Welcome to Predictive Marketing University

Jacob Shama, PhD
Learn how to leverage data science to improve your marketing and boost revenue

Components of Predictive Marketing

TRANSCRIPTION:
The quantities of data that are generated online continue to grow to an ever-larger scale. Over 300,000 million tweets are sent each day, over 70 million new blog posts are written every month.

Storing and processing these amounts of data requires a set of technologies and processes that are different from those used with traditional data processing applications. Since Predictive Marketing algorithms use vast quantities of data mined from the web and this data is continuously updated, they require implementing big-data processing capabilities.

Data mining is a field in computer science that deals with the discovery of patterns and clusters in large data sets. Data mining also structures unstructured data, which is tremendously important in our digital world.

Today, most of the data comes in the form of unstructured data, which is scattered around in myriad of websites, blogs, social networks and online databases. Data mining gathers data from the web and helps to turn it into meaningful marketing indicators.

For example, let’s say that we would like to know if there are certain key positions in fast-growing companies. We can look, for example, at the most prevalent job titles in the Inc. 5000 list of America’s fastest growing companies.

Companies’ websites and social networks have a large list of profiles, each containing a person’s job title. It would be hard to gain meaningful insights by looking at one profile after the other. Data mining helps us extract this data and structure it in a manner that would make it easier to gain meaningful insights.

Predictive analytics may be the most impressive aspect of Predictive Marketing. It is the algorithms that take all of the data that was mined and standardized and use it to make meaningful prediction models about the future. Predictive analytics is where the magic happens.

A great example for predictive analytics is the price trend at Kayak, a travel reservation engine. Kayak predicts the future price trends by analyzing data on past dates and can tell you whether you should purchase your ticket now or wait.

If you have any question, feel free to contact me directly at PMU@mintigo.com or on Twitter using my handle @JacobShama.


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