Shopify Metrics to Track Daily, Weekly, and Monthly (Operator Playbook)

If you run a Shopify store, the problem is not lack of data. The problem is deciding which numbers deserve attention today, this week, and this month.

How to read this guide

• Daily: catch breakages and protect revenue.
• Weekly: optimize channels, offers, and merchandising.
• Monthly: judge profitability and growth quality.

Use weekday-to-weekday comparisons for daily data. For weekly and monthly, focus on trend direction and decision impact.

Daily, weekly, and monthly metrics to track

Cadence	Metric	What it tells you	Formula (simple)	Primary action if it worsens
Daily	Net sales and orders	Revenue health and demand volume	Net sales = Gross sales - discounts - returns	Check checkout, payment, traffic quality, and stock availability.
Daily	Conversion rate	How well sessions convert into purchases	Conversion rate = Orders / Sessions * 100	Audit landing pages, shipping friction, promo code issues, and checkout UX.
Daily	Add-to-cart rate	Product page buying intent	Add-to-cart rate = Sessions with add_to_cart / Sessions * 100	Improve product page clarity, pricing communication, and offer strength.
Daily	Checkout start rate and cart abandonment rate	Where buyers drop before purchase	Cart abandonment = (Initiated checkouts - Purchases) / Initiated checkouts * 100	Review shipping cost shock, payment failures, and trust signals.
Daily	ROAS (Return on Ad Spend) by channel	Fast read on paid spend efficiency	ROAS = Attributed revenue / Ad spend	Pause wasteful ad sets and validate with weekly profitability metrics.
Daily	Days of cover for top SKU	Stockout risk on best sellers	Days of cover = Inventory units / Avg daily units sold	Expedite replenishment or shift budget to in-stock products.
Daily	Refund rate	Product or fulfillment quality signal	Refund rate = Refunded orders / Total orders * 100	Investigate return reasons, fulfillment delays, and product expectation gaps.
Weekly	Week-over-week revenue, orders, conversion	Whether growth is volume-led or efficiency-led	WoW % = (This week - Last week) / Last week * 100	Rebalance acquisition vs conversion optimization priorities.
Weekly	CAC (Customer Acquisition Cost) by channel	Cost to acquire each new customer	CAC = Acquisition spend / New customers	Tighten targeting, refresh creative, or reduce spend on weak channels.
Weekly	AOV (Average Order Value)	Revenue per order	AOV = Net sales / Orders	Test bundles, upsells, and free-shipping thresholds.
Weekly	Contribution margin	Profitability after variable costs	Contribution margin = Net sales - COGS - fulfillment - fees - ad spend	Cut unprofitable campaigns or improve pricing and product mix.
Weekly	Repeat purchase rate	Customer quality and retention momentum	Repeat rate = Customers with 2+ orders / Total customers * 100	Improve post-purchase flows, lifecycle messaging, and retention offers.
Weekly	Revenue per recipient (Email/SMS)	Commercial impact of lifecycle marketing	Revenue per recipient = Attributed revenue / Recipients	Adjust send mix, segmentation, and offer strategy.
Monthly	Net revenue growth (MoM/YoY)	Overall business trajectory	MoM % = (Current month - Previous month) / Previous month * 100	Diagnose whether shifts are demand, pricing, or margin driven.
Monthly	MER (Marketing Efficiency Ratio)	Blended efficiency of all paid media	MER = Total revenue / Total paid media spend	Reallocate budget mix and align top-line goals with efficiency limits.
Monthly	LTV (Customer Lifetime Value) by cohort	Long-term value from acquired customers	LTV = AOV * Purchase frequency * Lifespan	Increase retention and repurchase via product and lifecycle improvements.
Monthly	LTV:CAC (Customer Lifetime Value to Customer Acquisition Cost) ratio	Sustainability of growth economics	LTV:CAC = LTV / CAC	Slow aggressive acquisition if ratio weakens; improve payback model.
Monthly	Gross margin and contribution margin trend	Whether growth is profitable	Gross margin % = (Net sales - COGS) / Net sales * 100	Fix discount strategy, COGS pressure, and channel mix.
Monthly	Inventory turnover and aging stock	Cash efficiency in inventory	Inventory turnover = COGS / Average inventory value	Clear slow stock and adjust purchasing forecasts.

Daily Shopify metrics to track

1. Net sales and orders

What it is:
Net sales is your real top-line after discounts and returns. Orders is the count of completed purchases.

Why it matters:
Most teams celebrate gross sales, but gross can hide discount-heavy days or return-heavy campaigns. Net sales tells you what you actually keep. Orders tells you whether demand volume is stable.

Formula:
Net sales = Gross sales - discounts - returns

Example:
If gross sales are $18,500, discounts are $1,900, and returns are $600, net sales are $16,000. If orders also fell from 420 yesterday to 330 today, you likely have both demand and conversion pressure.

Benchmark or context:
Use a weekday baseline, not day-over-day only. Compare today against the average of the last 4 same weekdays.

Red flag:
Net sales down for 3+ same-weekday comparisons while traffic is flat.

Action checklist:

• Check checkout uptime and payment gateway status.
• Check top landing pages for traffic-quality shifts.
• Check top SKUs for stockouts and variant availability.

2. Store conversion rate

What it is:
Store conversion rate is the percentage of sessions that end in an order.

Why it matters:
This is your fastest indicator of funnel efficiency. It helps you separate a traffic problem from an on-site buying-friction problem.

Formula:
Conversion rate = Orders / Sessions * 100

Example:
96 orders from 3,200 sessions gives 3.0%. If sessions stay near 3,200 but conversion slips to 2.2%, the issue is usually landing-page-to-checkout friction, not traffic volume.

Benchmark or context:
Use your own 4-week baseline by device and channel. Mobile conversion is usually lower than desktop, so blended numbers can hide mobile-specific issues.

Red flag:
Conversion drops >20% vs baseline for 2 consecutive days while sessions are stable.

Action checklist:

• Re-test checkout on mobile (coupon, autofill, payment flow).
• Verify shipping fees and delivery timelines are visible pre-checkout.
• Audit promo codes and auto-discount logic.
• Check product pages with highest traffic for slow load or broken variants.

3. Add-to-cart rate

What it is:
Add-to-cart rate measures how often sessions show first-step purchase intent.

Why it matters:
If this rate falls, the product page is usually the bottleneck. It is an early signal that pricing, value proposition, or merchandising is not landing.

Formula:
Add-to-cart rate = Sessions with add_to_cart / Sessions * 100

Example:
400 add-to-cart sessions out of 4,000 sessions equals 10%. If you launch new creative and this falls to 7%, your ad-to-product-page message match is likely weak.

Benchmark or context:
Track by top SKU and top landing page. Blended store averages can mask product-level issues.

Red flag:
Add-to-cart rate drops on high-traffic pages while conversion and traffic stay flat.

Action checklist:

• Improve first-screen product value proposition and benefits.
• Verify image quality, variant clarity, and size/fit guidance.
• Make shipping/return policy visible near price and CTA.
• Remove unavailable variants from default selections.

4. Checkout start rate and cart abandonment rate

What it is:
Checkout start rate shows how many sessions move into checkout. Cart abandonment shows how many checkout starters fail to purchase.

Why it matters:
These metrics isolate where intent leaks after a shopper is already interested.

Formula:
Checkout start rate = Sessions with begin_checkout / Sessions * 100
Cart abandonment rate = (Initiated checkouts - Completed purchases) / Initiated checkouts * 100

Example:
If 500 users start checkout and 180 complete purchase, abandonment is 64%. If this jumps from 58% to 64% in one week, checkout friction likely increased.

Benchmark or context:
Baymard often reports overall cart abandonment near 70% across ecommerce, but your own trend and step-level drop-off are more decision-useful.

Red flag:
Checkout starts stay stable but purchase completion drops across devices.

Action checklist:

• Check shipping cost visibility before checkout.
• Verify guest checkout and express payment options.
• Review checkout step where drop-off is highest.
• Trigger and test abandoned checkout recovery flows.

5. ROAS by active paid channel

What it is:
Return on Ad Spend (ROAS) tells you how much attributed revenue each ad dollar generated.

Why it matters:
This is a daily control metric for paid media. It helps you cap losses quickly during active campaign periods.

Formula:
ROAS = Attributed revenue / Ad spend

Example:
If Meta spend is $2,000 and attributed revenue is $6,000, ROAS is 3.0. If Google drops from 3.4 to 2.1 while spend rises, efficiency is likely deteriorating.

Benchmark or context:
Treat channel ROAS as directional. Validate with weekly Customer Acquisition Cost (CAC) and contribution margin because attribution windows can over-credit channels.

Red flag:
ROAS falls below your break-even threshold for multiple consecutive days.

Action checklist:

• Pause low-intent audience segments first.
• Reallocate budget to stable campaigns and proven creatives.
• Check landing-page message match for poor-performing campaigns.
• Validate whether attribution lag explains the dip before major cuts.

6. Top SKU sell-through and days of cover

What it is:
Sell-through shows inventory movement speed. Days of cover estimates how many days a SKU can stay in stock at current demand.

Why it matters:
Your best campaign cannot save revenue if hero SKUs stock out. This metric protects both demand capture and customer experience.

Formula:
Days of cover = Current inventory units / Avg daily units sold

Example:
240 units with 30 units/day sales gives 8 days of cover. If your replenishment lead time is 12 days, this SKU is already at risk.

Benchmark or context:
Define SKU-level guardrails by lead time. A common practical rule is to reorder before cover drops below lead-time + safety buffer.

Red flag:
Any top-20% revenue SKU has days of cover below reorder threshold.

Action checklist:

• Trigger replenishment or transfer inventory.
• Shift ad spend toward in-stock alternatives.
• Prioritize back-in-stock capture for low-cover SKUs.
• Update merchandising logic to avoid pushing near-stockout variants.

7. Refund rate

What it is:
Refund rate is the percentage of orders that are refunded in full or part.

Why it matters:
Refunds compress margin and reveal expectation mismatch across product, content, and fulfillment.

Formula:
Refund rate = Refunded orders / Total orders * 100

Example:
If 18 of 450 orders are refunded, refund rate is 4%. If this rises from 2.8% to 4.0% over two weeks, investigate by SKU and reason, not just store-level totals.

Benchmark or context:
Return/refund norms vary by category. Track against your own category-specific baseline and top-SKU history.

Red flag:
Refund rate rises for 2+ weeks and clusters around specific SKUs or promise claims.

Action checklist:

• Audit top refund reasons by SKU and campaign source.
• Fix misleading product content and sizing/fit guidance.
• Review fulfillment quality and delivery delay patterns.
• Update support scripts to capture structured reason codes.

8. Checkout or payment error rate

What it is:
Checkout or payment error rate measures failed checkout attempts caused by technical or payment failures.

Why it matters:
It is a priority operational metric. If checkout fails, every marketing and conversion analysis becomes unreliable.

Formula:
Checkout error rate = Failed checkout attempts / Total checkout attempts * 100

Example:
35 failures out of 700 attempts gives 5%. At scale, that can mean dozens of lost orders in a single day.

Benchmark or context:
Use gateway and device segmentation. A stable overall rate can hide severe failure on one browser, device, or payment method.

Red flag:
Error rate spikes after theme/app changes or gateway updates.

Action checklist:

• Segment failures by device, browser, and payment method.
• Verify third-party checkout apps after each deployment.
• Monitor authorization decline reason codes by gateway.
• Create real-time alerts for sudden failure spikes.

Weekly metrics to track

Weekly numbers smooth day-level volatility and are better for optimization and budget decisions.

1. Week-over-week revenue, orders, and conversion

What it is:
This is a three-metric weekly check on revenue, order volume, and conversion efficiency.

Why it matters:
Revenue alone can hide problems. You need all three to know whether growth came from better traffic, better conversion, or both.

Formula:
WoW change (%) = (This week - Last week) / Last week * 100

Example:
Revenue rises from $72k to $79k (+9.7%), but conversion drops from 3.1% to 2.7%. That usually means traffic volume increased while efficiency weakened.

Benchmark or context:
Use 4-week moving trend, not single-week wins, especially during promos.

Red flag:
Revenue up but conversion down for 2+ weeks.

Action checklist:

• Audit channel mix for lower-intent traffic growth.
• Review landing pages driving the largest traffic increase.
• Separate promo-driven spikes from durable performance changes.

2. Customer acquisition cost (CAC) by channel

What it is:
Customer Acquisition Cost (CAC) by channel is the average cost to acquire one new customer from each paid channel.

Why it matters:
CAC tells you where growth is efficient and where spend is getting expensive.

Formula:
CAC = Acquisition spend / New customers

Example:
If Google spend is $9,000 and it drives 225 new customers, CAC is $40.

Benchmark or context:
Include all meaningful channel costs where possible: media spend, creative, agency, and tooling allocations.

Red flag:
CAC exceeds first-order contribution margin for consecutive weeks.

Action checklist:

• Tighten targeting and exclude low-intent audiences.
• Refresh fatigued creatives and offers.
• Reduce bids on low-quality placements.
• Rebalance budget toward channels with stronger payback.

3. Average order value (AOV)

What it is:
Average Order Value (AOV) is the average net revenue per order.

Why it matters:
AOV shows whether upsells, bundles, and pricing structure are increasing basket value.

Formula:
AOV = Net sales / Orders

Example:
$52,500 net sales from 1,250 orders gives AOV = $42.

Benchmark or context:
Review AOV by channel, new vs repeat customers, and device type to find profitable mix shifts.

Red flag:
AOV increases but conversion drops enough to reduce overall contribution margin.

Action checklist:

• Test bundle composition and pricing ladders.
• Revisit free-shipping threshold against margin math.
• Review discount strategy for unnecessary basket erosion.

4. Contribution margin by channel

What it is:
Contribution margin by channel is what remains after variable costs and channel spend.

Why it matters:
This is the profitability truth metric. High revenue with weak contribution margin is not healthy scale.

Formula:
Contribution margin = Net sales - COGS - variable fulfillment - payment fees - ad spend

Example:
$40,000 net sales, $14,000 COGS, $4,000 fulfillment, $1,200 fees, $12,000 ad spend leaves $8,800 contribution margin (22%).

Benchmark or context:
Track contribution margin by channel and by campaign cohort to find where volume is diluting profitability.

Red flag:
ROAS looks healthy but contribution margin trends downward.

Action checklist:

• Cut spend on channels below contribution targets.
• Raise price or reduce discount depth on weak-SKU campaigns.
• Shift budget toward high-margin SKUs and proven segments.

For deeper modeling, use contribution margin formula guide.

5. Returning customer rate or repeat purchase rate

What it is:
Repeat purchase rate is the percentage of customers with 2+ orders in a defined period.

Why it matters:
It indicates retention quality. Repeat buyers usually improve unit economics and reduce dependence on expensive acquisition.

Formula:
Repeat purchase rate = Customers with 2+ orders / Total customers * 100

Example:
180 repeat buyers from 900 total customers gives 20%.

Benchmark or context:
Benchmarks vary by category, so prioritize cohort trend: are new cohorts repeating at higher or lower rates than prior cohorts?

Red flag:
Repeat rate declines for recent cohorts while CAC rises.

Action checklist:

• Strengthen post-purchase email/SMS flows by product type.
• Launch replenishment and win-back sequences for consumables.
• Review early-order experience and delivery reliability.

6. Product performance funnel

What it is:
This funnel tracks key SKU performance from product view to add-to-cart to purchase.

Why it matters:
It pinpoints exactly where a product loses buyers, so fixes become specific instead of generic.

Formula:
Add-to-cart from view = Add-to-carts / Product views * 100
Buy-to-detail rate = Purchases / Product detail views * 100

Example:
For 1,000 product views, 120 add-to-carts, and 40 purchases, add-to-cart-from-view is 12% and buy-to-detail is 4%.

Benchmark or context:
Use SKU-to-SKU comparison inside the same category before using external benchmarks.

Red flag:
High product views with weak add-to-cart and weak purchase rates.

Action checklist:

• Improve product page narrative, social proof, and FAQs.
• Test image order, PDP layout, and price framing.
• Validate stock and variant defaults on high-traffic SKUs.

7. Email/SMS revenue efficiency

What it is:
Revenue per recipient measures lifecycle channel efficiency in commercial terms.

Why it matters:
Open and click rates are engagement signals. Revenue per recipient shows whether messaging is producing actual sales.

Formula:
Revenue per recipient = Campaign-attributed revenue / Recipients

Example:
$8,400 attributed revenue from 14,000 recipients gives $0.60 per recipient.

Benchmark or context:
Compare by flow type (welcome, abandoned checkout, win-back, campaign blast) because expected value differs significantly.

Red flag:
Sends increase but revenue per recipient declines for 2+ weeks.

Action checklist:

• Improve segmentation and offer relevance by intent stage.
• Reduce over-sending to fatigued segments.
• A/B test creative angle and CTA clarity by segment.

Monthly metrics to track

Monthly review is where founders and operators test whether growth is healthy, not just visible.

1. Net revenue growth (MoM and YoY)

What it is:
Net revenue growth tracks business momentum month-over-month (MoM) and year-over-year (YoY).

Why it matters:
MoM gives speed, YoY gives seasonality context. You need both to avoid false conclusions.

Formula:
MoM growth (%) = (Current month net revenue - Previous month net revenue) / Previous month net revenue * 100

Example:
If net revenue rises from $180k to $210k, MoM growth is +16.7%.

Benchmark or context:
Pair growth with margin trend. Fast growth with deteriorating margin is usually expensive growth.

Red flag:
Revenue grows while gross margin and contribution margin both decline.

Action checklist:

• Split growth into price, volume, and discount effects.
• Review channel mix and promo dependency.
• Adjust growth targets to margin guardrails.

2. MER (marketing efficiency ratio)

What it is:
Marketing Efficiency Ratio (MER) is total revenue divided by total paid media spend.

Why it matters:
MER is the cleanest blended view of how efficiently the paid system supports top-line growth.

Formula:
MER = Total revenue / Total paid media spend

Example:
$250k revenue on $50k paid spend gives MER 5.0.

Benchmark or context:
Set a brand-specific MER floor based on margin structure. Track trend, not only one-month absolute value.

Red flag:
MER declines for multiple months while spend keeps increasing.

Action checklist:

• Rebalance spend toward channels with stronger contribution outcomes.
• Tighten audience quality and creative testing standards.
• Reduce low-intent prospecting during weak payback periods.

3. Customer lifetime value (LTV) by cohort

What it is:
Customer Lifetime Value (LTV) estimates expected revenue generated by a customer over their relationship with your brand.

Why it matters:
Cohort-based LTV shows whether newly acquired customers are getting better or worse over time.

Formula:
LTV = Average order value * Purchase frequency * Customer lifespan

Example:
If AOV is $45, purchase frequency is 4 orders/year, and customer lifespan is 2 years, LTV is $360.

Benchmark or context:
Use cohort LTV (by acquisition month) instead of one blended LTV number.

Red flag:
Recent cohorts show weaker early repeat behavior than prior cohorts.

Action checklist:

• Diagnose cohort-level retention by channel and first SKU purchased.
• Improve onboarding and post-purchase nurture by cohort.
• Adjust acquisition sources that deliver low-LTV cohorts.

4. LTV:CAC ratio and CAC payback

What it is:
LTV:CAC ratio compares long-term customer value with acquisition cost. CAC payback estimates how quickly acquisition spend is recovered.

Why it matters:
This is a core sustainability signal for scaling decisions.

Formula:
LTV:CAC = LTV / CAC

Example:
With LTV $360 and CAC $90, ratio is 4.0. If first-order contribution is $30, payback needs roughly 3 similar contribution cycles.

Benchmark or context:
Many operators use 3:1 as a practical target zone, but acceptable ranges depend on cash cycle and margin profile.

Red flag:
Ratio trends toward 2:1 while CAC payback period lengthens.

Action checklist:

• Slow scale in channels with weak payback.
• Improve first-order contribution via pricing, bundles, and shipping policy.
• Invest in retention levers that lift cohort LTV.

5. Gross margin and contribution margin trend

What it is:
Gross margin reflects product economics before marketing. Contribution margin reflects profitability after variable go-to-market costs.

Why it matters:
Tracking both shows whether profitability loss comes from product economics or acquisition/fulfillment pressure.

Formula:
Gross margin (%) = (Net sales - COGS) / Net sales * 100

Example:
If net sales are $300k and COGS is $150k, gross margin is 50%. If contribution margin falls from 24% to 16% in the same period, channel or operational costs are likely driving erosion.

Benchmark or context:
Margin targets should be category-specific and set by finance, but trend direction month over month is universally important.

Red flag:
Revenue rises while contribution margin declines for 2+ months.

Action checklist:

• Reprice or reduce discount intensity on low-margin SKUs.
• Improve shipping and fulfillment efficiency.
• Shift spend toward higher-margin product lines.

6. Inventory turnover and aging stock

What it is:
Inventory turnover shows how often inventory converts into sales during a period.

Why it matters:
Low turnover ties up cash. Very high turnover can signal frequent stockout risk.

Formula:
Inventory turnover = COGS / Average inventory value

Example:
Monthly COGS of $120k with average inventory value of $240k gives 0.5 monthly turnover.

Benchmark or context:
Turnover expectations differ by category; use turnover together with stock aging buckets (30/60/90+ days).

Red flag:
Aging inventory accumulates while top sellers still face stock pressure.

Action checklist:

• Run clearance or bundle strategy for aging SKUs.
• Update demand forecasting by SKU velocity.
• Reallocate open-to-buy toward high-velocity categories.

7. Return and support burden

What it is:
This combines return/refund rate, return reasons, and support tickets per 100 orders into one operational quality lens.

Why it matters:
When support burden rises, margin and repeat purchase performance often deteriorate soon after.

Formula:
Support tickets per 100 orders = (Total support tickets / Total orders) * 100

Example:
If you receive 520 tickets on 2,600 orders, tickets per 100 orders is 20.

Benchmark or context:
Track by issue type (delivery, damaged item, wrong variant, billing). Total volume without category split is hard to act on.

Red flag:
Returns and support tickets rise together after campaign or product launches.

Action checklist:

• Link top support categories to specific SKUs and campaigns.
• Fix root causes in product page claims and fulfillment SLAs.
• Create monthly feedback loop across support, ops, and growth.

Benchmark Ranges for Ecommerce Operators

Use these as directional ranges for ecommerce operators. Treat your own baseline as the source of truth.

Metric	Typical directional range	How to use it
Store conversion rate	~1.5% to 4% (varies by category and traffic mix)	Segment by channel and device before acting on blended averages.
Add-to-cart rate	~6% to 12% for many stores	Use SKU-level diagnostics when high-traffic products underperform.
Cart abandonment rate	Often 55% to 75%	Pair with checkout-step drop-off to locate friction.
Repeat purchase rate	Commonly 15% to 35% by category and maturity	Track by acquisition cohort, not only store-wide rollups.
LTV:CAC ratio	Often targeted around 3:1+	Combine with cash payback timing before scaling spend.

Make your numbers trustworthy

Good ecommerce operators fail less on analysis and more on metric definitions. Use these rules:

• Lock one denominator per KPI and keep it stable.
• Keep one source of truth per metric family.
• Separate shop traffic metrics from ad platform metrics to avoid double counting.
• Align event tracking for funnel metrics (add_to_cart, begin_checkout, purchase) in GA4 and Shopify integrations.
• Write one action rule per KPI so reports lead to decisions.

If you are rebuilding your reporting setup, use analytics dashboard best practices.

FAQ

What is the minimum daily metric set for a Shopify store?

Use net sales, orders, conversion rate, add-to-cart rate, checkout completion, ROAS by active channel, top-SKU stock cover, and refund rate. This set catches both demand and operational issues.

Why do teams disagree on conversion rate?

The numerator and denominator differ by tool. Lock one definition (for example, orders divided by sessions) and keep it fixed in dashboards and meetings.

Can I use benchmarks from other brands?

Use benchmarks as context, not targets. Your own baseline and trend over time are more useful than copying a generic industry number.