Rethinking Your Rankings: What Defines a High Performer?

Most eCommerce businesses have some kind of analytics in place to inform their decisions. With Google Analytics or other tracking solutions, it’s never been easier to gather customer data and generate reports with the click of a button. The problem is that many of these metrics require context to produce helpful insights that can improve your business.

It’s tempting to think that revenue or conversion rates are all you need to make informed decisions. But the truth is that these metrics are biased by external factors, and you must account for this bias to get valuable insights. Otherwise, you risk drawing false conclusions that could leave money on the table or even harm your long-term prospects.

In this article, we will take a look at a few common eCommerce metrics, the problems with them, and how to more accurately rank your best products.

Common eCommerce Metrics

You probably already record basic metrics like pageviews and sales, which can be used to calculate conversion rates. These are undoubtedly important, as conversion rates help inform marketing campaigns and provide a baseline for measuring improvement. In fact, conversion rates are often regarded as the most important eCommerce metric given its utility in ranking products.

You probably also track revenue and profits to calculate revenue growth and profit margin trends. These data points can be helpful for prioritizing marketing campaigns and quantifying value creation for stakeholders. After all, success in business is often defined as maximizing revenue and profitability—nothing else matters in the eyes of investors.

If you’re more advanced, you may even track shopping cart statistics to calculate cart abandonment rates. These insights can help identify problems with the checkout process rather than just optimizing product performance. For example, visitors may prefer the addition of a certain payment method or desire a guest checkout option to avoid signing up. You might even track where a product appears within a category or search results page.

Google Analytics Enhanced Ecommerce has made it easier than ever to track these metrics and generate valuable reports.

Are You Measuring the Right Things?

These are all great metrics to track, but it’s important to understand their limitations before acting on their insights.

A product detail pageview is highly dependent on visibility and placement. For example, a product that appears lower within a category will inevitably receive fewer pageviews than a product that appears higher up in a category—regardless of its merits. The lower-ranked product could actually have a higher conversion rate than the higher-ranked product when placed on equal footing, which isn’t obvious when looking at the top-level statistics.

Conversion rates can also be influenced by design. For example, the mobile version of your eCommerce website may have a lower overall conversion rate due to its design. Products that appear on a disproportionately high number of mobile searches could, therefore, have an artificially low conversion rate. You might assume that these products are just underperforming unless you segment out mobile visitors. Mobile visitors may also view fewer products than desktop visitors, which means that products appearing lower in lists will have fewer chances to convert.

Revenue and profit figures may also be misleading without context. It’s easy to assume that your top-grossing products are the best sellers, but those numbers are dependent on the number of purchases. And the number of purchases can be heavily influenced by outside factors, including visibility and placement. This means that a different product may have the potential to be a best seller if it were on equal footing.

Alternative Metrics to Consider

The easiest way to incorporate visibility into your analysis is by using the number of actions relative to the number of impressions. For example, you could create a list of products with the highest revenue per product impression rather than just looking at top-grossing products. This will show you products that outperform similar products, but suffer from a lack of visibility on your site.

Some important metrics to track include:

• Cart to Product Impression Rate
• Cart to Product Detail View Rate
• Purchase to Product Impression Rate
• Purchase to Product Detail View
• Revenue to Product Detail View Rate

Once you’ve analyzed these metrics, use them to improve your overall conversion rates. For example, you can arrange your product category pages based on the highest revenue per product impression to show products that are likely to generate the most revenue. Or, you could look for an unusual difference between the cart to impression rate and the purchase to impression rate for your products, which might indicate a problem with the detail page of a certain product.

The second strategy for leveling the playing field is segmenting analytics based on other factors. For example, visitors coming from a paid advertising campaign may be more primed to purchase than visitors coming from search engine results. This can impact conversion rates if one product receives a lot more paid advertising than another product. It may be helpful to segment out these traffic sources to get a clearer picture.

Some important segments to consider include:

• Mobile vs. Desktop Visitors
• Paid Advertising vs. Organic Visitors
• Different Geographies

There are many other optimizations that can be made along these lines, but incorporating visibility and segmenting analytics are great starting points.

How Avatria Convert Can Help

The biggest problem with in-depth analysis of this type is that it can be difficult and time consuming to crunch these numbers. In some cases, it may even be impossible. How can you possibly arrange search query results by top-performing products when there is an infinite number of possible search queries? What happens when new products are added or old products are removed? How do you adapt to changing purchasing behaviors over time?

Avatria Convert does all of this analytical work for you to address these questions and more. Our machine learning technology is built to take in Google Analytics Enhanced Ecommerce data and figure out which metrics are most important in driving customer behavior. By predicting which products customers are most likely to purchase, Avatria Convert automatically and intelligently rearranges products in categories, search results, and other areas to maximize conversion, revenue, and profitability.

By letting us take care of the heavy lifting, you can focus on making tangible improvements to your business elsewhere.

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