The document discusses the significance of predictive solutions in retail, emphasizing the role of big data and machine learning in analyzing customer behavior and making informed business decisions. It highlights the importance of predicting product obsolescence and employee attrition using advanced algorithms, which can help companies adapt to changing market demands and workforce dynamics. Overall, it emphasizes the integration of technology in understanding trends and optimizing operations.

1. Predictive Solutions
By

2. Predicting trends
Customer behavior data has
become enormously vital for
retail companies in making
profitable business decisions.
Big data and machine
learning is used extensively
by retail marketers to
optimize analytics, provide
real-time insights, and to
predict a typical customer
behavior.

3. Obsolescence prediction
It is crucial to know when
the demand for a product
or service becomes
obsolete due to various
technological
advancements. As most
utility products become
connected to the internet,
there is steady data
developing on product
performance and
obsolescence of previous
products.

4. Attrition Prediction
• AI and machine learning
algorithms have offer
predictive models like
decision trees or random
forest methodologies to
classify and understand the
complexities associated
with decision making in
employee attrition. Several
parameters are taken into
consideration to begin
taking preventive measures
for employee attrition.