitting arbitrary benchmark then stopping optimization.

Device-specific "good" rates

Desktop versus mobile expectations

Desktop conversion typically 2-3x higher than mobile. Desktop: 2.5-4.5% conversion normal across categories. Mobile: 1-2% conversion typical. Tablet: 1.5-2.5% conversion, splitting difference. This doesn't mean mobile "underperforms"—it means mobile shopping behavior differs. Customers browse on mobile, purchase on desktop. Mobile conversion reflects discovery phase, desktop reflects completion phase.

Mobile-optimized stores narrow the gap but don't eliminate it. Excellent mobile experience might achieve: desktop 3.5%, mobile 2.2% (1.6x gap instead of 2-3x gap). Mobile will always convert lower due to device constraints—smaller screens, more distractions, less comfortable payment entry. Goal isn't parity—goal is closing gap from 3x to 1.5x through mobile-specific optimization.

When device gaps indicate problems

Mobile conversion below 0.5% or desktop-to-mobile gap exceeding 5x suggests mobile usability problems, not just normal device behavior. Test checkout on actual mobile device: can you complete purchase smoothly? Are buttons tappable? Do forms work? Is text readable? Mobile conversion gap becomes diagnostic tool—normal gap (2-3x) indicates expected behavior, extreme gap (5x+) indicates technical problems requiring fixes.

New versus returning customer rates

Returning customers convert 2-5x higher than new visitors. New visitor conversion: 1-2% typical across categories. Returning visitor conversion: 3-8% common due to brand familiarity and simplified checkout (saved payment info, known products). Overall store conversion rate masks this split—2.5% overall might represent 1.5% new + 6% returning. Understanding segment performance prevents misdiagnosing problems.

New visitor conversion rate reveals acquisition efficiency. If new visitors convert 0.5%, you have product-market fit problem or wrong traffic. If new visitors convert 1.8%, acquisition works—optimization should focus on increasing return rate. Returning visitor conversion below 3% suggests checkout friction or unclear value proposition for established customers. Segment analysis tells different optimization story than overall rate.

When "good enough" becomes complacency

Hitting category average then stopping optimization wastes opportunity. 2.1% conversion matching fashion average still means 97.9% of visitors leave without buying—massive room for improvement. Competitors optimize continuously. Standing still means falling behind relatively even if absolute rate stays constant. Market benchmark shifts upward yearly as collective optimization improves—last year's "good" becomes this year's "mediocre."

Calculate revenue opportunity from improvement. Current: 5,000 monthly sessions, 2.1% conversion, $75 AOV = 105 customers, $7,875 monthly revenue. Improving to 2.6% conversion (0.5 percentage point increase) = 130 customers, $9,750 monthly revenue. Additional $1,875 monthly, $22,500 yearly from 0.5 percentage point gain. Even "good" conversion rates have substantial improvement opportunity translating directly to revenue.

Red flags indicating problems regardless of benchmarks

Conversion below 1% for established stores

Sub-1% conversion for stores operating 12+ months indicates fundamental problems independent of category. Possibilities: wrong traffic (visitors aren't target customers), broken checkout (technical failures preventing purchase), trust issues (site looks unprofessional or scammy), pricing problems (dramatically overpriced versus alternatives). Requires diagnosis before optimization—fix root cause before CRO tactics.

Declining conversion trend over 3+ months

Sustained decline—2.4% → 2.2% → 1.9% → 1.7% across quarters—signals systematic problem beyond normal seasonality. Check: increased competition (new entrants lowering your conversion), degrading site performance (loading speed issues), traffic quality decline (wrong channels growing proportion), or broken tracking (measuring incorrectly, not actually declining). Trend matters more than single point—one bad month is variance, three declining months is pattern requiring investigation.

Extreme channel variance

Organic traffic converting 3.5% while paid traffic converts 0.6% indicates paid targeting problems, not normal variance. All channels should be within 2x of each other—organic 3%, paid 1.5-2%, email 4-5%, social 1-2%. When one channel underperforms by 5x+, channel strategy needs revision before more budget goes there. Normal variance shows quality differences, extreme variance shows misalignment between channel and product.

What to do with conversion rate data

Use it for prioritization, not judgment

Conversion rate reveals where optimization attention belongs. Mobile converting 1.1% versus desktop 3.8% = mobile needs focus. Product category A converting 3.2% versus category B converting 0.9% = category B needs attention or discontinuation. Traffic source X converting 0.4% = stop buying from source X. Conversion rate is diagnostic tool showing where problems hide, not report card judging your worth.

Track alongside customer acquisition cost

Conversion rate and CAC together determine viable business model. 2% conversion, $30 CAC = $1,500 CAC per customer cohort (50 customers from 2,500 sessions). If customer LTV is $120, $1,500 ÷ 50 = $30 CAC is viable. Same 2% conversion with $60 CAC = $3,000 per cohort, $60 CAC per customer—potentially unprofitable depending on margins and LTV. "Good" conversion rate enables acceptable CAC, not exists independent of acquisition economics.

Set improvement targets, not benchmark targets

Target: improve 10% quarterly. Current 1.7% → target 1.87% next quarter. Achievable, measurable, focused on your performance versus past. Avoids comparison paralysis from industry benchmarks and focuses effort on continuous improvement. After hitting 1.87%, new target becomes 2.06% (another 10% gain). This framework works regardless of starting point—always room to improve another 10%.

While detailed conversion analysis requires your analytics platform, Peasy delivers your essential daily metrics automatically via email every morning: Conversion rate, Sales, Order count, Average order value, Sessions, Top 5 best-selling products, Top 5 pages, and Top 5 traffic channels—all with automatic comparisons to yesterday, last week, and last year. Monitor whether your conversion rate is "good" by tracking improvement trends without dashboard checking. Starting at $49/month. Try free for 14 days.

Frequently asked questions

My conversion rate is 1.3%. Should I panic?

Depends on context. New store (under 6 months)? 1.3% is reasonable—building awareness takes time. Established store with mostly organic traffic and sub-$100 AOV? 1.3% is low—investigate traffic quality and site usability. High-ticket store ($300+ AOV) with luxury products? 1.3% might be fine—expensive purchases require consideration time. Don't panic based on number alone—analyze your specific situation.

Should I benchmark against direct competitors or industry average?

Neither if you can avoid it. Best benchmark is your own historical performance—are you improving versus last quarter? If forced to external benchmark, find stores with similar: traffic sources, price points, and business model. Comparing luxury fashion site to fast fashion site because both are "fashion" creates meaningless comparison. Specific context matters more than category label.

Can conversion rate be too high?

Possibly. 8%+ conversion rate is suspiciously high unless you have: massive brand recognition (Nike, Apple level), heavily returning customer base (subscription model), or very warm traffic (email to existing customers). For typical small store with mixed traffic, 8%+ might indicate: tracking errors (miscounting sessions or orders), wrong traffic mix (buying only high-intent traffic), or prices so low you're leaving money on table. Extremely high rates warrant investigation ensuring measurement accuracy.

How long until I know if my conversion rate is improving?

Minimum 30 days of data needed for trend assessment. Weekly fluctuation is noise—conversion varies 15-25% week-to-week randomly. Monthly data reveals actual trends. After making optimization changes, measure next 30-60 days versus baseline 30-60 days before change. Statistical confidence requires time and sample size. Premature conclusions from 1-2 weeks of data create false patterns causing bad decisions.