CRM - Data mining Perspective

1. CRM - Data mining Perspective

2. Predicting Who will Buy Here are five primary issues that organizations need to address to satisfy demanding consumers: • Retaining customers and preventing them from defecting to the competition • Determining which products and services to bundle together to increase customer profitability • Attracting and retaining profitable customers • Treating customers as individuals • Implementing technology solutions that will achieve corporate objectives

3. The Challenge The following statistics relating to customer relationships reflect the challenges associated with attracting and retaining customers and how important this objective is to suppliers: • Most Fortune 50 companies lose 50 percent of their customers in five years. • It costs seven to ten times more to acquire a new customer than it does to retain an existing customer. • A 50-percent increase in retention rate can increase profits 25 to 125 percent. • Up to 50 percent of existing customer relationships are not profitable. • The average company communicates four times per year with its customers and six times per year with its prospects.

4. CRM Analytics in Data Mining • The CRM analytics model is an earlier concept that has evolved to meet modern-day requirements. • Analytical CRM is the mining of data and the application of mathematical, and sometimes common-sense, models to better understand the consumer. • By extrapolating useful insights into market and customer behaviors, companies can adjust business rules and react to customers in a relevant, personalized manner. Analytics can be derived through several different channels, including: • The Internet • Retail point of purchase • Direct marketing activities

5. Mine the Data • Typically the easiest and shortest phase, this step involves applying statistical and AI tools to create mathematical models. Data mining typically occurs on a server separate from the data warehousing and other corporate systems. • In a data mining environment, data warehouse, query generators, and data interpretation components are combined with discovery-driven systems to provide the capability to automatically reveal important yet hidden data. The following tasks need to be completed to make full use of data mining: • Create prediction and classification models • Analyze links • Segment databases • Detect deviations

6. The Technology of CRM Highlights Four major areas of technology contribute to a successful CRM project: • Data warehousing • Database management systems • Data mining • Business analysis software

7. Examples of Applications of Data Mining via Relationships and Patterns • Retail / Marketing • Identifying buying patterns of customers • Finding associations among customer demographic characteristics • Predicting response to mailing campaigns • Market basket analysis

8. Examples of Applications of Data Mining via Relationships and Patterns • Banking • Detecting patterns of fraudulent credit card use • Identifying loyal customers • Predicting customers likely to change their credit card affiliation • Determining credit card spending by customer groups

9. Examples of Applications of Data Mining via Relationships and Patterns • Insurance • Claims analysis • Predicting which customers will buy new policies. • Medicine • Characterizing patient behaviour to predict surgery visits • Identifying successful medical therapies for different illnesses.

10. Examples of Applications of Data Mining via Relationships and Patterns • Customer profiling: characteristics of good customers are identified with the goals of predicting who will become one and helping marketers target new prospects. • Targeting specific marketing promotions to existing and potential customers offers similar benefits. • Market-basket analysis: With Data Mining, companies can determine which products to stock in which stores, and even how to place them within a store.

11. Examples of Applications of Data Mining via Relationships and Patterns • Customer Relationships Management-Determines characteristics of customers who are likely to leave for a competitor, a company can take action to retain that customer because doing so is usually for less expensive than acquiring a new customer. • Fraud detection- With Data Mining, companies can identify potentially fraudulent transactions before they happen.

12. Predictive Modelling - Value Prediction • Used to estimate a continuous numeric value that is associated with a database record. • Uses the traditional statistical techniques of linear regression and non-linear regression. • Relatively easy-to-use and understand.

13. Predictive Modelling - Value Prediction • Linear regression attempts to fit a straight line through a plot of the data, such that the line is the best representation of the average of all observations at that point in the plot. • Problem is that the technique only works well with linear data and is sensitive to the presence of outliers (i.e.., data values, which do not conform to the expected norm).

14. Non-Linear Value Prediction • Database Segmentation • Link Analysis • Deviation Detection

15. Database Segmentation • Aim is to partition a database into an unknown number of segments, or clusters, of similar records. • Uses unsupervised learning to discover homogeneous sub-populations in a database to improve the accuracy of the profiles.

16. Database Segmentation • Less precise than other operations thus less sensitive to redundant and irrelevant features. • Sensitivity can be reduced by ignoring a subset of the attributes that describe each instance or by assigning a weighting factor to each variable. • Applications of database segmentation include customer profiling, direct marketing, and cross selling.

17. Example of Database Segmentation Using A Scatter Plot

18. Example of Database Segmentation Using A Visualization

19. Link Analysis • Aims to establish links (associations) between records, or sets of records, in a database. • There are three specializations • Associations discovery • Sequential pattern discovery • Similar time sequence discovery • Applications include product affinity analysis, direct marketing, and stock price movement.

20. Link Analysis - Associations Discovery • Finds items that imply the presence of other items in the same event. • Affinities between items are represented by association rules. • e.g. ‘When customer rents property for more than 2 years and is more than 25 years old, in 40% of cases, customer will buy a property. Association happens in 35% of all customers who rent properties’.

21. Link Analysis - Sequential Pattern Discovery • Finds patterns between events such that the presence of one set of items is followed by another set of items in a database of events over a period of time. • e.g. Used to understand long term customer buying behaviour.

22. Link Analysis - Similar Time Sequence Discovery • Finds links between two sets of data that are time-dependent, and is based on the degree of similarity between the patterns that both time series demonstrate. • e.g. Within three months of buying property, new home owners will purchase goods such as cookers, freezers, and washing machines.

23. Deviation Detection • Relatively new operation in terms of commercially available data mining tools. • Often a source of true discovery because it identifies outliers, which express deviation from some previously known expectation and norm.

24. Deviation Detection • Can be performed using statistics and visualization techniques or as a by-product of data mining. • Applications include fraud detection in the use of credit cards and insurance claims, quality control, and defects tracing.