Examples

Travel Industry

In the travel industry, every stage of the customer cycle contains data points that can be stored and analyzed by artificial intelligence. Patterns can be established and acted upon. These include qualitative information such as the motivation for travel and numerical data like the booking date, along with personal information from birthdates to primary language and marital status. Trawling through all of this and coming up with valuable insights can be a daunting task, but machine learning can analyze them with ease.

 

  • Revenue forecasting
  • Reducing delays
  • Advanced sentiment analysis
  • Lost luggage turnaround
  • Improved operational efficiency
  • Advanced travel offers
  • Optimal demand forecasting
  • Customer satisfaction and loyalty
  • Recommender systems
  • Fraud detection
  • Passenger & other travel data enrichment
Energy & Utilities Industry

Energy is a very special commodity. It is economically non-storable, and power system stability requires a constant balance between production and consumption. At the same time, electricity demand depends on weather (temperature, wind speed, precipitation, etc.) and the intensity of business and everyday activities (on-peak vs off-peak hours, weekdays vs. weekends, holidays, etc.). Machine Learning can train models and help energy companies to deal with a complex variety of variables and data.

 

 

  • Asset performance & reliability
  • Energy demand forecast
  • Maximize power generation
  • Uncover hidden energy patterns
  • Customized incentives
  • Energy theft detection
  • Appliance efficiency
  • Billing forecasting
  • Optimize energy programs
  • Prevent customer churn
  • Customer sentiment analysis
Healthcare Industry

Healthcare is a great target for machine learning to both greatly improve care delivery and optimize processes. While robots and computers will probably never completely replace doctors and nurses, machine learning is transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. ML is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care.

 

 

  • Patient risk migration
  • Hospitalization risk diagnosis
  • Follow-up visit frequency
  • Personalized treatment plans
  • Prescription error reduction
  • Anomaly device detection
  • Reduce unnecessary hospitalizations
  • Personalized medication co-pay
  • Design treatment plans
  • Diagnosis through images
  • Medication management
  • Increased drug effectiveness
  • Healthcare fraud prevention
  • Patient sentiment analysis
Financial Services

Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for Machine Learning. Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and the development of accessible machine learning tools, such as ML for z/OS.

 

  • Customer risk scoring
  • Automated loan underwriting
  • Credit monitoring
  • Product recommendations
  • Planning assistance
  • Customized withdrawal limits
  • Portfolio tax optimization
  • Spending patterns
  • Credit increase worthiness
  • Customer retention
  • Fraud & ID theft detection
  • Identity management
  • Sentiment & news analysis
  • Spending impact influencers
  • Risk detection in FS
  • Documentation review
Manufacturing Industry

The manufacturing industry today is experiencing a never seen increase in available data. These data compromise a variety of different formats, semantics, quality, e.g. sensor data from the production line, environmental data, machine tool parameters, etc. Through ML the manufacturing industry can benefit from the increased data availability for quality improvement initiatives, cost estimation and/or process optimization, etc., so that it can handle the high dimensionality, complexity and dynamics involved.

 

 

  • Responsive machines
  • Anomaly reduction
  • Increased production capacity
  • Internal defect reduction
  • Accelerated price determination
  • Better integrated process flow
  • Improving preventative MRO
  • OEE improvements
  • Quality production forecast
  • Demand forecast accuracy
  • Optimized product customization
Retail Industry

As consumers increasingly reveal their shopping habits online, retailers can access social media, purchase history, consumer demand and market trends to better understand their customers, maximize spending and encourage repeat purchases. In the hands of forward-thinking retailers, the possibilities for advanced machine learning are limitless, from sourcing, buying and supply chain all the way to marketing, merchandising and customer experience, retailers can make significant improvements by deploying machine learning solutions.

 

 

  • New store locations
  • Shelf, store and package optimization
  • Seasonal planning & forecasting
  • Consumer trends
  • Optimal product blend
  • Personalized promotions
  • Inventory forecasting
  • Promotional strategies
  • New label products & product categories
  • Price optimization
  • High-end purchase anomalies
  • Product lifecycle

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