Product Analytics Built for E-commerce
Track customers, not sessions. Attribute revenue, not clicks. See the full purchase journey.
Product Analytics for E-commerce: What GA4 and Shopify Reports Won't Tell You
Most e-commerce brands run their analytics on two tools: Google Analytics 4 and whatever reporting their platform provides — usually Shopify, WooCommerce, or BigCommerce. Between the two, you get traffic numbers, conversion rates, and order totals. That covers the surface.
But neither tool tracks individual customers across their full lifecycle. GA4 is session-based. It tells you what happened during a visit, not what happened across visits. Shopify reports tell you what was purchased, not why — and not by whom in any meaningful behavioral sense. You see aggregates, not people.
This gap is where e-commerce brands lose money. Not because they lack data, but because the data they have cannot answer the questions that actually drive revenue: Which acquisition channels produce customers who come back? What does the path from first visit to third purchase look like? Why do some cohorts retain and others churn after one order?
These are person-level questions. Answering them requires person-level analytics. That is what KISSmetrics was built to do — and has been doing for over 15 years.
What E-commerce Analytics Actually Needs to Do
The phrase "e-commerce analytics" gets thrown around loosely. Vendors use it to describe everything from page view counters to full behavioral intelligence platforms. Here is what the term should mean — the capabilities that actually move revenue for online stores.
Customer LTV by Acquisition Channel
Knowing your customer lifetime value as a single number is a start. But the number that matters is LTV segmented by how the customer found you. Customers acquired through branded search behave differently from those acquired through a Facebook prospecting campaign. If you cannot break LTV down by acquisition channel, you are optimizing ad spend with incomplete data. You might be scaling the channel that produces the most first orders while starving the one that produces the most repeat buyers.
Cart Abandonment Cohort Analysis
Every e-commerce platform reports a cart abandonment rate. That number is almost useless on its own. What you need is the ability to group abandoners into cohorts — by traffic source, by product category, by whether they are first-time or returning visitors — and track what happens next. Do they come back? How long does it take? Which recovery emails actually work, and for which segments? This is cohort analysis applied to cart abandonment, and almost no out-of-the-box reporting supports it.
Repeat Purchase Frequency and Segmentation
The difference between a one-time buyer and a three-time buyer is the difference between a break-even customer and a profitable one. You need to see purchase frequency distributions, identify what separates repeat buyers from one-and-done customers, and build segments around purchase behavior — not just recency, but the full pattern.
Campaign Attribution at the Person Level
GA4 attributes conversions to sessions. That means if a customer clicks a Facebook ad on Monday, comes back via organic search on Wednesday, and buys on Friday through a branded search, GA4 credits the branded search. The Facebook ad — which introduced the customer — gets nothing. Person-level attribution connects the dots across sessions, giving credit where it is actually due.
Revenue Tracking Tied to User Journeys
Revenue is not just a number at the end of a checkout. It is the outcome of a journey — a series of visits, interactions, emails opened, pages viewed, and decisions made. Tying revenue back to the full journey lets you understand which touchpoints matter, which pages drive purchases, and where customers drop off before converting.
Cross-Device and Cross-Session Identity Resolution
A customer browses on their phone during lunch. They revisit on a laptop that evening. They purchase on their tablet over the weekend. Session-based analytics sees three separate anonymous visitors. Person-based analytics sees one customer with a complete journey. Without identity resolution, you are working with fragmented data that overstates your audience size and understates your conversion rates.
How a Typical GA4 Setup Compares to KISSmetrics
Most e-commerce brands treat GA4 as their primary analytics tool. Here is how it stacks up against KISSmetrics for the capabilities that actually matter for online stores.
| Capability | Competitor | KISSmetrics |
|---|---|---|
| Customer LTV by acquisition channel | Requires BigQuery export, custom SQL, and manual stitching of user IDs across sessions | Built-in LTV reports segmented by any property including UTM source, campaign, or custom channel groupings |
| Cart abandonment cohort analysis | Reports aggregate abandonment rate only; cohort-level analysis requires third-party tools or manual data work | Create cohorts of cart abandoners by any dimension — traffic source, device, product category — and track recovery behavior over time |
| Repeat purchase frequency | No native repeat purchase reporting; must be built with custom events and explorations | Native purchase frequency distribution and segmentation by number of orders, time between orders, and product category |
| Person-level campaign attribution | Session-scoped attribution with limited cross-session support; data-driven attribution available but operates on aggregated data | Every conversion attributed to a person with full cross-session journey, supporting first-touch, last-touch, and multi-touch models |
| Revenue tied to user journeys | Revenue tracked at session level; connecting revenue to multi-session journeys requires BigQuery and custom modeling | Revenue events connected to complete customer timelines showing every touchpoint from first visit through purchase |
| Cross-device identity resolution | User-ID feature available but requires significant implementation; default reporting remains session-based | Automatic identity stitching when users authenticate — merges anonymous and known activity into a single profile |
Person-Level Revenue Attribution
Most analytics tools can tell you which campaigns produce the most clicks or even the most first-order conversions. That is not the question that matters. The question is: which campaigns produce the highest-LTV customers?
A Facebook campaign might generate hundreds of first orders at a $40 average order value. A podcast sponsorship might generate fifty first orders at $35. On a session-based dashboard, the Facebook campaign wins. But if those podcast customers come back four more times over the next year, they are worth three times as much. You would never know that without person-level attribution that connects first-touch source to long-term revenue.
KISSmetrics tracks every dollar back to the person who spent it, and every person back to the channel that brought them in. You see revenue attribution not just for the first order, but across the entire customer relationship. This is how brands stop wasting budget on channels that produce one-time buyers and start investing in the channels that build a customer base.
Cohort Analysis Built for Commerce
Cohort analysis is not a new concept, but most implementations are built for SaaS — grouping users by signup date and tracking monthly retention. E-commerce cohorts need to work differently.
In KISSmetrics, you can group customers by first purchase date, acquisition channel, first product purchased, or any custom property you define. Then you track what actually matters for commerce: repeat purchase rate, time between orders, revenue per cohort over time, and which cohorts are trending up versus fading out.
This is how you answer questions like: Do customers acquired during Black Friday actually retain, or are they one-and-done discount seekers? Do customers who buy from a specific product category have higher repeat rates? Is our Q1 cohort performing better or worse than Q1 last year?
These are not theoretical questions. They are the ones that determine whether your marketing budget is building long-term value or just buying short-term revenue.
$7,500/yr — Fully Implemented
Enterprise analytics platforms charge $50,000 to $150,000 per year and take months to implement. Hiring an analytics consultant to build custom dashboards on your existing stack costs $10,000 to $30,000 for a single project — with no guarantee the setup will be maintained after they leave.
KISSmetrics costs $7,500 per year, flat. That includes the platform, the implementation, and the ongoing support. Our team handles the setup: event taxonomy, tracking installation, dashboard configuration, and integration with your e-commerce platform. You do not need a data team to get started, and you do not need a data team to keep it running.
For context, $7,500 per year is roughly what a mid-size e-commerce brand spends on a single A/B testing tool. Except this gives you the behavioral data to know what to test in the first place.
How KISSmetrics Works for E-commerce
Getting from zero to actionable e-commerce analytics does not require a six-month implementation project. Here is the process.
Step 1: Install Tracking
KISSmetrics integrates with Shopify, WooCommerce, BigCommerce, and custom platforms. Our team installs the tracking code and configures it to capture the events that matter for your business — product views, add-to-cart actions, checkout steps, purchases, and any custom events specific to your store. This is handled for you as part of implementation. Learn more about our product capabilities.
Step 2: Map the Customer Journey
Once tracking is live, KISSmetrics automatically builds customer timelines. Every visitor action is recorded and stitched into a person profile. When an anonymous visitor becomes a known customer (by logging in, entering an email, or completing a purchase), their entire history is merged into a single profile. No manual data work required.
Step 3: Build Your Dashboards
Our team configures your initial dashboards based on your business priorities. Common setups include funnel reports for checkout flow analysis, cohort reports for retention tracking, revenue reports segmented by acquisition channel, and customer LTV dashboards. These are ready to use out of the box — see our reporting suite for details.
Step 4: Analyze and Act
With person-level data flowing and dashboards configured, you start finding answers. Which campaigns are worth scaling. Where customers drop off. Which cohorts retain and which churn. What distinguishes high-value customers from one-time buyers. Then you act on those insights — adjusting spend, refining targeting, optimizing the experience.
For a deeper look at how KISSmetrics serves online stores, visit our e-commerce industry page.
E-commerce Brands Need Analytics Built for Commerce
GA4 is a general-purpose web analytics tool. Shopify reports are transactional summaries. Neither was designed to answer the person-level questions that e-commerce brands need to grow efficiently. KISSmetrics was — and has been helping online stores understand their customers for over 15 years.
If you are spending money to acquire customers but cannot tell which channels produce the ones who come back, you are making decisions in the dark. Person-based analytics turns the lights on.
Ready to see how KISSmetrics works for your business?
Person-level analytics, fully implemented for your business, $7,500/year.
1-hour onboarding included. No implementation fees. No surprises.