Most enterprise software buyers are flying blind when it comes to adoption of their enterprise applications. That is a shame as so many technology investment decisions are then made (or not made) based upon politics and squeaky wheels. In the course of discussing how organizations can move to a more data-driven management approach, I am I am often asked, what passes for good user adoption analytics? What data is necessary? Is there a specific dashboard that you recommend? The following is a short-list of concepts that I think are important – the adoption analytic BASICS.
#1: Focus on Time & ensure 100% Usage Transparency
It is really not enough to know who has logged in or who has called the help desk. Actionable user adoption insight is predicated on understanding TIME. Where are users spending their time? Where are user wasting their time? If I take action-Y how much time do I estimate I can save? Time, with the addition of few user demographics is powerful - organizations suddenly have a way to communicate focus and set priorities in an objective manner. Critical to comparing relative time is 100% coverage of the user base. It is no good only analyzing desktop users when many user have gone mobile, no use in focusing on one GUI over a web portal and so on. You need to have 100% usage transparency.
#2: Align Insight to Actionable Operating Decisions
Turning thousands of user transactions into information takes skill, but if the resulting insights don’t impact any meaningful decision then the insights are worthless. So making sure your insights are aligned to specific business questions or outcomes is critical. For example, understanding you have inadequate super user coverage is interesting, but there has to be some intent to recruit new super users on other side of the finding. Figuring out you have a systemic usability issue is validating, but if you cannot dig into the source of the problem and then make configuration changes what is the point. Insight needs to be connected to actionable decisions.
#3: Use Information Asymmetry to create some Sizzle
It is often the case that an adoption analysis reveals something that was suspected or already known. Nothing wrong with this. It proves the analytics work. But to really make a breakthrough impact your adoption analytics have to reveal something you didn’t know. Or even better - something the rest of the organization doesn’t know. Whether it is process benchmarking against other companies, uncovering the hidden source of unexpected costs or finally understanding the impact of service model decisions, needs to be that A-HA moment. Some sizzle with the steak that helps attracts attention and creates excitement.
The data-driven approach to user adoption is still in its infancy but worth the effort. With the above adoption analytic basics achieved decision makers should be able to better understand complex environments, predict the impact of changes and prove the worth of their services.