Data mining or data discovery is a concept that businesses use to acquire and analyze specific information about their market – the relationship between behaviors and variables – and use it to their best advantage. Mining is successfully applied in sectors such as banking, medical/pharma, insurance, retail, and telecommunication.
CNN recently reported on a business that successfully used data mining to boost revenue by up to 300%! Fashion retailer Sway made use of predictive analysis software (data mining software) to boost the results of its email marketing campaign, which was proving futile because only a small proportion of customers opened their emails. Analyzing customer behavior using mined data and planning the email delivery schedule accordingly increased the response rate from 20% to 40%.
One classic example of success with data mining is that of a large North American chain of supermarkets. They identified customer buying patterns using Oracle software. Results showed that men who buy diapers on Thursdays and Saturdays tend to pick up some beer too. Also, they found that people did most of their grocery shopping on Saturdays and that by Thursdays, they bought fewer items. So, the retailer came to the conclusion that buying beer on Thursdays was to stock up for the upcoming weekend. So they moved the diaper and beer displays closer to each other and made sure that both products were sold at their full price on Thursdays.
Main Steps in the Data Mining Process
- Extract, transform, and load transaction data
- Systematic data storage in multidimensional database system
- Allowing data access to authorized professionals
- Analyzing data using application software
- Presenting data in useful formats such as graphs or tables
Outsourcing companies offer data mining services for all types of businesses. They gather basic information about customers, assimilate buying, and track demographics based on customer/business location. Mining services are offered operational or transactional data such as sales, cost, payroll, inventory, and accounting, non – operational data like forecast, sales, and economic data, and meta data like data dictionary definitions and logical database design
Some Applications of Data Mining
In Insurance Sector
- Identification of potential buyers
- Detection of risky customers
- Detection of fraudulent behavior
In Retail / Marketing Sector
- Finding buying behavior patterns
- Detection of associations among customer characteristics
- Prediction of the probability that clients answer to mailing
In Medical / Pharma Sector
- Computer Assisted Diagnosis
- Characterization/prediction of patient’s response to product dosage
- Identification of successful medical therapies
In Banking / Finance Sector
- Detecting usage patterns of fraudulent credit cards
- To identify hidden relations between different financial indicators
- Identification of stocks trading rules from historical market data
- Risk management due to attribution of loans using scorecards
Target, Grasshopper, and Walmart are examples of other firms that have successfully used predictive analysis. Consumer behavior is constantly shifting with the changing economic scenario. With businesses facing budget constraints, small and medium – sized businesses with budget constraints are seeking ways to optimize revenue and streamline their marketing investments. Professional data mining services have immense significance are crucial for better targeted marketing strategies, customer retention and improved ROI.