eCommerce analytics is the process of collecting data on an online business and using it to find actionable insights. eCommerce analytics helps you measure and improve growth strategies, sales trends, user behavior, revenue targets, and more.
Data analytics is used in eCommerce to help business owners understand user behavior and optimize for more sales and engagement. It's a key component for any data-driven online store or retailer.
Every eCommerce business should measure a few critical metrics in each of these categories, to help you make data-driven decisions.
The key performance indicators (KPIs) you choose to measure depend largely on your situation, goals, and industry.
Every marketing channel differs, but you'll likely find yourself paying very close attention to these business metrics, since they most accurately report the overall health of any eCommerce business.
Your customer acquisition cost tells you the average cost to acquire a new customer.
CAC is one of the most important metrics because it connects expenses to revenue.
For example, if you spend $2,000 on a campaign that acquires 100 customers, your CAC is $20.
This is a broad metric that counts the percentage of people who take the desired action.
For example, if 1,000 people visit your website and 14 of them sign up for your newsletter, your newsletter subscription conversion rate would be 1.4%.
Most eCommerce business owners use paid advertising to find new customers.
Common choices include social media platforms like:
Search engines and marketplaces are also popular advertising platforms:
In addition to the marketing analytics listed above, you'll also want to look for these additional metrics when using paid ads.
Click-through rate is a type of conversion rate specific to click behavior.
If 10 people click on an ad shown to 200 people, your click-through rate would be 5%.
This is the total number of times your ad has appeared, or been exposed to eyeballs.
One person who is shown your ad ten times counts as ten impressions.
The exact definition depends on the platform, but usually, an impression counts even if a user scrolls past too fast to have even seen it.
This counts the number of people who saw an ad---you can think of it as unique impressions.
So if 500 people see your ad twice, it counts as a reach of 500 but 1,000 impressions.
This is the price paid, on average, for each click.
If you spent $10 to show an ad to 1,000 people and 25 of them clicked, your cost per click would be $0.40.
Engagement measures all kinds of interactions with content, including comments, likes, clicks, shares, or even watching a few seconds of a video.
If a post with 1,000 impressions gets 3 comments, 24 likes, and 7 clicks, its engagement rate would be 3.4%.
The exact definition differs across platforms but generally doesn't represent unique users.
So if a single person clicked 7 times, the engagement rate would remain the same.
This metric determines the total return on an advertising campaign.
For example, if you spend $500 on an ad campaign that generates $2,000 in revenue, you have a ROAS of 4:1.
Promoting your brand using email campaigns is a more cost-effective strategy compared to paid ads.
And since you own the email addresses of your customers, it's more reliable since you don't need to depend on a network's algorithm.
In addition to click-through rates, customer acquisition cost, and conversion rates of web visitors to subscribers, you'll want to look out for these metrics below.
This is the total number of people who have signed up for your emails, minus those who unsubscribed.
If 500 people have signed up, but 11 have unsubscribed, your number of subscribers is 489.
Open rate is the percentage of recipients who have opened your email.
If you send an email to 1,000 subscribers and 482 of them open it, your open rate is 48.2%.
These are the people who have unsubscribed from your emails. According to U.S. federal law, you cannot contact these people again unless they re-subscribe.
Usually, unsubscribe rates are calculated for each email. So if you send an email to 1,000 recipients and 6 unsubscribe using the link in that email, your unsubscribe rate is 0.6%.
Ranking on Google and other search engines is a way to bring organic traffic to your site. Whether you're a startup or fast growing company, SEO matters. Click-through rate, impressions, and other types of marketing metrics still apply.
This measures the average placement of a page on Google's results for its keyword.
If you have five pages in position #1, #4, #15, and #24 for their respective keywords, your average rank position would be 11.
This estimates how many people search for a keyword each month.
So a keyword with 3,200 monthly searches means there are roughly that many searches each month.
This predicts how difficult it will be to rank for a keyword.
Every keyword tool uses a different formula, but a keyword with a difficulty of 12 will be much easier to rank than one with a difficulty of 68.
There are several metrics to measure how users engage with your site. The eCommerce platform you use, like Shopify or WooCommerce, probably has some analytics features built in. You can also use third-party applications like Google Analytics.
Data on customer behavior and the general performance of your eCommerce website will help you optimize your site to improve sales and engagement.
This metric divides the total revenue generated on your site by the number of visitors in a given period.
For example, if you had 1,000 visitors in one day and sold $5,000 in revenue, your revenue per visitor for that day would be $5.00.
This measures the percentage of website visitors who click away after only visiting one page.
So if 1,000 people visit your website's home page, but only 283 click to see another page, your bounce rate would be 71.7%.
This measures how long visitors stay on your page, on average.
So if you have three visitors who stay on the page for 30 seconds, one minute, and one minute 30 seconds, respectively, your average time on page would be one minute.
This represents how long it takes for a page on your site to load.
There are a few different variations of this metric, including First Contentful Paint, Time to Interactive, and more.
This is calculated by the total number of visitors to your site minus those who have visited before.
So if you've had 1,500 site visitors in a given period and 500 have been on your site earlier, you have 1,000 unique visitors.
This can also be expressed in a percentage, which would be 66.6% unique visitors.
Sales and revenue largely determine the long-term success of your business. Marketing metrics and website performance can improve these numbers indirectly, but you also need to measure them individually.
This is the percentage of prospects who add an item to their shopping cart but never successfully complete the order.
If 1,000 visitors add something to their cart but only 317 place an order, your cart abandonment rate would be 68.3%.
Cost per acquisition measures the average price of a conversion based on the total spend in a marketing campaign.
If you generate 200 customers from strategies that cost $2,000, your CPA would be $10.
This is to calculate the revenue generated from an average order.
You'll divide the total revenue by the number of orders, so if you generate $2,500 from 25 orders, your AOV is $100.
The customer journey doesn't end after a purchase. Here's what to measure to ensure you can continue to stay top-of-mind with customers and encourage future purchases.
This metric measures the average total spend from a customer's first purchase to their last.
For example, if an average customer spends $100 a year and stays with you for five years, you can estimate the customer lifetime value at $500.
This measures customer retention, specifically the percentage of customers who return to make another purchase.
Unless you have a subscription product, you can define the window for this metric or measure it indefinitely.
For example, if 10,000 customers bought last season, and 2,500 of those customers have bought since, your repeat customer rate would be 25%.
Your churn rate is the number of customers who cancel subscription---essentially the opposite of your repeat customer rate.
In the above example of a 25% repeat customer rate, your annual churn rate would be 75%.
This scores customer responses from 0-10 for a single question: __"How likely is it that you will recommend our company to a friend?" __
You subtract the percentage of 0-6 answers from the 9-10 answers. So if 55% responded with a 9 or 10 and 30% responded with a number between 0-6, your NPS would be 25.
There are several benefits to having an eCommerce analytics system in place. Here are a few improvements you can expect from a better understanding of the metrics.
Basic tools can only tell so much about how customers interact with your site. Combined with data from other platforms, you can understand customer segments and user behavior on a much deeper level.
Data can tell you which marketing strategies work best for increasing online sales and profit. Optimize pricing and add-on offers using A/B testing and real customer data.
Spot trends early and prepare. Reduce the inventory you're carrying for products that won't sell as much and stock up on what's trending beforehand. Run a leaner inventory but prepare even better for stocking issues.
See insights into how products and categories are performing over time. Spot trends and adjust pricing, inventory, and promotions for products rising or falling in popularity.
Learn which of your marketing efforts are doing the best and discover new improvements. Adjust marketing campaigns based on data and optimize your marketing strategy using data-driven insights into attribution and customer retention. Optimize campaigns for a better return on ad spend.
Make informed decisions to improve the customer experience. Reduce friction during your checkout process, automate personalized product recommendations, and create landing pages that resonate with your potential customers.
Deliver enhanced eCommerce forecasting to better predict trends and new growth opportunities. Project growth in specific categories in the coming months or years and develop a clearer picture of upcoming bottlenecks to resolve.
Using data for eCommerce analytics allows your company to make better decisions.
Without a clear strategy, robust infrastructure, and a team to execute, your team will be flying blind.
It's always better to have data at your fingertips when you're making critical business decisions.
Always focus on a business outcome.
The two most important business outcomes are:
Pick a business problem that drives revenue or saves money, and then tackle it.
I want to increase click-through rates by optimizing the copy I use in my advertisements.
I want to drive referrals by identifying our most engaged clients and reaching out.
At the most basic level, you need:
These tools are tailor-made for eCommerce companies, they have off-the-shelf integrations to common systems, pre-built data transformations, and insights ready-to-go.
They're great if you want to do zero work. However, over time, most companies move on to one of the other options below as complexity increases.
An out-of-the-box eCommerce analytics tool is fast and easy but will never provide the advanced functionality you'll grow to need.
These solutions are broader than just eCommerce. This has pros and cons.
The pros are that you can expand across your company to help with marketing, HR analytics, financial planning, etc.
The cons are that you might be missing high value eCommerce integrations that are critical to your business.
An out-of-the-box platform can work for various use cases. A broader tool gives you more insights but will be restricted to supported data sources.
With this path, you can purchase an ELT solution, a data warehouse, and a visualization tool to build your own tech stack.
It takes more expertise than the alternatives above, but provides unlimited flexibility as you quickly scale your analytics initiatives.
A custom stack is ideal for increased complexity.
You'll eventually need to migrate to this option. This involves a custom data warehouse, ETL tools, and business intelligence software.
Whether you're just starting with data analytics for your eCommerce store or building on an existing system, here are the most important data integration strategies for success.
Before beginning, have a clear goal in mind. For most eCommerce businesses, this is usually making money or saving money. For example, you might want to increase ad click-through rates or run a leaner inventory without affecting shipping timelines.
Make your goal as clear as possible, and start with data directed to that goal. If you gather data without setting priorities, you won't have the direction you need to make clear business decisions.
You can only improve what you measure, so have a benchmark you want to beat. Most businesses can use the last reporting period, but eCommerce data is unique because trends usually happen in year-long cycles. Setting up a year-over-year baseline can be a better way to measure progress.
And consider general industry changes as well. For example, the beginning of the COVID-19 pandemic saw a surge in demand for at-home workout equipment. Don't attribute industry-wide growth trends to a specific campaign.
Data analysis quickly becomes complex. If each department is measuring its own eCommerce reports, you'll find silos that keep information from other departments and result in duplicate work and confusion. Choose a single source of truth to store all data, and make sure it's available across your business.
Behind every data set lies a story. Use analytics to see broader patterns, find reasons behind the trends, and develop hypotheses to test. To do this, you'll want to combine data from several platforms for a clearer picture.
You can also balance quantitative metrics, like site visitors and conversion rates, with qualitative metrics, like heatmaps, screen recordings, and customer interviews.
Don't fall prey to analysis paralysis. You've made discoveries about your most profitable demographic---now what? Always tie insights to clear, concrete action. Interesting statistics and glossy eCommerce reports won't help your business grow unless they're connected to next steps.
Doing data analysis for brief stints won't bring the long-term results you're looking for. Use eCommerce tracking to measure category and specific product performance over time. Keep regular reviews of customer data like user flows. And constantly look for ways to improve.
If you need to manually import and review your data, the process will inevitably hit a bottleneck. The solution is to automate as much of the process as possible, from extracting, transforming, and loading (ETL) to presenting data in meaningful dashboards.
Portable can help when you want to build your own eCommerce analytics stack, but we can also augment your out-of-the-box eCommerce analytics stack.
We have 35+ pre-built, no-code connectors from eCommerce applications to data warehouses (Snowflake, BigQuery, Redshift) that you can use for analytics - tools like Gorgias, Klaviyo, Friendbuy, and Cin7. We have over 225+ no-code connectors across our entire catalog.