Desktop Analytics is the answer. Its one of the best kept secrets of the BPO industry. Desktop Analytics comprises of a set of softwares that analyze how people interact with applications on their desktops. This analysis yields a wealth of information that can be used to improve various metrics such as productivity and quality, or for streamlining workforce to better manage workloads. This post provides data on from a real company that is using such a software.
Background: This an example of a team that conducts research on the web with respect to various industries. This team is expected to work for 8 hours a day. They are primarily expected to use Google Search, Linkedin , MS Access, MS Excel and MS Outlook. Overall, management was satisfied with the output of this team. However, when the management tried out the desktop analytics software for just one week, some interesting insights that were gleaned. Here is this insight for one individual in the team:
1. Overall Productive Time for a day:
This table shows how productive the individual has been on a particular day (the 15th of the month in this case). We can clearly see that there is an opportunity to improve productive time by about 2 hours 18 minutes (15m 12s + 35m 52s + 1h 25m 51s). This kind of information is available every day of the week or the month. Average and trends can be determined for each category of time (productive, unproductive etc.).
2. Productive time during every hour of the day:
This graph shows the hourly activity of the individual, along with the duration of use, of various applications in each hour. As can be seen, the individual worked only for about 20 minutes in the first hour of the day on the 15th. Of these 20 minutes, about 10 minutes were spent on unknown and unproductive applications.
3. Desktop Activity in 1st hour of the day:
If you want more details on what exactly were the unproductive and unknown usages, that information can also be seen in this chart. It is obvious that this individual is spending the first hour of the day on personal work on various shopping sites and on personal email.
4. Workload Analysis:
Workload analysis will show you how busy your people are, on productive applications in each day of the week. This data can also be viewed in a similar graphical format for each hour of the day. Ideally, we would like to see greens everywhere. The predominance of blues shows the opportunity for improvement here.
5. Weekly Application Usage:
One of the most powerful features, is to look at trends over time. The above graphs shows application usage of the individual through the week. It is strange to notice the individual’s usage of all google search coming down over the week. It is also interesting to see that most of 19th was spent on Excel and on Linkedin. If this is the expected usage every week, then it is fine. If not, it deserves a closer look by the team leader or the manager.
6. Weekly Efficiency:
Efficiency of the employee is calculated as a a ratio between (a) the weighted average of the time spend on productive and unproductive applications and (b) the total potential time available for productive applications. The above graph shows how the efficiency of the employee varies across the week. The same efficiency graph can be viewed for the entire group that this individual belongs to. The group could be a process, a department, a function etc.
The above example is just a minor snapshot of what can be done with desktop analytics. There is a lot more If you are not leveraging it already, you are definitely missing something.