What does ‘personalised’ mean to you? Is it your name etched onto the back of your smartphone? Is it a website that suggests additional products based on what’s in your cart? Is it a video game that automatically adjusts the difficulty as you play?

Personalisation is an umbrella term for a collection of techniques that address the same goal: to provide a tailored experience in order to promote a desired outcome. For instance, personalised marketing is proven to reduce customer acquisition costs by as much as 50%, increase revenues by 5 to 15% and improve the efficiency of marketing spend by up to 30% percent according to Harvard Business Review. Today, personalisation is near-ubiquitous in our consumer experiences. But how can this concept be applied to help people do their jobs better? After all, learning is very complex. What would it take to provide every employee with a personalised learning experience? The answer: more than Netflix.

The wrong metaphor
Netflix has become a popular metaphor for personalisation in L&D. There’s nothing wrong with Netflix. They’re very good at what they do. However, the idea of creating a ‘Netflix for learning’ doesn’t make sense when you consider what Netflix actually does. Their totally-justified goal is to keep you watching by providing you with a never-ending supply of content recommendations. The more you consume, the better. While these recommendations are very sophisticated, they’re based on limited data. After all, Netflix only knows what you do while you’re using Netflix. Netflix is a bad metaphor for personalisation in learning.

“L&D must adopt personalisation as a foundational tenant of their modern learning ecosystem.
JD Dillon

Frankly, it’s the opposite of what L&D should be trying to do. Netflix wants to keep you watching. L&D should keep people focused on their jobs and limit the time needed for training. Netflix applies limited data to recommend content. L&D should apply a robust data profile that includes a variety of business metrics in order to identify and close proven skill gaps. Finally, the Netflix experience is all about digital content. Online activities are just one of the many ways people develop their knowledge and skills in the workplace.

An ecosystem approach
Digital learning. Coaching. Performance support. Job training. Practice. People improve their performance in a variety of ways. Because learning is a personal process, the same activity can have different impacts on different people. This is why one-size-fits-all training falls so flat. The odds of every person having the same development needs at the same time are limited, especially after they’ve been in a job for some time. This is why personalisation, when done right, has such great potential for workplace training. But personalisation doesn’t begin with technology or content. It starts with a mindset shift. L&D must adopt personalisation as a foundational tenant of their modern learning ecosystem.

The specific tactics used to personalise learning will vary by organisation and audience. But many common learning ecosystem elements can benefit from a personalised approach.

 

Push Training:
An employee should not have to waste their time in training if they have already mastered the topic (unless there is an established compliance requirement). Personalisation can combine evidence of real-world capability with performancebased assessments to reduce an employee’s overall training requirement and focus on just what they need.

Reinforcement:
People forget. It’s human nature. But everyone forgets at different rates. Reinforcement training, such as daily microlearning, can strengthen an individual’s long-term knowledge retention and confidence in areas of proven need.

Performance Support:
Hundreds of courses. Thousands of documents. There’s a lot of information to sift through when trying to solve a problem at work. Personalisation tactics, such as content recommendations and software tool tips, can help employees find their answers and get back to work more quickly.

Digital Learning:
Many employees do not need to view every slide in order to understand the topic being presented. They may already have some familiarity with the topic. Digital learning
can adapt to each individual based on how they navigate through the content. For example, problem solving scenarios can assess their current knowledge and allow them to accelerate through the content when appropriate. Navigation options are a low-tech way to personalise a learning experience through user choice.

Coaching:
A manager is typically not present to observe team member performance at all times. Therefore, to personalise their coaching discussions, they need actionable insights and recommendations regarding each employee’s performance. Otherwise, they may recommend generic, one-sizefits- all solutions, such as additional training that a person does not actually need.

Data: The key to personalisation
Personalisation tactics all have one thing in common: data.
L&D cannot provide a solution that fits an individual’s needs if they cannot accurately identify those needs. Unfortunately, traditional learning data, such as course completions and test scores, is not sufficient to enable meaningful personalisation. Consider content recommendation as an example. Just because someone else on your team completed (and maybe liked) a course does not mean that you should automatically take the same course. L&D needs more data about a person’s current knowledge and skill in order to make an informed, high-value recommendation.

To create a learning ecosystem grounded in personalised experiences, L&D must improve its data practices. This starts with expanding the definition of ‘learning data’ to include a wider range of organisational metrics, including job performance and business results. Then, L&D must identify the problems it wants to solve and find the data needed to address these challenges. For example, if you want to provide a salesperson with personalised training on how to improve their sales numbers for a particular product, you will need data on their knowledge, behaviour and results related to that product.

Every L&D team will collect, analyse and apply data differently based on the needs of their organisation. But you don’t have to solve the data problem by yourself or become a data scientist. Instead, L&D can partner with existing data experts within their organisations and partners. They can help you find available data and determine how it may be used to improve your learning personalisation and measurement strategies.

But remember …
Personalisation represents an opportunity for L&D to expand its value proposition. The ability to provide each employee with the help they need, when and where they need it is a powerful idea. However, as with any new concept, L&D must pause to consider how their tactics may impact their audience.

Let’s examine the Netflix metaphor one more time. The platform provides highly personalised recommendations for content you are proven to enjoy. But what about all of the other movies and TV shows available on the system? What if there’s something new and different available that you would love to watch, but the system never recommends it for you? Personalisation is an important part of a modern learning strategy, but it’s not the ultimate goal.

Personalised learning is more than recommended courses and branching eLearning modules. It’s an acknowledgement that learning is not a one-size-fits-all proposition. It’s a willingness to invest in the advancement of L&D capabilities — including data, technology and content — needed to provide right-fit solutions. It’s an opportunity to provide each employee with the support they need (and deserve) to do their best work every day.

“Personalisation is an important part of a modern learning strategy, but it’s not the ultimate goal.”

About the author:

JD Dillon is one of the most prolific authors and speakers in the global L&D community. For 20 years, he worked in operations and learning roles within highly regarded organisations, including Disney, Kaplan, Brambles and AMC Theatres. JD is now chief learning architect with Axonify and founder of LearnGeek.

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