Ecommerce Analytics at Scale: The Art and Science of Using Data to Grow

Ecommerce Analytics at Scale: The Art and Science of Using Data to Grow

There’s no shortage of ecommerce data to track. And that’s exactly the problem …

Companies don’t struggle with collecting data. Instead, the trouble is drawing insights and actions from the data they already have.

As Avinash Kaushik, Digital Marketing Evangelist at Google and author of Web Analytics 2.0, says:

Most businesses are data rich, but information poor.

Ecommerce analytics — especially at scale — is both an art and a science.

To guide you through the flood, let’s take a big-picture look at (1) the fundamentals of ecommerce analytics, (2) how to create a KPI hierarchy to prioritize your efforts, and (3) how to optimize as well as visualize the metrics that matter most.

Ecommerce Analytics At Scale: The Art And Science Of Using Data To Grow

Ready to grow through analytics?

This article is an abbreviated excerpt from the Shopify Plus Data Analysis Course, one of many courses available exclusively to Shopify Plus merchants.

Inside, you’ll learn how to utilize platforms like Google Analytics, Google Data Studio, Facebook Analytics, and more.

Even better, all of the custom Google Data Studio dashboards you’ll see below are available for instant access.

Access the full course

Not on Shopify Plus? Connect with us today

Ecommerce Analytics Fundamentals

Collecting quantitative and qualitative data is the starting point, not the finish line. You then should overlap this information with context.

Absolute vs Relative Analysis

A simple data point might look straightforward on the surface, but different people can draw different conclusions based on the type of analysis they’re using.

Here’s a simple, math-based scenario to illustrate this problem.

If a business increases its conversion rate with a new landing page from 1% to 1.5%, that is a 50% relative increase but a 0.5% absolute increase.

  1. Relative increase: 0.5% ÷ 1% = 50% relative change.
  2. Absolute increase: 1.5% - 1% = 0.5% absolute change.

That might seem like a tiny, insignificant difference, but it has massive implications.

Let’s take, for example, an analysis that shows that conversion rates only decreased from 4% to 3% (effectively 1%) this month. That doesn’t sound too bad initially.

An absolute change of 1% doesn’t accurately reflect the real change in performance because the relative change ends up being a 25% decrease. A 1% relative decrease, in comparison, would only mean the conversion rate dropped from 4% down to 3.96%.

This problem becomes exacerbated at scale when dealing with larger numbers. One analysis might show a seasonal fluctuation, while another would indicate a massive failure.

And that additional context is critical to correctly interpreting the story the data is telling you.

High-Level KPIs for Ecomerce Analytics

Key Performance Indicators (KPIs) are measurable values that demonstrate how well a business is achieving their set objectives. KPIs should always reflect higher-level business objectives like growth or profitability.

At Shopify, we have created a KPI hierarchy for businesses that are optimizing for revenue growth:

revenue ltv kpi hierarchy

Revenue is at the top of this KPI hierarchy because it's one of the more common business objectives among ecommerce brands.

One level down, you'll see the two metrics that influence revenue: customer lifetime value (LTV) and the number of active customers. Let’s take a look at each to see how they affect ecommerce analytics at scale.

Customer Lifetime Value

Customer lifetime value (CLTV or LTV) is made up of both purchase frequency and average order values.

kpi frequency aov

For example, if a customer you acquired last month goes on to spend $500 each year for the next three years, they have a lifetime value of $1,500.

In a study by RJMetrics, the top-performing businesses (as classified by producing more than $45 million in revenue in their first few years) had on average, five times higher customer lifetime values than other businesses.

clv data chart

The study found that the top producers began to generate over half of their revenue from repeat customers by the second year, and that the top-performing customers for a given business spend up to 30x more than an average customer over their lifespan.

best customer spending graph

That means the most successful businesses are experts at retention and reselling. You can’t and won’t convert everyone in your space, but focusing on repeat customers with higher lifetime values is what separates ecommerce that scale from all other small shops.

To identify this information, start by looking at your “Customers over time” report in Shopify:

customers over time data

Dividing revenue by customers in each period will provide a simple way to calculate lifetime value each month.

Diving deeper, you can also pull up individualized spending reports on each of your customers to isolate which individuals are your highest purchasers. Then, you can target them with specific customer appreciation campaigns to build loyalty and increase repurchases.

Customer Acquisition Cost

Your cost of customer acquisition (CAC) includes the amount of money you have to spend to acquire a customer.

Unfortunately, most companies either can’t answer this question or get it wrong because they’re working with incomplete data sets.

big and small data infographic

Image via Kissmetrics

Technically, your cost of customer acquisition doesn’t just focus on ad spend. It also includes soft costs like labor, variables ones like outside agencies, and even contractors or sales commissions.

In other words, the effective “marketing spend” number is a lot bigger (and more nebulous) than many realize.

marketing spend new customers

Cost of acquisition can also be slightly misleading. A higher number doesn’t mean it’s bad or wrong or that your activities aren’t working. Instead, it needs to be set in context against the lifetime value of a customer to see if you can afford it.

A $1,000+ cost of acquisition might be high for a transactional B2C ecommerce shop. But for a disability insurance company that gets a commission plus residual amount for 20-30 years, it’s nothing.

Both metrics are critical for deciding what to do next. Here are two ways to put these metrics to work for your business growth today …

Optimizing through Ecommerce Analytics

Using tools like Google Analytics and Search Console are fine for getting basic leading indicators. They don’t tell you how to grow or scale a business year over year, though. For that, you’ll need to understand the trickle-down effect from changes in SEO rankings to traffic fluctuations and conversion rates.

Here are two examples that illustrate how ecommerce data analysis can help you prioritize efforts to grow your business.

(1) Optimizing Customer Lifetime Value

If each one of your customers spent an average of around $500 per year, how much revenue would your business generate?

Would it be more than expected, or enough to put a heavy focus on increasing LTV instead of generating new customers?

Head to the “Customers over time” report inside Shopify to see the customers and revenue over the last 12 months.

revenue over months analytics

Simply multiply your total number of customers for the last 12 months by $500 (for the annual spend metric). In this example above, the result is $1,040,500 in revenue as opposed to roughly $55,000. 

Lifetime value can act as leverage for increasing your conversion rates because a conversion has more value if the average customer spends more over the course of a year. If this analysis shows that your annual spend and lifetime value is high, it means you can more aggressively spend higher on ads to get people in the door, or even invest in lower converting traffic sources and still remain profitable.

For example, take a look at Amazon’s customer lifetime value for Prime and Non-Prime members:

ltv present value

Image via The Motley Fool

A typical, non-Prime customer might be worth less than $1,000 to them. Yet Prime members are worth over twice as much, coming in around $2,283 according to an analysis by The Motley Fool.

By including things like free two-day shipping, free returns, one-click purchasing, and consistently competitive prices, Amazon keeps customers around for the long haul. They even personalize your shopping experience by suggesting new deals tailored specifically to your order and viewing history.

recommended product deals ecommerce

All of this works to keep you coming back for more, or even ordering items you never knew you needed. Each feature has been designed with the explicit objective to get you to spend more, more frequently with them.

That means on the front-end, they’re fine paying more to acquire users. A ~$25 cost per click isn’t too bad when you know each successful conversion brings you back 10x.

Conversely, if your annual customer value is lower, you can shift the priority to increasing average order values or repurchases, first, rather than blowing your budget on big-scale acquisition campaigns.

Then, these strategic decisions can trickle down to your tactical ones.

For example, Facebook Optimization Rules allow you to set limits and pause campaigns when acquisition costs start to pass a certain threshold. You can limit the cost you’re willing to pay per customer, and shift the rest of your budget into retargeting existing customers to purchase more.

campaign manager ecommerce analytics

Automating your retargeting campaigns is easy with an app like AdRoll.

Your product catalog can be integrated with new campaigns in just a few clicks. The targeting for each product campaign can even extend to previous visitors, people who’ve purchased similar products in the past, or people who’ve added the exact product to their cart but haven’t yet purchased.

Adroll app web ads

Image via AdRoll

(2) Tracking and Increasing Repeat Purchases

Bootstrapped businesses rely exclusively on sales to fuel growth.

They’re forced to fund new business acquisition through cash flow, which depends largely on profit margins. That means you need to balance keeping the customer acquisition costs lower while increasing the yield (or revenue per customer) you’re receiving.

Unfortunately, you don’t always know which side of that equation you fall on. It’s not always clear if your business is heavy on acquisition (new customers) or retention (existing customers).

But using a few analytical reports, you can look at the repeat customer rate and determine if your current strategy makes sense or needs changing. Inside Shopify, head to the analytics dashboard and you’ll see your repeat customer rate directly on the “Overview dashboard:”

overview dashboard repeat customer rate analytics data

If you’re running a relatively new shop, or sell a higher priced item, don’t be frightened if you see a large portion of your revenue is from first-time buyers. The trends often average out over time.

If your business has been around the block, but still generates a high percentage of revenue from new customers, and you don't sell a high priced product, you can switch focus to increasing retention and LTV. Thereby lessening the burden on new customer acquisition spend.

In Shopify, you can locate the Customers report and select the “One-time customers” or “First-time vs. returning-customer sales” sections. These reports will show a detailed breakdown of all customers in your system.

customers data returning first time

Leveraging these reports, you can create a new remarketing audience or email list to help drive more sales from one-time buyers. Increasing the average order value from new customers can also give you more room to profitably increase ad spend on new acquisition, too.

Conversely, if your business generates revenue from repeat purchases, you might want to think about acquiring new ones even at higher costs. Death Wish Coffee took this approach, winning a contest for a commercial slot in Super Bowl 50 that generated a quarter of a million dollars in sales in just the first two hours.

The cost per acquisition would be extremely high, factoring in production, videography, and labor. But their subscription-based model is so good at keeping customers around that they can afford to spend more on new customer acquisition.

As you can see, data is critical for making marketing decisions that move the needle. Simply throwing money into social because it’s “hot and trending” won’t make the difference.

Let the data inform your next tactical move.

Integrating Google Data Studio

Google Data Studio takes the basic, raw data you’re used to seeing and brings it to life.

google data studio

For example, instead of just looking at raw site analytics, you can also bring in data from Google Sheets, AdWords, YouTube, and more. That means you can build out complex dashboards, showing everything from acquisition all the way through your entire sales funnel.

Currently, Shopify Plus offers a few Data Studio dashboards that help to filter and segment specific Google Analytics data. Each report helps you visualize your visitors' behavior and gain insight into how users at each stage of the funnel interact with your business. 

ecommerce analytics KPIs

Charts and custom reports allow you to access nearly any data set imaginable. Plus, you can sort each column by your preferred KPIs.

ecommerce data KPIs

Getting a clear picture of cause and effect isn’t a blur anymore. Try using the Shopify Plus and Google Data Studio to create reports that will help you determine which campaign, demographic or activity will produce the next best results.

Final Thoughts on Ecommerce Analytics

Analytics platforms are just that … analytics platforms. They provide raw data and plenty of it.

Unfortunately, that data is often only skin deep. It leaves more room for biases to distort your judgment. Confusing a relative change from an absolute one means you might miss the underlying trend that threatens to derail sales.

Worse, too much data is just as bad as too little.

Using Google Data Studio, you can quickly create custom ecommerce analytics dashboards to highlight growth-based metrics. Data analysis at scale is necessary because it helps you ditch superficial metrics. It demystifies and clarifies. It directs and informs.

And that’s why …

Ecommerce Analytics At Scale: The Art And Science Of Using Data To Grow

If you’re ready to grow?

Then check out the Shopify Plus Data Analysis Course, one of many courses available exclusively to Shopify Plus merchants …

Inside, you’ll learn how to utilize platforms like Google Analytics, Google Data Studio, Facebook Analytics, and more.

Even better, all of the custom Google Data Studio dashboards shown above are available for instant access.

Access the full course

Not on Shopify Plus? Connect with us today