- In this lecture, we'll focus on insight applications. These involve analyzing large amounts of data, including unstructured data such as text or images, to discern patterns or make predictions. An example can be found at Intel, which uses machine learning to improve sales effectiveness and boost revenue. They developed a predictive algorithm that automatically classifies customers into categories that are likely to have similar needs or buying patterns. System prioritizes sales efforts and tailors promotions to particular customers.
Company expects this approach to result in $20 million in additional revenue when rolled out globally. Another example is a North American bank that uses a natural language processing system to track and understand what consumers are saying about the bank and its competitors online. Automatically identifies salient topics of consumer chatter and the sentiments surrounding those topics. The insights delivered by the system influence decisions on setting fees, on offering customer perks, and how customer service reps should respond to certain customer inquiries about services and fees, and it helps improve the bank's marketing and customer service.
A third example, in health care, can be found at Aetna, which developed a machine learning application focused on metabolic syndrome, a condition that significantly increases the risk of developing heart disease, stroke, and diabetes. Using data from claims and biometric measures for 37,000 Aetna members, they learned models that could predict patient risk of developing metabolic syndrome, predict the probability of developing any of the five conditions associated with the disorder, and could determine which medical interventions were most likely to improve a specific individual's health outlook.
These insights enable the company to improve their care management programs, to improve the health of patients, and reduce the cost of caring for them. Insight applications make entirely new kinds of analysis possible. We know a start-up company, for instance, that uses computer vision to analyze satellite photos of parking lots to predict retail store financial performance. The benefits of insight applications are better and faster decisions that can improve operating and strategic performance. Where can you find opportunities for insight applications in your organization? Well, look where you might have large data sets that have not yet been fully analyzed or unstructured data sets that couldn't have been analyzed using traditional techniques, and look for processes where the value of improved performance is high.
Summing up, insight applications involve analyzing large amounts of data and using the analysis to discern patterns or make predictions. Machine learning is a key cognitive technology here. The benefits can be better decisions and better operational performance.
- Artificial intelligence explained
- Cognitive technologies explained
- Supervised, unsupervised, and reinforcement learning
- Machine learning models and algorithms
- Language, speech, and visual processing
- Business applications of cognitive tech
- The impact of cognitive technologies at work
- Future of cognitive technologies