Content marketers are increasingly tasked with making sense of large and unwieldy data sets.
However, they often lack the skills to process this data, creating a paradoxical relationship between executive decision-making and on-the-ground implementation.
On the one hand, of businesses feel that data is essential to their growth.
Yet, at the same time, of employees say they struggle to process data in a workable timeframe.
As digital publishing moves towards a data-driven model, deep analysis is required for companies that want to remain competitive.
Content marketers must adapt their skillsets and build advanced, privacy-focused tech stacks that can handle first-party data.
This, in turn, enables them to create highly relevant, credible, and engaging content that meets and ranks well in search engines.
Evolving Data: A Story of Complexity And Opportunity
Data analysis as it relates to content marketing presents a multifaceted picture.
Many factors come into play, including government regulations, growing concerns around privacy, and the upcoming (to name just a few examples).
Nonetheless, both the prevalence of data and its use in content marketing are expected to grow exponentially in the coming years and decades.
- The CAGR (compound annual growth rate) for spending on analytics solutions will increase by between 2021 and 2025.
- of marketers anticipate an overall increase in content marketing spend in 2022.
- of marketers say their business sees content as a “core strategy.”
- of customers want brands to use only first-party data.
- of consumers experience anxiety around data privacy.
These figures highlight both the possibilities and challenges of a future in which data is widely available, yet restricted in the scope of its use.
are in a precarious position when balancing competing concerns. As a result, first-party data is taking center stage as the primary driver of decision-making in the digital space.
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 shift in little more than the time it takes to say “world wide web.”
A veritable cacophony of conditions affects consumer tastes, from political events to passing pop culture fads.
provide something of a bulwark against this uncertainty.
They enable marketers to adapt content strategy by measuring specific types of user behavior and accessing the right platforms.
Furthermore, point solutions are largely displaced with comprehensive CDPs (customer data platforms) aggregating inputs from numerous sources.
These apps typically include AI (artificial intelligence) and automation mechanisms for generating insights without the direct involvement of data scientists.
Content marketers can generate useful insights without necessarily relying on advanced infrastructure or in-depth technical knowledge.
Let’s look at five key types of data insight that have relevance for content marketers.
1. Industry Trend Projections
Analysis of historical data enables content markers too, the emergence of new distribution channels, changing fashions and emphases within industries, seasonal keyword variations, and more.
“Time series” data tracks a set of data points over a consistent period, thus providing insights into long-term user behavior and laying the groundwork for detailed forecasts.
Because time series analytics typically requires large volumes of data, trend projection represents one area where prediction engines and machine learning algorithms are essential to translate raw information into workable insights.
Metrics that provide insights into 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 subjects and themes offer insights into audience engagement.
This has obvious implications for the direction of your content strategy and editorial choices.
In a similar vein, understanding which categories your visitors navigate to after they leave a page means you can add content that is lacking on primary landing pages.
Where topic category data provides general insights into user engagement, specific performance metrics like conversions allow for high-level analysis of content ROI when aggregated into categories.
Metrics that provide insight into engagement: bounce rate, time on page, ROI, conversions.
3. On-Site Behavior And Experience
Data about on-site behavior provides an immediate window into the effectiveness of content types, formats, and channels.
Machine learning has also enabled the speedy processing of qualitative feedback.
One example is sentiment analysis, which relies on advanced technologies like 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 bounce.
Working with this data to track the customer experience provides opportunities for remedying fall-off points and solidifying high-converting parts of a website’s sales funnel.
Metrics that provide insight into on-site behavior: shares, engagement, qualitative feedback.
4. Data, Content, Customer Profiles, And Segmentation
Clearly defined user segments that incorporate data points like location, visit times, purchase frequency, interests, and so on enable content marketers to create tailored, highly specific content that is likely to excel in performance measures like engagement and conversions.
In addition to providing into the nature of users’ current interests and preferences, detailed profiles also form a strong basis for predicting future behavior.
Automated technology found in data platforms is particularly effective at streamlining this process.
Metrics that provide insight into profiles and segmentation: location, visit times, purchase frequency.
5. Data And Content Performance In Search Engines
Search engine performance is typically conflated with rank tracking.
But there’s more to measuring the effectiveness of content than simply monitoring
Insights geared towards improving search performance need to account for various data points.
These include zero-position rankings, long-tail distribution, click-through rates, the prevalence in featured snippets, content longevity, and more.
Research by my company, BrightEdge, shows that content preferences can vary by industry. Hence, it is vital to utilize data to inform your content strategies.
All-in-one SEO analytics platforms (as opposed to point solutions) carry out this function and enable content marketers to replicate top-performing topics and content formats.
Equally, they provide valuable, actionable data for optimizing promising but underperforming pages.
Metrics that provide insight into engagement: organic traffic, click-through rates, SERP positions, and the share of voice.
The Benefits Of Data-Driven Content Marketing Model
Advanced analytics are essential weapons 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 effectively you’re implementing innovative technology solutions and generating unique insights.
Content typically sits at the core of successful marketing, sales, and retention strategies.
And analytics platforms provide an invaluable chance to sharpen your competitive edge.
A data-driven approach to content marketing accounts for various factors, including evolving user interests, shifts in channel preferences, and applicable legal constraints.
As the world becomes ever more data-centric, digital companies need to take advantage of the opportunities on offer and measure content marketing ROI.