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 operations
 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.