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Greta Roberts is the CEO and co-founder of Talent Analytics, Corp. She is the program chair of Predictive Analytics World for Workforce and a faculty member of the International Institute for Analytics. Follow her on twitter @gretaroberts.

Greta Roberts

Greta Roberts offers some advice on how to use predictive workforce analytics to solve real, line of business challenges.

The Beginner's Guide to Predictive Workforce Analytics

Greta Roberts explains how hiring is like lending money for mortgage. Businesses need to be able to predict—before extending an offer—if candidates will likely be successful in their prospective role.

Hiring? Approving Mortgages? It’s the Same Thing!

Who Are Your Gatekeepers?

Greta Roberts offers five important steps to follow when selecting a predictive talent analytics project.

5 Ways to Select a High-Value Predictive Workforce Project

Greta Roberts outlines some important tasks for working on a predictive workforce analytics project.

The Talent Analytics Work Flow Needs Nonanalytical Talent Too

We all walk around the world with certain expectations. We have a sense of how the food will taste at a fast food restaurant. We know what our favorite slippers feel like on a cold morning. We brace ourselves when we get a letter from our accountant. When our expectations aren’t met, we struggle. We complain that the line at our go-to coffee shop is longer than normal. We can’t believe that a particular clothing store no longer sells red T-shirts.We also struggle when something or someone exceeds our expectations because we don’t know how to react. The entertainment industry plays on this mismatch of expectations and reality. But often, we celebrate when faced with such unexpected scenarios. Julia Roberts can afford to shop in the pricy boutique in Pretty Woman. Frodo is much more than a just a hobbit in Lord of the Rings and defeats the greatest evil in the land_._We carry around the same expectations at work. Learning and knowledge teams often begin projects with deeply held beliefs about the other. Prior interactions between the two teams (like the mass of ERP projects in the 1990s and 2000s) have built and reinforced such preconceptions. Yet in reality, both teams exceed these expectations.Learning teams see knowledge managers as organizers of information, building categories and subcategories to put knowledge into well-identified bins. Knowledge managers historically controlled the content necessary for the learning team to develop training, managed access to that knowledge, and ensured that all of the knowledge was formatted properly. The learning team saw the knowledge managers as keepers of policies, processes, and procedures for knowledge. The demands of these procedures frustrated learning teams, creating an impression that knowledge managers were more worried about the format and categorization of knowledge than whether it fit a particular need. Knowledge managers were perceived as gatekeepers, not enablers. Learning teams felt compelled to create supplementary content to fill in what they perceived to be gaps in the “fit” of the knowledge.Knowledge teams, on the other hand, saw learning managers as trainers only, adapting content that they had created. Learning teams might change the order of the presentation of knowledge to make it easier to consume, which would affect the context of the knowledge presented. Knowledge teams also felt an intense pressure to deliver “complete” knowledge to the learning team. This was particularly difficult in complex projects where changes to the deliverables would alter the knowledge required to build training, creating numerous back-and-forth interactions between knowledge and learning teams, and frustrating both. Knowledge teams also were frustrated by the amount of time that the learning team required to build training.Obviously, neither of these sets of expectations is true. Today, both learning and knowledge teams are driving toward the same goal—delivering the knowledge and skills that team-members need as close to when they need them as possible. Just-in-time knowledge and learning delivery systems mirror one another in intent.Knowledge and learning teams need to recognize their limiting expectations and learn to exceed them. Can you imagine the applause you can get if the teams move past their expectations?Would you like to know how to build this partnership and achieve exceptional results? Check out our March issue of TD at Work: “Enabling Success through Learning and Knowledge Sharing.”

Great Expectations: Move Past Preconceptions and Forge a Healthy Partnership

December 2022 - TD Magazine

Reduce the friction that is preventing learners from engaging with your learning.Businesses spend billions of dollars trying to understand consumer behavior, resulting in marketing strategies that aim to get consumers to buy their products. However, in those companies, many L&D functions are not leveraging the same insights and strategies to reach their learners. Treat your learning solutions like products, and minimize the friction between the solutions and learners. One way to do so is using BJ Fogg's behavior model to evaluate your learning initiatives.Identify the frictionThe Fogg behavior model looks at three conditions that must be present for someone to engage in a behavior: motivation, ability, and a prompt. When one of them is absent or diminished, so is the likelihood that the desired behavior will occur.For example, let's say you have a missed call on your cell phone. Why didn't you answer the phone? Maybe you heard it ring, looked down and saw who was calling, and decided you didn't want to talk to that person right now. You heard the ring—that was the prompt. Nothing was keeping your from answering the call (that is to say, you had the ability). You just didn't want to—you didn't have the motivation to engage in the behavior.Now let's say the phone rings, and you're expecting the call and want to take it. But you can't find your phone, or it's in another room and you can't get there in time. Again, you heard the ring (the prompt), and this time you wanted to take the call (motivation), but you weren't able to get to the phone in time (ability).Finally, let's say you're expecting the call, and your phone is right next to you. However, the ringer is set to silent. The phone rings, you want to answer and are able to do so, but you never hear the ring—you received no prompt to answer it.That's how Fogg's behavior model works. The presence or absence of any of the variables can contribute directly to the likelihood that you'll engage in a given behavior.What if we look at learner behavior through that lens? You've created a great learning initiative and spent a lot of time and money on the learning ecosystem. However, you don't seem to be getting the traction you're hoping for. Here are some questions to consider:Are learners aware of the learning solution? Are there prompts that alert learners to its availability?Are learners motivated? Do they want to engage in learning?Are they able to engage with the learning solution?Is there anything preventing them or making it more difficult for them to access it?If you have opportunities in any of those areas, then you have friction between learners and the learning solution. Friction, in this context, is the absence of prompts or is anything that impedes motivation or ability when it comes to a learning initiative.PromptsIf learners don't know about your learning initiative, it may as well not exist. These days, people are inundated with prompts—pop-ups on smartphones, ads on social media, digital billboards, and the like. How can we ensure that learners are prompted to our initiatives? The traditional methods are static and push prompts.A static prompt is one that is in a fixed physical location, such as a poster in a breakroom. It works fine when your audience tends to be isolated in one particular location. But if someone is not in that exact location, they won't see it.A push prompt is much more targeted. It entails identifying your audience members and sending a prompt to them, such as an email reminder that a course is available. However, you often can't customize that prompt by audience member—everyone gets the same message. And you can't time the prompt so that it reaches an individual right when they need it most. Thus, there are some limitations to what we have traditionally used as prompts in our learning ecosystems.This is an area we should pay close attention to because it has the potential to change rapidly. Imagine if we could activate a prompt for a specific learner at a specific time and in a specific location with a learning solution that is designed precisely for the activity that they are engaging in in that moment? It would be similar to finding an exclusive offer from Starbucks popping up on your phone whenever you're within 50 yards of a store.MotivationMotivation should be the easiest variable in this equation. If your initiative meets a learning need, learners should be inherently motivated to engage with it. However, that isn't always the case. In my experience, there are two major reasons. The first is that the solution does not meet a learning need (which is a subject for another article). The second, and more egregious, reason is that learning solutions get bogged down with demotivators that dilute learners' eagerness.Given the massive increase in video use for learning, there are some lessons to be learned from how people engage with YouTube and how we think about the videos and other learning initiatives we create.I often use the example of having a hole in my drywall and turning to YouTube to see how to fix it. Not surprisingly, there are numerous options from which to choose. We've all become accustomed to using filters to narrow down internet search results, although we often do our own visual filtering first. For example, I can take a quick look at a results list on YouTube for drywall repair and immediately rule out, or at least deprioritize, some of the videos. The first thing I'll look at is time. I expect that this should be a relatively simple task, so I'm not looking for a long video. The shorter the better.Likewise, learners have an expectation of how simple or complex a task is and at least some sense of how long it should take to learn. If they see that a video is 30 minutes long, and their perception of the task doesn't line up with that amount of time, you may lose them right away.I start playing a six-minute video from the list. After a lengthy introduction, a bio of the host, and several examples that don't apply to what I'm looking for, the host eventually starts the process of explaining how to execute the repair. It took over one minute to get there. That is an informal measurement I call "time to learning"—or how long it takes before I get to the actual learning. Time to learning is especially important when considering whether our goal is knowledge retention or knowledge acquisition. In other words, are learners in this for the long haul, or do they just need a quick one-off fix that they will rarely if ever need again?When looking at learning from a knowledge retention standpoint, time to learning is slightly less important. We know we're going to get deep, and participants learning about the instructor's credibility can be helpful to set the tone. But if we seek knowledge acquisition, or disposable knowledge, time to learning matters a lot—especially if there are several options from which to choose.For example, imagine I need to generate a report for my manager, who is out sick. I've never done the task before, and it's highly unlikely that I'll ever have to do it again. After some digging, I find a few videos that will help me figure out what to do. However, the videos don't cover only the report I need to generate; they cover all the reports in the entire program and explain where the data from those reports comes from and why they're important to the business.Are those bad things for me to know? Of course not. But they're not relevant to what I need to do right now. Getting what I need is like searching for that how-to needle in a haystack of video instruction. I may resort to other options, such as trial and error (risky) or asking a colleague (now two of us are wasting time trying to figure this out).Going back to the drywall video: I stuck with it for more than a minute only to see how long it would take for the purposes of this example. If I had a real need, I doubt I would have held out for more than 10–15 seconds.We can and should apply the same lessons to our video content libraries and consider where we are adding friction that we should remove. In most cases, those are demotivators that don't reflect the quality of our instructional design yet may deter learners from ever finding that out.AbilityAnother go-to source for information in people's nonwork lives is Google. Here's what happens if I search for "how to repair drywall"—let's count the clicks: I open the browser to Google and type the search term, then click Search. One click. What I see first are a few links to associated terms like "water damage" and "peeling" that enable me to refine my search. Below that is a frame from a video already queued to a specific timecode that is likely to address my need. Below that are links to a few more videos, all with descriptions and timecodes of key moments in the videos. Next up is the "People also ask" section, which displays several ways that others have posed their question that may be more in alignment with my need. And finally, I get a list of results that I would normally expect to see on Google. All from one click.Now think about your learners and, more importantly, your learning ecosystem. Most likely your version of Google is some sort of learning management system or learning experience platform. How many clicks does it take learners to get there? Once someone arrives at your version of Google, how many of the features described above does it have? How easy is it for learners to find what they need?Many of us are presumably still in a situation where learners must go through six, eight, or even 10-plus clicks to find what they need. Number of clicks and speed to solution are ways we can assess learners' ability to engage in the desired behavior.Nobody wants to click through a never-ending menu. Individuals have been conditioned that they shouldn't have to, and they are far more likely to give up somewhere along the way than they are to reach the end.Other ability barriers include bandwidth and hardware. Sometimes our instructional design comes with ability barriers. If you have motivated learners who know about your initiatives, make it as easy as possible for them to access and engage with learning.Enhance learning's impactThis article has numerous questions you can ask about any given learning solution. It's going to be difficult to get satisfactory answers to all of them. But what you can do is identify an area that you believe may be causing friction for your learners and is something that you can influence.In other words, we all want better bandwidth, but you probably have an extremely limited ability to influence that yourself. You may have a better chance implementing some prompts for learners that can move the needle. Or you may be able to reduce the number of clicks required to access content with some adjustments to your internal websites or portals. Start small by looking for areas to reduce friction with existing learning solutions, and then incorporate the ideas into the design phase of future ones.If you treat your learning program like a product and view your learning ecosystem as a mechanism for reaching your audience and providing that product, it may change your perspective.

Are You Driving Learners Away!

If you’re searching for a new job or switching careers, how does this affect you? Here are five job search tips, trends, and strategies you need to know.

5 Job Search Tips for Career Switchers During the Great Resignation

Be intentional and action-oriented about developing a connection mindset that unites people so that they pull together and support one another.

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