The DMAIC Model: Understanding Amazon’s Metrics

Orchestrated disruption. It might seem like a contradiction in terms at first. But if anything has come to define the Amazon business model, orchestrated disruption may be the most applicable term.


Business models tend to be static entities. And innovation will either bolster an existing business model or shatter it altogether. Yet Amazon has managed to do both. Beginning in 1994 as a retailer selling one particular product line through an otherwise unexplored channel, it gradually morphed into a product line, an entertainment hub and a symbol whose sum is greater than its respective parts.


Yet that sum needs instrumentation in order to operate efficiently. That instrumentation needs to understand how those constituent parts interact fully and comprehensively. And those constituent parts need to understand just how their respective roles affect the greater whole of that sum.


That instrumentation is the key to understanding Amazon’s metrics. It’s called the DMAIC model and it’s part of a well established business philosophy also known as Six Sigma. It’s a strategy that’s driven by data. And it’s responsible in no small part for Amazon’s orchestrated disruption.


The DMAIC Model and Amazon


Define

The business model of a flywheel isn’t necessarily exclusive to Amazon. But they perfected it to such a degree that it’s become one of the most referenced examples of how retail growth can maintain its own momentum. And customer experience is the core of Amazon’s strategy.


In Amazon’s case, growth is generated by six specific factors: lower cost structure, lower prices, customer experience, traffic, sellers and selection. While each influences one another, it’s customer experience that is the sustaining momentum of that flywheel. And customer-centricity is the very foundation of Amazon’s success. Amazon defines itself and its own growth by the hallmark of customer experience.


Measure

In both science and business, a working definition simply isn’t enough. You need both a working model and the necessary instrumentation to measure the metrics of that model. And again, Amazon’s measurement is based on the yardstick of providing a superior customer experience. But there are three key elements to keep in mind when measuring the success of any business model:


  • Bias reduction. Bias reduction means understanding the results of your business model objectively. It means adaptation to those results, even (and especially!) if they don’t conform to your model.

  • Auditing. Are your metrics working correctly? Does any element of your model need to be refined? Because if anything is in the slightest bit off-kilter in your measurement, the end result will skew dramatically.

  • Investment. In particular, in the instrumentation of your model. You need to measure what works in your model—but more importantly, what doesn’t. And both take time, energy and capital if you hope to have a comprehensive understanding.


Analyze

Analysis is when you come to a unique understanding of the drivers behind your metrics. It’s built on both experience and perspective. And both demand complete and absolute objectivity.


Are all elements of your model in sync and working under control? Are the results of your model consistent and replicable? What are the root causes of any variant result? What is influencing those causes?


Bias confirmation is hardly unique to the business sector. Our brains are hardwired to expect our environment to work in conformity with our perspectives. When they don’t? Disruption occurs. That disruption can either be rejected altogether—or adapted to. And the latter is always going to be a gamble. In Amazon’s case, it was a successful gamble. But Amazon also had more than a few failures along the way. Without analyzing exactly why those failures occur, evolution simply isn’t possible.


Improve

Evolution implies innovation. It’s not a standalone phenomenon. It builds on what has worked in the past and has adapted to an already existing environment.


But something happens along the way. It can mean strengthening existing processes. But it can also mean a sudden flash of inspiration which redefines those very processes. That’s called improvement. Amazon’s retail model subsequently changed the game for good. They adapted to a new and virtually unexplored environment and came up with an entirely unique business proposition: customer-centricity.


But change can only come after the previous step of analysis. That’s because that change will be entirely contingent on variable results. There will be times in which a metric stops working altogether, yielding no useful information. That’s evolution as well. It’s never a static phenomenon. And if a metric stops working after thorough analysis, it needs to be either redefined or discarded altogether.


Control

Only after the previous stages of definition, measurement, analysis and improvement have been refined and perfected can the final stage of control be realized. It ensures that processes are operating normally and effectively, with no room for error. In fact, they can operate so normally that some of the previous elements can be automated.


Yet there’s a dilemma in the control stage: what happens when the entire DMAIC model runs so smoothly and efficiently that there’s no room for innovation? The answer, in Amazon’s case, is to take that model and expand upon it, applying it towards previously uncharted territory. And that’s precisely what they’ve done. Few retailers would even consider expanding into air logistics. But Amazon has. Few retailers would have considered developing network data infrastructure that is now standard in both private and public sectors. Amazon perfected it.


The DMAIC model is an infinitely elastic one, applicable to both internal metrics as well as a larger business macrocosm. But there’s always a potential crisis in innovation. It can fail altogether for any number of factors. Innovation is never anything less than a bumpy ride and its success is never guaranteed. Not only is reevaluation of internal metrics critical, it can result in discarding untenable models altogether.


That’s disruption, as well. But it’s never anything less than well orchestrated.


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