All forms of marketing, including content marketing, revolves around the collection and utilization of data. Regardless of your objectives, you need data to assist you in making informed decisions that can ultimately affect the success of your marketing efforts. When it comes to content marketing, data allows you to create content that reaches and engages the right audience. Now, most marketers track and measure some form of data analytics to monitor the performance of their marketing efforts. In fact, according to the Content Marketing Institute (Links to an external site.), around 73 percent of digital marketers regularly track website analytics to improve their content’s performance.
However, while data analysis is widely used by marketers, it tends to be more limited and simpler than its close counterpart, data science. Data science is often interchanged with data analytics but the two have clear differences. As defined by an article on No Good (Links to an external site.), data science examines data to predict future outcomes. It may use a combination of machine learning tools and mathematical methods to gain key insights. On the other hand, data analysis reviews past data for context.
Incorporating data science into your strategy can do wonders for your content marketing efforts, especially as the practice begins to rise (Links to an external site.) in use among digital marketers.
Better testing for better content
In order to learn which forms of content perform best on a platform or for a client, you’ll need to rely on testing. Remember: not all content is created equal. Content that works well on social media may not work well on another platform (or for a specific business). With data science, you have a wide net of different testing methods such as serial testing (Links to an external site.), which allows you to run several tests one after another. Since data science utilizes sophisticated algorithms, you’re able to derive clearer insights on specific metrics.
Effective tracking over time
Through data science methods like time-series forecasting (Links to an external site.), you can create models that allow you to effectively track and measure data and make predictions based on month, week, or day. Incorporating these types of models within your strategy allows you to predict your content’s performance in the future. This can include assessing keywords over time so that you know how your content may perform among your competitors.
Enhanced segmentation of customer base
Segmenting customers plays a huge role in content creation. With sufficient customer segmentation, you can create relevant content that attracts customers based on several factors, such as location, pain points, and buyer’s stage. Data science turns the customer segmentation process into a seamless experience. How? Well, through machine learning algorithms, you’re able to make accurate segments of customers, which, in turn, helps you predict their behavior.
In many ways, data science helps content marketers (or any marketer) drive their campaigns and practices by placing a special focus on data and present opportunities. From predictive analysis to customer segmentation, data science offers key benefits that go far beyond this blog. When implemented correctly into a strategy, utilizing data science methods allows marketers and business owners to understand their data so that they gain an edge in the competition.