Social media is great for sharing photos of your hoppin’ house party but terrible for providing insights into what your audience actually craves. It’s time we ignore the social-listening hucksters next door, turn up the volume on other data sources, and become antisocial.
There’s a cornucopia of online content marketing channels. Websites, blogs, podcasts, apps, email newsletters, etc. But in recent years, research into how people consume content has become dominated by social media metrics. More and more, analysts are looking to Facebook and friends to gauge audience preferences and engagement levels. But this ever-growing reliance – dare I say obsession – with social media data is skewing results and misleading content strategists.
How can social media data be a bad thing? Surely – I hear you say – social media is a fair representation of what people think, like and dislike.
From a channel-mix perspective, social media can be great for creating short-term PR responses or brand-driven campaigns. LinkedIn is often the perfect place to share your hot take on the latest news, and a number of platforms have become thriving marketplaces. From a data-analysis perspective, the social networks do provide some important insights, revealing sudden spikes in audience interest and up-and-coming trends. But it’s a huge mistake to make social media analytics the driving force behind your content-marketing strategy.
By focussing too heavily on social media channels, analysts undervalue the engagement levels of website-based content: articles, videos, infographics, forums, reports. And social media won’t tell you what the most popular content formats are. Or the different tones that are preferred in different markets. Nor will they compare content produced by your industry against content consumed from non-industry sources.
They keyword fallacy
Strategists may think keyword results provide enough insights into the web beyond social media. They don’t. Content isn’t just copy. It’s more than just blogs. It’s a rich tapestry of formats that keywords often ignore. By using keywords in isolation, strategists miss out on the important insights about audience preferences that are essential to developing an effective content strategy.
Often, we see companies only track the keywords they and their competitors use. Then, they proclaim success whenever they win more organic searches than their peers. This navel-gazing KPI leads organizations to focus on keywords specific to their industry or brand . The resulting content drones on endlessly about their offerings, crowbarring the product-focussed keywords into every paragraph. The content is seen as shallow advertising by their audience, and the true engagement is awful. This approach goes against the very ethos of content marketing:
“Content marketing is a strategic marketing approach focused on creating and distributing valuable, relevant, and consistent content to attract and retain a clearly defined audience — and, ultimately, to drive profitable customer action.
Instead of pitching your products or services, you are providing truly relevant and useful content to your prospects and customers to help them solve their issues.” Source: Content Marketing Institute
Social media and keywords are not enough to create an effective content strategy.
Not everyone is noisy
Are you a member of a group chat in a messaging app like WhatsApp? Have you noticed that the vast majority of the conversations are started by a small percentage of the group’s membership? And that the same few people are always the first to post a comment or share a meme, opinion or experience. Are those select people expressing the consensus of the wider group? More often than not, no.
The ‘Audience Participation Inequality’ theory says that 1% of an audience creates and publishes content; up to 9% visibly engages with that content (comments, likes, shares); the remaining 90% just read, watch or listen to the content without announcing their engagement. Also known as the ‘1:9:90 Audience Participation Rule’, the phenomenon was first referenced in an online context in 1997 by Jakob Nielsen. Then in 2006, it was used in a quantitative marketing context by McConnell & Huba. Today, it is central to how we approach content analysis.
Some researchers believe 90% is actually optimistic and that visible engagement levels are actually much lower than 9%. The ‘hidden audience’ is likely larger. This means that social media engagement data effectively ignore 9 out of 10 audience members. It makes no sense to build a content plan based on the needs, interests and requests of the noisy few.
Audiences are diverse, so diverse data is needed to understand them
Content marketing strategy must include a wide spectrum of sources, not just social-listening data from the noisy nine or product/brand/competitor-focussed text-driven keyword research. Thankfully, new technology can help broaden our understanding of what our audiences truly desire.
The New Zealand America’s Cup sailing team and McKinsey used machine learning to make sense of the millions of data points involved in a yacht race, turning their computer into an expert sailor who advised the team to victory in 2021. (Read the article here.) At Immedia Content, we decided to create our own artificial-intelligence expert – one who advises our clients on how to deeply engage with their customers. The technology platform identifies, harvests and analyses millions of data points about how 100% of their audience is consuming content.
We wanted to build an expert, not just a tool.
Tools find, identify and quantify.
Experts recommend, advise, learn and predict.
So we built Contrend, an advanced artificial intelligence and machine learning system that becomes increasingly wise about our clients’ goals over time. Contrend becomes an expert member of our clients’ teams, not just a supporting tool. It creates harmony between humans and software, making sense of a chaotic global content marketing landscape. It combines search with social, mobile and market intelligence to create a balanced, complete assessment of audience content preferences.
Audience-centric, data-driven, actionable recommendations
By combining complex, big data sets, our platform identifies what content topics, formats, styles, images and display types engage best with a target audience, and how to create differentiated, multi-format content across multiple markets to maximise long-term audience engagement.
We’re not really antisocial. We just want social media data to be accurately weighted so that our clients give their customers the very best content. Social media will always be invited to the party; they just need to learn that they can’t always be the centres of attention. Contact us here to discuss how we can help you build a balanced, audience-centric, data-driven content strategy that everyone can celebrate.