Process optimisation or troubleshooting?
Based on our experience supporting real-time analytics across almost 2M devices around the globe, Mobile device analytics can be used to help enterprises in two different areas: Troubleshooting and Process Optimisation.
Troubleshooting is relevant when a device user calls their support hotline because the mobile device, network or applications are not working and causing them to not be able to do their job. Having real-time, in-depth analytics from B2M available can help find the root cause of problems quicker, helping to cut support call durations, enable a mobile worker to get back working more swiftly and reduce unnecessary no-fault-found returns of devics.
Process optimisation on the other hand is when we take a more strategic view and look to improve operational efficiencies and return on investment for a mobile deployment. With process optimisation our goal is solving issues and improving processes by identifying problems before end-users notice or are impacted. This allows us to lower downtime, and eliminate help-desk calls and end-user dissatisfaction with their mobile device and apps.
With our enterprise mobile analytics solution Elemez we support our customers in both troubleshooting and process optimisation.
Drop and impact detection
A new feature we have recently been working on is drop detection. For drop detection we use data from a device accelerometer and analyse it with an Artificial Intelligence (AI) model to detect if a device is dropped or exposed to other forceful impacts or rough handling.
Part of the motivation for me to write this blog article is that people had varying expectation about what the drop detection feature could be used for. Should a device for which a drop was detected be send into repair immediately? Could it be used by a manufacturer to reject warranty claims? Was the employee at fault for the dropped device?
Most of these ideas approach the feature from a troubleshooting point of view: A problem with a particular device is detected and direct action is taken accordingly. There are solutions in the market that approach drop detection, or rather fall detection, from a troubleshooting standpoint. They focus on using a mobile device (often a wearable) to detect if a vulnerable person has fallen and alert emergency help. Those solutions are calibrated to a specific device and a very clearly defined alarm scenario; their main value proposition is operating a 24/7 emergency call centre that deals with alarms (usually by establishing a voice connection to the person wearing the device, confirming if a fall has actually happened).
But that is not the usage scenario we see for drop/impact detection in Elemez. Drops and impacts are not black and white: From personal experience I can tell that I drop my mobile phone all the time, yet in most cases it survives undamaged. A device, in particular a rugged device, being dropped is usually no reason to raise an alert. And what constitutes a drop or other impact is a grey area.
Yet, if there are constant problems with device handling or particularly demanding environments that will affect device health this might mean that there are operational problems to be addressed.
What we are trying to achieve with drop detection in Elemez is to help you better understand the context in which your devices are used. Questions you could ask are for example:
- At the next device refresh, do we need fully-rugged devices, or would cheaper semi-rugged or consumer-grade devices also suffice for the intended usage environment?
- Are devices sufficiently secured when in a vehicle?
- Are there any differences in device handling between locations; could that indicate a need for training or management improvements?
- Has there been an increase in device user frustration after the roll-out of a new application version?
- How often do drops result in RMAs and replacement of devices?
In summary: At B2M we think about drop detection in terms of process optimisation. The answer to the question, is the screen on a mobile device broken, is usually straight-forward and doesn’t require an analytics solution. But work processes, mobile devices and user behaviour form a complex system. To maximise return on investment we need to be able to measure how interventions affect this system, and drops and impacts are a core metric at the interface of processes, devices and users. If you cannot measure it, you cannot improve it.