Catalina Schveninger, Global Head of Learning, Vodafone

For anyone like me who spends lots of time scrolling through new titles undecided, Netlfix trying to personalize my watch list sounds great. They are often quoted as is Amazon as the gold standard for learning experience—make it seamless for people to binge learn as they binge watch.

But now the streaming giant stands accused of being creepy and racist by black users fed up with Netflix’s advertising of films and shows that they claim is targeting them by ethnicity. Netflix rolled out a new algorithm last December to serve up personalized images to its subscriber-base. “Artwork personalization” came out in their own research as the biggest influencing factor on a viewer deciding what to watch (82 percent). Multiple images are now generated for each and every title and change regularly to lure audiences depending on their tastes and previous viewing history. “It’s weird to try to pass a film off as having a Black principal cast (by creating a movie poster-like as featuring just the Black people) when it’s a white movie. A very white movie. I’d already watched this one last month so I knew it was a marketing trick. Still,” wrote Stacia L Brown, a blogger, referring to their promotion of the ‘Like Father film.

Another example on the more “creepy side” of personalization, is the The Magic Sauce—an algorithm that predicts the users’ psycho-demographic profile from digital footprints of their behavior. It reveals how you might be perceived by others online and provides insights on personality, intelligence, leadership, life satisfaction, etc. 

My ideal of personalization is to combine pushing relevant content, measuring the impact of learning on skills development and how the person is applying those skills

It was created by academics at the University of Cambridge— who actually ethically encourage users to understand how their digital footprint data is used by the likes of Facebook for targeting. This work inspired Cambridge Analytica—we know how that ended. Now, back to the learning tech industry—there are a couple of encouraging examples of personalization with purpose. I picked a couple of examples that I think are close to my own ambition in this space:

First, the folks at Filtered developed the learner’s skills signature— a live, continually updated best-estimate of which skills learners can most valuably develop. Once they have modeled a learner’s skills signature, magpie matches it with its model of the skills each piece of content (learning asset) is likely to develop. Where there is a good match— that is where the content builds skills that are particularly important to the learner content gets prioritized; where there is a mismatch they will de-prioritize the content.

Edmodo, the education app, announced that they will partner with IBM Watson to create individualized tools for educators to address each student’s needs, bridging the gap that educators and curriculum creators have not managed to produce a uniform level of academic performance.

I mentioned IBM: we have a long standing partnership with IBM here at Vodafone. It was recently announced that IBM will provide managed services to Vodafone Business’ cloud and hosting unit, in an eight-year engagement. Our corporate learning teams also decided to partner and put together a cross functional squad of bright young sparks from both companies who ideated on the future of work in a lab like experience.

One of the Minimum Viable Products they came up with addresses the need to deliver a deep personalization, not just of the learning content that is pushed, but also of the whole learning experience and how learning gets integrated in the flow of work.

The team had no previous knowledge of the theory of nudging— made popular by Laszlo Bock with his new venture Humu— but they came up with it, which I thought was amazing.

They used design thinking techniques and continue to work as a squad— until the MVP is ready for pilot.

Jaz, the persona, has got his personalized learning nudges that help him be at his best. VIM is an AI assistant that understands Jaz and their career aspirations, what the required skills are and what it takes to learn and apply these skills. It knows where Jaz spends their time and how they like to be contacted, to keep Jaz motivated even when it feels like there is no time. This assistant is personal to Jaz but it works across all organizations, pulling knowledge from company experts to the platforms Jaz uses the most, to upskill them in a collaborative way for the future. It will also integrate with the productivity tools and learn Jazz’s routines to better adjust even the timing the nudges are being sent. It will ultimately learn how Jazz’s learns best.

I am excited about this project not only because it caters to a need of embedding learning in the flow of work and personalizing the experience in a deep, meaningful way but also because of the way it was born— by non experts, young learners with a passion for tech from different functions and 2 different companies. It doesn’t get more agile than that.

I think this is hyper-cool, but I declare my bias. My ideal of personalization is to combine pushing relevant content, measuring the impact of learning on skills development and how the person is applying those skills whilst deeply understanding how individuals learn.