The document presents an overview of data mining in the retail industry, explaining its definition, significance, and various applications such as marketing, risk management, fraud detection, and customer acquisition. Data mining helps retailers analyze customer behavior, optimize services, and enhance marketing strategies to maintain competitiveness. It emphasizes the transitional role of data mining in managing vast amounts of consumer data to drive business decisions and improve customer satisfaction.

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.