What is data mining?
Why data mining is required?
Data mining Applications
Data mining in Retail Industry
Marketing
Risk Management
Fraud Detection
Customer Acquisition and Retention
ICT Role in 21st Century Education & its Challenges.pptx
Data Mining in Retail Industries
1. Data Mining In Retail
Industries
Presented By-
Rahul
Bca SemVI
23
2. Contents
What is data mining?
Why data mining is required?
Data mining Applications
Data mining in Retail Industry
Marketing
Risk Management
Fraud Detection
Customer Acquisition and Retention
3. What is Data mining?
Data mining refers to extracting or “mining”
knowledge from large amounts of data. Also
referred as Knowledge Discovery in Databases.
It is a process of discovering interesting knowledge
from large amounts of data stored either in
databases, data warehouses, or other information
repositories.
4. Why data mining is required?
Rapid computerization of businesses
produce huge amount of data
How to make best use of data?
A growing realization: knowledge
discovered from data can be used for
competitive advantage.
5. Data mining Applications
Data mining is an interdisciplinary field with wide
and diverse applications
There exist nontrivial gaps between data mining
principles and domain-specific applications
Some application domains
Financial data analysis
Retail industry
Telecommunication industry
Biological data and DNA analysis
6. Data mining in Retail Industry
Retail industry: huge amounts of data on sales,
customer shopping history, etc.
Applications of retail data mining
Identify customer buying behaviors
Discover customer shopping patterns and trends
Improve the quality of customer service
Achieve better customer retention and satisfaction
Enhance goods consumption ratios
Design more effective goods transportation and
distribution policies
7. Marketing
‘Market basket analysis’ is a marketing method used by many
retailers.
The study of retail stock movement data recorded at a Point of Sale
(PoS)—to support decisions on shelf-space allocation, store layout,
product location and promotion effectiveness.
Another marketing tactic employed by many retail stores is the use of
‘loyalty’ cards and coupons.
8. Risk Management
Retail organizations use data mining to understand
which products may be vulnerable to competitive
offers or changing customer purchasing patterns.
Data mining enables retailers to remain competitive
and reduce risks by helping them understand what
their customers are really doing.
Retailers can then target those customers who are
more likely to buy a certain brand or product.
9. Fraud Detection
Retail shrink because of dishonest employees.
Some super-markets use CCTV, along with data
mining, to enable retail loss prevention to expose
cashier stealing.
Loss of data, credit card fraud, duplicate payment
can be avoided with the help of data mining.
10. Customer Acquisition and Retention
Data mining helps in acquiring and retaining
customers in the retail industry.
Retail industry deals with high levels of
competition, and can use data mining to
better understand customers’ needs.
Retailer can study customers’ past purchasing
histories and know with what kinds of
promotions and incentives to target
customers.