Six Key Product Metrics Every eCommerce Team Should Track

Most online stores track the same handful of numbers: total revenue, sessions, overall conversion rate, and maybe average order value. These aggregate metrics tell you what happened. They rarely tell you why, or which products are responsible.

The gap matters because product-level decisions drive store growth in ways that store-level aggregates cannot. If you know Product A converts at 8% and Product B at 0.9%, you can fix Product B. If you only know your store converts at 2.3%, you are managing an average.

Here are six product metrics that every eCommerce team should be measuring, and what each one unlocks.

1. Product-Level Conversion Rate

Site-wide CVR is one of the most misleading numbers in eCommerce. A store converting at 2.3% overall can mask a hero product converting at 8% while three others sit at 0.4% and drag the average down. You can spend months optimizing your checkout when the real problem is one underperforming product page.

Product-level conversion rate measures what percentage of product page visitors complete a purchase for that specific SKU. Tracked by product, the variation is almost always surprising.

A summer apparel store running Stormly’s product analytics found one jacket with 13.4% CVR and another in the same category at 0.8%. Both had similar traffic, similar price points, and similar Google Ads spend. The difference: the high-converting jacket had three size-guide images and a fit comparison chart. The other had none. Applying size guides to six underperforming products lifted category CVR by 2.1 percentage points over the following month.

This kind of insight is invisible at the aggregate level. It only appears when you measure conversion rate by product.

[For context on where Shopify’s native analytics misses product-level CVR, read what Shopify Analytics gets wrong and how to reconcile the numbers.]

2. Repeat Purchase Rate by Product

Not every product builds customers at the same rate. Some are one-time buys. Others are gateway products that reliably produce a second purchase within 30 days. This is the most actionable product metric for a store that wants to grow through retention rather than paid acquisition.

Repeat purchase rate by product measures what percentage of customers who first bought a specific SKU returned to make any purchase within a defined window: 30, 60, or 90 days.

A candle brand tracking this in Stormly found that customers who started with a $12 wax melt sampler set returned within 30 days at a 61% rate. Customers who started with a $48 candle gift set returned at 18%. The sampler was not just a low-ticket item; it was the product that created loyal buyers. Once identified, the brand moved the sampler into their checkout upsell and first-purchase welcome email sequence. Store-wide repeat rate improved 11 points over three months.

The product with the highest repeat purchase rate is your aha moment product. It is the SKU that creates customers, not just transactions.

[For a deeper look at what the aha moment means for an online store and how to find yours, read how to find the aha moment that turns browsers into repeat buyers.]

Identify your store’s highest-retention product in Stormly. Start a free trial.

3. Retention Rate by First-Purchase Category

Where repeat purchase rate is granular (SKU-level), retention rate by first-purchase category answers a broader strategic question: which parts of your catalog build long-term customers?

Customers who first bought from your skincare line may retain at a fundamentally different rate than customers who first bought from apparel. In many stores, the answer is counterintuitive. High-margin categories often have high churn; consumables and everyday items produce the most loyal cohorts.

With Stormly you can build a retention curve for each first-purchase category and compare them side by side. An outdoor gear retailer did this and found that customers who first bought hydration packs had 3.4x the 90-day retention rate of customers who first bought tents. Tents were high-margin and high-ticket; hydration packs were lower-margin consumables. The insight reshaped paid acquisition targeting: drive new customers into the hydration category first, then upsell to high-margin gear on the second and third purchase.

[For category-level benchmarks on what retention rates typically look like, see eCommerce retention rate: how to calculate it, benchmark it, and push it past your category average.]

4. Product LTV (Revenue per Customer by First-Purchase Product)

Customer lifetime value is most often calculated as a single store-wide number: average revenue per customer over their full relationship with your brand. This is useful but it hides the variance that matters for acquisition decisions.

Product LTV measures the average total revenue generated by customers whose first purchase was a specific product. If customers who first buy Product A generate $340 in lifetime revenue and customers who first buy Product B generate $89, that difference should directly inform where you direct paid acquisition spend.

A subscription coffee brand found that customers whose first order included a “build-your-own” variety pack had 2.8x the lifetime value of customers whose first order was a single-origin bag. The variety pack customers subscribed more often and churned less. Once the brand saw this, they restructured their paid social creative to lead with the variety pack across all cold audiences.

Stormly’s recurring revenue report breaks LTV down by cohort, so you can compare first-purchase segments without custom SQL or a separate data warehouse query.

5. SKU-Level Cart Abandonment Rate

The average cart abandonment rate across eCommerce hovers around 70%. Most stores treat this as a single number to optimize with exit-intent popups and cart recovery emails. That approach is too broad and arrives too late.

SKU-level cart abandonment rate identifies which products are disproportionately abandoned after being added to cart. A product appearing in 32% of all abandoned carts when it only drives 11% of add-to-carts is a red flag. The cause is usually one of three things: a price surprise at checkout (shipping costs, fees), a trust gap (insufficient reviews or images for the price point), or a policy concern (return policy not visible).

One furniture store using Stormly identified a sectional sofa that appeared in 44% of abandoned carts. Investigation revealed the product page displayed free shipping, but the cart screen revealed an $180 white-glove delivery fee. The team updated the product page to show the delivery cost upfront. That product’s cart abandonment rate dropped from 44% to 27% in three weeks.

[For a step-by-step breakdown of diagnosing checkout leaks by product and category, read where your Shopify checkout actually leaks, broken down by product.]

6. Repurchase Frequency (Product Stickiness)

For SaaS products, stickiness is usually measured as DAU/MAU. For an online store, this metric does not map to buying behavior and should not be used.

eCommerce product stickiness is repurchase frequency: how often a buyer returns to purchase a specific product within a set time window. For consumables, this is the most important growth metric in the catalog.

If your protein powder product has an average repurchase interval of 34 days and your subscription default cadence is 30 days, you are slightly out of sync. Some customers will run out before their next shipment arrives, contact support, and cancel. Adjusting the subscription cadence to 35 days or adding a low-inventory email reminder at day 31 can meaningfully reduce churn without discounting.

In Stormly, you can see the distribution of repurchase intervals per SKU. If the distribution is tight (most buyers repurchase within 28 to 32 days), you have a strong subscription candidate. If it is wide (buyers come back at 20 days, 45 days, and 90 days), the purchasing pattern is episodic rather than habitual, which points toward different retention tactics like seasonal promotions or gifting angles.

[For more on how customer retention analytics works at the product and category level, read eCommerce customer retention analytics: the metrics that predict who stays and who leaves.]

How to Use These Six Metrics Together

The six metrics above work as a system, not in isolation.

Start with product-level CVR to find your underperforming product pages. Fix those first because they cost you customers before those customers even get to the retention stage.

Then look at repeat purchase rate and retention by category to identify your aha moment product and your highest-LTV acquisition entry point. These two should drive your acquisition creative and your onboarding email sequence.

Use product LTV to set your maximum cost per acquisition for each product-led segment. If customers who first buy Product A generate $340 LTV, you can profitably spend $80-100 acquiring them. If Product B customers generate $89 LTV, your paid acquisition economics look very different.

SKU-level cart abandonment catches the friction points your product pages are creating at the moment of commitment. Fix these and your CVR improvement shows up across every channel.

Repurchase frequency is the operational metric for subscription and retention teams. It tells you whether your subscription cadence, reorder reminders, and retention emails are timed correctly for actual buyer behavior.

Together, these six product metrics give an eCommerce team a clear picture of what is working, which products deserve more investment, and where friction is costing customers. The stores that track them at the product level consistently outperform stores that manage by aggregate numbers alone.

Stormly connects to Shopify and WooCommerce natively and surfaces all six in pre-built reports. No event configuration, no custom SQL, no waiting for a data team.

[For a broader foundation on what product analytics means for an eCommerce team, see what is product analytics and what can it actually do for an online store.]

See your store’s six key product metrics in Stormly. Start a free trial.