How to start using advanced content analytics
Analytics, in general, is key to performance optimization. Getting accurate feedback on what is working and what not helps companies make improvements that will lead to better outcomes. And when content is the center of your business, Advanced Content Analytics becomes the center of your Big Data analytics. By now, most businesses have heard of the Advanced Analytics and how it can generate value from large amounts of warehouse and fresh data. But despite this, most marketers today are struggling to measure whether anyone is using their content, how often their content is used, how much their content engages customers, and most importantly – whether their content has any impact on sales. Advanced Content Analytics using today’s sophisticated tools and techniques can make it easier to measure the outcomes of your content.
What are analytics and what are their types?
According to RapidMiner, analytics is a term that refers to the skills, applications, technologies, and practices used in the exploration and investigation of data in order to gain insight that will help drive future business planning. They also divide analytics into two major areas: Business Intelligence (BI) and Advanced Analytics. While BI uses a consistent set of metrics to measure past performances, Advanced Analytics goes beyond this using sophisticated modeling techniques that can help predict future events. A definition of Advanced Analytics by Gartner Inc. is autonomous or semi-autonomous examining of data or content using sophisticated tools that are beyond those of traditional BI. Examples of Advanced Analytics tools include data/text mining, pattern matching, visualization, semantic analysis, neural networks, and complex event processing just to name a few.
A perfect match of technology with expertise
However, not everyone agrees with the current definition of Advanced Analytics with some seeing it simply as a subset of BI. A Data Science and Analytics Company called Recovery Decision Science explains that while Advanced Analytics refers to future-oriented analysis, that the only thing that’s future about them is the actual business decision based on analyses of current or past data. With that being said, Recovery Decision Science argues that Advanced Analytics should not be defined in terms of time orientation neither by the techniques used. Instead, they offer a different definition of Advanced Analytics: any computer-based analytics technique relying on both modern software technology as well as the expertise of the analyst.
Analysis of content
When it comes to Advanced Content Analytics, in particular, the term simply refers to the use of Advanced Analytics used to measure the outcomes of their content. Businesses that rely on content to generate leads and sales are always on the search for new ways to measure the impact of their content marketing strategies. This will help direct their future endeavors such as determining the right content for the right customer. In Advanced Content Analytics, the goal is to measure lead generation, measure the number of direct leads compared to assisted leads, and measuring intricate details beyond page visits, views, and downloads such as time spent on a page, number of scrolls, and even benchmarking.
The controversy of Content Analytics
So, as you can see Advanced Analytics is that it is more efficient in solving business problems. However, Advanced Content Analytics have a long way to go before they are able to truly measure the impact of their content and with that, predict business outcomes. This is especially evident when you look at current statistics showing that 70% of marketers lack a consistent or integrated content strategy and most find measuring ROI from content marketing to remain a top challenge. Most businesses rely on views alone showing a clear lack of analytics tool that would help generate leads or at least measure ROI. A recent study by Content Marketing Institute even found that only 38% of B2B companies stated that content marketing was an effective strategy.
Tools used in Content Analytics
But who exactly needs Advanced Content Analytics? Well, in the early days of content marketing, simply putting out content was considered enough of an accomplishment. But today, internet noise and clutter means that any business using content marketing in their business strategy will need to put more effort in what content they put out in order to be heard. This is where Advanced Analytics step in as a valuable tool. While some may see Google Analytics as being sufficient to plan future strategies, other like to dig deeper into their data by using Google Analytics plugins such as Scroll Depth, Riveted, or tools like Piwik that features things like event tracking, keyword search, and visitor maps. When you combine these tools with the knowledge of a skilled data science, you just may get ahead in the completive world of content marketing.
Things to keep in mind
However, keep in mind that Advanced Content Analytics is difficult to master. It involves much more than relying on the flashy new tools and features to examine the impact of your content, be it for micro-influencer marketing or simple content marketing. Instead, you should focus first on establishing a good enough content strategy aligned with your business objective. Once you get serious about your Content Analytics strategy, you’ll start diving into things deeper than data mapping and data aggregation such as contextual discovery, predictive analysis, linguistic modeling, and semantic profiling and analysis among other things. But most importantly, you will see that outsourcing won’t work as well with Advanced Content Analytics as would hiring professionals to work within your company in the given context of your business.
Advanced Content Analytics is a relatively new term in the world of Advanced Analytics and that is lacking suitable tools and techniques. Businesses that rely on content in their marketing strategy find that measuring ROI is difficult and most seem to rely on views and downloads as their main metric. However, new technologies as well as advancements in the science of data analysis have made measuring the impact of content and determine future outcomes easier. Now, tools and techniques are used to help businesses put out only content their business can have use of.