“The fusion of these two technologies allows us to go from an anecdotal approach to a data-supported approach that enables us to create more meaningful interventions and better patient care moving forward.”
An analytics division in a privately held healthcare company wanted to use their vast amount of patient treatment data to help drive better care and outcomes. They monitored each patient’s progression over their entire course of treatment, storing vast amounts of data in many different formats and across many facilities. This led to a complex dataset, which the company needed to quickly cleanse, simplify and draw fast, actionable treatment conclusions to share with doctors.
RapidMiner’s unified data science platform was chosen for its easy to use drag and drop visual programming and ability to integrate with 3rd party software like Tableau. This gave them the robust data prep and predictive modeling functionality of RapidMiner along with the ability to operationalize results directly into the user friendly, interactive dashboards of Tableau.
Austria’s leading mobile phone service provider, Mo-bilkom Austria, received more than 800,000 emails every month; even after spam filtering more than 80,000 customer requests remain. Of course, customers expect a timely reply, especially when communicating through this medium.
Using RapidMiner‘s Data Science Platform, Mobilkom was able to analyze the textual content of incoming customer requests and automatically determine the topic of each request. This way, the email requests are automatically and quickly forwarded to the support person in charge for this topic and a competent answer is guaranteed within the shortest time possible.
Millions of patents exist and new ones are granted every day. These documents are publicly available, but it is still difficult to track and monitor the huge number of possible violations. Is someone violating one of our patents? Are we possibly violating someone else’s? Big corporations need to know.
This multinational chip manufacturer collects patent documents filed by competitors and uses RapidMiner’s text analytics and predictive capabilities to sort and track them.
This multinational pharmaceutical company sells thousands of different drugs. In order to optimize logistical operations and storage needs, the company needed to know future sales. If the company just looked at sales from the previous month as a predictor for the next month, the error could be 20 percent off in either direction. Better predictions = better process and huge logistical savings.
By using RapidMiner and looking at a variety of factors (not just the previous month’s sales), the company was able to predict its upcoming month’s sales within three percent across multiple product lines.
This giant pharmaceutical firm was looking for customer feedback. It wanted to know what people liked about its products. Did people prefer the company‘s product over other products? Did these preferences develop and change over time? In addition, the company was legally required to report any adverse product reactions, so a connection to customers was doubly important.
This firm focused on collecting publicly available information with RapidMiner, primarily from the diabetic community, specialized diabetes forums, blogs and the major social networks. The information was in the form of millions of individual texts and posts per year, far more than could be reviewed by human eyes. Is this text about the company’s product? Is it about a competitor’s product? Is the post about the consumer’s desires about the product or is it from real experience? Once the appropriate texts were identified, RapidMiner’s sentiment analysis tools were used to determine whether each one was positive or negative.
In the area of aircraft maintenance, it is vital to be able to predict airplane component or equipment failures and maintenance needs in order to reduce costly downtime, avoid unplanned out of service times, and to optimize service crew schedules. With over 1,000 airplanes to be maintained, Lufthansa had hundreds of thousands of log entries, sensor data, error messages, and maintenance reports that needed to be evaluated in order to accurately predict & prevent failures.
Lufthansa uses the RapidMiner Data Science Platform to offer predictive analytics services to their customers. Using RapidMiner’s real-time analytics of time series data, feature extraction, machine learning for regression, classification, and frequent item set mining, on the available airplane and service data, they were able to develop accurate models for predicting when maintenance should be performed.
“The seamless integration of RapidMiner’s lightning fast data science platform and QlikView’s strong visualization capabilities provides a Customer Segmentation solution to drive revenue optimization.”
One of the most important challenges people are facing is customer segmentation due to multiple data sources and different business needs. Because IT has to prepare the data and modify the model to fit each business scenario manually—time and money is lost in process.
Connect, combine and analyze both structured and unstructured data coming from multiple datasources with RapidMiner. Create an automated segmentation system based on users specified parameters provided directly from QlikView dashboard. Use QlikView connection to enable users to trigger a workflow directly from QlikView and evaluate different models based on those parameters.
Modern Marketing Concepts, Inc. (MMC) is a global leader in the business-to-business marketing services industry, offering innovative marketing solutions across multiple industries, including the building trades and healthcare. Based in Binghamton, NY, MMC has over 25 years of experience changing the way its clients market and sell their products, with turnkey, full-service marketing applications and services that fine tune campaigns to optimize results.
“RapidMiner is extremely powerful, has the best operators, and can handle Big Data from wearables. It also allows us to rapidly prototype very sophisticated analytics, machine learning, and classification applications, saving significant time and money.”
Based in Stonington, Conn., Body Biolytics is focused on applying activity-recognition software to the sport, fitness and health industries. The company’s technology is field-proven, having been installed on over 40 U.S. Navy ships to keep a close watch on machinery health by collecting data from hundreds of on board sensors. Using predictive analytics on this data, the software predicts machinery failures, allowing maintenance crews to take corrective action in advance of any problems.
Established in 2012, with the project lead headquartered in Stuttgart, Germany, SustainHub provides a systematic and efficient approach to collect compliance and sustainability data for products and manufacturing processes through the supply chain, and integrates these into the internal systems and processes of companies. This leads to better management of supply chain data and sustainability data, and improves the eco-efficiency performance of product design and production.
Founded in 1998 and acquired by eBay in 2002, PayPal is the faster, safer way to pay and get paid online, providing simpler ways to send and receive money around the world. With 143 million active accounts in 193 markets and 26 currencies around the world, PayPal enables global commerce, processing more than 8 million payments every day.
For Han-Sheong Lai, Director of Operational Excellence and Customer Advocacy at PayPal and Jiri Medlen, Senior Text Analytics Specialist at PayPal DT, driving customer satisfaction and reducing customer churn are never ending challenging tasks. Han and Jiri knew that figuring out what drives product experience improvement without adequate knowledge of customer perspective and feedback is like “shooting in the dark,” hoping that opinion-based actions translate into tangible business improvement. By applying basic voice-of-the-customer-concepts and text analytics to customer feedback in over 60 countries worldwide, Han, Jiri and their team were able to identify, classify and count customers as “top promoters” and “top detractors,” according to their feedback verbatim.