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This Issue's TLDR...

• The origin story of the name "Best @ Amazon" and why it should remind you to contextualize your data.
• There are three broad categories of data contextualization: internal benchmarks, external benchmarks, and transformations.

What’s in a Name? Best @ Amazon explanation

(Time to Read: 4 minutes)

I can't think of a better way to kick off this newsletter than to provide a little context for the name: "Best @ Amazon."

Is it an expression of my ego? A boastful statement that, of all the super smart Amazon experts out there, I'm the best?

Not at all. In fact, I hate the term "Amazon expert" (and its close cousin, "Amazon guru") and I'm quick to remind people that I'm just a guy that has been around a lot of Amazon brands, has been around a lot of smart people, and has picked up some cool things along the way.

But I digress…

The name "Best @ Amazon" comes from my time working at Amazon and a KPI classification system that we used to contextualize our performance.

Essentially, for every imaginable input or output metric (more on those another time), I would see how my business — Amazon Canada Consumer Electronics — was performing relative to, e.g., Amazon US Consumer Electronics, Amazon France Consumer Electronics, and so on. And, for quick reading of these cross-marketplace comparisons, we visually flagged the marketplaces that were the best performers and worst performers with the tag: "Best @ Amazon" and "Worst @ Amazon."

And with that little piece of trivia, with that little "What's in a Name" explanation, comes my first tip, trick, best practice, whatever you want to call it…

➡️ Always seek to contextualize your data

How can you do this?

I like to focus on three broad categories of contextualization:

1️⃣ Internal Benchmarks

• Vs Budget or Plan -- If you're part of a larger organization, you probably have a Budget or Plan, and are measured against it (whether it is reasonable or not). If you're a small or medium enterprise, you might think you don't have a Plan, but there's a hidden Plan that you're not thinking about: Your Demand Forecast. Presumably, you have to forecast demand to fine-tune your purchase orders (if you don't do this, you must enjoy having capital tied up in inventory). So, at minimum, tracking unit sales velocity relative to a Plan / Demand Forecast is a useful exercise for your business.
• Period-Over-Period -- How are you performing Week-over-Week (WoW), Month-over-Month (MoM), Quarter-over-Quarter (QoQ), or Year-over-Year (YoY)? You can get infinitely granular with this -- e.g., a period-over-period comparison to 8 weeks ago -- but my experience is that WoW, MoM, QoQ, and YoY get you 98% of the insights you need.

2️⃣ External Benchmarks

• Peer Groups -- This is a broad term, and you only really get mileage out of peer group context when it is precisely defined and highly relevant to your business. Share of Search (SoS) and Share of Voice (SoV) are versions of peer group comparison (i.e., with SoS, you're looking at your brand's search impression share of total impressions, relative to your peers). Peer group comparisons on Amazon used to be somewhat nebulous, imprecise, and reliant on third-party data sources. Over the past 12-18 months, however, Amazon has opened up the flow of (first-party!) data to brands, including a Brand Metrics report, which allows for easy advertising funnel peer group comparisons, like the one below.

3️⃣ Transformations

• Ratios -- As your business scales, ratios become more important as an audit and inspection mechanism. When you only focus on absolute amounts -- especially in a growing business -- you lose the ability to intuitively understand if that growth is actually value accretive. For my Amazon brands, my top 3 ratios to track are: Contribution Profit Margin (Sales less COGs less Amazon Referral Fees less Amazon FBA Fees), Net Profit Margin, Cash Payout %.
• Common Basis -- Think of this as holding one "variable" constant, while adjusting all others. As an example, one common basis view of my Amazon brands that I like is my Daily Sales Average (the constant variable), looking back over different periods of time (the variable I'm changing). This gives me a quick sense of whether sales are flat, decelerating, or accelerating.

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