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DATA MINING IN
RETAIL INDUSTRY
1
►INTRODUCTION
►APPLICATION OF RETAIL INDUSTRY
►MAJOR PROBLEM OF RETAIL INDUSTRY
►RISK MANAGEMENT
►FRAUD DETECTION
►CONCLUSION
2
INTRODUCTION
 Retail industry collects large amount of data on sales and
customer shopping history.
 The quantity of data collected continues to expand rapidly,
especially due to the increasing ease, availability and
popularity of the business conducted on web, or e-
commerce.
 Retail industry provides a rich source for data mining.
 Retail data mining can help identify customer behavior,
discover customer shopping patterns and trends, improve
the quality of customer service. 3
Applications of 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 4
EXAMPLE FOR RETAIL: CRM
(Customer Relationship Management)
 Customer Relationship Management (CRM) is a business
philosophy involving identifying, understanding and better
providing for your customers while building a relationship with
each customer to improve customer satisfaction and maximize
profits. It’s about understanding and responding to customers’
needs.
 To manage the relationship with the customer a business needs
to collect the right information about its customers and organize
that information for proper analysis and action. It needs to keep
that information up-to-date, make it accessible to employees,
and provide the knowhow for employees to convert that data
into products better matched to customers’ needs.
5
6
MAJOR POBLEMS OF RETAIL INDUSTRY
 Employee turnover
Employee turnover refers to the number or percentage of workers who leave an
organization and are replaced by new employees.
► Auditing
Financial auditing is the process of examining an organization's financial records to
determine if they are accurate and in accordance with any applicable regulations, and laws.
 Economic Challenges
Economic issues facing the world economy include prospects for growth, energy and the
environment, labor issues and the impact of new technologies.
 Technology
Speed and efficiency are expected of today’s retail businesses.
7
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
8
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
9
CONCLUSION
 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.
10
THANK YOU
11

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Data mining in retail industry

  • 2. ►INTRODUCTION ►APPLICATION OF RETAIL INDUSTRY ►MAJOR PROBLEM OF RETAIL INDUSTRY ►RISK MANAGEMENT ►FRAUD DETECTION ►CONCLUSION 2
  • 3. INTRODUCTION  Retail industry collects large amount of data on sales and customer shopping history.  The quantity of data collected continues to expand rapidly, especially due to the increasing ease, availability and popularity of the business conducted on web, or e- commerce.  Retail industry provides a rich source for data mining.  Retail data mining can help identify customer behavior, discover customer shopping patterns and trends, improve the quality of customer service. 3
  • 4. Applications of 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 4
  • 5. EXAMPLE FOR RETAIL: CRM (Customer Relationship Management)  Customer Relationship Management (CRM) is a business philosophy involving identifying, understanding and better providing for your customers while building a relationship with each customer to improve customer satisfaction and maximize profits. It’s about understanding and responding to customers’ needs.  To manage the relationship with the customer a business needs to collect the right information about its customers and organize that information for proper analysis and action. It needs to keep that information up-to-date, make it accessible to employees, and provide the knowhow for employees to convert that data into products better matched to customers’ needs. 5
  • 6. 6
  • 7. MAJOR POBLEMS OF RETAIL INDUSTRY  Employee turnover Employee turnover refers to the number or percentage of workers who leave an organization and are replaced by new employees. ► Auditing Financial auditing is the process of examining an organization's financial records to determine if they are accurate and in accordance with any applicable regulations, and laws.  Economic Challenges Economic issues facing the world economy include prospects for growth, energy and the environment, labor issues and the impact of new technologies.  Technology Speed and efficiency are expected of today’s retail businesses. 7
  • 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 8
  • 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 9
  • 10. CONCLUSION  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. 10