marketing

59 posts

A guide to content analytics: Why content professionals should care about their metrics

A guide to content analytics: Why content professionals should care about their metrics

ke it or not, it's up to us, the people who enjoy making things, to advocate for what we value and to value what we create. We must, on a base level, understand our numbers.

Making impressions a thing of the past: Grounding your content performance data in reality

Making impressions a thing of the past: Grounding your content performance data in reality

Everyone else's numbers are inflated, but your own data is your gold. Learn to trust your owned data and make decisions with verifiable performance metrics.

Keep brands weird: The research framework for exceptional data-driven content

A diagram of the Content Research Framework, showing categories of self-collected and co-collected insights.

If you’re feeding yourself the same inputs as everyone else, you run the risk of homogenous, bland, boring content. This content research framework will help you find the right data.

Setting the stage for content project success: Best practices for a strong kickoff meeting

Setting the stage for content project success: Best practices for a strong kickoff meeting

Leave every participant with a clear understanding of what the team aims to accomplish. However, reaching that point involves research and stakeholder input. Check out our tips for running kickass kickoffs.

Engagement rate and beyond: The best TikTok, Facebook & Instagram metrics for content analysis

Engagement rate and beyond: The best TikTok, Facebook & Instagram metrics for content analysis

Can’t remember when you last took a look at your social analytics numbers? Today’s the day for a health check-up.

On iteration: Why digital business folks and content producers don’t always see eye-to-eye

On iteration: Why digital business folks and content producers don’t always see eye-to-eye

Iteration is a repetition of a process to solve a specific problem, and it’s a big part of how machine learning algorithms are developed. But the way devs and data scientists think about iteration doesn’t always match with editorial and entertainment industry production methodologies and norms.

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