5 Types of Data Insights

Content marketers are increasingly responsible for making sense of large and unwieldy datasets.

However, they often lack the skills to process this data, creating a conflicting relationship between executive decision-making and implementation on the ground.

on the one hand, 94% of businesses believe data is critical to their growth.

However, at the same time, 63% of employees say they struggle to process data within a feasible time frame.

As digital publishing moves towards a data-driven model, companies that want to stay competitive need to conduct in-depth analysis.

Content marketers must adapt their skill sets and build advanced, privacy-focused technology stacks that can handle first-party data.

This, in turn, enables them to create highly relevant, credible, and engaging content that meets Google’s EAT (Expertise, Authority, Trust) criteria and rank well in search engines.

Evolving Data: A Story of Complexity and Opportunity

Data analysis related to content marketing presents a multifaceted picture.

Many factors are at play, including government regulations, growing concerns about privacy, and the impending devaluation of third-party cookies (to name a few).

Nonetheless, the popularity of data and its use in content marketing is expected to grow exponentially in the years and decades to come.

  • The CAGR (Compound Annual Growth Rate) of analytics solutions spending will increase 12.8% Between 2021 and 2025.
  • 66% of marketers expect an overall increase in content marketing spending in 2022.
  • 81% of marketers say their businesses view content as a “core strategy.”
  • 85% of customers expect brands to use only first-party data.
  • 86% of consumers are anxious about data privacy.

These figures highlight the possibilities and challenges of a future where data is widely available but limited in its scope of use.

Content marketers are in a precarious position balancing competing issues. As a result, first-party data is becoming a major driver of decision-making in the digital space.

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The role of data and analytics in content marketing

Access history and real-time data enable content marketers to navigate a digital environment where user interests can change in less time than it would take to say “the World Wide Web.”

From political events to pop culture fads, the dissonance of conditions affects consumer tastes.

A data-driven approach provides some safeguards against this uncertainty.

They enable marketers to adjust content strategies by measuring specific types of user behavior and accessing the right platforms.

Additionally, point solutions have largely been replaced by a comprehensive CDP (Customer Data Platform) that aggregates input from numerous sources.

These applications often include AI (artificial intelligence) and automated mechanisms to generate insights without the direct involvement of data scientists.

Crucially, content marketers can generate useful insights without having to rely on advanced infrastructure or deep technical knowledge.

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Let’s take a look at five key types of data insights relevant to content marketers.

1. Industry trend forecast

Historical data analysis enables content tagging to predict topic trends, the emergence of new distribution channels, changing fads and priorities within the industry, seasonal keyword changes, and more.

“Time series” data tracks a set of data points over a consistent period of time, providing insight into long-term user behavior and providing the basis for detailed forecasting.

Since time series analysis often requires large amounts of data, trend forecasting represents an area where forecasting engines and machine learning algorithms are critical for turning raw information into actionable insights.

Metrics that provide insight into industry trends: traffic, keyword searches, and retention rates for products and services.

2. Engagement by content trends and categories

Disaggregated data related to well-defined topics and themes provides insights into audience engagement.

This has clear implications for the direction of your content strategy and editorial choices.

Likewise, knowing which categories visitors navigate to after leaving the page means you can add content that’s missing from your main landing page.

Where topic category data provides general insights into user engagement, specific performance metrics like conversions allow for advanced analysis of content ROI when aggregated into categories.

Metrics that provide engagement insights: bounce rate, time on page, ROI, conversion rate.

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3. On-site behavior and experience

Data on field behavior provides an instant window into the effectiveness of content types, formats and channels.

Machine learning is also able to process qualitative feedback quickly.

An example is sentiment analysis, which relies on advanced techniques such as biometrics and text analysis to extract data about customer attitudes.

User behavior data enables content marketers to visualize the entire customer journey from initial search to purchase or return.

Using this data to track customer experience provides an opportunity to close churn points and solidify high-converting parts of your website’s sales funnel.

Metrics that provide insight into field behavior: sharing, engagement, qualitative feedback.

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4. Data, content, customer profiles and segmentation

Well-defined user segments contain data points such as location, visit time, purchase frequency, interests, and more, enabling content marketers to create customized, highly specific content that may excel in performance metrics such as engagement and conversion rates.

In addition to providing real-time insights into the nature of a user’s current interests and preferences, detailed profiles provide a solid foundation for predicting future behavior.

Automation technologies in data platforms are particularly effective in simplifying this process.

Metrics that provide insight into profiles and segments: location, time of visit, frequency of purchases.

5. Data and Content Performance in Search Engines

Search engine performance is often confused with rank tracking.

But measuring content effectiveness goes beyond simply monitoring SERP positions.

Insights aimed at improving search performance require consideration of various data points.

These include zero ranking, long-tail distribution, click-through rate, popularity in featured snippets, content longevity, and more.

Research by my company BrightEdge shows that content preferences can vary by industry. Therefore, leveraging data to inform your content strategy is critical.

All-in-one SEO analytics platforms (as opposed to point solutions) perform this function and enable content marketers to replicate top-performing topics and content formats.

Likewise, they provide valuable, actionable data for optimizing promising but underperforming pages.

Metrics that provide engagement insights: organic traffic, click-through rate, SERP position, voice share.

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The benefits of a data-driven content marketing model

Advanced analytics is an essential weapon in the modern content marketer’s arsenal.

It’s no longer about whether you’re leveraging data – that should be a given.

Instead, you should consider how to effectively implement innovative technology solutions and generate unique insights.

Content is often at the heart of a successful marketing, sales, and retention strategy.

Analytics platforms offer valuable opportunities to improve your competitive advantage.

The first-party, data-driven approach to content marketing takes into account a variety of factors, including changing user interests, changes in channel preferences, and applicable legal restrictions.

As the world becomes more data-centric, digital companies need to capitalize on the opportunities presented and measure the ROI of content marketing.

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