Evaluating product performance based solely on units sold can be deceptive...

As an eCommerce merchandiser, imagine you're tasked with organizing your collection page. Your goal is to feature the star performers at the top, while those not faring as well are subtly nudged...

As an eCommerce merchandiser, imagine you're tasked with organizing your collection page. Your goal is to feature the star performers at the top, while those not faring as well are subtly nudged to the bottom.

Now, consider the role of an email marketing manager, eager to craft an email that showcases your top-selling products to your loyal subscribers.

Or picture yourself as an advertiser, strategically selecting the cream of the crop among your products for your upcoming prospecting campaign.

In each of these scenarios, you're faced with a pivotal question: What truly defines a “best performing” product? This article delves into the heart of product performance metrics. We'll explore whether the best metric is as straightforward as the volume of units sold, or if there are other, more nuanced factors at play.

Our approach to analyzing product performance

Fundamentally, product performance analysis can be as straightforward as arranging your products in a table, ranked by total sales volume or generated revenue.

For example, to evaluate the Month-To-Date performance of shorts by revenue, your starting point would be a table akin to the one illustrated below:

The most significant limitation of this analysis lies in its sole focus on the output, which is the number of units sold for specific products. It overlooks the inputs, such as the extent of exposure each product received.

By incorporating 'Product Views'—the number of times a product has been viewed on the website—we gain a valuable estimate of the cumulative marketing effort deployed across all channels for a particular product.

The concept of the 'best performing product' shifts considerably when we expand our analysis beyond just the number of Units Sold. Take 'Shorts 2,' for instance. Initially, it appeared to perform on par with 'Shorts 1' based solely on sales volume. However, after factoring in Product Views, a startling insight emerges: 'Shorts 2' achieved nearly identical sales as 'Shorts 1' with roughly just a third of the exposure.

To quantify the relationship between Product Views and Units Sold, we calculate a ratio by dividing the two, yielding a metric we've termed the Product Conversion Rate (CR%).

Product CR% = Units Sold / Unique Product Views

The term 'Unique' in Product Views adds a layer of precision to our analysis. It implies that if an individual views the same product multiple times in a single session, we count it only once. This method refines the Product CR% metric, offering a more accurate measure than if we were to consider all repeated views within the session.

The problem with the Product CR% as a metric

Take two hypothetical products as an example: Product A is priced at $5, and Product B is priced at $100.

Intuitively, which one might boast a higher Product CR%? Typically, more affordable products tend to have higher conversion rates since they are more accessible to a larger audience.

But here's the conundrum: if Product A has a Product CR% of 5% while Product B's CR% stands at 1%, should we automatically allocate more exposure to Product A?

Suppose both products have a profit margin of 70%. A single sale of Product A yields a $3.50 profit, whereas Product B earns $70 per sale.

With a 5% CR%, Product A would sell 50 units from 1,000 views, generating a profit of $175. In contrast, Product B, with a 1% CR%, would sell 10 units from the same number of views, but would result in a $700 profit.

Below is a table to visualize the comparison more clearly:

Should we then invariably prioritize products with the highest dollar margin per unit sold? It's not quite that straightforward.

Consider a scenario where Product B's CR% dips to 0.2% rather than 1%. Let's examine the impact on the numbers:

In such instances, Product A's substantially higher CR% resulted in a greater total profit from an equivalent number of Product Views.

Hence, we advocate for a balanced approach that weighs both Product CR% and profitability. Integrating these metrics offers a more comprehensive assessment of product performance.

Putting it all together

We've distilled our analysis down to a key metric: Profit per View (PPV)

To calculate PPV, we take the Total Profit and divide it by the number of Product Views:

PPV = Total Profit / Product Views

Alternatively, we can arrive at the same figure by multiplying the Product Conversion Rate (CR%) by the Profit per Unit Sold

PPV = Product CR% * Profit per Unit Sold

(If you're curious about the math behind this equivalence, I'm just a message away for a detailed breakdown!)

Essentially, PPV reveals the average profit generated from each product view.

The table above illustrates that evaluating the PPV metric leads us to a conclusion that aligns with what we observe when we consider total profit.

Revisiting the initial report on shorts from the beginning of our article, it would be beneficial to incorporate both the profit derived from each product's sales and the PPV metric into our analysis.

We now face an intriguing question: What increase in profit could we achieve if 'Shorts 2' and 'Shorts 3' received more exposure at the expense of 'Shorts 1' and 'Shorts 4'?

Imagine redistributing exposure so that 'Shorts 1' gets 8,000 fewer Unique Views and 'Shorts 4' gets 5,000 fewer, while 'Shorts 2' and 'Shorts 3' each gain an additional 6,500 Unique Views. What impact would this shift have on our profit margins?

By strategically reallocating exposure among these four products, we could potentially elevate our profits from $19,045 to $28,280—an impressive 48.5% surge.

It's essential to recognize, however, that such an outcome represents an ideal scenario. Real-world conditions might not permit such precise adjustments in exposure. Additionally, a surge in traffic for 'Shorts 2' might lead to a slight decrease in its PPV.

While reaching a 48.5% increase in profit may be ambitious, achieving even half of this growth would still be remarkably beneficial for our bottom line.

TL;DR

To sum up the most important take-aways of this article, here are the most important points:

1. Evaluating product performance based solely on units sold can be deceptive, as this metric doesn't account for the product's exposure within the same period.

2. A more informative measure is the Product Conversion Rate (CR%), which requires careful consideration as cheaper products often exhibit higher CR% than their costlier counterparts.

3. For us, the Profit per View (PPV) emerges as the preferred metric for assessing product performance. It adeptly integrates sales, exposure (via product views), and profitability into a singular, comprehensive metric

In upcoming articles, we will share a variety of use cases where we have applied, or plan to apply, the PPV metric.

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