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JD Dillon

Now what are the different things that we may be able to do as Axonify in order help inform and improve the practices that you apply everyday in order to support your people on the job. That’s where I spend a lot of my time in addition to speaking at events and writing articles and doing various things like that. When it comes to the conversation around adaptive learning, I think it’s interesting, especially, because I’ve been in the space for about six years now actually ever since I got involved with the Axonify. I was actually Axonify’s seventh customer. I did a lot of work in the early days of how can we leverage technology and data to provide a more informed and targeted support and learning experience. Now I’ve evolved that story over the past six years through a variety of conversations and now the work that I do every day.

Again, thanks for hanging out with us for the next hour or so. As I said earlier, if you’re just joining, our session is being recorded so the recording will be made available to everyone who registered. The slides will be made available via a link at the end of the presentation and I’ll also provide my contact information. If you have any questions that don’t get answered today during our presentation or if you want to dig in a little bit deeper talk about how some of these things may apply specifically to your context and your used case, always happy to support in any way that I can.

Overall, we’re here today to talk about really one simple fact. Strangely enough, I don’t know where that background audio is coming from but we’ll work on that. If anyone out there is actually on a phone, feel free to mute to make sure but we’ll tackle that. But, anyway, we’re here to talk about the simple fact that every employee that you’re supporting in your organization is unique. They come to the table with different backgrounds, different capabilities, different foundational knowledge, and even if they’re coming into the same function, the same job, even from the same school, there’s a unique element in nature to what they do and who they are. The question is how do we support that as a corporate learning team?

If you take this example, so we have two people including a gentleman with a very junkie mustache. Let’s assume that these two people are hired in on the same day against the exact same job but when you take a look at who they are and their backgrounds and why they’re here, there’s noticeably different profiles. The gentleman on the left side, recent college grad brand new to the industry, and he’s really here because he needed a job. I think that’s one of the false assumptions we make a lot as corporate learning people, is that everyone is in here for the career. Some people are in here, because they needed a job at this particular time. They need to support their family, they need to do things to do.

That’s the situation for the gentleman on the left. The person on the right is her sixth professional role. They’ve got 12 years in this industry, they’re building a career, they’re here to take the next step forward, they want to evolve as part of this profession, or maybe within your organization, but they’re starting where they’re starting. These two folks, exact same job, hired on the same day. They’re coming from two radically different places. The unfortunate nature of this story, and again, this is just a simplified version of it, is that when we look at how we support them, in oftentimes we’re providing the exact same mechanisms, the exact same training programs. They’re going through onboarding in the exact same way, completing all the same content.

Then their continuous learning often looks the exact same. Even though they’re there for different reasons coming in with radically different backgrounds and capabilities, their support structure doesn’t match the unique nature of them as individuals and as employees. That’s because historically we’ve had a really hard time balancing out these two considerations of corporate learning. The idea of how do you provide a personalized experience? What is the right fit for this particular individual? But then how do you do this at scale? I’ve yet to meet an organization or a corporate learning team that says things to me like, “We have all the money, we have all the time, we can add all the people we want.” Usually you’re a very small mighty team of 10 trying to support an organization with 100,000 people in it.

When you have a set amount of resources, a set number of people and only so much you can do, how do you support an organization that is growing in terms of size, complexity and all the other considerations that could potentially be there? Again, a perfect world would be the mentorship apprentice model, right? For every person who needs to learn how to do the job, we would have another person who’s an expert in not only doing that job but at helping people understand how to evolve their skills. It’d be next level of mentorship and everyone would have a person like this. But when it comes to the speed of our current organizations, the scale of our resources, the global nature of business, we simply can’t do that. If you can do that, you’re extremely lucky. In certain roles, you can do that but at the scale of the organization, it’s just not realistic. Then you take onto that and add an additional factor which is the growing nature of customized personalized experiences in everyday life.

Again, if you’ve been to a mall lately, you haven’t experienced probably where your cell phone knows where you are and triggers certain deals and special based on your physical location and noticeably personalized shopping experience. This image is near and dear to me because I live at Disney World, so this is an image from the queue at the Rock ‘n’ Roller Coaster and Disney Hollywood Studios. Because everyone that goes to Disney nowadays is wearing an RFID chip on their wrist with different elements of the attraction, customized and personalized to their experience. In this case, someone named Adam from Orlando walked past this poster case and it actually changed in order to represent them as an individual and enhance their experience. Again, this is a far reaching example but just simply the nature of your interactions as a consumer with all types of products and services are leaning more and more towards a personalized experience.

How can we take what is happening in that consumer experience and apply it in a meaningful way to improve the way that we support employees? Ultimately, the goal should be, and I believe everyone deserves the right support at the right time from the beginning. As soon as they join an organization, we should be able to maximize their talents and capabilities and at the same time provide them the right level of support to help them grow based on what they’re trying to achieve with our business. Our conversation today is based around the idea of going from the lack of fit, that one size fits all really provides and the idea that everyone completing the same content doesn’t really benefit anyone to figuring out how we get to a right size fits one. A customized, personalized fit to the way that we’re supporting people, the way we’re helping them learn, and ultimately the way we’re helping them achieve their goals and drive the results for the business.

In our conversation around adaptive learning, that’s the transition we’re trying to make and the idea here is hopefully we can walk away with a couple of ideas as well as the perspective on how Axonify approaches this story so you can start introducing this conversation within your organization. To kickoff the details of our presentation, we’re going to talk about learning personalization. We specifically try to bucket or break down this concept into four distinct categories. There’s one thing you’ll notice, and maybe this is why you’re here today, everyone and their mother is starting to slap the words personalized or adaptive onto their learning solutions and products.

Almost every vendor is saying they do some type of adaptation. It’s a growing level of interest in the market in general into the idea of personalized learning. But at the moment, there’s a lot of noise which is not foreign to corporate learning.

We tend to have every six months a new thing and we try to figure out how can we maximize that trend, or is it just a trend, or is there something actually underneath there that’s going to help us do our jobs better and help us help people more effectively? I try to help you break through the noise, I’m going to try to break down what are the different arguments around learning personalization, what are the different mechanisms that are currently being used? Then where’s the value and where can you try to take that in what you’re doing every day in your particular role in your organization? To start off, the first type of personalization we see a lot is content personalization. This is pretty familiar to folks because this is the idea of branching content. I know that years ago when I was doing hands on e-learning development as the bulk of my role, I was always finding ways for people to try to …

To help people not have to complete all the slides. Maybe there are 100 slides that have to be completed [inaudible 00:07:44] but does everyone really has to be all 100 or can you help them based on the decisions that they make within the content grants in a certain direction only have to complete a certain element. But this experience, again, is contained within the content. There’s a limited scope, again, it’s better than someone seeing all 100 slides, they don’t need 100 slides but the adjustments are based on their specific decisions or the limited amount of data informing this particular content experience. It’s limited in scope but it’s usually the one that a lot of people are familiar with because if you’re using any type of rapid development tool, you’ve probably tried to go down this path in order to again personalize a little bit to what the particular user may want to see.

Type number one, relatively limited scope is content personalization. You expand that out a little bit, you’re starting to see more and more conversations around the idea of personalized curriculum. We come out of the individual content object. In this case, this is where we start to talk about things like pre-assessment. Let’s say that a new employee to your organization is expected to complete 10 different modules but before they go through those 10 modules, they take some type of an assessment. The assessment dictates they only need to take five and now they can get through potentially twice as quickly because you’re again appreciating their background, what they already know at that moment in time, and they’re able to move through the curriculum that much faster.

That’s what I’m defining as personalized curriculum. Again, it’s better than someone taking all 10. The limitation, again, is the fact that we’re in that moment in time. It’s not continuing through the experience and the ROI or the value proposition around this one is really the idea of getting through more quickly. There’s appreciating what you know right now and at the same time you’re reducing the amount of training time per se. So adaptive or personalized curriculum and then the other one you’re hearing a lot about nowadays and Netflix is the apt analogy is the idea of recommendation. Something, again, we see a lot in consumer experience. Netflix, Amazon, basically, anything else that tries to match you with a particular object purchasing decision piece of content. So based on data around what you’ve completed in the past, will people like you? People who do your job have completed serving up a recommendation to say, “This is probably an object that you might want to address.”

Again, positive in the fact that helping you find what you may want to consume when there’s a lot of potential content out there. The limitations are the challenges, so this one I tend to make the reference to the fact that how often does Netflix actually recommend something you really want to watch? Netflix has this habit for me of recommending Boss Baby and that right there may be analogy [inaudible 00:10:20] because I have no interest in watching Boss Baby. If you like that film, we should have a conversation about cinema after this webinar. But simply the idea of how far can you go based on recommendations, based on the limited data set. I always say if Netflix knew how my day was, knew what mood I was in, I would have a much more powerful set of recommendations provided there. But the overall Netflix of learning idea in that recommending from a large collection of content and what may be applicable to you based on largely what you’ve done in the past and what people like you have consumed.

When you take those first three into account, the commonality that I see is that we’re still not completely breaking out of the place and time model for learning. We’re still a content first experience. There’s a lot of content, how can we help you do as little of that content as possible at a moment in time? You still have to get people to a place in order to go complete the curricular or login to the particular learning object or to have any type of improvement or personalization to the experience. For me, there’s a natural limitation to those first three categories based on the fact that it still requires a place in time content centric approach to supporting people on the job. And it’s more about getting people through faster or getting people to complete things more quickly as opposed to what’s the outcome we’re looking to achieve which is really making the business improve and enjoying driving business results.

Which drives me down the path of suggesting that to make the most of this idea of personalized learning and the whole concept of adaptive technology, it doesn’t start with the technology component. It starts with how we think about how we support people in the workplace every day. In thinking we can do these different types of things, technology is allowing us to go in different places and down different paths. But what does that do to the fundamental experience of the workplace and then how we support within that context. If you’ve ever seen me do a presentation before, I talk a lot about leaning our support into the day to day reality of work because we know how hard it is to get people to step away from work and go do anything for the sake of training or learning. If you look at what’s the utilization of maybe of your current LMS content? How hard is it to get people moving the operation? What’s that conversation look like with management?

For me, it’s about a balancing act of how we try and get closer and closer to the day to day experience with the employee, and make sure that if we’re going to ask them to do anything, it’s going to be as high value and experienced that fits into their day as humanly possible. That for me is what takes us to the fourth concept around personalized learning, which is what I’m terming adaptive experience. Going a step beyond the curricular-based approach or the content-based approach to really holistically look at the entire support experience of how we help a person day to day throughout their time with the organization really get the right fit support, whatever that needs. Whether it is content or something else that’s going to help them do their job more effectively.

I’m going to spend most of our time today breaking down the idea of what is an adaptive experience? How it ties and defines adaptive learning, and what this can potentially look like with the resources and technology available today. Where this comes from is it’s not new. For me, the idea of an adaptive experience is actually a natural extension of other conversations taking place in workplace learning right now. I think one of the challenges we have as a profession is that we tend to put things into buckets and we try to talk about different scenes or trends in isolation. We’ve done this for years, we did it with mobile learning and gamification and social learning and brain science. The idea that these are somehow different pieces and parts that don’t touch each other or that one is better than the other, I think is why we don’t get as much value out of them as we should.

For me, all of these concepts, these overlap and plug-in together in order to inform that greater experience that we’re trying to create. In the case of adaptive learning and adaptive experiences, the three big conversations that have been happening for a while that I’ll point out are, one, micro-learning. Specifically the idea that it’s about focusing on target objectives to solve specific problems with specific assets. Again, forget about a lot of the fluff and the noise around micro-learning. That targeted nature of providing support I think even to the story. The second element is self-directed learning. The concept of allowing agency of making it okay for a person to get what they need, whether it’s a push or pull experience. But the fact that we’re providing autonomy and accountability to the end user and the employee as opposed to on the L&D side, I think is a meaningful shift for this conversation.

The third and most obvious is data. The continuing conversation around you can put quotes around big data, or just the fact that we’re using different types of data and an expanded data profile in new ways in order to inform what we’re doing. If you take these three concepts as well as some others, but these three big topics of conversation in learning and development, and you pull them together and ask the question, “How does this change the way that we support people every day?” That’s right, I see that we get the adaptive conversation we’re talking about today. When we transition out into the bigger story around adaptive learning and we have to question, “What’s the definition?” That’s the other place that we have learning and development tend to be challenged. We don’t have dictionary specific definitions around a lot of the concepts that we apply.

I always try to provide very simple high level applicable regardless of your circumstance high definitions to these different concepts. For adaptive learning, the operating definition I’m working on today is using everything we know about a person to provide a personalized targeted value add support experience. A couple things to note in that definition. I’m saying everything we know, we’re going to talk, break that down a little bit in this conversation today. But, again, that could mean a bunch of different things based on your organization and where you are in this journey. The other thing I point out is the word support. I didn’t use the word learn in the definition because it’s not just about providing content, this is not about completing a course, it’s about the different mechanisms we can use in the workplace to help people get better at the job so the business can achieve its results.

What you’ll hopefully see in examples in a little bit is that it’s more than just content, it really has to be holistically thinking about how we support our people every day in order to drive the right fit experience. When you transition that into what does it look like to provide an adaptive experience? I’m going to try to diagram this out. This is, again, I use in a lot of different conversations to speak to what should continuous learning feel like for an individual employee. Well, we’re talking about personalization and adaptive, it again is really about this one person and what it feels like to work and be supported in their particular environment. Before I start adding to the image, note that this will obviously be a little bit different in your organization. This is more for effect an example. The tactics that you may apply may be a little bit different based on the people and the organization you’re trying to support.

But what does a continuous learning experience feel like? And then where do these big trends that I’ve talked about really fit in and how does it lead to an adaptive experience? Well, first it’s about introducing tools and tactics into the day to day experience for the individual that’s going to allow us to do things in different ways. Again, none of these are new, but the idea is that we’re looking to reinforce content continuously to make sure people retain long-term. Measuring non-technology [inaudible 00:17:44] making managers more effective coaches. Allowing employees to provide feedback and preference into the equation and also introducing the right motivational factors. Because your employees today are likely used to going to a place in time learning model. They get scheduled for a class and they go once in a while as opposed to doing something consistently every day.

But in order to adapt and provide right support at the right time, it has to be something that is always within reach, that is always available as opposed to something that has a wait to be scheduled for. I always recommend it’s based on the access point to share knowledge so people can pull. It’s a balancing act between push and pull information available that really drives the day to day continuous nature of learning. Because, again, learning isn’t a place and a time, it’s constantly happening so this is layering on support mechanisms that will support that constantly engaged employee. Into this puzzle, we insert things that we’re more familiar with, so nothing goes away in this puzzle so it’s still job training, online content. People are still going to events, but when you layer in these other tactics that are driving continuous learning, it allows those customers you’re more familiar with to become that much more purposeful.

If we’re going to do a classroom event, we can use it much more specifically because if we’re going to pull someone out of the job into a place in time moment, it can be used for a specific purpose that it has the specific capabilities wrapped around that event as opposed to something that we could do in our day to day experience. For me and the work that I do with Axonify, is this experience that drives the results that we see. I’ll share some examples of not just knowledge change, but business results, tangible, measurable outcomes based on a shipping experience. And then again the ability for people to contribute back into the community. When we support one person in a more personalized way, we’re enabling them to become support agents within the organization. The one thing I want to make sure we don’t get lost on today is the fact that this is a conversation about people and about how we can use our tools and tactics and evolving capabilities in order to help people become even more human in the way that they do their jobs.

It’s not just a technology conversation, it’s a conversation about people. It’s this experience that then again you layer all these different ideas we talked about in terms of the big conversations and trends in learning, this is how it comes to life. Like I said, the pull access point for shared knowledge or materials when you need it enables a self-directed learning idea, that agency for the employee. The fact that we’re targeting content to solve specific problems over long term for retention and behavior change is really what micro-learning is all about. Then on the right side, the fact that we have a continued touch point with the employees, we’re constantly reinforcing and measuring their knowledge. We’re measuring how that knowledge transitions into behavior and we’re using that data in order to inform the experience that’s at the heart of adaptive learning.

For me, this diagram or model really brings to life these three big conversations in workplace learning and shows how they can be shaped together to form a continuous learning and support experience that really helps drive home the needs of this particular employee. So, hopefully, this will come to life for you a little bit as we continue through our conversation today. Again, just to show the idea that what does this experience create? From the Axonify side of the conversation, here is just a couple examples from organizations that we’re working with in terms of the outcomes they’ve seen from implementing this type of experience. I’ll mention it a little bit later as well, but what I think is interesting about this slide is not just their big, big fund numbers, but the fact that each organization is trying to achieve a different outcome.

At home, reducing onboarding time was a critical importance in their world. At Bloomingdale’s was really about reducing safety incidents. So you’re seeing the introduction of adaptive learning and an adaptive experience solve very specific problems that are important to those organizations based on the fact that these ideas work. If you’re sitting there wondering, “Will this work in my world or maybe work in a highly regulated industry?” At Kaiser, you have a lot of requirements and compliance considerations, can this work for you? This slide and these stories are really meant to say these principles approved and implemented correctly can help you solve problems regardless industry or used case. It’s about finding the experience that’s going to work for your people as part of the process.

That’s a little bit of explanation around what the experience starts to look like, but what are the key considerations when we’re building that experience? How do you go from today, maybe you are that place in time learning, content centric model, how do you go from there to the continuous, personalized adaptive experience that I’m trying to paint in our conversation today? For me, there are four huge considerations when we’re making that shift and the work that we do with clients at Axonify. One is data, two, content, three, the technology itself, and the four, the person. I’m going to keep saying that because if we leave the person behind, this doesn’t work. It’s about enabling people to solve business problems, not just about delivering content in different ways. Let’s dig into each of those elements for just a bit.

One, we’re going to talk about data. This puzzle doesn’t work if we don’t use data in new and increasingly effective ways in order to shift the overall experience of the employee. Like I said, coming back to the definition of adaptive learning, I said using everything we know about a person, everything we know basically speaks to an expanded data profile. In a lot of cases when we talk about learning data or learning analytics, it tends to be completions and scores and progress in these types of ideas. That definition has to expand in order for us to be able to be there at the right moment with the right type of support for the employee. This idea of a multidimensional data profile includes a bunch of different potential data sets to really help us expand that idea of what we know about this person and how we can support them.

Again, connecting this to consumer experiences, let’s think about how targeted marketing has become in a lot of the things that you see. When you log in and you’re on a browser, you’re on Facebook, wherever you may be, the unnecessarily specific as you get even in the mail, how are they doing that? They’ve got a huge data profile as much as you’re willing to provide in order for them to customize and personalize the experience to you. It’s a similar concept, but now we’re looking at what is the data profile we can build for an individual at work? What do we know and how can we use that data? What is included in a multidimensional data profile? Let’s break down the data elements. One is demographic data, this is probably something you already have. The question is how are you using it today?

Demographic is basically who is this person? When did they get hired? What job are they in? Who do they work for? What department do they work in? All of those different basic fundamental elements of their HR like data profile. Again, something you’re probably using today, maybe you’re using it to target content, maybe certain contents going at certain people’s direct reports or you’re targeting it through the LMS of certain departments. But that is the critical consideration in terms of who is this person and how much do we know about them demographically that’s going to inform the overall quality. Next piece up we’ll have is consumption and again we talked a little bit about this earlier, what has this person done in the past? What content have they consumed? Again, very familiar LMS like data in terms of completion. There is value in knowing that information.

It’s not enough to do what we need to do but we still need to understand what is this person’s consumption patterns. Again, a lot of this type of data is used on the internet in order to target recommendations to you every day, so consumption data. Context data, this is where we start to get into the pieces of puzzle which tend not to be there today in how we do what we do. What else is happening around this individual that may be informing their need from a learning and performance perspective? This is what is happening inside of the organization, did we just go through potentially a layoff inside of the organization? Did this person get moved recently? What else is potentially influencing this person that may shift their needs or the way that we’re supporting at that particular time?

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Feedback. This, again, is making sure that it’s not just about pushing down on the person, it is about giving them the ability to provide data into this puzzle. This is feedback, this could be as simple as level one feedback, it could be about their preferences or what information or what content in the past that they have enjoyed, particularly, or that they have found particular value in. Allowing them the ability to add into this data profile. Then we start getting to knowledge, so this is not score, this is different than scores. If you give someone a task at a particular moment in time, you’re testing what I know at that moment in time. We’ve all had this experience when you’re taking a test in school is you study for the test, you get a decent score on the test and then what would have happened if I gave you that same test a couple weeks later? Your score would probably decline.

This is about measuring knowledge continuously, understanding are people retaining the knowledge necessary to change their behavior or is there a decline that we need to work on and make sure that they can retain and grow their knowledge over time, so a continuous probing into their knowledge to make sure that we understand what they know at any given moment as part of their experience. Next up is behavior, how is that translating into real world activity? How are they behaving on the job? Are they taking the actions we need in order to help hit the business results we’re trying to achieve? This can be measured in a variety of different ways whether it be through observation or other mechanisms. For example, I work in a call center environment. A lot of time is spent recording and evaluating calls, that would be a behavior observation.

What are the different ways we can grab data about how this person is performing on the job and start to make the connection? Then, finally results, What is this person achieving? If this is a salesperson, are they able to achieve their goal? If this is a call center agent, are they hitting all of the marks on the rubric? If this is a hasty potential implications, are they staying safe on the job or are they potentially experiencing workplace injuries? This, for me, starts to complete the overall puzzle of what type of data do we need in order to solve the beginning of the adaptive learning definition, which is everything we know about a person. Again, there’s a lot to come into effect about understanding this person, which is why it’s not just an [inaudible 00:27:47] conversation or just a content conversation. But if we’re looking to round out and expand how we view data, these are the different categories of data that we are using in the work that we’re doing at Axonify.

And then I think are critical to really adapting the continuous learning experience and not just resorting to a place in time content centric consumption model of recommendation and learning personalization. Hopefully, that makes sense. Again, the data points and elements will look different in your organization so that gives you some categorization or things to look for. Then there’s always the big question of how do we collect this type of data especially when you’re looking at behavioral data as a great example. A big part of this journey for you should be looking for what are your current access points to data, because I went way past just what learning and development measures into operational dat. Most organizations of scale today have some type of business insights teams and data is their life. There’s a potential plugin point for you there to get access to and start to be informed by data.

Like I said, this may be manual observations. I can actually relate to this from my experience as a frontline manager at Disney, one of my big responsibilities was to take an index card out into the operation, observe people executing specific behaviors, mark down how they were performing, coach accordingly, and provide that data back into the operation so it can be measured at scale. That was a manual observation point that was fit into my workflow as a manager and an expectation of my job every day. There’s also the automation consideration, so I use a big truck picture as an example just because it’s a fun demonstration of how much data is available when there maybe isn’t a person watching. If you work in trucking, logistics, distribution, you know how much data is fed off of a big red truck nowadays. The truck knows when the driver breaks hard or swerves or does anything outside the norm speed in terms of what’s expected of them behaviorally on the job.

It’s a great example of a potential again plugin point to get access to a different type of data that can again inform the overall experience we’re trying to architect. Because if someone is trained to drive effectively and pass all the tests but they’re swerving all over the road, there’s a missing piece to the overall conversation we’re trying to build. With an adaptive experience, we can understand there’s a data point there and then insert the right type of support along the way. That’s the data part of the conversation. That leads us directly into the content puzzle. In order to make an adaptive experience work and adaptive learning really work, we have to change the way that we build content or shift out of what I would term the course mentality. Because big inflated, everything you need to know courses can’t solve targeted problems.

That’s where you start to get into that story in the beginning where people are taking content they don’t need in order to check a box to make sure that everyone has everything. That doesn’t work in this model, it’s again shifting towards a more of a micro-learning approach to targeted specific solutions and the content necessary for us to do it. Let’s take safety as an example, rather than have a course about workplace safety and all of the things potentially including the being space in your workplace, we’re instead going to focus down on a specific element of safety. So it may be listening as an example or it may be ergonomics and specific ergonomic behavior depending on your environment. It’s that breaking away from the big inflated course model into specific objects and types of content that help us solve specific problems that can be plugged into this overall experience.

How do you build that? This is the results based or outcome based approach that we apply within Axonify and the idea here is to create the value chain between what you’re trying to achieve on the right as an outcome and the content you ultimately build. How do you go from the big safety for everyone course to a specific object? We focus on what are you trying to achieve? The answer is not safety, the answer is you want people to lift more effectively on the job because lifting is often a common source of injury. In order to lift more effectively, what do they have to do? They have to execute certain behaviors, they have to keep their elbows in, they have to bend at the knees. There are specific things that you break down that equals and effectively. In order to do that, you have to know those basic elements. Maybe you need a demonstration and you can see visuals.

Whatever is going to help drive that fundamental knowledge, that’s going to dictate what you build. Again, rather than build a course about lifting, you may build a video that shows the demonstration of being a job aide. But whatever the specific content is, it’s going to reconnect these pieces of the value chain and drive the outcome you’re trying to achieve. This model will allow us to align these specific content objects to the data that we’re collecting on the individual. Like I said, if a person is driving your big link truck and they’re having a consistent issue with speeding as an example, you’re not going to want to send them a course about how to drive a truck effectively. You’re going to want to specifically address the thing they’re doing wrong which in this case relates around speed limits and the potential negative outcomes of that.

You would potentially be looking at how do we push the right solution that’s going to support improvement on that behavior to that person when we know that they’re not doing something correctly. As you can see, it’s changing the way that we build the content and match up against the data profile that we’re collecting that allows us to target more effectively. I always use the example of driving school which relates to that speeding example. I don’t know about you, if you’ve gone to driving school, I have completed driving school in every format available since the late ’90s. I’ve done VHS tapes, I’ve done it in person, I did it on DVD, and I did on the internet. At this point, none of you may want to get in a car with me. I’m fine, I swear but I got a couple of tickets. Every time they made me take a multi-hour course about everything that you could possibly want to know about driving.

What did I do wrong? I was speeding slightly, it should have been specifically about what I did wrong. But, instead, I had to sit there and enjoy hours of all the rules about driving. It’s that example from real life that we’re trying to overcome here as part of the adaptive personalized experience. Third piece up is technology. Again, in the beginning I talked about the challenge of balancing personalization at scale, and in order to do this, again, it’d be great if I could sit a human being that was hugely knowledgeable and a great mentor next to every employee, you likely can’t do that. This is where technology becomes a critical part of this overall equation, so how are you going to select and apply technology in a way that feeds into this overall experience? Again, from an Axonify perspective, this is something that we’ve been doing for six years now and is at the core of how our platform operates. I’ll give you a little bit of demonstration or example as to what that can look like in a couple of minutes.

But what are some things you should be looking for in technology if you’re interested in providing an adaptive experience? One, this is where algorithms and machine learning come into play. How can the technology keep up with a robust data profile in order to identify the models and the patterns and the rules that are going to help provide the right recommendation or the right support at the right time? This is not something that is scheduled, so at no point did I say in order to make sure people have the right thing at the right time we’re scheduling anything because we don’t know. Again, every person is different, every person evolves at a different pace within different topics that are important to your company. How can we have the machine understand when is the right time and when can we provide the right support? That’s where an algorithmic model and machine learning comes in critical.

Two, is variety in content format. As I continue to stress, it’s not just about providing a video at the right time. A video may not solve the problem, what are the different types of objects that you may be building in order to solve those specific problems? I always say, why go the course when a video will do and why build a video when a job will do the trick. Whatever technology you’re looking at has to be able to support and serve up different content objects that are going to help people overcome that specific problem at that time. Continuous and engaging experience. If you expect someone to step away from the job and go behind some type of a firewall in an HR system and eventually go find the content, this doesn’t work. This has to be something that people are continuously engaging with. We have to continuously measure where they are, what they know, how they’re performing in order to them support and provide the right recommendations, the right types of content and the right overall experience.

It has to be something people are engaging with every day or potentially multiple days a week in order for them to get real benefit out of this. Because that they have to go log into something they never use, this doesn’t play. Data collection options. Like I said, it can’t just be about technology that collects what people did and what scores they got. It can’t just be about giving people a test and it’s only giving them the content they need at that time. It’s about how much of that multidimensional profile can we put together and bring together from potentially different systems, from manual observation, from all these different input in order to build the profile that’s going to shape the experience. But you should be looking for organizations or technology that leverages APIs or maybe ingest data inside of a platform itself in order to help you build out that overall profile on the individual employee.

Again, a couple of check boxes that I believe are necessary for any technology to play into the story and to allow you to create an adaptive learning experience at the scale of a large organization to really support your employees. Then finally, again, I will continue to reiterate the importance of the person. This is a very different story, not just for corporate learning professionals but for employees. Again, we work in an interesting field because everyone has an opinion on how we do what we do. Unless you are an accountant, how much of an opinion do you have about how your corporate accountant does their job? Probably not a lot, let the accountants do it. But when it comes to learning, everyone has experience, everyone is continuously learning and everyone has gone to school for the most part.

They tend to have an opinion when it comes to how they learned based on their experience. I’ve had this experience as a corporate learning team, having conversations with frontline employees who ask me the question, “Can’t we just go into a classroom once a quarter and get this information as opposed to doing something [inaudible 00:37:51]. We know why this is an improved story. We have it [inaudible 00:37:56]. However, we have to help the individual as part of the organization understand how we’re evolving this model and how we’re going to help support them in a new way that is not what they’ve seen in the past in other jobs or an academic model for learning. A couple of different considerations when we’re looking at the human side of an adaptive personalized learning experience. One, this is about value. If we’re going to try to articulate, why are we going to do this?

The answer is not learning theory, the answer is not technology we’re going to get up there. The answer is we’re providing a value added experience and that’s why it’s in the definition. So that when we ask someone to do something, we ask them to consume the content, we ask them to log into our platform. It’s about them getting the most value out of that time they’re spending because we know regardless of job, people are overwhelmed and just under resourced. If we’re going to ask them to do anything, it has to provide clear value to them, not just the organization. That’s the story, it’s about value to the individual. It is about trust. If we’re asking them to use data about them, again, all collected from within the workplace but in order to shape the way that we’re helping them, there is a trust factor to that. Think about you and the way advertising or social media works and how sometimes that trust seems to blur in certain phases.

Because you wonder how they got that data? How did that website know that about you? That can’t be the case when we’re applying a similar set of principles inside the workplace. We have to establish the trust through the value that we’re trying to provide to make sure people understand how we’re using data and how we’re shifting to an experience that is targeted and meant for them and trust that we are going to provide the right experience and help them go in the right direction. This again is about helping people focus, and not just on the content experiences we provide, but the idea that an adaptive experience can help knock away a lot of those challenges people face every day. Why those fundamental issues? Like I said, I’ve talked about safety and product information, those basic pieces and parts of doing the job that they can focus on the greater goal. They can try to continue evolve and grow the larger pieces of their puzzle and add to this overall experience.

It’s not about adaptive learning solving every potential challenge, we’re really helping them focus down on what’s going to be critical and necessary for them. There’s been a lot of cases that people simply don’t know where they have a knowledge gap. Maybe they’ve been there a long, long time and they don’t realize there’s certain areas they could improve upon. That’s what this experience is going to help them do, focus not only what they need today, but give them the opportunity so they’re not completing unnecessary content to focus on their greater goals and the success of the overall organization. Again, it leads to enablement. It’s about that person being able to do what they need to do, giving them the agency and the focus for their needs as opposed to wrapping them into the one size fits all needs of the organization.

Finally, accountability. When we shift to a model where we’re providing the right support mechanisms for the right person at the right time and we’re confident in that experience. Accountability shifts from learning and development to the end employee. It’s about them taking advantage of this experience and really driving their own development. Again, balancing that push and pull reality of workplace learning. It’s no longer about us getting content to everyone, it’s about us providing the right experience and helping the employee take advantage of that and be accountable for their own performance along the way. Again, a couple of different considerations while talking about that human side of adaptive learning and an adaptive experience that if this gets lost, we’re not going to be a successful long term. Those data and content and technology are critical considerations. But, ultimately, it’s about how we help people do what they need to do to be successful and help organizations achieve our goals.

Like I said, overall, it’s both a balancing act of personalization and scale but also the idea of balancing the pull and push reality of learning. There’s, again, a lot of conversation around self-directed learning and providing autonomy and people’s ability to pull the right content to solve problems at the right time. I completely agree, but I don’t think that’s the entire puzzle. I think it is again about what experiences do we push and provide based on the data profile, and then where do we allow people to give people the opportunity to pull and support their specific needs when they know they need to solve a particular problem at that moment of need. The overall adaptive experience is balancing, helping us balance these two considerations out.

 

To start to move into a couple of examples, they’re just a recap for critical considerations that you’re looking to evolve an adaptive learning experience. You have to take into account how we’re collecting data and building out a robust data profile. How that’s going to help us shift the way that we target and build the right content experience, how we’re going to use technology in order to both collect the data and to serve up that right fit content experience and how we’re taking into account the needs, the trust and the value for the individual. To start to transition into Axonify to give you a couple of examples from the work that we do. Again, we’ve been doing this for about six years now really looking at how do we enable data experiences, not just to improve learning and retention, but to ultimately drive business performance. Because when I talked about the content model and how we’re focusing on outcomes, it’s really about enabling people to solve business problems so your businesses can achieve what they’re looking to do.

When we look at the experience that we’re creating, this is a pretty good visual for what Axonify looks and feels like every day. The idea that because we’re fighting a continuous experience, it’s critical for people to engage as frequently as possible. The more touch points we have with the individuals, the more data we can collect and the more opportunities we have to provide the right support, the right knowledge, the right motivation, the right coaching in order to help change behavior and get the results we’re trying to achieve. To demonstrate that, this is some simple facts about what Axonify engagement looks like. On average, more than 80% of Axonify users across our entire user base, access Axonify two to three times per week. That gives us a tremendous amount of flexibility because of the amount of data we’re collecting. We’re collecting hundreds of data points on each individual every time they access the platform to build out that data profile I spoke to earlier in order to inform the adaptive experience.

To the point where 30% of our users complete additional content beyond what that adaptive experience provides up, and I’ll show you what that potential looks like. There’s some specific examples, again, demonstrating the idea that this isn’t just about a specific industry or a specific used case whether it is professional sales, retail, manufacturing, pharmaceuticals. The idea of engagement rises considerably and you get a lot more activity and a lot more motivation from your employees when you’re providing an experience that has clear value because it’s important and meaningful to the person, not just what the organization is trying to deliver overall, it’s what that person needs to help them achieve their goals. Two examples really quickly of what this experience starts to look like. Let’s take a grocery retail example.

You maybe work in retail so you know what the day to day operations feels like, you know you tend to have as little staffing as possible to lift a huge cost consideration in this type of business. It’s hard to pull again people from the operations. Once they’re on the floor, they’re on the floor, so it’s really about how do we embed an experience in the day to day so that it’s for this individual, not everyone getting the same experience to get the engagement we spoke to. For this individual what it may look like is that when they come in for the day next to the time clock they may login to a platform. Maybe on a mobile device examples on screen, it may be a desktop computer. But basically there’s an access point right where they’re coming in to do their job every day so they can complete their brief adaptive learning experience. As you see on the screen here, when the individuals login, they will be served up a different experience based on what that data profile says.

What the platform is doing is saying, “What’s the right experience for this individual?” And it is serving up that recommended experience. On the left you see maybe there’s a new topic, maybe in your department there’s a new product that was released. It’s something that those individuals are going to take a look at today. In the middle maybe it’s time for certification, maybe it’s the anniversary of this person’s hire date so they need to complete some type of certification exercise to make sure that they check that box and that all regulations are met. Or maybe it’s simply a reinforcement exercise, there isn’t anything new to focus on today, so instead we’re going to look at where there are particular topics that maybe this individual’s struggling on or that just needs to be reinforced to make sure that they’re retaining that knowledge long term.

So, again, it’s just a visualization to show that when the individual logs into Axonify, the experience changes immediately. So day to day, it is never the same for two employees, regardless, though if they’re on the same team or doing the same job because we’re adapting using as much as we know about the person from minute one. Again, visualize this in a little bit of flow chart, the individual in the grocery store will login, the platform is making that priority determination to say, “What do you need today based on who you are, what’s required of you and where your profile, where your knowledge and behavior sits?” It’ll deliver the appropriate digital experience, in this case, they saw three examples so that certification maybe is going to be a video and a set of questions, or maybe the new topic is going to be an interactive module.

Again, the content objects vary based on what the specific topic is. Then from there two different things happen. One, we automatically update manager reporting so that the managers know immediately not only what people have done, who’s completed their certification training and these types of ideas, but how the person’s knowledge is changing over time. Because we’re, again, measuring these elements every time a person comes in so we’re building that robust real-time profile as opposed to just testing people when they specifically deliver content. Then the data inside the platform is updated for the next session so that when the employee comes in tomorrow, they’ll do what’s appropriate for them tomorrow. It could be reinforcing what they saw the day before or it could be completely different based on how the organization has set priorities and what we know from a data profile perspective about how the individual goes along.

Also, by the same technology, so again, we’re looking at how Axonify’s working to provide an adaptive experience too. Now we’re going to go into logistics distribution warehouse type facility. In this case, we’re sitting on a different type of experience with different content but we’re talking, let’s say, it’s about safety in the scenario. In this case, we’re going to introduce a couple of different elements. On the screen, you see a couple of different images. On the top left, you see a reporting dashboard for managers. When I talked about the data automatically updating managers reporting, this is the type of data that the manager could see in order to inform how they’re going to engage with the employee. Because again it’s not just about delivering digital content, it’s about providing different types of support to fit the needs of that particular individual.

In this case, the person could maybe need a coaching conversation and the data is proving that. It helps the manager intervene appropriately. In the middle of the screen, you see a behavior observation form example. This could be an example where in addition to the experience we talked about in the grocery retail story, the managers are walking around the facility observing people execute behaviors, maybe it’s safety behaviors, and noting when people are doing things correctly into standards and incorrectly. We’re going to use that data as part of the overall story, and maybe on the right you see a new version of the login screen. In this case, it’s a refresh screen. In this case, maybe the individual has been observed not performing to the desired level of safety and maybe it’s lifting.

 

We’ve seen them repeatedly not lift effectively on the job, so we’re going to change maybe when this person comes in the day after being observed to a refresher moment where they can specifically look at the thing they were not doing very well recently and really make sure they’ve got the knowledge necessary in order to be able to execute on those behaviors. Turning them into a flow chart we’re again using the same experience you saw earlier, maybe in this case, the employees are logging in once a day near where they pick up their equipment every day in order to do their job inside the warehouse. But at the same time manager reserving performance and the platform is asking the question does this meet the threshold? We’d never expect anyone to ever perform for pretty much never perform at 100% perfect all the time, right?

For a particular behavior we’re looking at what is acceptable? The organization will step back and say, “When does the person … How many times does it require a negative observation to break the threshold in order to create action?” If the individual is meeting the threshold for those specific behaviors, they get their regularly scheduled experience. That is coming back to what I said about the grocery employs, they’ll get new training if available, compliance training, reinforcement training, whatever the right need for them is and then we continue to the next day. If they do not meet the threshold, so they’ve broken below what is acceptable behavior for this particular topic, two things will happen. One, that refresher session will be delivered so the next time the person acts as Axonify, they’ll get a refresher session on that specific behavior. Again, the content is directly tied to the data which gives them the specific information they need, and then soon the manager is notified.

In their reporting dashboard, the manager will see that there’s a potential behavior gap on their team and they’ll be able to take action. Again, clearly observing the fact that it’s not just about training, maybe the person does know. We’re going to make sure they got the knowledge but we’re also going to want someone to have a conversation to identify, “Is this a motivational challenge? Is the person simply not doing it for some other reason?” We’ll make sure that we’re closing that entire gap so we can make sure the person is improving their behavior on the job, and then continue the cycle of collecting the data and observing performance to make sure the improvement takes place. That, again, shows you the element of adding in performance data and behavioral data into the overall Axonify puzzle. Then two other things to point out. Like I said, it’s a balancing act of push and pull when we’re talking about adaptive experience.

It’s about serving up the right recommended experience on a continuous basis based on what we know about the person. But also giving that agency whatever they want to do some additional work, solve a particular problem at the moment of need, close the gap that they’ve identified. A couple of things we do are, one, we allow extra training to be integrated into that experience on demand. They’re able to go into the platform and search for and identify specific topics they want to progress on. If time is available and the organization allows, they can do that extra training when available. Again, take quick quizzes, really refresh their knowledge on specific topics. Maybe they’re about to go in to a specific sales call and they want to take a quick quiz on that particular product we’re going to be talking about, or maybe they want to view a quick video or do a quick interactive module because they’re looking to evolve their skills.

The great thing about when I say integrated is that this is feeding our data profile, so when they do this extra training, we’re still collecting the data so we see how their knowledge is evolving and growing over time and then we use that to inform the push side of the experience. When they log in the next day, we know they’ve done that work the day prior and we can use that to inform what we serve up next. Then there’s the idea of performance report. We also have an element inside the platform that allows people to search for and ask questions in order to get that moment of need help. When it’s not about training, it’s more about accessing information that you don’t need to retain but you need to reference at the moment of need. Again, both these elements give people the option to balance the push and pull of their overall experience.

Hopefully, you see it as we start to wrap up the idea that adaptive learning and adaptive experience helps you balance what the business prioritizes, so what the business needs to complete whether it’s checking the box for compliance and certification reasons or specific priorities they have to achieve specific goals with the needs of the individual. Working across all different topics or used cases that may be available. Coming back to this slide again, just to demonstrate whether it’s about professional sales, it’s about onboarding, it’s about a manufacturing or technical environment, it’s about strength or safety. The experience and the way that people evolve their behavior is pretty consistent across. If these principles work, you just have to build the experience that’s going to best supply for what your people need.

This is a relatively young conversation in workplace learning which is why again you see a lot of the noise, you see a lot of people trying to jump onto the train and publish articles about it, attach the word personalizer adaptive to the work that they’re doing. Hopefully, this conversation helps you dig in and really identify can the solution you’re looking at or the person that you’re working with really help you get to this level of an adaptive learning experience? A couple of final tips. One, it’s about crafting a continuous experience without the experience and people continuously engaging, continuously measuring, continuously collecting data. There’s a limited scope and a limited value that we can provide. So, one, how do you craft the right experience for people? How do you find those data insertion points whether it’s something that’s already happening or maybe something you need to augment in terms of process so you can build out that full profile.

How do you make it okay to personalize? Again, if you work in an organization where everyone is used to doing everything and you’re used to checking those boxes and the stakeholders feel good because everyone’s done it, how do you evolve to a place where you can say everyone’s not necessarily going to do everything, they’re going to do the things that are going to help them solve their problems and improve their performance. How do you identify what the critical business problems are so you can focus down on those issues and not try to solve everyone for everything? This helps us work at the scale of a large organization. How do you break that down to specific behaviors that we’re trying to help people improve that are going to get to the results? It’s not about a course about safety, it’s about the specific behaviors that are going to help people be more safe on the job.

Finally, this all helps us better empower the individual. This is about people and how we support them through data, content, and technology, not the other way around. Before we take a couple questions, just to sum up. I firmly believe this is the next frontier or the next big conversation in workplace learning. It’s about how we meet the needs of the individual because, again, not only are people experiencing this in the consumer world, but in order to be able to keep up and be agile with the needs of a modern business and therefore the needs of a modern employee, we really have to figure out how we can break away from one size fits all to a true right size fits one adaptive experience. These slides are available at this link. Feel free to download and use these if you’re going to try to build your own adaptive experience story.

 

We have additional resources on the Axonify website, so check out our resources at axonify.com. You can see adaptive learning as well as other topics that we do a lot of work around and it’s great resources from multiple different places if you’d like to inform your overall story. Again, I’m happy to have any additional conversations, feel free to reach out to me. If you have any questions that we’re not able to address today in the couple minutes we have left, I’m happy to continue to engage in conversations to help you evolve to a more adaptive right fit experience. That said, you got a couple of minutes, I’ll throw it to the Axonify team and see if any questions came up while I was just talking.

 

Speaker 2

All right, thank you JD. So we do have a couple of questions. Unfortunately, we’re not able to get to all of them, but the first question is how long do you foresee it taking a company to effectively switch to a fully adaptive experience?

 

JD Dillon

This is one of those it depends answers. But again what it means to shift to fully is going to vary depending on scale of the organization and a variety of different things. For me, it’s about starting with specific problems that you need to address that the business really wants to overcome and slowly evolving the mentality and the approach the organization takes. I think in order to introduce some of these, a lot of these elements are at fray. You have content, you have data, you have different support structures in place. I think it’s about shifting them in specific ways to start overcoming a challenge, which I think is a question of just months in order to start shifting that and then slowly over time it’s shifting the mentality in the overall strategy that you’re approaching.

When I did it as a corporate learning professional, this was more than a year conversation to really bring a lot of the people I was supporting across the finish line but we were able to address specific problems within weeks and month because we were applying principles to help evolve our strategy over time.

 

Speaker 2

Okay, great. The next question is, there’s lots of other vendors that status solution use as adaptive, so what makes Axonify different?

 

JD Dillon

Sure. The thing that I will point out toward Axonify that’s noticeably different is that fundamental experience. When I shared that slide about the level of engagement that Axonify receives and how often people are coming in, it gives us two big strengths. One, the massive amount of data that we’re collecting from individuals on multiple fronts. So not just consumption, knowledge data, behavior data, results data. And then two the fact that we have such a frequent touchpoint that we can provide and serve up support experiences on an almost daily basis. We don’t have to struggle to get people to do anything different. We’re embedding a habit inside of the day to day and able to use that in multiple ways. It’s that fundamental adaptive experience that I think is noticeably different from other providers.

 

Speaker 2

Okay, great. That’s all we have time for. Thank you everyone for attending. Stay tuned, the webinar recording will be sent out to everyone who registered for the event. Any questions that we are unable to answer, please feel free to reach out to JD directly.

 

JD Dillon

Thank you.

 

Speaker 2

All right, thanks and enjoy the rest of your day.