I’ve just finished reading an excellent Harvard Business Review article on Performance Measurement. It’s quite a long article, so I though it worth summarising the key points (and pointing you to some of my articles that are closely aligned).
The Harvard article ends by saying this: “Companies have access to a growing torrent of statistics that could improve their performance, but executives still cling to old-fashioned and often flawed methods for choosing metrics. In the past, companies could get away with going on gut and ignoring the right statistics because that’s what everyone else was doing. Today, using them is necessary to compete. More to the point, identifying and exploiting them before rivals do will be the key to seizing advantage.”
At the start of the article is the view that people’s confidence in their own judgement is often at odds with the reality and that good data and appropriate statistical analysis is required in order to make better business decisions. I’ve written previously about “Management by Fact“.
Some of the other measurement issues raised in the article include the “recency effect” where people’s view of performance is biased towards what happened recently. I’ve experienced this when facilitating discussions around process activity costing where staff tend to recall the recent, difficult case they dealt with last week, and use that to inform their judgement on how long a process takes.
Whereas a dog may be for life, a measurement may need to be changed as organisational circumstances change. For example, start-ups are likely to need to measure new customer acquisition rates, but more mature businesses are more likely to be interested in measuring loyalty or churn. In the same way that processes need to be reviewed, measurement systems also need to be reviewed to ensure they support what the organisation is trying to achieve.
Probably the most important theme in the article is that of “cause and effect”. This underpins the design of an effective Balanced Scorecard system, which I’ve also written about several times. One of the most popular articles on my blog is about Lead and Lag indicators where I explain how they differ and also how there should be a cause and effect chain of objectives and supporting measurements. To be useful, your measurements must predict the result you’re seeking.
There are five steps that this HBR article proposes for selecting the right measurements:
- Define your governing objective (what is it you’re ultimately trying to achieve?)
- Develop a theory of cause and effect to assess presumed drivers of the objective (what are the internal leading indicators that will predict external lagging performance?)
- Identify the specific activities that employees can do to help achieve the governing objective (what skills and capabilities are needed?)
- Evaluate your statistics (are the measurements you chose last week/month/year, still the right ones for what you’re trying to achieve today?)
Overall, this article reminded me of the Golden Rules of Measurement that I was introduced to a long time ago:
- Decide what you’re trying to achieve, then decide what you need to measure
- No measurement without recording
- No recording without analysis
- No analysis without action
You’ll find more of my articles on Performance Management here.