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Best Guide of 2020

E-commerce Analytics for Shopify Stores: A Complete Guide

Why do your e-commerce customers buy your products? When do they need to hear from you? And how can you get closer to your customer behavior?

Automated e-commerce analytics answers these questions and can unlock massive growth for your DTC brand. This full e-commerce analytics guide will walk you through: 

  • How to Use E-commerce Analytics to Improve Your Shopify Store
  • How Segmenting Your Data Elevates Your Marketing Strategy
  • How to Get Started with Automated E-commerce Analytics

If you want to understand more about your customers, how your products are performing, and which of your marketing channels are driving conversions, this is the place to start! Peel opens the door for deep analysis and segmentation with easy-to-use tools.

This 5-part guide will walk you through everything you need to optimize your analytics in Shopify, using Peel to drive major growth for your business.

Want to jump ahead and try Peel’s e-commerce analytics tools? Start a free 15-day trial now!

E-commerce analytics guide - sections

Part 1: What is E-commerce Analytics?

E-commerce analytics is a system and set of tools for understanding your customers’ purchasing behavior.

Ask yourself these questions:

  • What are my customers purchasing? 
  • How often are they making purchases?
  • Why are they buying certain products?
  • How much value are my customers bringing to my store?

E-commerce analytics answers these questions by gathering and analyzing data from all aspects of your online store that have an impact on your business. This includes (but is not limited to):

  • Sales data
  • Marketing data
  • Acquisition data
  • Conversion data
  • Cohort analysis
  • Retention data
  • Product data
  • Attribution data
  • Cost data
Why do you need E-commerce Analytics?

The goal of e-commerce analytics is to provide you with the full picture of your store’s performance with customer behavior, revenue, and marketing channel performance at its core. This is meant to inform better business decisions across your sales and marketing efforts.

Think of it this way: if you know who your customers are based on their behaviors, which of your marketing channels and campaigns are performing well, and you have a handle on all costs vs. revenue, you're in great shape for faster growth. 

Part 2: E-commerce Analytics Method

Successful e-commerce analytics strategy is about more than just having the right tool in place. Sure, automated analytics tools will make life a whole lot easier, but it’s really all about having a method that sets you up for success. 
Peel has come up with an ideal marketing method that’s driven by e-commerce analytics: 1. Market > 2. Analyze Results > 3. Gain Insights > 4. Build Audiences > 5. Hyper Target > 6. LTV Growth

E-commerce analytics - method

Think of this as more of a cycle versus a linear flow. After you experience lifetime value growth (more on lifetime value, aka LTV below), you should use all that prior experience to start a new lifecycle with your customers where you market, test, and refine again. Let’s expand upon each step in the e-commerce analytics method:

Step 1: Market 

You’ve either created or sourced a great product and you’ve started your brand. Now, how do you communicate it to your prospective customer base? You write about it. Get other authorities in your niche to write and talk about it. You promote it on various channels. Eventually, you start bidding on keywords in PPC campaigns and spending on Google Ads and social media ads for your product. Perhaps you test a variety of discount codes and work with influencers to get faster traction. But how do you know this is working? Are all your efforts actually helping you grow fast?  

Step 2: Analyze Results

Connecting the dots between your customers’ purchase behavior and your marketing efforts can be tricky. With an automated e-commerce analytics platform like  Peel, this becomes a lot easier. You can look at sales data, cohort analysis, average order value (AOV), retention data and more. The idea is to see where your numbers are trending positively and how those improvements line up with different experiments you’ve tried across your marketing channels.

Step 3: Gain Insights

Analyzing the results of your marketing experiments should no doubt lead you to informative and actionable insights. By looking at trends within your data, like how revenue for certain cohorts is affected by different ad campaigns, or monitoring the repurchase rate of customers who buy a bundle compared to a single product, or finding ways to convert more one-time-purchasers to lifelong subscribers, or discovering which discount code brought in the highest value clients, etc, really any connection you can draw between your marketing efforts and the results, will help you formulate your marketing strategy going forward. 

Step 4: Build Audiences

After you have gained insights on how certain groups of customers, products, channels, or discount codes perform, you have the fuel for using your segments to create custom audiences. This creates better structure for marketing engagement as you have more focused groups with similar needs. For example, you can take customers who have placed 2+ orders of a specific product in the last 2 months and create an audience based on their loyalty to that product and healthy repurchase rate. These customers present the perfect opportunity for retention emails suggesting new products that complement what they already purchased.

Step 5: Hyper-target

Creating audiences allows you to send out hyper-targeted messaging to smaller groups of customers based on patterns and opportunities in their behavioral data. Audience-based marketing campaigns perform better than generalized marketing as you are speaking to the needs and interests of your customer segments based on their behavior. This is a better avenue for engaging your customer with content that’ll resonate and get them to return for more purchases. This process will help you unlock the answers to questions like: Did a specific customer's LTV grow since the start of the campaign? What about the LTV of the audience group overall? And so much more.

Step 6: LTV Growth

With more informed data and better segments and audiences built based on your early marketing experiments, you now have a better engine for growth. With your hyper-targeted messaging, you should experience lifetime value (LTV) growth. LTV is the total expected value (in revenue) that you can expect your customers to spend with you over time based on their purchase data. The more you are catering to their wants and needs with tailored marketing campaigns, the more your LTV will grow.

Part 3: E-commerce Analytics: What are the metrics that matter?

The most important e-commerce analytics for your DTC site boil down to 3 main categories:

  • Cohort Analysis
  • Marketing Metrics
  • Subscription Analytics

Let’s jump into each one, what they do, how you can use them, and some highlights in each category.

Cohort Analysis - Understanding Group Behavior

A cohort analysis looks at the behavior of a group of individuals who have a common trait ahead of analysis.

When it comes to your DTC site, cohort analytics give you insight into the behavior of your customers based on common groupings. In Peel’s case, your cohorts exist based on the month in which you acquired your customers.

For example: your July 2021 cohort consists of the customers who made a purchase for the first time with your brand in that month, and now you are following their purchasing and spending behavior throughout their lifetime.

E-commerce analytics - cohort analysis AOV
Example of Cohort Analysis for Average Order Value (AOV)

Cohort analysis is a tremendously important method within your e-commerce analytics. Looking at the groups, aka cohorts, and finding patterns in purchasing behaviors, trends, and seasons that result from your marketing experiments provide you with a wealth of knowledge for engaging your customers and getting more value out of them.

Two Types of Cohort Metrics

In Peel, cohort metrics are broken down into two categories: Cohorts Retention and Cohorts Revenue.

E-commerce analytics - cohorts retention & revenue

Cohorts Retention metrics focus on how many of your customers are coming back and repurchasing as well as how often.

This data is particularly illuminating for understanding when customers are typically coming back to make purchases. You can use this to find the right cadence for email drip campaigns or SMS campaigns to encourage those who haven’t come back by the expected time to return and make a purchase. 

New product suggestions that complement what they’ve already purchased, new pricing, bundles, or discount incentives are also great ideas to improve repurchase and retention.

Remember: It’s estimated that acquiring a new customer is up to 5x more expensive than retaining a customer. So, understanding your Cohorts Retention metrics is critical for success. 

Cohorts Revenue metrics are all about how much value (in profit) that your customers are bringing into your business. 

These metrics help you understand which of your cohorts are the most valuable to your brand, which leads to the important questions: Did we attract these customers with a discount? Did we have an email campaign that drove in more valuable customers? Is our new product bundle attracting higher value customers?

The analysis shows you the patterns, behaviors, and attributes of your customer base, so you can identify your best customer. 

E-commerce analytics - go-to metrics

3 of the most commonly used metrics that work in tandem to help bands see their overall cohort value are: Average LTV per Customer + Repurchase Rate + Cohort AOV per Month.

Lifetime Value (LTV) - Represents the value (in revenue) that a single customer brings to your business over the course of their lifetime with your brand. Average LTV per Customer is a cumulative metric that tells you the average $ amount that your customers are spending each month. When you know the LTV of a customer, you know how much money they spend with your business over a period of time, and this allows you to develop a customer acquisition strategy that targets customers who will spend the most with your business.

Repurchase Rate - Tells you how often your customers are coming back to make more purchases. This gives you the % of customers that are  coming back to make a purchase each month. 

Average Order Value (AOV) - Shows you how much your customers are spending per order. Cohort AOV per Month tells you how much customers in your monthly cohorts are spending per order. This gives you a more detailed view of how much value their transactions are bringing in on an individual basis.

Why we love LTV + Repurchase + AOV: The bottom line is that you want to know how much your customers are spending, how often they are spending with you, and how much their transactions are worth. 

From there, you can dig into what marketing experiments you made from new ads, to channels, to first product purchased, to email and SMS, to discounts and so much more to figure out exactly what drove in your most valuable customers. Check out our extended dive into how these metrics can help you better understand the efficacy of your discount strategies.

Marketing Metrics - Measuring the Efficacy of Your Campaigns

One of the biggest uphill battles for any marketing team is knowing how much to spend on acquiring new customers and retaining current customers. 

Within your e-commerce analytics, marketing metrics open the door to understanding the return on your marketing investments based on the value of customers acquired via ads and paid marketing campaigns.

Key Marketing Metrics 

E-commerce analytics - key marketing metrics


  • What it tells you: The value your customers will bring over their entire relationship with your brand compared to the $ spent to acquire those customers (shown as a ratio). Calculating, monitoring, and optimizing your LTV:CAC ratio is critical because it indicates how effective your marketing efforts are along with your brand’s profitability. It measures the return on investment for each dollar your brand spends to acquire a new customer.
  • How to use it: If LTV is high compared to your CAC, you can spend more to acquire those valuable customers. On the other hand, if CAC is too high compared to LTV, it’s time to pull back spending or find a better way to attract the right customers – referral programs, SEO, affiliate programs, focusing on retention efforts, etc.
  • What is a good LTV:CAC Ratio: The ideal LTV:CAC Ratio is 3:1 (or 3x customer LTV compared to the cost of acquisition). This can fluctuate depending on the industry, but that’s a good rule of thumb. 

Return On Ad Spend (ROAS)

  • What it tells you: How much profit you’re making compared to how much you’re spending on ads to attract those customers.
  • How to use it: Monitoring your ROAS is essential for improving your ad strategy. If your return isn’t high enough, you need to adjust the content, placement, budget, or other component of your ad strategy.
  • What is a good ROAS: The average ROAS is 2:1 (or $2 earned for every $1 spent on ads). So, anything above 2:1 is considered a good ROAS, with many companies aiming for 4:1.

Return On Investment (ROI)

  • What it tells you: How much profit you’re making compared to your overall marketing spend - including ads, PPC campaigns, marketing tools, and all other costs included.
  • How to use it: ROI is a great way of monitoring your total spend compared to the value you are getting back from your customers. You want to make sure your ROI exceeds your costs or else you’ll experience net loss.

Signal Loss

  • What it tells you: How much visibility you are losing into critical touchpoints in your customer’s path to purchase due to lack of attribution or privacy updates.
  • How to use it: The signal loss metric provides you with a look at link clicks to your landing page compared to page views. This gives you your true signal loss that your brand is experiencing, which you can use to make improvements to your ads and content.
E-commerce analytics - marketing analysis

One of the most common analyses for DTC brands entails understanding the value of their advertising efforts. With Ad Spend + ROAS + Total Sales metrics, your team can see exactly how your ad spend is translating to value.

The Ad Spend metric aggregates ad spend from your digital platforms and tells you what your total spend is for a given period. You can look at this as focused as daily, or as broad as quarterly  (and everything in between). 

ROAS tells you how you are making back for every ad dollar you are spending. That ratio, as we mentioned above, is critical for teams who are looking to manage and optimize their budget. Looking at total ad spend and seeing the return on that ad spend provides a great picture of the state of your marketing ads.

The final piece of the puzzle is Total Sales. Not technically a “marketing metric,” but a critical part of the analyses nonetheless. The idea here is simple. Did you set a goal for your ad spend? What was the ROAS ratio you were looking to hit? Did that help you hit a total sales goal that validates your marketing spend? 

The answers to these questions are some of the best places to start for optimizing your marketing strategy, along with finding and filling the gaps in your efforts. 

Subscription Analytics - The Ultimate Retention Strategy

One of the top customer retention techniques for e-commerce sites is a subscription-based business model. Converting one-time purchasers into subscribers who make purchases on a regular schedule is the best way to guarantee more monthly recurring revenue (MRR).

Subscription-based e-commerce businesses need a whole host of analytics support to better understand their customers' purchasing behavior along with details about how and when they became subscribers. This is where subscription analysis comes in.

What you want to know about your subscribers:

  • When did they become subscribers?
  • How many of your one-time purchasers are becoming subscribers?
  • How much MRR are you getting per subscriber?
  • What’s your lifetime Value (LTV) per subscriber?
  • What’s your LTV per subscription cohort?

Peel answers these questions and more!

If you know who your one-time purchasers are, and you know the cadence at which your customers and your cohorts are converting to subscribers, you’re in businesses! Check out this new subscriber acquisition flow:

E-commerce analytics - subscription analysis

As you can see in this typical use case, the biggest benefit of your subscription analytics is that it empowers you to engage with your customers when they are primed to convert to subscribers. You know this by metrics that tell you how many days on average it takes for one-time purchasers to convert to subscribers. Then it’s time for you to re-engage them via email or SMS.

You can’t just rely on your product to do all the talking. Sure, your customers may have a great experience with the product, but if they don’t know the value of subscribing, or worse, don’t even know that you have a subscription option, you may not be setting yourself up for success. One-time purchasers or sporadic purchasers don’t guarantee long-term value the way subscribers do.

Part 4: Segmentation - Digging for Deeper Stories

So, after you have your e-commerce analytics at your fingertips, broken down into: cohort metrics, marketing metrics, and subscriptions metrics, what can you do with that data?

Making high level assumptions about your customers’ behavior will only take you so far. Data segmentation is the next step in getting the granularity you need to drive insight-driven actions.

What is Segmentation?

Segmentation is the process of chopping up your data into smaller bits to get more specific answers. More stories live in your data that are waiting to be discovered; segmentation allows you to mine these stories and use them for your advantage.

For example: if you are looking at your July 2021 cohort, and notice a high LTV, you may be wondering what exactly drove in higher value customers.

You can dig into the first product purchased via Product Name, Sku, Product Type or segment by Marketing Channel to see which dimension drives your most valuable customer. 

To find out, you can segment your data by discount codes or product tags to discover which products might be bringing in the most value or which discount codes were a hit. From there, you can dig back into your data to see what channels these customers came through to repeat and improve upon your successful efforts. 

How You Can Segment Your E-commerce Data?

There are many ways to segment your e-commerce analytics. In fact, the more creative you are with your tagging in the backend of your Shopify store, the more specific you can get with your custom segmentation in Peel.

When it comes to your preset segmentation in Peel, you have a wide array of options to dig into the exact segments you need:

E-commerce analytics - segment by customers
  • Segmenting by customers allows you to dig into specific customer tags that you’ve set up in your Shopify backend, discounts issued to customers, and subscriptions activated by your customers.
  • How to use it: This helps you understand what types of customers are making purchases and bringing certain amounts of value to your business. Did you attract valuable customers with a discount? Did you tag a certain type of customer who could benefit from a retention email based on what they purchased? Are subscriptions driving more value across certain customer segments?
E-commerce analytics - segment by location
  • Segmenting by location gives you specific data on how certain locales where your customers are making purchases. You can go as specific as a physical store that you have registered in shopify to as broad as countries where you customers are purchasing.
  • How to use it: Digging into the location of your customers allows you to understand how people are responding to your products in different regions. Maybe your California customers have different needs than your Vermont customers. You can use this information to create geographical audiences for hyper-targeted emails and Google Ads based on your customers’ regional preferences.
E-commerce analytics - segment by multi-touch attribution
  • Segmenting by multi-touch attribution provides you with a full look into your customer journey. This helps you account for everything from clicks to referrals to landing pages that your customers encounter ahead of their purchase. UTM tags allow you to track specific content, sources, campaigns, media, and keyword terms that contribute to your conversions. Multi-touch attribution is critical for understanding your customer journey.
  • How to use it: If you know every touchpoint that your customers are exposed to ahead of their purchases, you’ll have a better idea of which channels and campaigns are your most effective. Remember to tag your URLs intentionally with UTMs which makes this a whole lot easier. From there, you can lean into and put more emphasis on the channels that are driving your most valuable customers.
E-commerce analytics - segment by orders
  • Segmenting by orders allows you to take a closer look at your order types and how those impact your business. Segment by discount codes, online store, order tags, and payment gateways to get a better understanding of what specific elements of your site are working well and which can be improved. 
  • How to use it: Order data gives you a direct line into how your customers are purchasing. Combining this with your go-to metrics like LTV and AOV can unlock opportunities for growth. Do customers acquired with a discount bring a higher LTV over time? Do customer buying bundles with a specific order tag bring in a higher AOV? Segmenting by orders helps answer these questions to better understand your customer behavior.
E-commerce analytics - segment by products
  • Segmenting by products gives you the opportunity to deep dive into what products, collections, types, and variants are resonating with your customers. 
  • How to use it: Product segmentation is probably the most straightforward to use. Which products are performing the best? Do those products yield high LTV customers or do they drive in one-time purchasers? Do you have certain variants or types of products that are boosting your repurchase rate? Does a certain vendor provide you the best products? Answers to these questions can propel your marketing strategy for months and quarters ahead.

Part 5: Getting Started with Automated Analytics

This is the easy part! Now that you have a better understanding of e-commerce analytics, it’s time to see how your business is performing and start optimizing.

With a single click on the Shopify App store, you can install Peel for a free 15-day trial and start answering your most critical questions: Which products, discounts, or channels drive in my most valuable customers? Where are my upselling opportunities based on AOV? When do certain customers need to hear from me again? 

Peel’s full suite of automated e-commerce analytics will help you unlock the answers to these questions and more to grow your business.

E-commerce analytics - automated Shopify analytics free trial