Productivity is the thing we want most, but understand least. Just as there is no one version of what “being productive” means, there is no universal way of measuring it. But we have to start somewhere if we want to keep strengthening our performance and staying competitive. This detailed guide is all about how to comprehensively quantify and measure your personal version of productivity in a way that makes useful practical sense.
Before we dive in
The productivity methods we’re going to look at do not constitute hyper-complex, mathematically perfect productivity indices required by big plants and factories (for that, look here). Instead, they are geared towards more “abstract” types of work – like creative services and “knowledge work” – whose output can’t easily be reduced to identical units on a production line.
What even is productivity?
In order to measure productivity, you first need to know what it means. From a traditional economic perspective, productivity can be summarised as total output divided by total input(s). Some have interpreted that to be the yield or efficiency of your output in relation to the effort you put in.
But while this transactional “rate of work” is useful when producing definable units, it doesn’t easily lend itself to creative services. It’s a bit easier if we focus instead on inputs – the resources you use to create your work, like people, time, technology, capital and raw materials. Higher productivity here means doing more with the same number of inputs or using fewer people to achieve a set result.
But this definition is still flawed. Like the first, it overlooks important mediating factors, like the actual quality of output, whether the “correct” products were produced, and how individual team members contributed to the output. A higher output rate is not productive if the value of what’s being produced is inferior, and effective production flows aren’t productive if your output has no actual market demand.
Clearly, productivity needs to factor in some idea of “effectiveness”: improving the value of your efforts – whatever you interpret that to be – by working smarter, instead of harder. But effectiveness is also something that is subjectively interpreted, rather than mathematically “true” or “false”.
So productivity is harder to define that we think. There’s no single definition of it, since no two businesses operate under the exact same conditions with the exact same end-goals, so we have to create our own. So, before you measure productivity, establish what it does and doesn’t mean to you personally.
Ways of measuring productivity
Here are some of the most popular single factor measures used to track productivity:
Average task length / total hours used
This essentially comes down to labour productivity – finding an average benchmark for how long a certain task should take from previous examples, and then measuring ongoing performance against it.
Number of tasks completed / time spent
This measures the most traditional view of productivity – the amount of units produced against the time put in. The value here is measuring how much has been achieved or how much people complete within a set timeframe.
Time spent on high-value task A vs low-value task B
Productivity can be interpreted as putting more effort into "the right" work. You can easily keep tabs on this by seeing the proportion of time that goes towards the high-yield, important work that advance your projects and goals, versus the time that spent on low-value "shallow" tasks that contribute little materially.
Total hours worked / hours budgeted
Similar to above, this helps you work out the capacity of your workforce against their contracted totals and highlights overtime.
Average number of tasks per standard project
This is great for identifying broken workflows and needlessly complex project plans across similar types of work. It’s really useful for finding out which processes hold productive work back.
Number of tasks completed / number of employees
This uses the same understanding of productivity above, but exchanges time for employees to get an idea of labour productivity.
Total hours available / active work time
This gives you a utilization benchmark – seeing how many of the set hours you have available each week are actually spent on productive work (as opposed to unproductive tasks like meetings, email, admin etc.).
Time spent per project / profit generated
This takes the “savings made” approach to productivity – with the idea that you should be able to use less of your input (in this example, time) while increasing your profits. Swap “time” for “employees” for a gauge on team effectiveness.
Time spent on project per employee
This one is useful for seeing the breakdown of value as contributed by each member of the team. Obviously, you need to account for the skills spread and requirements of that project to really assess “individual effectiveness” – if it’s a predominantly technical task and only one person has the necessary skills, they’re going to appear to have carried the entire project.
As you’ll notice, there’s a lot to do with time in here, and it’s really important to measure time even if you don’t use it to value your output. Time can’t ever be removed from the production process – it is the only universal resource that all businesses share. To save you the hassle and extra burden of trying to work out where your business uses time, try getting an automatic time tracker to capture it all for you. It’s especially useful when it comes to working out performance averages for tasks and projects.
There are three main approaches to measuring productivity:
Single factor productivity: this looks at one ratio of input to output (like hours taken to complete a task). The data is easy to access and track, but produces a one-dimensional view of productivity.
Multi-factor productivity: this combines the ratio of output based on a group of different inputs – like time, labour and budget. Data is also quite easy to access, but the added level of complexity makes it harder to calculate.
Total factor productivity: this is the (usually unattainable) ideal – combining the impact of all inputs used in production. It’s usually extremely difficult to measure and still falls short of being able to show interactions between different inputs which affect productivity.
All of single factor productivity measures from the previous section are pretty useless on their own. Used in isolation, they can’t deliver a realistic “bigger picture” of your performance and they can’t individually explain where your production inefficiencies lie – like a skills shortage, juggling an excessive amount of projects simultaneously, slow processes, or unmanaged client expectations.
So, to get a richer understanding of what productivity means for you, we would always recommend using multivariate measures wherever possible while keeping your measures as simple as possible. A mathematically perfect, multi-layered productivity index sounds great, but if your employees can’t understand or relate to it when making decisions, it’s essentially pointless. The whole point of quantifying productivity is to explain the individual factors that influence output in a way that everyone understands.
To that end, only measure that which actually informs your end goals. There are a ton of work variables you can measure, but the only ones that correlate reasonably to your personal definition of productivity. To be effective, your productivity measurement should identify the contribution of every production factor and combine them.
Towards a personalized productivity measure
It all sounds ridiculously complex, but it doesn’t need to be. Use these 4 steps to create a meaningful multi-factor productivity measure that works for you:
Choose your most important performance ratios (like task length, projects completed, and utilisation)
Establish current performance and long-term goals for each (your benchmark)
Add weight to each ratio to show its relative importance (together totalling 100) to create a single score
Track performance within each ratio against this weighted average
Whatever measure you end up with, always qualify it with contextual information – like resources available, difficulty or uniqueness of projects, number of active projects and experience among your team. Remember, the goal isn’t to have a scientifically perfect measure; it’s to have a practically useful and understandable general measure which shows you how to improve.