Marketing uses big data to gather insights about user preferences and behaviours. Learning relies on intuition.
At their cores, both Marketing and Instructional Design have a similar goal: inform an audience in order to influence a change of behaviour. Whilst the outcome in marketing is the completion of a sale, and the desired results of a learning intervention are far more varied, the principles are still aligned. Marketing automation, via Big Data analysis, has refined much of the business of converting leads to sales from an art to a science. Yet L&D still relies on theory, or trial and error.
What if learning professionals borrowed from marketers when it comes to building online content? Are there lessons from digital marketing that can translate to how we better design? Why do we rely on a Level 1 Evaluation after a learning event? That's like waiting for an autopsy instead of getting a diagnosis.
What Marketing Does that Learning Doesn’t
Marketers analyse online behaviour to determine a user’s Digital Body Language (DBL)*, which, in basic terms, is understanding a user’s preferences by tracking activity such as where they click, and more importantly, don’t click. Through this data, they can begin to predict what will best attract a user’s attention. For example, if a user more likely to watch a video or read an article on the same topic; the time of day when a user is likely most active; and what types of emails influence action, such as downloading a whitepaper or visiting a website. Once the DBL of a user is better understood, then marketing can better respond based on these preferences. Content can be delivered in the demonstrated preferred media type or pushed out at a time of day when the user is likely to be active. Responding effectively to DBL correlates directly with proven increased sales outcomes. For more details on DBL, read Steven Woods' book "Digital Body Language".
It’s one thing to understand and respond to an individual’s DBL, but there’s also a great deal of insight to be discovered in comparing and aggregating the online behaviour of large audiences. Consider the following demographic profile:
- mostly female
- living in Central America
- between the ages of 26-34
Rather than using a “one size fits all” approach, algorithms from previous marketing campaigns can reveal trends for specific audiences. In the case of this particular demographic, choices about type, length, and media, can be made in a more informed manner based on concrete data and not intuition.
What Should Learning Do?
Learning has had 70 20 10 for years but lacks a framework to support that effectively. Learning needs to step back and approach learning culture in the same way marketing approaches lead nurturing. Learning must leverage other sources of data about user activity to gather and gain insights. This will lead to better solutions that are more calibrated to how learners will engage.
Start With What You Have...
What data about users do you have access to right now that can be mapped and leveraged to analyze beyond learning assessments? What social aspects can be leveraged in your technology? What data do you track that goes beyond completion (e.g. drop off, etc)?
...Then Make Informed Decisions
Using the data you can procure, analyse it to uncover the insights to build learning that your users will prefer and increase their engagement.
Co-written with Adrian Celentano