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 Retail experience and e-commerce: Retailers study customer
behaviour, including browsing history and previous purchases,
to predict which products or content a customer is most likely to
choose. Sales data is also connected with local factors such as
weather conditions and events to improve stock control. In addition,

prices can be adjusted dynamically based on market demand and
competitor pricing.

 Financial experience and banking: Banks and financial institutions
use data analytics to detect suspicious transactions and prevent
fraud. It also helps in assessing a borrower’s credit risk by analysing
their past financial records and transaction history.

 Healthcare experience and problem-solving: Hospitals analyse

patient records, lifestyles and medical histories to predict the risk of
diseases. Similarly, AI technology can quickly scan large volumes of
medical data to match patients with suitable clinical trials, instead
of spending weeks or months on repeated searching. This supports
faster research and improved treatment outcomes.

 Social media and marketing: Brands use sentiment analysis to
examine online reviews and comments in order to understand public
opinion. User behaviour is closely monitored so that personalised
advertisements and content can be delivered, increasing engagement
and relevance.

 Tech and services for food: Machine learning is used by food
delivery services to estimate preparation and delivery times by
analysing order details, restaurant workload and time of day. This
helps improve efficiency and ensures faster, more reliable service for
customers.

 Business forecasting: Business forecasting uses data science to
examine historical data and market trends, helping organisations
predict future sales, demand and growth, which supports informed

decision-making, reduces risks, improves planning and guides
effective long-term business strategies overall.

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