Learn five key data-driven insights fuel content marketing success, performance, and measurement.
Content marketers are being increasingly challenged with the task of making sense of complex data sets.
They cannot often process the data, creating a paradoxical relationship between executive decision-making and actual implementation.
94% of businesses believe that data is vital to their growth.
However, 63% of employees claim they have difficulty processing data within a reasonable timeframe.
Companies wishing to stay competitive in digital publishing must conduct deep analysis.
Content marketers need to adapt their skills and create privacy-focused tech stacks that can handle first-party data.
This allows them to create high-quality, credible, and trustworthy content that meets Google’s EAT (Expertise Authoritativeness and Trustworthiness) criteria. It also ranks well in search engines.
The Story of Complexity and Opportunity
Content marketing data analysis presents a multifaceted picture.
Many factors come into play. These include government regulations, privacy concerns, and the upcoming loss of third-party cookies (to name a few).
However, the use of data in content marketing and the prevalence thereof are expected to increase exponentially over the next few decades.
These figures show the challenges and potential of a future where data is readily available but limited in scope.
Content marketers face a difficult task when trying to balance competing interests. First-party data has become the main driver of digital decision-making.
Data and Analytics in Content Marketing
Content marketers have access to real-time and historical data to help them navigate the digital landscape in which user interests can change quickly.
Consumer tastes are affected by various factors, including political events and passing pop culture trends.
Data-driven approaches offer some protection against uncertainty.
These tools allow marketers to adjust content strategy by accessing the right platforms and measuring user behavior.
Point solutions are being replaced by comprehensive CDPs (Customer Data Platforms), aggregating inputs from multiple sources.
These apps often include AI (artificial Intelligence) and automated mechanisms to generate insights without data scientists’ direct involvement or supervision.
Content marketers can create valuable insights without relying on technical expertise or advanced infrastructure.
1. Industry Trend Projections
Analyzing historical data allows content markers to predict topics, the emergence and changing distribution channels, industry emphases, seasonal keyword variations, and more.
Time series data is a collection of data-points tracked continuously. This provides insight into user behavior over time and allows for detailed forecasts.
Time series analytics often require large amounts of data. Trend projection is one area in which prediction engines and machine-learning algorithms are crucial to converting raw information into valuable insights.
Industry trends can be analyzed using metrics such as traffic, keyword searches, and retention rates for products or services.
2. Engagement by Content Trend and Category
The ability to link categorical data with well-defined themes and subjects provides insights into audience engagement.
This will have obvious consequences for your content strategy and editorial decisions.
Similar to the previous example, knowing which pages your visitors go to after leaving a page can help you add content, not on the primary landing pages.
While topic category data can provide general insights into user engagement and conversions allow for specific performance metrics such as conversions to enable high-level analysis on content ROI when aggregated in categories.
Metrics that give insight into engagement include bounce rate, time on site, ROI, and conversions.
3. On-site Behavior and Experience
On-site behavior data provides immediate insight into the effectiveness and efficiency of content types, formats, and channels.
The speedy processing and analysis of qualitative feedback have been made possible by machine learning.
Sentiment analysis is one example. It uses advanced technologies such as biometrics and text analytics to extract data about customer attitudes.
Content marketers can visualize the customer journey from initial search through purchase or bounce using user behavior data.
This data can improve customer experience and strengthen high-converting areas of a website’s sales funnel.
Metrics that give insight into the behavior of site visitors include engagement, shares, and qualitative feedback.
4. Data, Content, Customer Profiles, and Segmentation
By using clearly defined user segments, content marketers can create highly targeted content that will be effective in performance measures such as engagement and conversions. These data points include location, visit times, and purchase frequency.
The detailed profiles provide valuable insights into users’ preferences and current interests. They also serve as a strong base for predicting future behavior.
This process can be streamlined by automated technology found in data platforms.
You can gain insight into your profiles and segmentation using metrics such as location, visit times, and purchase frequency.
5. Search Engines: Data and Content Performance
Search engine performance and rank tracking are often conflated.
But it’s not enough to monitor SERP position.
For search optimization to be effective, insight must take into account a variety of data points.
These include zero-position rankings and long-tail distribution, click-through rates, the prevalence of featured snippets, content length, and many other factors.
BrightEdge’s research shows that industry preferences can differ in content preferences. It is therefore vital to use data to inform your content strategies.
This function is performed by all-in-one SEO analytics systems rather than point solutions. They enable content marketers to duplicate top-performing topics or content formats.
They also provide helpful, actionable data that can be used to optimize pages with promising performance but low page numbers.
Analytics that gives insight into engagement include organic traffic, click-through rates, SERP positions, and share of voice.