Beyond the Conversion Rate — powerful Analytics E-Commerce Metrics to Rank Winners and Losers

How “Runner” and “Bummer” scores rank products and make simple reports actionable

If you have followed @frederikwerner’s blog, you have seen some powerful examples of Adobe Analytics Metrics. I am a huge fan of Adobe’s Segment-based Calculated Metrics, because you can condense so much complex info into a single column.

My favorite examples are the “Renner” and “Penner” metrics, which loosely translate to “Runners” and “Bummers”. I have been using them for years, and the simplicity and actionability that goes with them makes them an every-day tool for my E-Commerce clients.

These metrics allow to rank Products, Categories, Brands, Subcategories and even Campaigns, Search Terms and more in terms of how well they utilize the traffic they get in terms of generating Revenue.

So a “Bummer” is not simply a product that sells poorly, and a “Runner” not simply one with a lot of Revenue. Instead, the scores put their performance in relation to traffic and revenue. E.g. an expensive Order boosts the “Runner Score” more (and lowers the “Bummer Score” more) than a cheap Order (assuming that cheap products have higher Conversion Rates).

If you only looked at simple Metrics like Product Conversion Rate (in GA “Order-to-Detail Rate”) or “Revenue per Detail View”, you always run into problems with all those zero-Order products which all have 0% Conversion Rates, and you have to compare all products as if they were made equal, i.e. as if they were expected to reap e.g. the same Conversion Rate.

Bummers” (Penner) sorted by the Bummer Score (highlighted) allow to not only see those products on top that require immediate attention, but also where they get their traffic from (blue channel segments), thus immediately giving a hint to where the lever for optimization can be. E.g. the Product in row 5 gets most of its traffic (Product Detail Views [Visit]) from the “SEA” Channel and has sold not once (so it is rightfully a Bummer). By viewing the last 7 days together with Today and Yesterday (purple), you can quickly see if the problem has been solved already (e.g. Product 3’s days of Bummer traffic from the ”Referred” Channel are over).
By breaking down Bummer n° 5 by “Marketing Channel Detail”, the Category Manager can see the bad traffic is caused by 2 Google Shopping campaigns. He can send this info to Performance Marketing and ask whether it makes sense to stop bidding on this product in Google Shopping.

The formula for the “Bummer Score” (formulas are explained in the comments):

Product Detail Views [counted once per Visit] * e^((-Orders/3)*(Revenue/Prod Detail Visits))

In Adobe’s Metric Builder:

Bummer Score in Adobe Analytics Calc Metric Builder

You can experiment with this score for your shop (e.g. try another constant than 3, which is there to not make a single Orders have too much impact).

Likewise, the “Runner Score” shows Products/Product Groups/Categories etc. not simply in terms of how much they sell, but how well they sell compared to the traffic they get, and the price at which they sell:

“Runner” Products allow a niftier way to rank than simply by Revenue, Orders or Product Conversion Rate. Product 4 has e.g. brought only 6,554 CHF, but it has done so on fairly little traffic (less than 200 Product Detail Visits). Product 7 has brought 27,928 CHF, but it is at 7 and not higher because it needed a lot more traffic to get there (about 900 Product Detail Views). So it uses the traffic less effectively compared to the other products.

The formula for the “Runner” Score is:

IF (Product Detail Views>25):
THEN: (Orders * (Product Revenue ^ 1.5) * Product Conversion Rate) / 100

Inside Adobe Analytics:

Runner Score in Adobe Analytics Calc Metric Builder

That being said, you can apply these Scores not only to Product-scoped Dimensions, they are also useful to rank Marketing Campaign Performance or even Search Terms. Example:

Ranking Channels by “Runner Score” (“Prod Renner Score”) gives a more adequate view of how efficiently and effectively they turn their traffic into Revenue than by simply looking at Revenue or Conversion Rate.
“Bummer Scores” (“Prod Penner Score”) can also be applied on On-Site Search Terms. Term number 1 would not be the biggest Bummer if we looked only at its 0.3% Conversion Rate (here, Orders divided by Search Result Clicks). But considering we made only 1 Order for 339 Clicks, it IS a Bummer! Search Term 3 led to 7 Orders, but needed 298 Clicks for that, and 203 CHF in 7 Orders is not that much either, so the Bummer status is well-deserved here as well.

The scores are by no means perfect. Potentially some Data Scientist can find an even better score for you. E.g. Bummer Scores work less well for large categories and the longer the time frame. But that is not their aim. They are made so you can quickly react to short-term trends on a low-granularity level. And they do a great job for me every day and are part of every Category Manager dashboard.

At my previous employer, we even used Bummers and Runners as signals for Marketing Campaigns. By automatically exporting the Top x Runners and Bummers per Category (with the marvellous ProductsUp in-between to join that Adobe data with the rest of the product catalogue), we gave the algorithms a signal to bid more on Runner products and throw Bummers out for a while. The Runners were the products shown in Display Prospecting campaigns (for acquisition of new users). They were also used in Product Recommendations in Newsletters for users where there was not enough user-specific data to show personalized products — and I remember cases where recommending the overall Runners performed better than recommending something based specifically on that user’s data (e.g. based on which product she had bought/seen etc.).

So Product Rankings go beyond better Ad-hoc Analysis and Reporting. Since they rank products better than a simple Conversion Rate or Revenue metric, they are also helpers for Marketing Campaigns.

So take some time and try to use your creativity and some math to go beyond the simple, but short-sighted (and boring) standard metrics. ;)

Digital Analytics Expert. Owner of dim28.ch. Creator of the Adobe Analytics Component Manager for Google Sheets: https://bit.ly/component-manager