Predictive Analytics — more success with statistics

Jens Kuerschner
4 min readApr 9, 2015
Photo by Ruthson Zimmerman on Unsplash

Big Data, Data Mining, Predictive Analytics. These buzzwords are all arount the economic and IT news for some time now. The idea behint those words: Use of statistics, mathematics and modern computer technology to automatically analyze a huge amount of data, recognize patterns and to forecast future events.

Why I write a whole post about this topic?

Because this development is very exciting, and with my previous startup Placedise, I used these techniques to simulate advertising effects and predict their impact myself. For this reason, I want to tell you more about predictive analytics below.

Predictive analytics is a very broad term that describes no specific technology, but rather the idea that’s outlined above. The idea to predict or simulate future events and behavior with historical data from different areas is nothing new. Insurance companies and banks have used statistics for decades to determine credit risk or the credit worthiness of individuals. Given the complexity of these issues, it is not sufficient to simply consider the last 30 bank transfers of a credit applicant. In addition to the residence, or age, numerous complementary statistics are used. If the person has successfully demonstrated a similar application recently, for example, and how likely it is that the loan amount will be refunded in such cases. The situation is similar with advertising effects, which just can not be determined simply by scoring models with 7 parameters.

The basis for such data analysis models is always build on historical data. These can be derived from studies or directly by respective companies. In the end, it is simply important that the number of data is as large as possible, as with any additional information the validity of the statements increases. Thus, these predictive models can be even more meaningful at the end, than a small “real” experiment, which takes place as it were “live” and directly interacting with the object of interest.

Accordingly, for example, retail companies have great potential for such techniques, since the usually have recorded a lot of data due to their large trade turnover. But also in other areas, statistics can provide valuable results. So maybe you have heard about “Predictive Policing”. This describes the…

Jens Kuerschner

Tech Founder, Leader, End-to-End Product/Program Manager, Full-Stack Developer, Marketing and Digitalization expert. 🚀

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