Data mining allows for the discovery of hidden patterns and relationships in large amounts of data.
Data mining uses powerful analytic technologies to quickly and thoroughly explore mountains of data, isolating the valuable, usable information — the business intelligence —
Ex. Data mining tells you which prospects are likely to become profitable customers and which are most likely to respond to your offer. ROI is increased by making offers to only those prospects likely to respond and become valuable customers.
Spyware is any technology that aids in gathering information about a person or organization without their knowledge. On the Internet (where it is sometimes called a spybot or tracking software ), spyware is programming that is put in someone's computer to secretly gather information about the user and relay it to advertisers or other interested parties. Often done via adware applications. Free spyware scan
What Business Problems does Data Mining Solve?
You can use data mining to solve almost any business problem that involves data, including:
Increasing business unit and overall profitability
Understanding customer desires and needs
Identifying profitable customers and acquiring new ones
Retaining customers and increasing loyalty
Increasing ROI and reducing costs on promotions
Cross-selling and up-selling
Detecting fraud, waste and abuse
Determining credit risks
Increasing Web site profitability
Increasing store traffic and optimizing layouts for increased sales
Monitoring business performance SPSS
Data Mining Definitions and Uses
Data mining refers to a wide range of techniques that look at underlying patterns or associations among elements within large data sets. These patterns are then used to form rules or guidelines for use in a wide range of marketing decisions. Ex. Insightful Miner demo*
Data mining tools can improve marketing management decisions such as:
segmentation and target marketing
improving sales force performance
customer relationship management (CRM)
and many others
CRISP-DM Figure: Phases of the CRISP-DM Process Model Cross Industry Standard Process for Data Mining: Project Overview
What Can be Done with Data Mining?
Through various algorithms , data mining software sorts through thousand of data points, organizes it, then summarizes complex relationships for the user.
Data mining software typically follows one of five different analytical approaches:
Forecasting via Trend Analysis, Reporting and OLAP
Reporting and Online Analytical Processing (OLAP)
Reporting (a.k.a. summary methods or baby stats) is one of the most basic, but extremely useful, techniques for data analysis.
Provides simple views of the data such as counts, sums, percentages, and averages.
Sample query: How many units did we sell last month?
OLAP (think multi-dimensional cross-tabulation) is useful because it provides “cubes” of “ reports” that can break down one variable by another.
Differs from traditional cross-tabs because it is interactive and you can “drill down” through the live reports to get more specific views of each cube (cell).
See SPSS example in class.
Traditional Cross-tab vs. OLAP Days per week * SUBHT Cross-tabulation Count 56 114 170 90 116 206 56 42 98 41 132 173 1 1 2 244 405 649 daily 2-3 times once Sunday 5 Total no yes SUBHT Total Days per week
The basic premise of associations is to find all relationships such that the presence of one set of items in a transaction implies other items, while controlling for extraneous factors
Ex. 75% of consumers who buy beer also buy corn chips; 26% of consumers who buy beer and corn chips also buy salsa
Easy to do simple data analyses to discover these relationships
Data mining software can simultaneously control for other variables that impact these relationships. Ex. Sale items, competitor actions, etc. to get true effects.
Classification or profile generation uses data to develop profiles of different groups.
Can be used for segmenting and targeting, market evaluation, product management, etc.
Typically uses historical data to form rules that define groups. Those rules are then applied to new data to find similar groups.
Ex. Based on past results, a “hot prospect” is a person who has an advanced degree, earns $150K or more, has made three online purchases over the last month, and has purchased computer related equipment within the past year. Find person who fits that profile and you have a good prospect.
This technique looks at purchases, or events, occurring in a sequence over time and tries to uncover patterns of behavior.
Greatly useful for tracking effects of promotional activities; also for work force allocation, inventory management, and pricing/valuation.
Ex., Through data mining, company notices that when a customer buys a new DVD player, 85% of them return within three months to purchase speakers.
Ex., a company with a brand new potato chip is trying to decide what time of the year to launch it. Might look for events, trends, etc.
Clustering will segment a database into subsets or clusters, creating a set of groups which have the maximum similarity within them and the maximum difference between them.
Allows for consideration of multiple variables simultaneously.
Great for building consumer segments, perceptual mapping, brand image assessment, etc.
Ex. A company gathers consumer opinions on 25 product attributes, then develops clusters based on those attributes
Web Miner * - data mining consultancy utilizing cluster analysis with pre-mined demographic information
Data Mining Tools/ Web Mining
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