The Importance of Predictive Analytics in Social Media – Why Views and Likes Do Not Tell the Whole Story

3 September, 2015

At Parrot Analytics, we use predictive analyses to discover future TV content demand around the world. Predictive analytics includes a variety of methods that analyze existing and historical data in order to make predictions about trends and customer behavior to answer questions such as ‘how much demand will there be for Scandinavian drama in Portugal next year?’.

Predictive analysis involves testing a large number of variables, developing models and generating meaningful insights from massive amounts of data. Predictive analytics is not new. Financial companies have used predictive analytics to predict capital market activity and make decisions about loans. However, the rise of big data has unlocked new strategies and methods in predictive analysis, making it an essential tool for Fortune 500 companies to achieve their business objectives.

Predictive analysis is revolutionizing R&D within a range of disciplines such as machine learning, statistics, data modeling and data mining. We are putting it to work in media and entertainment sector. The most impactful variations of predictive analytics uses data sets that were not available a few years ago, including such variables as TV content Demand Rating™ and Demand Expressions™ to predict what is likely to happen with a new OTT original (or a linear series) in Denmark (vs other countries) in its micro-genre.

Predictive Intelligence vs. Business Intelligence

Predictive analysis is often confused with business intelligence (BI) or even considered as a subset of business intelligence. Predictive analytics differs from BI in a number of ways. To illustrate, BI involves slicing and dicing data to answer questions such as what happened or why something happened. The outcome of BI is in the form of static reports or dashboards, which in general are not actionable with regards to the future. On the contrary, predictive analytics tries to answer questions such as what factors can affect the churn rate of a company’s customers, or what demographics should a company focus on to increase their revenue or what marketing strategies work the best on target audiences. In essence, it involves a deeper and contextually focused analysis of both structured and unstructured data. Predictive analytics is all about shifting from reactive to proactive and from the past to the future. For instance, it is about being empowered to know what social media activities will have the biggest impact on TV content demand across 249 markets broken down by market and audience profiles.

Predicting the Future based on Social Media Data

Social media is based on collective wisdom of people which, when translated into social demand, can be extremely useful to anticipate and plan for the future. Predictive analysis comes into play here as well. It is used for spotting trends and patterns in social media data. It involves sophisticated tools and techniques to transform social media data into meaningful insights. Although social media analytics is still in its infancy stage, predictive analysis is fast providing a decisive advantage for achieving a range of business objectives such as predicting customer’s demand, marketing campaigns, sales promotions, reaching the right customer demographics, customer churn rate control, to name a few. Businesses are increasingly turning to predictive analytics in order to stay ahead of competition. To illustrate, we are able to pinpoint which social media participation variables have the most influence on customer behavior when interacting with their favorite TV shows. It is generally accepted to interpret video views or post likes as most valuable indicators of consumer behavior. However, our research indicates that this is not the case. In fact, efficacy of different types of demand expressions on social media differs from country to country, TV series and demographic groups.

Determine Your Next Strategic Step

To stay ahead in this digital era, companies need to move beyond the historical understanding of business performance. The success lies in peeking into the future and understanding what is likely to happen based on a multitude of disparate data sources. Today, organizations want to be predictive. They want to use today’s data to predict what is going to happen in the future. The entertainment industry can benefit from predictive analytics expertise to help explain and predict consumer behavior, measure the performance of content, and analyze the impact of marketing and promotional campaigns return on investment.

Using our global content demand data, we are able to predict how TV & media content will perform in a specific market before it’s launched in that market, as people express their demand for content across a multitude of digital ecosystems that we track. Being proactive and predictive is a game-changing factor in the media and entertainment sector.

Putting Predictive Analytics to Use

We apply state-of-art predictive analytics techniques to analyze multiple data streams from numerous social media data sources to produce meaningful and, more importantly, actionable insights. Outputs are used to estimate pre-production/post-production demand, register the impact of pre-release/post-release demand for TV shows and target the right demographic audience for newly released TV content. Together with our customers, we are shaping the entertainment industry’s future. Just imagine what you could do when you are able to better understand the most in-demand TV shows in each of 249 markets among female audiences aged 25-34, and how their participation on social media sites translates to actual content consumption.


Social media represents a fundamental shift in how information is produced, consumed and acted upon. Knowing how to couple social media data with global content demand insights, provides companies with an edge in organizing impactful campaigns that reach the right audience at the right time to deliver on their business goals.

– Shahida Jabeen, Data Scientist

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