Big data and Predictive analytics

Authors: Jacob Zingeser, Tenzin Tsering

Prof.Dr. Adalbert F.X Wilheim

big-data-analyticsWhat is Predictive models and analysis? Why are they applied to businesses?

Predictive models and analysis are methods of extracting valuable information from an existing data sets with an aim of determining patterns and predicting future outcomes and trends.It uses a number of techniques, data mining, statistical modelling and machine learning to help analysts make  calculated business forecasts.

In business, predictive models are used to analyse the data and historical facts to understand the customer, products and partners better. Consequently, the models can help company identify the opportunities to excel in market and also prevent potential risk.

Big data and predictive analytics 

In today’s digital era, businesses collect enormous amounts of real-time data from customers. Predictive analytics uses this historical data, combined with customer insights, to predict future events. In other words, predictive analytics enable organizations to use big data to move from a historical view to a forward-looking perspective of customers.

Case Study – Netnoc – How do they tackle big data and content analytics?

Netnoc is an Italian marketing startup – still in its early stages – that provides a platform for predictive and content analytics. It analyzes contents’ various key performance indicators (KPIs), including: reach, engagement, conversion, and retention of digital content, which is designed to ultimately calculate a single score – the “Netnoc Score” – that measures the return on investment (ROI). For example, Nike Football has a Facebook page that produces a single piece of content (POC) every day. The Netnoc platform is able to identify the highest performing content in the past year, and analyse the factors that contributed to it its success. To do this requires the help of machine learning algorithms that are capable of calculating figures for each KPI and implementing them into a single score, giving an easy-to-understand overview of particular digital content’s performance. Also, these machine learning algorithms must analyze previous digital content to give educated predictions of the performance of future digital content, based on different KPIs, such as keywords, optimal content posting times, etc..

Essentially, Netnoc looks at content across various platforms – Facebook, Instagram, Blogs, Websites, etc. – and interprets underlying relationships between variables into a simplified scores that marketers and investors alike can understand.

This platform is an invaluable tool for marketers, as it translates inconceivable amounts of data into actionable insights. These actionable insights allow marketers to better perceive opportunities and threats for future content and also helps to improve the efficiency and quality of future content.

How Does This Look in Action?

It is easier to understand how the Netnoc platform works with some visual aids. The following diagrams are produced by Moz – another content analytics company – using machine learning algorithms, which calculate up-to-date insights of the highest performing hashtags, based on their engagement.


Similarly, Netnoc can produce diagrams such as this, along with others, that are simple to understand for various other KPIs. For example, they could measure the engagement of frequently used words or terminology, as opposed to hashtags.

In conclusion, the possibilities are immense for marketers using machine learning and content analytics. Implementing these tools is leading to a new age for marketers, who are now able to produce far higher quality content for less cost than ever before, by understanding the nature of how customers see and engage with their content.






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