In modern-day marketing, data is the new oil.
The more customer data you capture, analyze, and act upon, the better you get at creating influential marketing narratives. In fact, 63% of marketing executives feel strongly that data-driven marketing is crucial to success in our hyper-competitive global economy (source).
But—as the best content marketers will tell you, analyzing and applying customer data is complicated and time-consuming. Fortunately, we live in an age of technological innovation—an age where artificial intelligence and machine learning have quickly become the gold standard. Why do we bring this up? Well, the latest and greatest in marketing technology may be the solution to your content marketing needs.
Ready to learn more? Today we give you a guide to content marketing and predictive analytics—what this means, how to use predictive analytics, and other important considerations. Keep reading!
What is Predictive Content Analytics?
Predictive content analytics are the metrics used by marketers to assess consumers’ content demands and optimize their content supply accordingly.
The content marketing ecosystem is highly competitive. As a result, organizations often blindly invest in content creation. Yet, many businesses have found that less than 5% of their content marketing efforts account for around 80% of the desired results—website visits, content downloads, free-trial sign-ups (source). In other words, a sizable portion of marketing content is wasted.
In short, a predictive content analytics model helps improve content efficiency, reduce content wastage, and boost content engagement.
Predictive Analytics vs. Traditional Analytics
Traditional marketing methodology recommends building content marketing strategies based on past data. But, in today’s fast-paced, digital world, consumption patterns change so rapidly that historical data quickly becomes irrelevant and inaccurate.
On the other hand, predictive analytics works in real-time to capture the ongoing content consumption patterns of your audience. By combining real-time data with your current content repository, predictive analytics quickly delivers insight into the future content consumption patterns of your audience.
In practice, this means content marketers who leverage data and personalize their content to match impending demand drive significantly more engagement than those who rely on a traditional analytics model. Yet, only 18% of marketers say they use predictive analytics for content personalization (source).
If you’re among the group of marketers who have yet to use predictive analytics as part of their content marketing strategy, keep reading. Here’s where things get interesting.
How to use Predictive Analytics to Drastically Improve Your Content Marketing
From understanding behavioral consumption patterns to forecasting future trends, predictive content analytics helps content marketers in a number of ways. Check out these examples:
1. Personalized content:
By combining key engagement signals (conversions, CTA clicks, views, etc.) with the interest profiles of your audience, you can create personalized content for particular audience segments. On average, when you serve targeted content based on your audience’s interests, you’re likely to see a boost of approximately 88% in conversions (source).
2. Identify existing trends:
Predictive analytics can help generate new content ideas, but they can also be applied to your existing repository. Predictive analytics makes it easier to analyze your existing content and identify what your audience is reading, allowing you to focus on aligning your content with your audience’s core interests.
3. Diversify content formats:
Research suggests companies use 8 different content formats to cater to the varied tastes of their target audience (source). This is a challenge for content marketers, especially those who have limited insight into the types of content their audience prefers to consume. Use predictive analytics to prioritize the content formats that perform best with your audience.
4. Accelerate lead nurturing and sales cycles:
Understanding a prospect’s status in the buying cycle helps both content marketers and sales reps do their jobs more efficiently. By offering content marketers and sales reps deep insights into prospects’ interests and motives, predictive content analytics empowers an entire organization with actionable intelligence.
5. Boost content marketing ROI:
According to Vertical Measures, hiring an in-house content marketing expert can cost you anything from $48,000 to $150,000 annually (source). Considering only a small percentage of content assets generate results, many companies fail to achieve their desired content marketing ROI.
However, with predictive analytics, your content marketing team is in complete control of your customer’s data. This puts the power of advanced reporting, informed decision making, and clarity back into your hands. When you optimize your supply of content based on audience demand, you will see a boost in conversions.
Final Thoughts About Predictive Content Analytics
The intense competition in the content marketing space means marketers must improve their content strategy or run the risk of falling behind their competitors.
Despite its potential benefits, many marketers are hesitant to adopt this technology. However, modern platforms, make it easy for marketers to implement and understand predictive content analytics.
About the Author: Michael Bibla is a Content Strategist at Atomic Reach, the content optimization platform helping marketers & agencies measure and create content that consistently converts and drives more leads.