Content marketers are increasingly tasked with making sense of large and unwieldy datasets.
However, they often lack the skills to process this data, creating a paradoxical relationship between executive decision-making and implementation on the ground.
On the one hand, 94% of companies believe that data is essential to their growth.
Yet at the same time, 63% of employees say they struggle to process data in a timely manner.
As digital publishing evolves into a data-driven model, in-depth analysis is needed for businesses to stay competitive.
Content marketers need to adapt their skills and build advanced privacy-focused tech stacks that can handle first-party data.
This, in turn, allows them to create highly relevant, credible and engaging content that meets Google’s EAT (Expertise, Authoritativeness, Trustworthiness) criteria and ranks well in search engines.
Evolving Data: A Story of Complexity and Opportunity
Data analysis as it relates to content marketing presents a multi-faceted picture.
Many factors come into play, including government regulations, growing privacy concerns, and the upcoming deprecation of third-party cookies (to name a few examples).
Nonetheless, the prevalence of data and its use in content marketing is set to grow exponentially in the years and decades to come.
- The CAGR (compound annual growth rate) of spending on analytics solutions will increase by 12.8% between 2021 and 2025.
- 66% of marketers predict an overall increase in content marketing spending in 2022.
- 81% of marketers say their company views content as a “critical strategy.”
- 85% of customers want brands to only use first-party data.
- 86% of consumers are anxious about data privacy.
These figures highlight both the opportunities and the challenges of a future in which data is widely available, but restricted in the scope of its use.
Content marketers are in a precarious position when it comes to balancing competing concerns. As a result, first-party data takes center stage as the main driver of decision-making in the digital space.
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The role of data and analytics in content marketing
Access to historical and real-time data allows content marketers to navigate a digital landscape where user interests can change in little more than the time it takes to say “World Wide Web.”
A veritable cacophony of conditions affect consumer tastes, from political events to passing fads in pop culture.
Data-driven approaches offer a kind of bulwark against this uncertainty.
They allow marketers to tailor content strategy by measuring specific types of user behavior and hitting the right platforms.
Additionally, point solutions are largely being replaced by comprehensive CDPs (customer data platforms) aggregating inputs from many sources.
These applications typically include AI (artificial intelligence) and automation mechanisms to generate insights without the direct involvement of data scientists.
Basically, content marketers can generate useful insights without necessarily relying on advanced infrastructure or deep technical knowledge.
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Let’s look at five key types of data insights that are relevant to content marketers.
1. Projections of industry trends
Analyzing historical data enables content markers to predict hot trends, the emergence of new distribution channels, changing fashions and accents within industries, seasonal changes in keywords, and more. .
“Time series” data tracks a set of data points over a consistent period, providing insight into long-term user behavior and laying the foundation for detailed forecasts.
Since time series analysis typically requires large volumes of data, trend projection represents an area where prediction engines and machine learning algorithms are essential to translate raw insights into actionable insights.
Metrics that provide information on industry trends: traffic, keyword search volumes, and retention rates for products and services.
2. Engagement by content trend and category
Categorical data tied to well-defined topics and themes offers insight into audience engagement.
This has obvious implications for the direction of your content strategy and your editorial choices.
Along the same lines, understanding which categories your visitors navigate to after leaving a page means you can add content that is missing on primary landing pages.
Where topic category data provides general insights into user engagement, specific performance metrics such as conversions allow for high-level analysis of content ROI when aggregated into categories.
Metrics that provide insight into engagement: bounce rate, time on page, return on investment, conversions.
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3. Behavior and experience on site
On-site behavior data provides an immediate window into the effectiveness of content types, formats, and channels.
Machine learning also enabled rapid processing of qualitative feedback.
One example is sentiment analysis, which leverages advanced technologies like biometrics and text analytics to extract data about customer attitudes.
User behavior data allows content marketers to view the entire customer journey, from initial search to purchase or bounce.
Working with this data to track customer experience provides opportunities to address falling points and solidify the high-converting parts of a website’s sales funnel.
Metrics that provide insight into on-site behavior: shares, engagement, qualitative feedback.
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4. Data, content, customer profiles and segmentation
Clearly defined user segments that incorporate data points such as location, visiting hours, purchase frequency, interests, etc. allow content marketers to create personalized and highly specific content that is likely to excel in performance metrics such as engagement and conversions.
In addition to providing real-time information about the nature of users’ current interests and preferences, detailed profiles also provide a solid basis for predicting future behavior.
The automated technology found in data platforms is particularly effective in streamlining this process.
Metrics that provide insight into profiles and segmentation: location, visit time, purchase frequency.
5. Performance of data and content in search engines
Search engine performance is commonly confused with rank tracking.
But measuring the effectiveness of content is more than just monitoring SERP positions.
Insights to improve search performance should consider a variety of data points.
These include position zero rankings, long-tail distribution, click-through rates, prevalence in featured snippets, content longevity, and more.
Research conducted by my company, BrightEdge, shows that content preferences can vary by industry. Therefore, it is essential to use data to inform your content strategies.
All-in-one SEO analytics platforms (as opposed to point solutions) fulfill this function and allow content marketers to replicate top-performing content topics and formats.
Likewise, they provide valuable and actionable data to optimize promising but underperforming pages.
Metrics that provide insight into engagement: organic traffic, click-through rates, SERP positions, share of voice.
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The benefits of the data-driven content marketing model
Advanced analytics are essential weapons in the modern content marketing arsenal.
It’s not about whether you’re mining data anymore – it should be a no-brainer.
Instead, you need to assess how effectively you implement innovative technology solutions and generate unique insights.
Content is usually at the heart of successful marketing, sales, and retention strategies.
And analytics platforms offer an invaluable chance to boost your competitive edge.
A data-driven approach to content marketing takes into account a variety of factors, including changing user interests, changes in channel preferences, and applicable legal constraints.
As the world becomes increasingly data-centric, digital businesses must take advantage of the opportunities presented and measure the return on investment of content marketing.
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