How Personal Technologies Enable Individualized Performance Tools

For The Employee, By The Employee

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In one of my previous blog posts, I explained that contextual learning was useful because it addressed a user’s specific questions at the moment of need.

But what happens AFTER the learning phase when you want to move into actual performance support on the job? And is it possible to customize the data, preferences, and user experience for every single user when you take that next step?

To help you answer that question, allow me to showcase personal technology is used in our everyday lives, and how the following software has made it possible in the workplace.

A Personal Experience In Theory

If you have Netflix, you are familiar with the recommendations area, which lists movies and shows that the software thinks you could enjoy based on your previous viewing history. This section appears on every version of the application for every platform, regardless of the user. My recommendations will differ from yours. But what value does this offer? Why not just let the user search for the content they want?

In 2013, Wired dove into The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next. The piece explores how Netflix keeps its catalog updated so that almost anyone can find what they are looking for. Xavier Amatriain, engineering director at Netflix, confirmed that with future updates, they planned on introducing contextual recommendations. Amatriain says Netflix has data that “suggests there is different viewing behavior depending on the day of the week, the time of day, the device, and sometimes even the location.” In 2015, The Netflix Tech Blog provided an update that explained how they implemented contextual recommendations.

A Personal Experience In Practice

Now, let’s take this idea of personal recommendations and give it more context.

Imagine that you and your spouse are trying to do some landscaping, and you end up at a home improvement store. You intend to buy a dozen concrete blocks, so you go to the (typically outside) area where all the brick pallets are stacked. The nearby clerk asks you what kind of lawn border you are seeking, and he pulls up a few options. The bedrock border looks ideal, but then you remember that you also want some tempered fire glass for a specific design you saw in a magazine earlier that day. The clerk pulls out his smartphone and opens an application that allows him to search for the glass, which is located on the opposite side of the store. He explains that besides tempered fire glass, you might also need weed barrier fabric, possibly mulch, and a few metals anchor stakes for what you need to do.

The clerk seems to know exactly what you need for your project, but how could he know all that based on one search?

Well, the algorithms in the software determined the next best set of items based on the initial search for lawn borders. And for every customer that comes in that day, the clerk will know which products complement their final purchase, making every experience much more personal for everyone involved. It also proves useful – because now you know the tempered glass is offered in pink. Knowing there are options available can play a significant role in your buying decision. This is a system that doesn’t just make random changes. It learns and behaves based on the actions you perform while using it.

A Personal Experience On A Broader Scale

How could something like this play out in the corporate training space? Most instances of individualized training in the workplace occurs in two ways: Data-driven adaptive systems and user-driven open systems.

Data-driven, adaptive systems learn and adapt as a user completes more training or interacts with the system in other ways. This is the direction where most learning platforms used in businesses are heading. If your company is sufficiently large, and you are using a corporate learning management system, the chances are that you are using a system that leverages data-driven personalization.

The user-driven, open systems put content personalization in the hands of the user. They can forge their path within a system that is not all that intelligent – just public and social. There are multiple platforms and resources available for a diverse range of skill training. Key players in the space to keep an eye on are Grovo and Lynda.com (now a LinkedIn company – talk about personalization). The open system is often seen as having the disadvantage of being harder to control or measure because the user, not the system, is in control. Geoff Stead, a co-author of the CHAMPIONS framework and senior director of mobile learning at Qualcomm, explains how his organization uses such a system in their L&D efforts. Think of it as a productivity (?) food court. “Learn as much as you want from any of these sources we are providing you.”

Not all companies and systems are up to speed in this area, though. Learning Solutions Magazine published an article that showed how the most commonly used learning management systems could be fixed and future-proofed by making their software personnel in three ways:

  1. Using learner analytics as to the primary data collection method
  2. Introducing the Experience API so that users can track their progress in a task
  3. Contextualizing information so that someone can access it from anywhere. These solutions can be supplemented by using a mobile platform to deliver the content.

There will be a few considerations when implementing personal technology. For example, knowing which content is best accessed by face-to-face interaction instead of using the technology, and determining how much control you want to give to the user. In the end, you should not have to micro-manage performance support tools, but let the personal technology itself help the process happen efficiently and intuitively. The best part is that your employees can feel like an active participant in shaping how the technology works.

Personalization is only one aspect of emerging digital technology, and there are eight more, which I have only partially covered. If you want to learn more about the next steps you should take for personalizing what your company’s productivity tools, this is where the CHAMPIONS framework can help.


Watch How CHAMPIONS Can Help You

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Dorsey Dixon

Dorsey Dixon graduated from Bradley University with a degree in public relations and organizational communication. Dorsey also holds a degree in network systems administration from DeVry University. He is an avid reader who is currently working on his first novel.

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On September 21, 2015
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