By: Jeremy Moser
There’s no shortage of ecommerce data to track.
And that’s exactly the problem.
Companies don’t struggle with collecting data anymore. Instead, they often have more trouble drawing insights and conclusions from the data they already have.
Ecommerce analytics at scale is both an art and a science. Learning how to understand the fundamental relationships between each data point leads to sound strategic decisions, from marketing to onsite sales to product develops to retention and lifetime customer value.
Here’s why ecommerce data analytics are key for scaling any ecommerce business and how you can use the right tools to understand consumer behavior and capitalize on it.
Ecommerce Analytics Fundamentals that Inform Decisions
Most marketers claim to be data focused, and they might be correct to a certain degree… They have attribution reports up-and-running, along with dashboards covering every KPI imaginable.
But more often than not, people don’t know what to do with that data. The problem is that raw data isn’t good enough on its own. Instead, you should focus on the revenue-generating insights you can derive from the numbers.
As Avinash Kaushik says,
Most businesses today are data rich, but information poor.
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.
Guide Your Next Steps with High-Level KPIs
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 is at the top of this KPI hierarchy because it's one of the more common business objectives among ecommerce brands.
All data-based business decisions should revolve around your vital metric, whether that's revenue or number of new customers - it all depends on your high level objectives. If your activities or campaigns aren’t ultimately leading to an increase in your main KPIs, they aren’t working.
One level down on this chart you'll see the two metrics that influence revenue - the lifetime value of a customer (LTV) and the number of active customers. The number of active customers can be broken down in many ways, but most businesses find their customer acquisition cost (CAC) to be useful when assessing their business model. These two metrics tell you how much a user is spending over their entire history of doing business with you, and how much it costs you to acquire them in the first place.
Let’s take a deep dive into 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.
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 $45million in revenue in their first few years) had on average, five times higher customer lifetime values than other businesses.
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.
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:
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.
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.
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 …
Ecommerce Analytics to Fuel High-Paced Growth
Becoming a successful merchant means leaning on data to draw insights that inform decisions.
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.
Read More at: https://www.shopify.com/enterprise/ecommerce-analytics-at-scale
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