Two ‘new media’ experts have suggested a way of resolving a core contradiction facing content marketers – the dilemma of whether to ‘go niche’ and tailor compelling content to specialist platforms catering for relatively small audiences with strong preferences, or ‘go wide’ on more generic platforms and risk submerging unique content in a sea of other, unrelated topics.
Both approaches have flaws: many niche platforms draw small audiences, not large enough to generate sufficient returns for rich, authentic and engaging content that requires so much talent and effort to produce. Big brands might be able to attract a sufficiently large audience, but smaller outfits will usually struggle.
Yet the apparent way out – placing marketing content on more generalist platforms – also has its drawbacks. They tend to cover huge varieties of different topics, leaving people to wade through an ocean of content that doesn’t really float their boat to find a particular article or video that does the trick for them.
Aileen Lamb, CEO of the South African digital content marketing agency New Media, and Hendri Lategan, COO of Swipe iX, a digital solutions and development house specialising in scalable and secure technology for helping businesses grow, suggest a way forward: machine learning.
Machine learning technology has advanced in leaps and bounds over the last few years, making it possible for content marketers to train their most relevant content on consumers who would value and benefit from it most. As the authors put it, machine learning has given content marketers “a quantum leap forward in increasing audience engagement and, ultimately, ROI for clients”.
The technology enables a switch of focus: instead of the often thankless task of pumping as much content as possible ‘out there’, hoping for interested parties to simply happen upon it, it lets content creators target highly particular content with laser accuracy – straight at the right end users. It has even evolved to the point where creators can dynamically fashion custom publications for individual end users in real time.
The effect, Lamb and Lategan suggest, is akin to going to your favourite generic news site but being spared the tedium of sifting through numerous layers of irrelevant categories to reach the content that inspires you. Machine learning can ensure instead that you get a complete homepage already customised exclusively to your specific interests.
The technology can help creators build up a detailed profile of each person’s likes and dislikes, their user preferences and browsing habits, and what content they find most engaging.
As the authors put it: “You can do this without asking the user to fill out a survey or ever tell you what they want. Ever wonder why your own Netflix profile brings up such different content from when your partner logs into theirs?”
Machine learning can collect discrete data points tracking a user’s pathway through a platform, pinpointing what issues they engaged with the most, how much time they devoted to specific pieces of content, and what they’ve felt moved to comment on or hit the like button over. The beauty is that no user will have their anonymity compromised by this technology: it uses ones and zeros, not user IDs or names.
The time has never been better for content marketers to marry great storytelling with machine learning’s technological wizardry.