Today, RPA is often seen as a flawless solution to a company’s many problems. Indeed, your business can benefit greatly from automation. However, bots without a proper RPA deployment monitoring strategy can quickly become a nuisance and even cause your automation initiative to stall. That’s exactly why you need RPA analytics. 

Analytics is the “voice” of your robots: they cannot communicate their status to you, but through constant tracking of multiple indicators, you can evaluate their performance, detect errors, and take proactive measures to avoid potential problems. More importantly, RPA analytics gives you valuable insights into the actual flow of your RPA deployment and the extent to which you were able to reach your business goals. There are two sets of metrics that are commonly tracked in this regard: 

  • Operational KPIs 
  • Business KPIs 

Let’s see how you can assess your RPA initiative’s state and the impact it has on your business. 

RPA analytics deployment success KPIs

Operational KPIs for RPA deployment monitoring 

Operational KPIs are quantifiable values that provide you with information on your robots’ performance. The exact selection of operational KPIs may depend on many factors, but we will analyze the metrics RPA analytics works with most often. 

  • The overall number of automated processes reflects how many business processes have been delegated to RPA bots, giving you a reference point to measure your RPA deployment scale. 
  • Utilization rates display the schedule of bots and the frequency of automated processes being run. Use it to determine whether your robots are being underutilized or overutilized. 
  • Productivity shows how many tasks your digital workforce can complete during a set period. You can use it for work planning and assessing the efficiency of your virtual assistants. 
  • Bot accuracy reflects the frequency and quality of errors your bots make. The analysis of trends and patterns in the bots’ flaws lets you detect and eliminate the reasons causing malfunctions so that you can improve your automation outcomes. 
  • Success rate is the percentage of processes that your bots complete without errors from start to finish. If your success rate is always 100 percent, there might be something wrong with the error detection system. 
  • Transaction handling time indicates how fast a bot is. In this regard, you can measure: 
    • Average time to complete a process itself (an essential element for planning deadlines and meeting SLA requirements) 
    • Average time to complete a process step (a great way to identify spots where delays and bottlenecks occur) 
  • Break-fix cycle length shows how much time on average passes between a bot malfunction and its repair. This greatly affects the average downtime value. 

Business KPIs for RPA efficiency measuring 

This group of RPA analytics metrics serves for measuring the value and benefits your company gains from automation. Here are some of the commonly tracked ones. 

  • Process outcomes are a quick and easy way to measure your RPA deployment success, as they indicate the state of things before and after process automation. 
  • Cost savings illustrate how much money you can save by deploying bots annually per employee or per process. In this context, the cost of a process executed by a human employee includes factors such as salary, taxes, insurance, equipment expenses, paid vacations, and more. 
  • FTE resources are the number of hours that human employees are freed up due to RPA. One FTE equals the number of hours a full-time employee worked on the process during a week. 
  • Customer/employee satisfaction is crucial today. So, conducting surveys before and after automation will show you whether RPA helps you achieve higher satisfaction rates among your customers or employees. 
  • Average downtime value indicates your losses in business value and money when bots cannot work on their assigned processes due to downtimes. 


More and more companies choose to facilitate their business processes with RPA solutions, but not all of them succeed in maintaining digital workforce in the long run. You might want to conduct regular health checks for your RPA initiative to ensure it does you more good than harm. We recommend you keep an eye on both operational and business KPIs throughout the whole RPA implementation process.  


Are there useless KPIs that everyone still measures? 

Some of the KPI examples that can be misleading or useless for RPA data analytics include: 

  • The number of detected errors. There are too many ways to interpret this KPI. E.g., if you detected little to no defects, it could mean either that your RPA bots are awesome or that the error detection system does not work well. 
  • The number of active bots. An ambiguous metric that gives you little insight into your bots’ actual throughput, productivity, and work capacity. 
  • Percentage of automated test cases. This metric is useless for RPA deployment monitoring because automated testing does not guarantee successful process automation. 

Why do RPA initiatives stall? 

According to Ernst & Young, up to 50 percent of initial RPA projects fail. Some of the common reasons include: 

  • Governance issues 
  • Shadow deployments 
  • Seeing automation as a one-time project instead of an integral part of software development 
  • Poor choice of automation candidates 
  • Trying to “automate everything” 
  • Setting unrealistic automation goals from the beginning 

In our opinion, the utmost reason why RPA projects fail is governance issues. Regulating each aspect of building and deploying bots, as well as managing digital workforce appropriately are what help RPA initiatives succeed in most cases. 

Are there any RPA analytics tools? 

There are many tools for RPA deployment monitoring. Examples include Bot Insight by Automation Anywhere, UiPath Insights, Alteryx, and many others. Before choosing, feel free to contact us for a detailed consultation on making the best use of your RPA analytics.