Predictive Analytics – 5 Examples of Industry Applications
Businesses today around the worldhave some portion of their operations being automated, which concurrently has meant that a lot of data about these processes is being collected (from sensors or internal company data etc). A combination of AI, big data analytics, and data science techniques seem to be a growing trend in many industry sectors, with predictive analytics being one of the most well-known.
According to adefinition from SAS, predictive analytics uses statistical analysis and machine learning to predict the probability of a certain event occurring in the future for a set of historical data points.
Businesses today seem to have a multitude of product offerings to choose from predictive analytics vendors in every industry, which can help businesses leverage their historical data store by discovering complex correlations in the data, identifying unknown patterns, and forecasting. This is hardly surprising considering the fact that predictive analytics can help businesses answer questions such as “Are customers likely to buy my product?” Or even “Which marketing strategies might be most successful?”
We highlight some use cases from the following industry segments with the aim of painting a possibility space for what predictive analytics can really do for business:
Below are five brief use cases for predictive analytics applications across five industry sectors. Each provides a fraction of a glimpse as to how AI technologies are being used today and which are being created and piloted as potential predictive analytics standards in these industries.
Boston-basedRapidminerwas founded in 2007 and builds software platforms for data science teams within enterprises that can assist in data cleaning/preparation, ML, and predictive analytics for finance. The 102-employee company provides predictive analytics services such as churn prevention, demand forecasting, and fraud detection, and they recently worked alongside PayPal. They claim that their predictive analytics software might help businesses with:
RapidMiner claims that they can help businesses achieve the above results by leveraging the client’s historical enterprise data. For example, In predicting the impacts of customer engagement for a retail firm, RapidMiner would first have to work with the retailers marketing team to gather all historical promotional and transactional data, including any marketing flyers, in-shop promotions, and purchase histories for a particular product.
The data is then cleaned in order to mold it into a structure that can be plugged into the machine learning algorithms. Those algorithms then perform statistical operations such as regression, classification, and frequent item-set mining aimed at identifying patterns in the historical data. These patterns can allow for determining the effect of perhaps promoting hamburger buns over hot dog buns for a particular week.
The system then derives actionable insights by working with a retailer’s marketing and IT teams in order to suggest the potential best practices for new promotional campaigns. The marketing team can then create a dashboard based on these and other insights that provides them metrics and analytics related to decisions such as choosing which products to market in the coming week or to whom they should market based on past history. RapidMiner claims their software can learn more such patterns over time, improving the accuracy of its predictions.
Below is a 3-minute video from Rapidminer giving a brief demonstration of how their predictive analytics software can help businesses:
PayPal collaborated with Rapidminerto gauge the intentions of top customers and monitor their complaints. According to a case study from Rapidminer, Han-Sheong Lai, Director of Operational Excellence and Customer Advocacy, and Jiri Medlen, Senior Text Analytics Specialist at PayPal, wanted to gain a better understanding of what drives product experience improvement.
