7 ± 2 : [HBS Summary] Choosing the Right Goals: Identifying Your Critical Performance Variables.

In this article I will simplify and summarize the HBS : Choosing the Right Goals which is part of the Harvard Strategy execution program and further explain the link to the Magical 7 ± 2 by George A. Miller .

It is usually understood to argue that the quantity of objects an average human can hold in short-term memory is 7 ± 2 . This has often been referred to as Miller's law. [ Below you will find both the full paper and additional video for your reference ]

Harvard Executive Program called Driving Corporate Performance asked executives to bring their balanced scorecards, so that they can work with them to improve their design. They find that some executives proudly bring scorecards with 50,60, or even more measures. Unfortunately, when you measure this many variables, you lose your ability to focus on the things that are most important. People often ask, how many measures should a manager be accountable for? The answer is found in a famous article by psychologist George Miller, with the title, "The Magic Number Seven, Plus or Minus Two. “The idea is straightforward. People can easily remember seven things, or even as many as nine, if they have to. Think of all the things in our daily lives that are configured in sevens--days of the week, notes on the musical scale, colors in the rainbow, wonders of the world. What does this mean for you as a manager? If someone can remember what they’re accountable for, this will influence the way they make choices as they go about their daily work. This is very easy to test. Stop someone who works for you in the hallway and ask them, what measures are you accountable for? If they have 20 or 30 measures on their scorecard, there’s no way they can remember them all. But if the answer is five to seven, they’ll have no trouble reciting back to you what those measures are. On the flip side of the coin, Miller's article suggests that we shouldn’t have fewer than five measures. Why? Because with fewer than five, there’s not enough variety to stimulate creativity. [Source HBS] .

To summarize, an individual manager should be held accountable for no more than seven measures, plus or minus two. The rest can and should be delegated.

Selecting Measures and Targets

Much of the power of using a balanced scorecard as a diagnostic control system lies in its ability to translate strategy into goals that clarify for employees what they are expected to achieve. To accurately track people’s success in achieving those goals, however, you must also ensure that measures link to critical goals.

A measure is a quantitative value that can be scaled and used for purposes of comparison. When designed properly, measures are an important tool for communicating, aligning, and motivating employee behavior to ensure the successful execution of strategy.

Imagine a recreational boat manufacturer looking to diversify its product lines. Managers set a goal to introduce a new line of mid-size sailboats for the cruising market. How will they evaluate their success in hitting this goal? They could do this in a number of ways, but they decide to select as their measure the number of new orders received from customers.

Once you have selected one or more measures for a goal, you need to decide on a target—the specific level of performance and timeframe against which to gauge progress. Drawing on its own sales history and industry benchmarks, the boat manufacturer set a target to have 20 firm orders in hand for the new model in the next nine months. [ HBS ]

Below are some examples of selecting measures and goals for a marketing department .

To set these measures and targets effectively, businesses should identify which firms in their industry set the standards for the most effective use of resources and calibrate their own efforts accordingly. For example, a business seeking to streamline its transaction processing might commission or consult a study of similar firms to determine which ones excel at transaction processing and identify their levels of efficiency and effectiveness with various indicators. This process is called benchmarking.

Tests of an Effective Measure

When designing measures, there are three tests you should apply to see how effective your selective measures will be in tracking progress to key goals.

Test #1: Does the Measure Align with Strategy?

• Ask yourself, if I picked up a scorecard and examined the measures on that scorecard, could I infer what the business's strategy was. If you've designed measures well, the answer should be yes.

Test #2: Is the Measure Objective, Complete, and Responsive?

• Objective : If a measure is objective, you can independently verify it. You and I could look at the same set of data and draw the same conclusion.
• Complete : A measure is complete when it captures the full range of activities you need to undertake to achieve a goal.
• Responsive : A measure is responsive when the employee can directly influence the measure. In other words, when you step on the gas pedal, does the speedometer move?

Test #3: Does the Measure Link to Economic Value?

The third and final test, does the measure link to economic value? This takes us back to the value creation process and our inputs-process-outputs (refer to previous posts ). The more that a measure focuses on outputs, the more confident you can be that it is creating value. For example, if you increase revenue and profits, it's very likely that you've created economic value for the business. As we move upstream to consider processes, we're less certain about the link with value creation. If, for example, we build new relationships with our customers, will that create economic value? The answer is probably yes, but we might not be 100% sure. Not all new relationships will lead to increased revenue and profit, so maybe the correlation is something like 80%. Moving further upstream to inputs, let's say we decide to invest in more training for our employees. We like to think that this will build long-term value for the business, but how confident are we that every dollar spent on that training will lead to economic value? Of course, we can't be totally sure, so our confidence level might decline to a 50% probability. There's an important implication here. The further you go to the right, the closer you are to outputs, the more you can use objective measures derived from formulas to measure and reward performance. You can link those measures to employees' bonuses and know that you were doing so fairly. The more that you move to the left, however, the more you need to rely on your own judgment when evaluating performance.

Additional Resources :