Most ecommerce stores collect plenty of data. But much of that data is fragmented.
Ask how last week's campaign did, and your ecommerce platform, your marketing automation platform, and your ads manager will likely generate different numbers.
More often than you’d like to admit, you might end up reconciling your performance metrics in a spreadsheet. As a result, you may not be getting a clear picture of what’s performing well, how each channel is contributing to revenue, and where to allocate future spend.
This guide breaks down what ecommerce analytics are, how predictive analytics can forecast behavior, how to set up omnichannel attribution, and how to choose an analytics platform that speeds up reporting.
What are ecommerce analytics?
Ecommerce analytics is the practice of collecting, measuring, and analyzing data generated by an online store to understand customer behavior and brand performance. Marketers use ecommerce analytics to make decisions about marketing, product, pricing, and operations.
Ecommerce analytics typically spans a few categories of data:
- Traffic and acquisition: percentage of visitors that come from different channels (organic search, paid ads, email, social, referral), customer acquisition cost (CAC)
- On-site behavior: pages and categories browsed, time on site, cart adds, cart abandonment, funnel drop-off points, wishlisted items
- Sales and revenue: conversion rate, average order value (AOV), revenue per visitor/recipient, sales by product/category, discount code performance
- Retention: customer lifetime value (LTV), repeat purchase rate, cohort retention, overall retention
- Customer service: customer satisfaction (CSAT) score, net promoter score (NPS), first response time, first contact resolution rate
- Marketing performance: return on ad spend (ROAS) and return on investment (ROI) by channel, email and text message engagement, attribution across channels
- Inventory and fulfillment: sell-through rate, stock-outs, return rates, shipping times
The ecommerce metrics that indicate growth
Not all ecommerce metrics are indicators of growth. If growth and acquisition are your first priorities, you’ll want to build a dashboard and generate reports that highlight these metrics. For example:
Ecommerce metric | What it measures | Why it matters |
|---|---|---|
LTV | Total revenue generated by each customer | Sets the ceiling on what you can afford to spend on acquiring and keeping each customer |
Repeat purchase rate | Percentage of customers who make two or more purchases | Leading indicator of loyalty and future revenue stability |
AOV | Average value per order | Indicates whether up-sell and bundling efforts are working |
Conversion rate | Percentage of shoppers who complete a purchase | At every lifecycle stage, reveals points of friction in your customer journey |
CAC | Total spend to acquire each customer | Only meaningful when compared to LTV (CAC should be less than LTV) |
Retention rate | Percentage of customers who buy again over a period of time | Predicts whether your business will grow or contract over time |
Keep in mind that conversion rate and AOV are lagging indicators, which means they report on sales that have already happened. Repeat purchase rate and retention rate are leading indicators, meaning they can tell you whether the revenue generated this quarter is likely to remain stable during your next quarter.
It’s a best practice to read these metrics alongside each other. A rising AOV next to a falling repeat purchase rate, for example, means you're extracting more from customers who aren't coming back.
Ecommerce predictive analytics: forecasting customer behavior
Predictive analytics use historical purchase and behavior data to forecast future customer behavior. They include metrics like predicted number of orders, expected date of next order, predicted LTV, and churn risk.
When you can predict this kind of customer behavior, you can get ahead of it by sending messages that are more likely to resonate closer to when they’re likely to happen.
Here are 3 ways you can use ecommerce predictive analytics to boost revenue:
- Use predicted LTV to estimate lifetime spend, so you can allocate more resources to shoppers who are likely to spend more.
- Use churn risk to flag at-risk customers who may need certain types of offers to purchase again.
- Use expected date of next order to personalize the timing for your replenishment and reorder flows.
Personal care brand Every Man Jack adjusted their replenishment flow to send on or slightly before each customer’s predicted date of next order. As part of a broader AI-powered marketing strategy, their predictive messages helped boost revenue from flows 25% YoY.
Omnichannel attribution: how to stop double-counting revenue
Ecommerce analytics need to take into account that the customer journey doesn’t happen in a straight line. Shoppers interact with multiple channels before they make a purchase, and each of those channels can take credit in different ways, depending on the brand’s marketing strategy.
This is where omnichannel, linear multi-touch attribution comes in. This type of attribution gives each channel its fair share of credit, without double counting revenue. When you use this type of attribution model, you can avoid pouring budget into the wrong channel just because it was the last one a customer interacted with.
Instead, you get a more accurate, balanced view of what’s really influencing purchases. You start to see not just what channels were involved, but how much credit each one deserves. Instead of giving all the glory to the last click, omnichannel, multi-touch linear attribution evenly distributes credit across all meaningful touchpoints.
You should be able to set up omnichannel attribution in your marketing analytics platform. Here’s how to get started:
- Choose a platform with built-in marketing analytics, like a B2C CRM with a built-in customer data platform (CDP). This is your data infrastructure that centralizes every customer interaction with your brand. When this data lives in one place, your attribution models are more accurate because they’re pulling from complete data.
- Make sure your on-site tracking script is installed. This may include a custom snippet on the check-out page specifically, since that's where conversion data is ultimately tied back to a session.
- Use accurate UTM tags. Since attribution relies on UTM source/medium, campaigns with missing or inconsistent tags might be labeled as unknown.
- Update your attribution model settings. Choose which attribution model makes the most sense for your brand, and set your attribution window accordingly. You should also customize which types of messages count as meaningful touchpoints (you might, for example, exclude transactional messages or bot clicks).
How to choose an ecommerce analytics platform
Your ecommerce analytics are only as accurate and comprehensive as the customer data infrastructure it sits within.
Depending on the size of your brand, you may just need a B2C CRM that comes with built-in CDP and marketing analytics functionality. If you’re working with a larger brand, you may need your data warehouse to sync with platforms such as Snowflake, BigQuery, Databricks.
When choosing a platform, ask the following questions:
- Does the ecommerce analytics platform unify data across every marketing channel you use?
- Does it pull ecommerce analytics in real time, or does it work from batch updates?
- Does it include predictive analytics you can act on from within the same platform?
- Can the platform build audience segments directly within its analysis?
- Does the platform come with a built-in AI marketing agent that can make suggestions based on performance data?
- Can you pull performance data by having AI conversations through an MCP server?
With more advanced analytics, dash cam brand Nextbase is able to understand customer behavior based on details like recency, frequency, and monetary value (RFM) of purchases. RFM analysis means Nextbase can reach out at the right time to retain customers and pay particular attention to subscribers with the highest potential value.
“We can build trigger flows based on when a customer moves into a different RFM group,” says Rory McMenigall, web manager at Nextbase. “It means we can drive revenue around the clock.”
To help you understand the difference between ecommerce analytics platforms, here’s a cheat sheet of definitions:
Type of platform | Best for |
|---|---|
Store analytics | Order, product, and check-out reporting inside your ecommerce platform |
Web analytics | Site traffic, page performance, and on-site behavior inside a platform like Google Analytics |
CDP | Unifies customer data across channels into one real-time profile |
Marketing analytics | Measures and activates data across channels |
Data warehouse | Central storage of raw data for analysts and business intelligence teams |
Activate your ecommerce analytics with Klaviyo
Klaviyo is the autonomous B2C CRM that brings together real-time customer data, analytics, marketing automation, customer service, and built-in agentic AI, so that brands can measure and act on their data from the same place.
With Klaviyo, you can:
- Build segments directly from funnel analysis. Target customers at their exact conversion drop-off point in one click.
- Act on your data, faster. Access pre-built recommendations and flow templates to act on your real performance data without starting from scratch.
- Build advanced reports. Use unlimited groupings, custom profile properties, and combined flow and campaign reporting without a business intelligence tool.
- Customize attribution. Measure attribution by joining two or more metrics or filtering for specific events across your POS and online store.
Start measuring and acting on your data from one place with Klaviyo. Get a demo