Measuring productivity is a complex problem. This is nothing new, but the problem has reached new heights in the knowledge economy where output is harder to measure. A hundred years ago you could more easily see how many cars a factory had produced or how much wheat had been harvested. It is a lot harder to determine how efficient an HR manager is or why a sales team is performing badly – there are infinite variables.
The emergence of big data has the potential to be a potent weapon in addressing this problem. Big data can help by tracking and analysing what employees are spending time on and then evaluating the results.
The interesting point here is that a small sample size is not likely to give useful results. One salesman may be doing poorly and spending only two hours per day on outbound telesales – which tells us very little. But if there is a direct correlation across 300 salespeople on performance versus time spent on outbound telesales, then that could be the cause, and would certainly be worth investigating further.
Effective tools now exist which are easier to use than ever before; there are hundreds of time tracking platforms (like Toggl) which slice and dice information into different reports so management can see who is doing what.
The need is huge, and the tools are there – businesses need to configure their resources in the most efficient way. Here is a guide on how you could start using big data to track time and boost productivity.
BLS is a fictional Surrey-based lift parts manufacturer, making and selling lift parts to a large range of clients across the UK and Western Europe. Sales have taken a downturn in recent months and management are worried. When they speak to the sales director, he claims that worries about Brexit and a poor economy have led to the decrease in sales.
The management team have decided to implement a month-long study on sales, account managers and customer services to see how people in those departments spend their time, then use this data to try and make conclusions on performance. The process for setting up the big data project, collecting results and initiating improvements is laid out in the following ten steps:
1. Set goals
2. Define metrics
3. Appoint a project leader
4. Engage team members
5. Use simple technology
6. Track progress
7. Collate data
8. Analyse results
9. Provide feedback
10. Take action
BLS needs to define what they’re looking to achieve from the process;
1. Attain a clear view on how time is being spent in the organisation across three teams.
2. Determine if there is a correlation between time spent on activities and performance.
3. Generate two to five recommendations on how team members can change productivity habits to optimise performance.
This entails looking at how both productivity and performance are measured.
Productivity can be measured in time spent on an activity. Some more detailed studies may ask employees to track energy or motivation levels – this requires accuracy and honesty, which can be difficult if employees feel under scrutiny.
Knowing who is performing well is important because BLS is trying to understand how the top performers behave, so they need to be clear on who the top performers are.
The study needs to be designed to engage team members, track progress and report to management.
One person should have ownership and accountability for the project; long studies of this nature often drift as more pressing operational matters get in the way so the project manager must have clear objectives and the time to deliver the project.
This may be the most difficult step. If the initiative isn’t introduced well, then team members may feel as though the project is looking for bad performance and that there will be consequences. The project manager has an important job to explain that the project is designed to improve performance across the organisation.
To deliver this message effectively the management team and project manager should approach it in the mindset of improvement rather than uncovering poor work.
There are many tracking tools on the market, some with extensive feature-sets. BLS need to choose simple tools for a few reasons:
1. The least technically competent team member should be able to use the tool easily.
2. Tracking time is fiddly for team members, especially those switching tasks so the process to track this should be easy.
It’s important to decide on the level of detail required when tracking – when it doubt go for less. It’s better to have slightly less detailed but complete data rather than a sophisticated method that hasn’t fully captured what is required.
There should be a set of milestones during the project to check that team members are completing the tracking effectively, that the process isn’t having a negative effect on motivation and to see if the project needs iteration.
As the project is underway, the management team or project manager may start to see early trends and want to dig into them or have an idea to improve the process – keeping up to speed with key milestones allows for these ongoing improvements.
If the project is set up well, then this part should be easy. Ideally, the team members’ data will automatically feed into a reporting module.
If the data is a little rawer (some businesses choose to do this using a pen and paper), someone needs to collate it into a format where it can be analysed.
Someone should be tasked to draw conclusions from the data; there should already be some questions to answer so tackle these first, then consider additional conclusions that can be drawn from the data. Examples may be:
General: Are there traits high performers have in common?
Specific: Do poor performers spend more time on proposals or sales meetings?
Highlight limitations of the study, depending on sample size you’ll be faced with making judgements on causation versus correlation. This should be one part of a wider process that looks at productivity.
This can be done company-wide and individually. It should always be positive:
- Our best salespeople do x, let’s learn from them.
- We found x was a trait of team members in the bottom 20% of performance.
Focus on what you’ve learned and what will change as a result.
It’s generally better to take the clearest and simplest conclusions and implement those first. For example:
- The ratio of prospecting to meetings should be 65/35.
- Customer service reps should aim to spend 3 hours following up on tickets per day.
Once clear and obvious improvements have been ratified and implemented then a more detailed approach can begin.
If you think this may work in your organisation, then spend time working out key objectives and designing the study. Big data isn’t going to solve every productivity problem overnight, but it can be a very useful tool to help paint a picture of activity in your organisation.
We’ve described a one-off process in this article, but you can and should embed this into your company where possible, so employees track time – study after study has shown that just the act of tracking time makes employees more thoughtful and productive.
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