General Product Performance KPIs

General Product Performance KPIs

General Product Performance KPIs

Marketing Managers & Merchandisers in every e-commerce team ask themselves the following big questions about their products:

  • According to which metric should we evaluate the combined marketing effort across all channels for a particular product?
  • According to which metric should we decide which products should we buy more of for the next season?
  • According to which metric should we decide which products to invest more $ on online advertising?
  • According to which metric should we decide what products are most optimal to promote through on the Web and via Email?
  • How much money would I need to spend on paid media in order to sell 1 unit of a promoted product?
  • Is a product selling as a result of direct promotion, or are customers discovering the product by browsing the website?

If you are asking yourself these questions in your team about your products then this is the page for you. This page is dedicated to answering the questions mentioned above and others related to the performance of products with data. But first, a little introduction about ourselves:

How do we know these are the burning questions of e-commerce CMOs?

At Datacop, we are dedicated to helping eCommerce teams in their journey from raw data to revenue. Over the course of the last 6 years we have worked with 25+ eCommerce companies and with that experience we developed the Datacop Analytics Framework (DAF). DAF is a set of analytical practices developed specifically for e-commerce marketers that aims to provide maximum value from their data. A key business area for e-commerce companies is their product catalogue. Which products should we have and how do we best market them? That has been at the core of the many hundreds of conversations we have had.

DAF: Product Metrics is our best practice set of recommended processes on how to deal with business questions e-commerce companies may have regarding their products. Adopting these practices for your team will mean:

  1. Faster Time to Insight: Time is a very valuable resource in the e-commerce business and e-commerce marketers have to make a lot of product decisions in a short period of time. Facing such complexity it is easy to make mistakes without the help of data. DAF users are provided with cleaned, transformed and automatically updated datasets and visualizations that they can quickly confront their business questions with data insights without worrying about the “data-backend”.
  2. Improved Online Ads ROAS: Overpromoting the wrong product on online ad channels may cost a lot of wasted money. DAF users can see quickly identify products that are underperforming the worst from the perspective of online ads promotions. DAF users then turn budgets off or decrease them and re-use them on underfunded but well performing products.
  3. Improved Web & Email Merchandising: Choosing the wrong products to have displayed at important website touchpoints, email campaigns may result in disinterested visitors and lost conversions. DAF users can quickly identify what are the best products to select for which campaign and which website touchpoint, without having to guess. This enables marketers to learn from their past merchandising decisions and continuously drive the CR% of their channels up, as they learn what works and what doesn’t.
  4. Improved Product Buying: Understocking the right products for the season may result into products being out of stock too soon. DAF users use on-site product data in addition to external sources of information to guide their product buying decisions. They can evaluate the performance of specific categories across time and seasons and better gauge when to stock up what.

Data challenges how well each e-commerce team overcomes them are a key predictor to their success. Consider how difficult it is to make the right decisions in e-commerce companies with large catalogs (1000+ active products). The larger the e-commerce company, the more complex the marketing operation becomes. The larger the e-commerce company, the more costly the “sub-optimal” decisions are. The larger the e-commerce company, the more valuable the insights become. The more channels and touchpoints an e-commerce companies is using the harder it becomes to merchandise all of them effectively.

With the help of DAF, marketers can regain control and visibility over their product catalogue. Instead of “hoping and spraying in all directions” marketers can focus on using their limited time to capitalize on insights and opportunities that directly impact their bottom line. The 40+ Product metrics are indicators that measure how products are behaving and performing. Together they provide the most advanced e-commerce digital analytics on the market to evaluate e-commerce products performance.

Product Group - 5x Levels

Note that product metrics can be “viewed” across a number of “product levels”. Each e-commerce company has its own product grouping logic, depending on the vertical and strategy. A metric such as “Units Sold” → can be answered at each level of the defined product level, depending on the granularity of the question or the needs of the situation.

An example of a fashion e-commerce company that has 5 level of product groups:

  • category_level_1 → Gender → Male (Women, Kids, Unisex)
  • category_level_2 → Clothing Type → Shirt (Jackets, Pants, Shorts, Accessories,…)
  • product_level_3 → Specific Style → “Datacop Logo Shirt”
  • product_level_4 → Specific Style and Color → “Datacop Logo Shirt - Black”
  • variant_level_5 → Specific Style, Color and Size → “Datacop Logo Shirt - Black - Large”

General Product Performance KPIs

This section includes all metrics related to the general product performance on the e-shop. What are all the observable (and useful) input and output variables of a product in an e-commerce site?

In addition to each metric, we sometimes mention specific call outs of interest. There are 3 categories of callouts:

⚠️
Common Errors There are mistakes in applying data to practice in we see occurring commonly in the industry. Examples of relevant errors have been provided to metrics, where appropriate.
icon
Business Questions For different questions and situations, you would want to “focus on” metrics that best correspond to the question in mind. Examples of relevant questions have been provided to metrics, where appropriate.
📈
High Value-Add Practices Certain metrics and practices have proven to be useful in providing product optimization insights. Examples of relevant high value-add practice have been provided to metrics where appropriate.

1. Gross Revenue $

The Gross Revenue Metric measures how much revenue a particular product (group) brought in over the course of a [selected time-frame] if it wasn’t discounted. It is calculated as (All Units Sold * Inventory Price of Item (MSRP)).

2. Product Discounts $

The Product Discounts $ Metric measures how much discounts were applied to a particular product (group). Depending on the e-shop, there are two types of Product Discounts $: one based on the customer applying a coupon and the other is based on a “store-wide” discount on the product.

icon
How much of our gross revenue (MSRP) of a particular product was discounted?

3. Revenue $

Revenue is what customers paid for a product over the course of a [selected time-frame]. It is calculated as (All Units Sold * Real Price of Item). Revenue does not include returns nor discounted revenue.

⚠️
This metric is often used to make a lot of product-related decisions. The limitation of this metric is that it does not take into account any input that was required to achieve reported revenue numbers. When we evaluate companies, we look at company revenue in relation to their expenses. We recommend doing the same thing when it comes to Product Performance evaluation. Take a look at PPV metric for more information.

4. Units Sold #

The Units Sold Metric measures how many units of a particular product (group) have been sold in the course of a [selected time-frame]. It is simply the count of all units sold per product.

5. Unique Views #

The Unique Views Metric measures how many times a particular product (group) have been viewed in the course of a [selected time-frame]. A view of a product is counted when its corresponding product page has been loaded. This metric is counting unique views per session per user, as opposed to all views per user per session.

The advantage of counting this metric as unique is that it eliminates the problem when somebody sees one product multiple times during one session. This is often the case, customers often return to the product they are interested in. This can be kind of random and not really reflection of a product’s performance. If the metric was not counting unique views, it would make the product look like it has worse performance than it really has.

📈
This is the go-to metric for evaluating the combined marketing effort across all channels for a particular product.
icon
What is the combined marketing effort for a particular product?

6. Unique Units Sold #

The Unique Units Sold Metric measures how many unique units of a particular product (group) were sold in the course of a [selected time-frame]. This metric is counting unique units sold per user per session as opposed to all units sold per user per session.

The advantage of counting this metric as unique is that it eliminates the problem when somebody buys the same product multiple times during one session. This is particularly true of un-usual customers that buy in bulk. Consider a situation where a single customer has ordered 40 of the same product, which later turned out to be a fraudulent transaction. If the metric was not counting unique unit sold, it would make the product look like it has better performance than it really has.

7. Unique Add To Carts #

The Unique Add to Carts Metric measures how many unique units of a particular (group) were added to the cart in the course of a [selected time-frame]. This metric is counting unique units added per user per session as opposed to all add to carts per user per session.

The advantage of counting this metric as unique is that it eliminates the problem when a visitor adds the same item multiple times (often by accident) into the cart.

8. Product CR %

The Product Conversion Rate % Metric measures the relationship between the number of users who saw a product and the number of users who bought the product in the course of a [selected time-frame]. Is is calculated as (Unique Units Sold / Unique Views).

icon
If a product is viewed in a 100 sessions, in how many of the sessions will it be purchased?
icon
What is the ratio between product exposure and product sales?

9. Product Add To Cart CR %

The Product Add to Cart Conversion Rate % Metric measures the relationship between the number of users who saw a product and the number of users who added the product to their cart in the course of a [selected time-frame]. It is calculated as (Unique Add to Carts / Unique Views).

10. Unit Cost $

The Unit Cost $ is an uploaded value that measures the cost for manufacturing/sourcing the product.

11. Profit per Unit Sold $

The Profit per Unit Sold $ Metric measures the difference between the price at which the product was sold at the cost at which the product was sourced. It is calculated as (Revenue $ - Unit Cost $).

12. Forecasted Revenue $

Forecasted Revenue $ measures the expected revenue to be brought in the course of a [selected time-frame]. There are a number of ways e-commerce companies typically forecast their revenue $:

  • YoY Baseline + Qualitative Assumptions about Marketplace + Growth Plans
  • Machine Learning Forecasting
icon
How well is our overall revenue comparing to our forecasted goals so far?

13. PPV $ (Advanced)

The Profit per View measures the expected profit per each Unique View of a product. It measures the relationship between the number product views, the number of product sales and product profitability in the course of a [selected time-frame]. It is calculated as (Product CR% * Profit Per Unit Sold $)

Organic PPV$

❗This calculation excludes any Units Sold or Product Views which occurred on sessions that arrived from Paid channels. This is because paid channel traffic is less qualified and products that are promoted more on Paid channels get unfairly downgraded.

📈
Organic PPV is the go-to metric to help guide merchandising decisions on the Web and on the Email.
  • Identifying Products that are “trending under the radar ” - low unique views, high O.PPV
  • Identifying Products that are “money-makers” - above avg. unique views, above avg. O.PPV
  • Identifying Products that are “a season mis-match” - high unique views, low O.PPV
  • Identifying “Products that potentially should be discounted” - below avg. unique views, very low avg. O.PPV
📈
Organic PPV is the go-to metric to help guide product buying decision for the next season.
  • Identifying product category mix for the season
  • Estimating best variants inventory levels within a particular product (sizes, volumes, colors)

14. Sales Velocity # (Advanced)

The Sales Velocity Metric measures the relationship per product (group) between number of days “active on site” and number of units sold. It is calculated as (Units Sold / Days available). Days available is identified as any day that the product was added to the cart at least by one user. This means that the product was displayed and in-stock.

icon
How many products a day can we expect to sell this product (group) in a given time period?
📈
This metric provides a perspective that eliminates the problem of “selling out too quickly”. Imagine you have a week long sale and after it has finished you are evaluating the products that were selected for the sale. Imagine a didn’t have a lot of stuck, but sold out really quickly in the first 2 days of the sale, would not show up very highly among total “Revenue” and “Unit Sold” counts. This metric lets you identify high performing fast selling products that are flying under the radar due to low inventory counts.

15. Unique On-site Impressions # (Experimental)

The Unique On-site Impressions Metric counts the number of time a product was “loaded” on category pages and recommendation shovelers. This is different from a Product View, because it refers to the step before when products are being listed on pages and a customer sees a lot of products at once they choose to view from.

icon
Which product (group) has the most unique on-site impressions?

16. “Browsing-CTR” % (Experimental)

The Browsing Click Through Rate % is a metric that measures the relationship of a product’s number of unique views and number of unique impressions. It is calculated as (Unique Views / Unique On-site Impressions).

📈
Browsing CTR% is the go-to metric to select category “hero” products on key website touchpoints.
📈
Browsing CTR% is a best practice metric to inspire and shortlist product candidates for Online Ad campaigns

17. % of Units Sold to New Purchaser

The % of Unit Sold to New Purchaser is a metric that measures how many of all of the units sold were sold to New purchasers. A New purchase is an Order done by a customer who has never purchased previously. It is calculated as (Units Sold to New Purchaser / Units Sold).

icon
How does this product perform as an “entry-level” product to acquire new customers? Some product may have a high % of Units Sold to New purchasers, while other products may be more purchased by repeat customers.

Contact us

If you need help building a reporting tool that evaluates product performance based on the KPIs mentioned above, please start by booking a demo meeting of the Product Performance Report here.

If you have any questions regarding this article, consider joining the free #ask-datacops Slack channel, where you can ask us any data-related questions. More info on our website.