We understand that Predictive Techniques will represent a significant investment. And it might be daunting to your team to emerge in concepts they may not fully understand. 

Engaging your people into the process is crucial, and finding the right way means to understand concepts crystal clear. This is a key factor to make your project a success.

Our Master Classes and Workshops, designed specifically to your Projects, can turn complex concepts like Predictive Analytics, Machine Learning or Artificial Intelligence techniques into accessible ones, people will adopt without fear.

We are fully prepared to motivate and engage your teams on your projects. The following are some examples we can use to engage and prepare your people to be a predictive savvy Team.

Case 1:

Predicting patients future needs: knee and hip replacement at Allianz

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Allianz, one of the top health insurance companies worldwide, is constantly designing new services to keep its high quality promise. One of these top quality program was the 'Knee-Hip Program.' It created an exclusive ecosystem around the patient that covered all the patients' needs before, during and after knee or hip replacement surgery. One of the greatest challenges the company had was ensuring that patients knew about these services exactly when they needed them. Allianz’s regular communication and advertising was not achieving this, so many potential customers remained unaware of the service's existence. The company designed a predictive model to anticipate which customers would, in the near future, need a knee or hip replacement. How could Allianz identify a patients' future needs? How did the company design the predictive model? What was the outcome of this new tool to attract customers to the 'Knee-Hip Program'?

Case 2:

Predicting customer tastes with Big Data at GAP

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CEO Art Peck was eliminating his creative directors for The Gap, Old Navy, and Banana Republic brands and promoting a collective creative ecosystem fueled by the input of big data. Rather than relying on artistic vision, Peck wanted the company to use the mining of big data obtained from Google Analytics and the company's own sales and customer databases to select the next season's assortment. Peck was betting that intelligence fueled by big data could outperform a fashion industry creative director at predicting the future fashion trends and tastes of consumers.

Case 3:

Data Analytics at DBS Group Audit

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The case reveals the data analytics initiatives at DBS Group Audit. Group Audit was used to assess the riskiness of the bank's branches based on seven attributes derived from the auditors' collective wisdom. The results could sometimes be misleading and inaccurate. To revamp this process, a machine-learning predictive modelling technique was introduced, and successfully correlated more than 130 risk-related attributes. In 2014, DBS Group Audit and A*STAR's Institute for Infocomm Research (I2R) reached an agreement to set up a joint lab, leveraging the research institute's capabilities in developing innovative products and services. The application of data analytics on risk profiling practices at DBS Group Audit is an anchor point for the bank’s vision of being predictive in risks. The bank is motivated to bring this data analytics initiative to other areas as well. Through this case, the participants will have an opportunity to learn the concept of data analytics and how the financial industry can leverage on data analytics, especially for auditing purpose, to deliver effective outcomes. This case would also be useful in understanding internal auditing concepts, such as risk profiling practices and reporting.

Predicting customer churn at QWE

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The VP of customer services for a successful start-up QWE wants to proactively identify customers most likely to cancel services or "churn." He assigns the task to one of his associates and provides him with data on customer behavior and his intuition about what drives churn. The associate must generate a list of the customers most likely to churn and the top three reasons for that likelihood.