You’re about to spend significant time and resources migrating your enterprise applications to power your organization’s digital strategy. But how will you know to what extent your employees are actually using the new solutions — and, more importantly — using them correctly and with greater efficiency?
User experience is the key to measuring and improving technology adoption. The business impact from your employees failing to understand and adopt a new enterprise platform can be costly in terms of money, time, and frustration for both your company and your clients.
According to Carnegie Mellon University, only 20% of enterprise software errors are related to the hardware, the network, the operating system, or incorrect system processes. The other 80% of errors are split between enterprise-introduced configuration or coding errors, capacity or throughput problems, or end user-related mistakes.
User Experience Analytics
The solution to gaining this insight is simpler than you might think: user experience analytics software. Before, during, and after a technology migration and deployment, user experience analytics can help you answer these simple, but critical, questions:
- Where is the right place to begin?
- Which strategies will most benefit my business?
- What is the best way to improve user adoption?
With employee experience analytics, you can begin to measure ROI with a small population before moving on to full deployment. These metrics enable you to identify business-critical issues that should be addressed as you migrate. They also help you to upgrade any obsolete, non-intuitive or inefficient applications before moving them to the new enterprise suite.
Navigating the Migration Journey
If contemplating a deployment for your enterprise, consider how user analytics can help make your project a success, with the following processes:
Before:
- Analyze current workflow roadblocks and less than optimum processes
- Identify which processes are most widely used
- Build out project scenarios with the greatest impact and develop business use cases with the most compelling ROI
- Set up specific criteria for success
During:
- Gather user data for the design thinking process
- Monitor and troubleshoot any new processes during the early stages, before full implementation
After:
- Ensure users are adopting and correctly using the new enterprise platform
- Compare KPIs pre- and post- implementation to gauge improvements
If I Build It, Will They Come?
Upon completion of your enterprise deployment, the operational improvement journey isn’t yet over. You now need to monitor to what extent users have properly adopted the new software and are using it successfully. Data-driven intelligence on how the applications are actually being used will give you visibility into where and how employees are experiencing bottlenecks and efficiencies, and where adoption rates are lagging.
In short, even though your organization is in run mode, there’s still a lot of work to be done to uncover both realized benefits and ongoing challenges like these:
- Are there any process inefficiencies causing us to lose money?
- Are users adopting slower than expected in any areas?
- In which ways has performance changed, either positively or negatively?
- Are there areas where software performance could be further improved?
- Which specific workflow steps are leading to user errors?
One of your primary goals should be to reduce employee service costs while sustaining or, even better, improving productivity. You can accomplish this by collecting and analyzing data that:
- Outlines how users are interfacing with applications and detects usability issues
- Evaluates the speed of software response in order to detect system problems
- Determines specific employee training needs in order to improve ROI
- Allows executives to better understand how applications are being used and adopted
- Lowers costs by decreasing the need for help desk support
According to IDCOpens a new window , organizations that analyze relevant data and deliver actionable information will achieve productivity gains that equal $430 billion over their peers. Having detailed analytics at your fingertips not only increases user productivity, it lowers the risk of performance degradation in complex systems, even when the makeup of your workforce undergoes rapid change.
Using data analytics after a deployment allows executives to hone in on whether their employees have successfully adopted the new applications. Only by looking at these statistics can you properly attend to any remaining issues – whether through removing unnecessary process steps, eliminating prompts that slow productivity, or clarifying changes in a more effective manner.
With improved user adoption, you will see:
- Improved return on investment
- Reduced end-user support issues
- Increased user efficiency and productivity
- Improved user satisfaction, which translates into…
- Better service for your customers
Remember: if there’s no way to measure it, there definitely won’t be a way to manage it.