Algorithm

Data Mining Demo for Marketing vertical: How to create a Targeted mailing list?

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Tools I’ll be using for the Demo:

Excel 2010

SQL Server 2012 (specifically SQL Server Analysis Services)

Excel Add-in for Excel.

Sample data-set that comes with the excel add-in

Scenario:

Marketing Department needs to create Targeted Mailing list.

What data do we need?

To create a Targeted mailing list – we’ll need a historical data-set of customer purchase history

What will we do with the data?

Based on the historical data-set, we’ll be able to find “patterns” in the past consumer behavior. E.g. A single male going to college living in Europe is likely to buy a bike. And the using these patterns – we would then classify NEW customers.

Technically, we’ll be using the classification method using the Microsoft’s decision Tree algorithm

(Read the difference between classification and clustering)

Let’s get in action!

STEP 1: Build a Model

Data Mining Tab > click on classify:

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Follow the steps:

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Select the data:

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In this case, since we want to predict the likelihood of buying a bike – our column to analyze is BikeBuyer

 

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For the Demo, I am going to just leave it default. There are “optimization” steps that you can do but for the demo I am going to keep it super simple

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Name the model:

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The Model has been created!

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STEP 2: Query the MODEL to predict the likelihood of bike purchase of a new customer

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Select the model:

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Select the data:

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Specify the columns that would be used in predicting the likelihood:

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Add the column that will have the “predicted value”

 

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And example of Data Mining Expressions (DMX):

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For the demo, I am just going to add the column to the existing table:

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Yay! Here’s our Targeted Mailing list – see the last column:

Screenshot 1

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Screenshot 2:

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Now what?

Marketers can now send “coupons” to ONLY those people who are most likely to buy a bike! And so that’s how you create a targeted mailing list using the Excel Data Mining add-in.

Related articles

Machine Learning VS. Data Mining

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For the Past couple of months, One of the things that I have thought about is “What is the Difference Between Machine Learning & Data Mining”. I have Studied Data Mining and Advanced Data Mining concepts at both Undergraduate and Graduate level and recently I started learning about Machine Learning via Coursera.org  – I was curious to know the difference between the two similar/inter-related fields. After, spending time understanding what Machine Learning is – Here’s what I am thinking:

When I learned Data Mining – The focus was on Taking a Data-set and using (more than one) Algorithm(s) to detect Patterns in the data-set. I am studying machine learning – Here, we’re asked to write algorithms (and build models). So To me, Data Mining seems to be deal with practical aspects of putting Machine Learning algorithms to use.

When I took Data Mining courses – I didn’t write algorithms. But learned what different Data Mining Algorithms can do and what kind of patterns each algorithm helps us find. In machine learning class, my focus is to learn how to write the algorithms (build the model) and optimize it so that it can predict well.

Also, in machine learning the goal is clear – the questions are mostly like “Build a model from Past Data that predicts X “. whereas I remember, For our Graduate Level class, My professor gave our Team a data-set of “fatal accident data” and said “Go play with it!”

These were my experiences. What are your experiences with Data Mining, Machine Learning – and how do you differentiate between these two fields which are similar in more than one ways?

Related articles

Data Mining: Classification VS Clustering (cluster analysis)

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For someone who is new to Data mining, classification and clustering can seem similar because both data mining algorithms essentially “divide” the datasets into sub-datasets; But there is difference between them and this blog-post, we’ll see exactly that:

CLASSIFICATIONCLUSTERING
  • We have a Training set containing data that have been previously categorized
  • Based on this training set, the algorithms finds the category that the new data points belong to
  • We do not know the characteristics of similarity of data in advance
  • Using statistical concepts, we split the datasets into sub-datasets such that the Sub-datasets have “Similar” data
Since a Training set exists, we describe this technique as Supervised learningSince Training set is not used, we describe this technique as Unsupervised learning
Example:We use training dataset which categorized customers that have churned. Now based on this training set, we can classify whether a customer will churn or not.Example:We use a dataset of customers and split them into sub-datasets of customers with “similar” characteristics. Now this information can be used to market a product to a specific segment of customers that has been identified by clustering algorithm

If you want to learn about Data Mining, check out the “free Book in PDF format: Mining the massive data-sets”.