Ironically, despite all of the effort put in to tracking progress against targets, there is often little data or analysis used to set those targets in the first place. 

One organization with whom I worked had a customer satisfaction target of 83.75 (out of 100).  I asked them why it was 83.75.  Why not 83 or 84?  Was there some data that said that 83.75 was the level at which they optimized their return on their investments in customer experience. Was 83.75 the point of diminishing return where it would start to cost more to improve satisfaction than they’d recover in sales?  Of course not.  The target was set based on the prior year’s result of 83.15.  Management wanted to do better and 83.75 seemed better (the word “seems” is generally a red flag that you’ve moved out of the realm of data).  They had no data as to whether it was attainable inside or outside of their organization or what impact a .6 increase might have. They just wanted a number that was higher than the prior year.  But higher isn’t always better.  Sometimes the incremental cost to improve on a metric doesn’t yield a proportional return.  Yet, I often find leaders who set targets based on an arbitrary increase or decrease from their prior year’s performance.  Simply using last year’s data as a baseline is not being data-driven. Being data-driven means that there is clear, factual evidence that hitting your targets will provide the outcomes that you desire.

Many years ago, the organization that administered our employee engagement survey sent us two interesting benchmark charts.  The first showed the level of employee engagement for those companies with the most highly engaged employees.  The second showed the level of engagement for the highest performing companies (from a business performance perspective).  The two charts were quite different.  The companies with the most engaged employees weren’t necessarily the companies with the highest business performance.  The higher performing companies did tend to have highly engaged employees and there is an increasing body of research supporting that.  However, they don’t need to have the most engaged employees.  There is a point at which increases in engagement no longer make a difference (from a business performance perspective).

My boss made the wise decision to target our engagement at the levels of high performing companies rather than the most engaged companies.  This is an example of a data-driven target.  He used the data to determine which target best met his goal.  His goal wasn’t to be on the list of organizations with the most employee engagement.  His goal was to improve business performance.  In doing so, he prevented us from over investing and over-optimizing our metric.

Unfortunately, when targets are set with little to no data or analysis, people misuse and misunderstand them.  Either they don’t get taken seriously (e.g., “It doesn’t matter that we missed it, it was unrealistic to start with”) or they are taken too seriously or misapplied (e.g., “Let’s see if we can beat the target by 50%).

The second problem might seem counter-intuitive.  What’s wrong with beating a target?  If you want $100 in sales and you make $200, isn’t your company doing better?  That depends.  If you had to sell the second $100 of merchandise at a loss, in order to get the sale, then exceeding the target hasn’t helped. 

Good targets should have meaning.  They are a guide as to where you want to be.  If they are truly based in data, then the goal should be to hit them or get as close as possible to them (just like a bulls-eye in darts) not exceed them.  Exceeding a data-driven target could be an indication that some other part of the business is being sub-optimized.

A few hints that your targets might not be data driven:

·         The precision of the metric is at a greater level of detail than your ability to perceive a difference in performance. (e.g., satisfaction targets that are expressed into the tenths or hundredths place or revenue/cost targets that are expressed to single dollar or cents place).

·         They are the same for disparate groups or business areas

·         They are based solely on an increase or decrease of the prior year’s performance

·         They don’t have an upper or lower limit