lecture 8

04/10/23 01:27 PM Ibrahim Elbeltagi Info 2007 1

Data Mining and Customer Relationship Management

“It is now conventional wisdom among marketers, and IT personnel who support them, that investments in customer data have

a demonstrable return” said Phillip Russom, director of data warehousing and Business Intelligence, Hurwits Group


Styles of data miningPredictive model

The virtuous cycle of data miningImplementing Data Mining for Better CRM

Outline


Two Style of Data Mining

Directed data mining: is a top down approach, used when we know what we are looking for.

Undirected data mining: is a bottom up approach that lets the data speak for itself.


Sometimes we do not care how the model works. It is a black box and we just want the best prediction possible.

Sometimes we want to use the model to gain insight into the data. We need to understand how the model works; it is more like a semitransparent box.

Data mining uses both black box models and semitransparent box.


Predictive Model

Prediction model answer questions such as: Who is likely to respond to to our next offer, based on the history of previous marketing campaigns?

What is right medical treatment, based on past experience?

Which machine is most likely to be the next one to fail?

Which customers is likely to leave in the next six months?

What transactions are likely to be fraudulent, based on known examples of fraud?


The Virtuous Cycle of Data Mining

The virtuous cycle consist of four major business process Identifying the business problemTransforming data into actionable resultsActing on the resultsMeasuring the results


Identify the Business Problem

Important that technical people understand what the real business needs are Do this by talking to domain experts: the people who understand the business Business people need to be kept informed of the development, so that they may make continuing contributions to the project and the focus remains on the business needs It is important to think outside the confines of domain experts' knowledge to understand the real problem



Business people's expertise allows you to answer the following questions:

Is the data mining effort necessary? Should we focus on a particular segment or subgroup? What are the relevant business rules? What do they (the business people) know about the data? What do their intuition and experience say is important?



Checking the opinion of domain experts:

The data can be used to check that the experience and intuition of domain experts is correct

Example: If a domain expert believes that the best customer is aged 24 to 31 with at least one university degree, we can check to see if this is supported by the data.


Transforming Data into Actionable Results



Identifying and obtain dataThe right data is often whatever is available, reasonably clean, and accessible Data must meet the requirements for solving the business problem Data must be as complete as possible When doing predictive modelling the data needs to be complete enough that we can determine the outcome of what we are modelling



Validate, explore and clean the dataIs there any missing data and will this be a big problem? Are the field values within legal bounds? Are the field values reasonable? Are the distributions of individual fields explainable? Data fields which are not used are often inaccurate compared to critical data fields



Transport the data to the right granularity

Granularity is the level of the data that is being modelled

Some data mining algorithms work on individual rows of data, so all data describing a customer must be in a single row



Add derived variables

Derived variables are calculated based on combinations of other values inside the data

Prepare the model set

Model set: data used to build the data mining models

The model set can be divided into training, test, and evaluation sets

Choose the modelling technique and train the model Check performance of the models


Acting on the Results

Insights: new facts learned during modelling may lead to insights about the customers and about the business One-time results: results may be focused on a particular activity and that activity should be carried out Remembered results: information in the results should be accessible through a data mart or a data warehouse Periodic predictions: periodically score customers to determine what ongoing marketing efforts should be Real-time scoring: model may be incorporated into another system Fixing data: may have to fix data problems that have been uncovered


Implementing Data Mining for Better CRM

Data mining provides companies with the ability to specifically target customer segments and their respective needs.



Identifying the customer: there are three levels of customersTransaction levelAccount levelComplete customer



The development and implementation of a successful data mining solution can be broken down into 5 steps:Setting goalsData collectionData preparationAnalysis and predictionMeasurement and feedback



Goals

Customer profiling and segmentation

Retention and attrition

Risk avoidance

Cross-sell

Shopping patterns


Data Collection

Types of data

Which data?




Data preparationData qualityData preparation


Analysis and prediction

Building the model

Testing the model

Score



Measurement and feedback

How well your campaign perform?

Are groups with the highest predicted likelihood respond at a significantly higher rare than the average?

Make sure that this information is returned as a component of your promotional history table in your database and used as learning experience.


lecture 8

Documents

customer data

data needs

right data

accessible data

data mart

missing data

data problems

business problem data