eCommerce KPIs are the metrics that show whether an online store is attracting the right visitors, converting them profitably and keeping them as customers. The most useful set covers traffic quality, conversion, order value, margin after all costs, acquisition cost, retention and operations, measured by channel and cohort rather than as a single blended number.
Most store dashboards are either too thin, showing only revenue and sessions, or too crowded, with dozens of numbers nobody acts on. This listicle sets out 25 metrics that matter, grouped by stage, with what each measures, how to calculate it and why it matters. Use it to build a weekly report the whole team can understand.
Key takeaways
- Revenue alone hides problems. Pair it with contribution margin, CAC and repeat rate.
- Measure conversion by step, device and channel; a single store-wide rate hides where buyers drop out.
- Include returns, COD losses and payment fees in profitability metrics, especially in fashion and electronics.
- Use monthly cohorts for retention and customer value instead of blended averages.
- Reconcile analytics with store orders weekly so reports match what finance sees.
What does a typical store funnel look like, and where are the biggest leaks?
Every store’s funnel is different, which is why benchmark tables mislead. What is consistent is that most visitors never reach an order and that checkout itself loses many interested buyers. Baymard Institute calculates an average documented cart abandonment rate of 70.22% across 50 studies and estimates that large eCommerce sites could gain a 35.26% increase in conversion rate through better checkout design alone. The chart below is a hypothetical store funnel to show how step rates combine.
Hypothetical store funnel: share of sessions reaching each step
Reading the funnel by step shows where to act. A low product-view rate points to landing pages and navigation; a low add-to-cart rate points to product pages; a low checkout completion rate points to costs, forms or payments. Our checkout optimisation guide and product page optimisation guide cover fixes for each.
Metrics 1–4: How do you measure traffic quality?
1. Sessions by channel
What it measures: How many visits each channel brings. How to calculate: Count sessions by default channel group in analytics, such as organic search, paid search, paid social, email and direct. Why it matters: Shows where attention comes from and which channels are growing or shrinking.
2. New versus returning visitors
What it measures: The mix of first-time and repeat visitors. How to calculate: New users ÷ total users, and returning users ÷ total users, for the period. Why it matters: A store that depends only on new visitors is exposed when ad costs rise.
3. Product page view rate
What it measures: How many sessions reach at least one product page. How to calculate: Sessions with a view_item event ÷ total sessions. Why it matters: Low rates suggest poor landing pages, navigation or search.
4. Organic search clicks to categories and products
What it measures: Search visibility for commercial pages. How to calculate: Search Console clicks for category and product URLs, grouped by page type. Why it matters: Shows whether SEO is growing the pages that sell; see our eCommerce SEO guide.
Metrics 5–10: How do you measure conversion?
5. Conversion rate
What it measures: Share of sessions that end in an order. How to calculate: Sessions with a purchase ÷ total sessions, by channel and device. Why it matters: The headline efficiency measure, but only useful when broken down.
6. Add-to-cart rate
What it measures: How often product interest becomes intent. How to calculate: Sessions with add_to_cart ÷ sessions with view_item. Why it matters: Points to product page issues: price, content, images, delivery information.
7. Cart abandonment rate
What it measures: Share of carts that do not become orders. How to calculate: 1 − (orders ÷ sessions with add_to_cart), or use carts created in store data. Why it matters: High rates suggest cost surprises, delivery concerns or comparison shopping.
8. Checkout completion rate
What it measures: Share of started checkouts that finish. How to calculate: Orders ÷ sessions with begin_checkout. Why it matters: Isolates the checkout itself from earlier stages.
9. Payment success rate
What it measures: Share of payment attempts that succeed, by method. How to calculate: Successful payments ÷ payment attempts, from the gateway dashboard. Why it matters: UPI or card failures can look like abandonment in analytics but are a technical problem.
10. Site search conversion rate
What it measures: How well on-site search leads to orders. How to calculate: Orders from sessions using site search ÷ sessions using site search. Why it matters: Search users often have high intent; poor results waste it.
Metrics 11–14: How do you measure order value and profitability?
11. Average order value (AOV)
What it measures: Typical order size. How to calculate: Revenue ÷ number of orders, before or after discounts (be consistent). Why it matters: Bundles, thresholds and recommendations can raise AOV, but watch margin.
12. Gross margin
What it measures: Profit after product cost. How to calculate: (Revenue − cost of goods sold) ÷ revenue. Why it matters: Shows whether pricing covers product cost before operations and marketing.
13. Contribution margin per order
What it measures: Money left after variable costs. How to calculate: Order revenue − product cost − packaging − shipping − payment fees − expected returns and COD losses. Why it matters: The money available to pay for marketing and overheads; the most important single profitability metric for D2C.
14. Discount rate
What it measures: How much revenue is given away in discounts. How to calculate: Total discounts ÷ gross revenue before discounts. Why it matters: Rising discount rates can hide weak demand and train customers to wait for offers.
Metrics 15–18: How do you measure acquisition efficiency?
15. Customer acquisition cost (CAC)
What it measures: Cost to win a new customer. How to calculate: Marketing spend aimed at new customers ÷ new customers acquired, by channel. Why it matters: Must be compared with contribution per customer to know whether growth is profitable.
16. Return on ad spend (ROAS)
What it measures: Revenue generated per rupee of ad spend. How to calculate: Attributed revenue ÷ ad spend, by campaign. Why it matters: Useful for optimising campaigns, but revenue-based ROAS ignores margin and returns.
17. Marketing efficiency ratio (MER)
What it measures: Overall marketing efficiency. How to calculate: Total revenue ÷ total marketing spend, for the same period. Why it matters: A blended check that does not depend on attribution models.
18. Payback period
What it measures: How long it takes to recover CAC. How to calculate: Months until cumulative contribution from a cohort covers its CAC. Why it matters: Shows how much cash growth ties up and how fast spend can safely scale.
Four questions every weekly report should answer
Traffic mix and product view rate.
Step rates by device and channel.
Contribution margin after returns and fees.
Cohort repeat rate and value.
Metrics 19–22: How do you measure retention and customer value?
19. Repeat purchase rate
What it measures: Share of customers who buy again. How to calculate: Customers in a monthly cohort with 2+ orders within a window (for example 90 days) ÷ customers in the cohort. Why it matters: Shows whether the product and experience earn loyalty; see our retention and loyalty guide.
20. Time to second order
What it measures: How long customers take to reorder. How to calculate: Median days between first and second order, by cohort. Why it matters: Sets the timing for replenishment and win-back messages.
21. Customer lifetime value (cohort contribution)
What it measures: Value of a customer over time. How to calculate: Total contribution margin from a cohort over 6 or 12 months ÷ customers in the cohort. Why it matters: A grounded version of lifetime value; compare with CAC by channel.
22. Email and WhatsApp revenue per recipient
What it measures: Value of owned channels. How to calculate: Revenue attributed to a flow or campaign ÷ recipients, and cost per order for WhatsApp. Why it matters: Shows which flows earn their place; see our email and WhatsApp guide.
Metrics 23–25: How do you measure operations?
23. Return and refund rate
What it measures: Share of orders returned or refunded. How to calculate: Returned orders ÷ delivered orders, by product, category and reason. Why it matters: High return rates erase margin and often point to sizing or description problems.
24. Delivery success and on-time rate
What it measures: How reliably orders arrive as promised. How to calculate: Delivered on or before the promised date ÷ shipped orders, by courier and region; include failed deliveries and RTO. Why it matters: Late and failed deliveries hurt repeat purchase and cost money, especially with COD.
25. Customer support contact rate
What it measures: How often customers need help. How to calculate: Support tickets or chats ÷ orders, by reason. Why it matters: Rising contact rates signal problems with delivery, product information or the website.
How should you build a weekly eCommerce dashboard?
| Section | Metrics | Breakdowns |
|---|---|---|
| Summary | Revenue, orders, contribution margin, MER | Week on week and year on year |
| Acquisition | Sessions, new customers, CAC, ROAS | By channel and campaign |
| Conversion | Product view, add-to-cart, checkout completion, payment success | By device and channel |
| Order economics | AOV, discount rate, contribution per order | By category and channel |
| Retention | Repeat rate, time to second order, cohort contribution | By monthly cohort and first channel |
| Operations | Returns, delivery success, support contact rate | By product, courier and reason |
Keep the weekly report to one page that someone can read in five minutes, with links to detail. Use GA4’s recommended ecommerce events, documented in Google’s ecommerce measurement guide, so product and funnel reports are consistent. Our marketing analytics and attribution guide explains how to set up reporting that leadership trusts.
How do you make sure eCommerce data is accurate?
- Reconcile orders and revenue in analytics with the store’s order report every week; investigate gaps above a few percent.
- Fire purchase events only on confirmed orders, and avoid duplicates on page reloads.
- Track refunds and cancellations so revenue reports reflect reality.
- Respect consent choices; consent mode and modelled data will make analytics differ from store data.
- Use one source of truth for each metric, for example the store for orders and the gateway for payment success.
- Document metric definitions so everyone calculates AOV, CAC and repeat rate the same way.
How should you set targets for these metrics?
Generic benchmarks are rarely useful, because category, price point, traffic mix and device split change every rate. Set targets from your own history: take the last three to six months by segment, decide which metric matters most this quarter, and set a realistic improvement linked to specific work, such as a checkout fix or a new retention flow. Review targets monthly and change them when the business changes, for example after a price change or a new channel launch. Pair every growth target with a guardrail, such as keeping contribution margin or return rate within a set range, so one number does not improve at the expense of another.
Which metrics need special attention for Indian stores?
- COD share and COD return-to-origin rate, because failed COD deliveries carry shipping costs both ways.
- UPI payment success rate, tracked separately from cards and wallets.
- Pincode-level delivery performance, since courier reliability varies by region.
- Marketplace contribution after fees and ads, compared with your own website.
- Mobile conversion on mid-range devices, which reflects many real buyers.
Context also matters when comparing channels. Bain and Flipkart’s How India Shops Online 2026 report notes that quick commerce shopping missions are driven by predetermined needs, with sessions under five minutes and about eight times higher visit-to-order conversion than broader e-retail. A D2C website, where many visitors are discovering a brand for the first time, should not expect quick commerce-style conversion rates. Compare each channel with its own history and purpose.
Who should own each group of metrics?
- Marketing: traffic, CAC, ROAS, MER and owned-channel revenue.
- eCommerce or product team: conversion step rates, site search and payment success.
- Finance: contribution margin, discount rate and payback, using agreed definitions.
- Operations: delivery success, returns and support contact rate.
- Leadership: the weekly summary and quarterly cohort review.
Named owners make it clear who investigates when a number moves, and stop the same metric being calculated three different ways in three different spreadsheets.
What are the most common KPI mistakes?
- Reporting revenue growth without contribution margin.
- Using a single blended conversion rate for all channels and devices.
- Trusting platform-reported ROAS without checking blended MER.
- Ignoring returns and COD losses in profitability.
- Measuring retention with averages instead of cohorts.
- Tracking dozens of metrics but acting on none.
Which related guides should you read next?
- Marketing analytics and attribution covers reporting models and attribution.
- Checkout optimisation improves checkout completion and payment success.
- Customer retention and loyalty for D2C improves repeat rate and cohort value.
- D2C growth strategy in India uses these metrics to plan growth.
- eCommerce SEO grows organic traffic to categories and products.
Frequently asked questions
What is a good eCommerce conversion rate?
It varies widely by category, price, device and traffic source. Compare against your own history by segment and focus on step rates, such as add-to-cart and checkout completion, to find where to improve.
What is the difference between ROAS and MER?
ROAS measures revenue attributed to specific ads against their spend. MER compares total revenue with total marketing spend, so it does not depend on attribution models. Use both.
How should I calculate customer lifetime value?
Use cohort contribution: the contribution margin a monthly cohort of new customers generates over 6 or 12 months, divided by the number of customers in that cohort. It is more reliable than formula-based estimates.
Which KPIs should a small store track first?
Start with conversion rate by device and channel, add-to-cart rate, checkout completion, AOV, contribution margin per order, CAC and repeat purchase rate.
Why does GA4 revenue not match my store?
Common causes are consent choices, ad blockers, duplicate or missing purchase events, refunds and time zone differences. Reconcile weekly and fix tracking gaps.
How often should I review eCommerce KPIs?
Review acquisition and conversion weekly, profitability and retention monthly, and cohort value quarterly.
Sources and further reading
- Baymard Institute: cart abandonment rate statistics
- Google Analytics: measure ecommerce
- Bain & Company and Flipkart: How India Shops Online 2026 (April 2026).
- The funnel chart is hypothetical and for teaching only. Metric definitions, the scorecard model and the dashboard layout are Bargaon’s editorial guidance.
Want a store dashboard that shows what to fix next? Bargaon’s eCommerce demand generation service sets up tracking, cohort reporting and KPI dashboards for D2C brands.