Step No. 2: Set important goals
Forget SMART. Establish the goals that matter. Goal-setting isn't the time to think about measurability.
It is, however, a good time to think about making them operational. For example:
- Goal: Satisfy our customers. Neither objective nor operational. Often, not even your customers will know whether you've achieved it.
- Goal: Make sure customers are happy they chose us. Better, but still not great. Happiness is elusive and sometimes not achievable. If, for example, you're a dental surgeon, your customers might be happier than if they still had a toothache. But happy? Don't get your hopes up.
- Goal: Customers come back and bring their friends. This is an operational goal. It's about how customers behave, not about how they feel. And you can tell if they have.
- Goal: Employee loyalty. Not operational.
- Goal: Happy (or proud or whatever) employees. Still not operational, but at least you can ask and hope to get a meaningful answer.
- Goal: Good, very good, and excellent employees stay, and recommend us to colleagues as a great place to work. Operational; also, highly desirable.
Step 3: Review for completeness
While you'll never reach the level of geometric proof, think through your list of goals, preferably with the team that reports to you -- more eyeballs help -- until you're all confident that if you achieve the goals on your list and do nothing else, you'll be successful.
Step 4: Translate goals to math
Goals are stated in plain language. Metrics are their equivalents, expressed in the language of mathematics. They are easier to develop when goals are operational than when goals are about attitudes and feelings. That doesn't mean goals for attitudes and feelings are impossible to develop metrics for -- but it's harder.
For customer satisfaction, you'd have to find a way to survey customers and ask them to rate their level of satisfaction on a numerical scale -- 1 to 5 is traditional.
For the operational version, you would ask customers as they buy merchandise why they chose to do business with you. If you're succeeding at your goal, the percentage of customers who are either repeaters or referred will rank high.
Targets -- specific numbers you want to hit or exceed -- are optional because they're a mixed blessing. They do give everyone something to shoot for. But they also define "good enough," which is generally the enemy of "better."
Step 5: Determine how to collect the data
Then think through how your data collection methods might lead to data quality problems.
If, for example, you plan on using surveys, think about why someone would take the time and effort to fill one out. The most likely reasons translate directly to possible sources of sample bias:
- Some companies offer customers a financial incentive to take Web surveys. The inevitable result: Survey-takers don't care whether they fill out the questionnaire accurately.
- Other companies don't. Their data quality challenge: Ticked-off customers are more likely to fill out the survey than happy ones.
- Also recall that if you plan on using the data to assess employee performance, you'll almost certainly end up with unusable data. Resist the temptation.