Customer Experience and Market Concentration

In 1945, an economist named Albert Hirschman invented a market concentration index” that shows the relationship between international trade and power. He recognized that even though it is convenient to consider trade as neutral, the basis of the “invisible hand of the marketplace” concept, trade is in fact always imbalanced because different economies have different starting points, advantages, and disadvantages. For this reason, though the “invisible hand” is a fun visual idea, if you really want to understand the impact of trade, you must look at the trading partners’ context in the market in addition to their simple trading numbers.

The same logic scales down to individual organizations and programs. Although it’s convenient to measure customer experience (CX) success in simple units like stakeholders served or products sold, those measures don’t capture success as much as they document simple trades. If you want to understand success, you have to look at context.

How to do that? For Hirschman, that meant taking firms and calculating their trades volume against the size of their market to understand their market shares. In individual organizations and programs, that means figuring out how to measure the market you’re operating in.

Two examples: If you are a federal employee working in contracting, typical milestone measures for your work might be how many contracts were successfully awarded or completed within a certain timeline. What that does not take into account is the context of those contracts or their nature. Whether or not those awards and completions were the best possible versions is unknown. The assumption is that they are, but there’s no context-driven measure for it. Each contract operates on its own.

As another example, when an organization goes on an efficiency drive, work requiring lots of staff will take hits, and those with long term goals of lowering staffing requirements will not show gains, but those that have the happy circumstance of not requiring much staff and being able to conform to that drive will look quite good to the strategic goal-setters–despite having done almost nothing to gain efficiency for the organization. That’s because the strategic goal is simply to find efficiencies; the different starting points of the programs subject to that goal are not typically examined.

Hirschman’s solution to this was to create his index with additional, context-driven data that illuminates the power differential between trading partners. I propose that organizations do the same before setting strategic goals. Hirschman did this with only human abilities to gather and synthesize data, but we can use AI implementations to achieve more precise program understandings than has ever been possible.

We’ve long used indices to track mission achievement, but what we’ve rarely used the same approach to program-level goals. Instead, we’ve stuck with the same, easily quantifiable key performance indicators, like click through rates and net promoter scores, to understand impact. But a dashboard of numbers with no meaningful link to strategic mission is as meaningless as no dashboard at all. It’s time to apply that same, index-based approach to individual programs in organizations that we do to overall mission achievement in order to better understand impact. The Human-centered Design Measurement Guide was researched, written, and reviewed by folks at NIST, OPM, EPA, and GSA to do just that. It’s not up on digital.gov, but we maintain a copy for your reference. Let us know if you have any questions– we would love to talk index-building with you.

What we’re into this week

For the first time in over a decade, it seems like staying at your job is the best path to a higher salary, from The Economist. [paywall]

Remembering that there’s nothing new here with physicist Angela Collier, a massive take down of AI hype. TL/DR: AI is amazing, but most of the commercial applications are just more and more about less and less.

Reviewing our humanity with a summer read of cognitive scientist Don Norman’s The Design of Everyday Things.