There's a word for content strategists who don't use data: Writers.
Writing is fine work, no shade intended. But for most of us in the contemporary content biz, developing content pillars without data is a best guess, and usually a wrong one at that.
Incorporating data is crucial for building a 2022-ready content strategy, and audience behavioral patterns and algorithmic insights are widely available to content strategists in the form of web analytics, trends tools, and free survey software. If you have the budget, additional data can be mined through subscription SaaS tools that scrape the web or distill social chatter.
With readily available audience and algorithmic data, content strategists can
understand what algorithms see
incorporate what audiences want
mix in business and brand goals
organize that information into buckets or themes that guide content creation, aka content pillars.
Content pillars make incorporating audience data manageable for long-term organic growth. Many publishers can't deeply keyword research every single article they publish or create a TikTok explainer for every fad, and that's ok! Incorporating data into strategic pillars that guide content creation for 1-2 years is enough for most publishers or content marketers. It certainly makes for a more cohesive, less frantic, high performing long-term content strategy.
Ideally, a holistic 2-year content pillar approach incorporates quantitative and qualitative data from multiple sources. Depending on resources, these pillars can be developed over a period of three days or several months.
To build pillars, use current, specific data. Personas or surveys from 3 years ago don't cut it to describe audience needs in "typical" years, let alone the massive behavioral and cultural shifts we've seen since March 2020.
Algorithmic data represents audiences at large
For effective content pillars that show up on algorithm-driven platforms, brands and publishers need to understand the human inputs that produce algorithmic results.
Organic content recommendation algorithms in search and social mine mass data from across the web and within their platforms, identifying the semantic patterns attached to a topic that spark audience engagement. In the early 2010s, these processing algorithms could only understand text. But in recent years major platform recommender algorithms have evolved to include image, video and voice inputs.
Content recommender algorithms are essentially a meta analysis of all human inputs into a specific platform. They represent a machine-powered call and response, an ever-evolving best guess of human intent or desire.
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