Enterprise Mobility Management, enterprise mobile app development atlanta

Picture the hero of our story. A tough, rugged, shiny new Enterprise Mobile Device. She’s reliable, she’s helpful, and she works tirelessly throughout the day to herald top-line growth and increase employee efficiency. She is truly a hero, but is she enough? How many heroes does it take to save the day?

(Superhero metaphor over…)

It can be fairly straight-forward for companies to observe top-line and bottom-line growth when evaluating a mobile device investment…
  • After a Mobile POS App rolled out, was there a resulting increase in basket size? Was there a decrease in abandoned carts?
  • After a Receiving App was released, did the average time to receive a truck decrease? Did overtime hours decrease?

What is more difficult to know about a mobile device investment is:

  1. Did I purchase the right number of mobile devices to enable my employees?
  2. Could get a good return if I purchased more?
  3. Are my devices correctly distributed across my sites?

Enterprise Mobility Management, enterprise mobile app development atlanta
optiko, enterprise mobile app development atlanta

These questions *used to be very difficult to answer. Until… Support Analytics. Support Analytics is a platform that tracks mobile device usage (and 60 other device metrics), and provides companies with the mobile device answers they are looking for.

We used Support Analytics to track how employees at Company 1 used devices over 2 months.

Now let’s explore what Company 1 learned about the sizing of their mobile device fleet.

Here is our plan for observing the fleet:

  1. Establish how many devices and users are at each site.
  2. Observe when and how many devices are used throughout the day.
  3. Understand if more (or fewer) devices are needed for the system.

How Many Devices and Users are at Each Site?

Company 1 has five sites. Each site has between 17-23 devices, and while each has a different manager, the processes are largely the same.

Site 1: 21 devices

Site 2: 23 devices
Site 3: 17 devices
Site 4: 18 devices
Site 5: 18 devices

Now while the number of devices at each site are similar, the number of distinct users to touch a device over the course of a week is much different, with up to 130 different people using devices at Site 1, while only 70 use devices at Site 5.

Enterprise Mobility Management, enterprise mobile app development atlanta

To give a better picture of users and devices, we can look at the user-to-device ratio. In the graph below, we see this average ratio calculated on a day-of-the-week basis (device usage tends to have a weekly cyclical pattern). What we see is that Site 1 has the most users per device, while Site 2 (not Site 5) has the fewest.

Enterprise Mobility Management, enterprise mobile app development atlanta

When are Devices Used During the Day?

While we know that Site 1 has the highest ratio of users per device on a daily basis, we can’t make any conclusions until we know what that looks like during the day. Below is a graph that shows the average number of devices in use throughout the day.

Enterprise Mobility Management, enterprise mobile app development atlanta

What we see here is interesting. Sites 1 and 4 have distinctly different usage patterns than Sites 2, 3 and 5. 1 and 4 have much more even usage throughout the day and night periods, while 2, 3 and 5 have a large drop-off between day usage and night usage. But would 1 and 4 have similar patterns if they had more devices to use during their peak hours?

One way to dig into this deeper is to look at the days with peak usage: Tuesday and Thursday.

Enterprise Mobility Management, enterprise mobile app development atlanta

This view paints a more extreme picture of what we were seeing before. Sites 1 and 4 utilize devices much more during the entire day than Sites 2, 3 and 5. And for Site 1, there appears to be a difference of only 3 devices in use, on average, between the night and the day.

To end our investigation of device use over time, we will take a look at the standard deviation for usage. We will use Thursday for our example.

As we see below, all sites have a standard deviation of between 1-2 per devices in use per hour, except for Site 2. In part, the higher standard deviation of Site 2 could be attributed to our small sample size (8 Thursdays), but we would also argue that the large standard deviation shows us that employees have enough devices to respond to fluctuations in their work environment. Ie, the other sites would also have higher standard deviations if they had more devices.

Enterprise Mobility Management, enterprise mobile app development atlanta

How to Right-Size this Fleet

Before we make a device recommendation for Company 1, there are a couple of areas we would want to investigate first.

  1. How are devices allocated to employees?
  2. What activities (by whom and when) are the main drivers for top-line or bottom-line growth for this company?
  3. How does device cost relate to revenue and savings resulting from device use?
  4. Are there any processes that are different between the 5 sites?

But, for the sake of this exercise, and given that the above questions relate to sensitive information, here are our conclusions!

Sites that would use more devices (in order of need): Site 1, Site 4, Site 3, Site 5

Sites where we would not increase devices: Site 2

During peak hours, 4 of the 5 sites would likely use more devices. The operative question is, if employees were given more devices, how much time would it take for their resulting activities to pay for the investment?

(Further Explanations)

Site 1: The highest number of users are at this site, with the highest user-to-device ratio, and the peak average use of devices during the day is high, with a low standard deviation, compared to the total number of devices. Site 1 appears to have a genuine need for more devices.
Site 2: With the most devices available (23) but the lowest user per device ratio, we are tempted to say that Site 2 does not need all of their devices. BUT, if you look at peak usage, Site 2 is right up there with Site 1, at about 17 users (6 away from total devices). So it would seem that Site 2 is right-sized.
Site 3: With the lowest number of allocated devices (17), Site 3 has the third-highest users per device ratio. Given that their peak average usage is only 2 devices away from their total devices, with the standard deviation being just below 2, we believe that Site 2 would utilize more devices if they had them.
Site 4: Having the second-highest user per device ratio, Site 4 is also a candidate for more devices. With 18 total devices and an average peak of 14 devices in use, the need is not as great as for Site 1, but it is still poignant.
Site 5: For some of the analysis, Site 5 appears to be right-sized. It has a similar user per device ratio to Site 2, and it has a low number of overall users, but we still see that the peak number of users is high compared to the number of phones, and this indicates that additional devices could be of use.

Now we are aware that we just concluded a statistical analysis by saying that a site needs ‘more devices.’ How many devices is that actually?

To provide that specific conclusion, we would need to have some on-site observation, to address what the optimum user-to-device ratio is, as well as what peak usage looks like in a healthy environment.

Kira Greco

Kira Greco

Business Experience Lead - Kira has experience in business analysis, user experience, marketing, operations and project management. She has worked in many industries and uses her strong analytical skills to drive process performance and organizational efficiency. She holds a B.S. in Civil Engineering and an M.S. in Construction Management and Engineering from Stanford University. Favorite restaurant: The Porter