a bank sends mailers to its customers offering a special deal on Personal Loans. In its previous campaign it got only about 9 positive response.
Objective How to target customers for increased conversion rate.
In other words the question to address is
what profile indicates a high-potential customer
3 XLMiner a quick tour
Past campaign data will be used to train the data mining model
This is called supervised learning in DataMining terms
Lets see how to build a model and use it for improving the response rate.
4 XLMiner Quick TourData description
Our past campaign data has the following customer attributes
Credit Card average annual spending
Did this customer accept past campaign offer
The last variable is the known outcome of the past campaign. Our Data Mining model will use this for Supervised Learning.
5 XLMiner Quick TourA view of the data
This is what the data looks like
The variable labeled as PersLoan is binary 0 means the customer was not interested in the Personal Loan. 1 means the customer was interested. 6 XLMiner Quick Tourthe Data Mining Process
Partition the data into Training Validation Partitions Fit the Model on Training Partition only Obtain results see if they look good enough Check if they are good for Validation data too! Study the outputs for validation data Try out several alternative models Choose and deploy the best model 7 XLMiner Quick TourStart the analysis
Lets get going with XLMiner.
Notice that XLMiner is as easy to use as Excel!
All we need to do is use the friendly menus. We follow just three simple steps to fit a model and see the outputs!
8 XLMiner Quick TourStep 1 Partition the data
Well create two partitions by choosing the records randomly.
The Training Partition will be used for fitting the model.
The Validation partition will be used for checking if the model gives a good fit for another piece of known data.
9 XLMiner Quick TourPartitioned Data
XLMiner creates a Partition Sheet that shows the data split into Two partitions.
Easy Hyperlinks on the Navigator facilitate viewing of either partition 10 XLMiner Quick TourStep 2 Fitting the Model
This is a Classification Problem where we want to predict customers as likely / not likely to take a Personal Loan.
Lets use one of the available techniques Classification Tree.
Later we can use other Classification techniques.
We select input (predictor) variables here and the outcome variable here 11 XLMiner Quick TourStep 2 Fitting the Model
The model fit guides us through easy wizard-like steps.
In these steps we choose technique-specific parameters and the output options.
In the end we click Finish to produce the results.
12 XLMiner Quick TourStep 3 Understanding the Outputs
The friendly Output Navigator lets us go over all the outputs.
The Summaries show us the classification error percentages i.e. how well the model is predicting Many other diagnostic outputs are available depending on options we choose. Other outputs (like the Tree here) will tell us the decision rules that the model is suggesting. 13 XLMiner Quick TourOutput 1 Validation Summary
First we look at how well the model predicted for the Validation data set
In the Training data where we already knew the outcome 156 will buy were predicted correctly and 38 wrongly. 1801 Wont buy were predicted correctly and merely 5 wrongly. Here are the corresponding error percentages. The errors are not very small but could still indicate a workable model. 14 XLMiner Quick TourOutput 2 the decision rule
Here is the Classification Tree that gives the easy-to-understand and implement Decision Rules
Cut-off points for different variables decide whether to go Left or Right 0 not likely to buy 1 likely to buy 15 XLMiner Quick Tourthe decision rule in table form
The same decision rule as shown visually can be converted into the table below. This is useful for implementing it in your information systems.
16 XLMiner Quick TourOutput 3 more details
Each technique (Classification Tree in this case) has additional helpful outputs
The example here shows the Prune Log how the percentage error reduced by pruning the tree
17 XLMiner Quick TourOutput 4 the Lift Chart
Lift tells us how much better the model did compared to a random targeting of customers. This is one of the most important outputs.
With our Tree model we get a much superior result. In less than 500 mailers sent to high probability customers we would get nearly 170 successes! If customers were targeted randomly we would expect this outcome. For instance 1000 mailers would probably yield less than 100 customers. 18 XLMiner Quick TourOutput 5 the Detailed report
The Validation data is scored in detail as shown below. Scoring means using the fitted model to classify each record of the data.
Probability of success is computed for each record. This is what helps XLMiner suggest selective records (customers) to target. Predicted values can be seen against the actuals here. 19 XLMiner Quick TourTry several techniques!
That was just one of the many techniques in XLMiner Classification Tree.
A typical Data Mining exercise involves several alternative approaches on the same data. This can be either with different techniques or with different parameters or both.
Comparing multiple approaches lets us assess which model to finally choose for implementation.
20 XLMiner Quick TourRich repertoire of techniques!
XLMiner supports a comprehensive array of supervised learning procedures
Multiple Linear Regression
Classification Regression Trees
k Nearest Neighbors
Naïve Bayes Classifier
21 XLMiner Quick TourRich repertoire of techniques!
... and several other features in Unsupervised Learning Data Reduction and Exploration
Principal Components Analysis
Self-organizing Maps (coming soon)
Affinity Market Basket Analysis
Here are some sample outputs from these methods
22 XLMiner Quick Toursample output - Dendrogram
Hierarchical Clustering produces a dendrogram an excellent visual representation of Cluster formation.
Height of the bars is a measure of dissimilarity in the clusters that are merging into one. Smaller clusters agglomerate into bigger ones with least possible loss of cohesiveness at each stage. 23 XLMiner Quick Toursample output cluster predictions
Cluster Analysis has many powerful uses like Market Segmentation. We can view individual records predicted cluster membership.
24 XLMiner Quick Toursample output BoxPlots
XLMiner supports powerful visualization. The example here shows BoxPlots of two variables.
Cluster 2 clearly shows higher Income Credit Card spend than Cluster 1. This is an excellent aid to characterizing the clusters 25 XLMiner Quick Toursample output Scatter Plots
Matrix Scatterplots in XLMiner give a visual insight into relationship among variables.
26 XLMiner Quick Toursample output Association Rules
For Market Basket Analysis XLMiner produces easy-to-read Association Rules
Rules are explained in simple English! Each rule tells us which offerings will go well together 27 XLMiner Quick Tour and thats not all!
XLMiner has handy utilities for Data handling
Missing data treatment
Transforming categorical data
Binning continuous data
Sampling from Databases
Scoring to Databases
28 XLMiner Quick TourXLMiner gt Versatility!
This was a quick demonstration of just a few things XLMiner can do.
It can do lots more. It is comprehensive in coverage like the best DM products around.
Get your free download for evaluation at www.xlminer.ncom
29 XLMiner Quick TourXLMiner gt Simplicity!
Daryl Pregibon had said Data Mining is Statistics at Scale and Speed.
Youll find that XLMiner is Statistics at Scale Speed and Simplicity!
If you know to use Excel you already know XLMiner. You can get started in minutes.
30 XLMiner Quick TourXLMiner gt Great Value!
Several comprehensive DM products are many times more expensive.
For exploring how Data Mining will work for you XLMiner provides a great start!
31 XLMiner Quick TourWhat others say
The American Statistician reviewed XLMiner along with other reputed products in the November 2003 issue
This is what it had to say
An easy to use an excellent inexpensive add-on that greatly expands the capabilities of Excel.
XLMiners documentation is remarkably good
32 XLMiner Quick TourMore Resources
For your initiation into Data Mining
Free evaluation download
Online Courses at www.statistics.com
Case Book in the making
Technical references on product website
33 XLMiner Quick TourThank you for viewing this Demo!
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