IBM SPSS 19: Marketing Analytics and RFM

What is RFM Analysis?

Recency Frequency Monetization is basically a technique to classify your entire customer list. You may be a retail player with thousands of customers or a enterprise software seller with only two dozen customers.

RFM Analysis can help you cut through and focus on the real customer that drives your profit.

As per Wikipedia

http://en.wikipedia.org/wiki/RFM

RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail.

RFM stands for

  • Recency – How recently a customer has purchased?
  • Frequency – How often he purchases?
  • Monetary Value – How much does he spend?

To create an RFM analysis, one creates categories for each attribute. For instance, the Recency attribute might be broken into three categories: customers with purchases within the last 90 days; between 91 and 365 days; and longer than 365 days. Such categories may be arrived at by applying business rules, or using a data mining technique, such asCHAID, to find meaningful breaks.

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Even if you dont know what or how to do a RFM, see below for an easy to do way.

I just got myself an evaluation copy of a fully loaded IBM SPSS 19 Module and did some RFM Analysis on some data- the way SPSS recent version is it makes it very very useful even to non statistical tool- but an extremely useful one to a business or marketing user.

Here are some screenshots to describe the features.

1) A simple dashboard to show functionality (with room for improvement for visual appeal)

2) Simple Intuitive design to inputting data3) Some options in creating marketing scorecards4) Easy to understand features for a business audiences

rather than pseudo techie jargon5) Note the clean design of the GUI in specifying data input type6) Again multiple options to export results in a very user friendly manner with options to customize business report7) Graphical output conveniently pasted inside a word document rather than a jumble of images. Auto generated options for customized standard graphs.8) An attractive heatmap to represent monetization for customers. Note the effect that a scale of color shades have in visual representation of data.9) Comparative plots placed side by side with easy to understand explanation (in the output word doc not shown here)10) Auto generated scores attached to data table to enhance usage. 

Note here I am evaluating RFM as a marketing technique (which is well known) but also the GUI of IBM SPSS 19 Marketing Analytics. It is simple, and yet powerful into turning what used to be a purely statistical software for nerds into a beautiful easy to implement tool for business users.

So what else can you do in Marketing Analytics with SPSS 19.

IBM SPSS Direct Marketing

The Direct Marketing add-on option allows organizations to ensure their marketing programs are as effective as possible, through techniques specifically designed for direct marketing, including:

• RFM Analysis. This technique identifies existing customers who are most likely to respond to a new offer.

• Cluster Analysis. This is an exploratory tool designed to reveal natural groupings (or clusters) within your data. For example, it can identify different groups of customers based on various demographic and purchasing characteristics.

• Prospect Profiles. This technique uses results from a previous or test campaign to create descriptive profiles. You can use the profiles to target specific groups of contacts in future campaigns.

• Postal Code Response Rates. This technique uses results from a previous campaign to calculate postal code response rates. Those rates can be used to target specific postal codes in future campaigns.

• Propensity to Purchase. This technique uses results from a test mailing or previous campaign to generate propensity scores. The scores indicate which contacts are most likely to respond.

• Control Package Test. This technique compares marketing campaigns to see if there is a significant difference in effectiveness for different packages or offers.

Click here to find out more about Direct Marketing.

Clustering Business Analysts and Industry Analysts

In my interactions with the world at large (mostly online) in the ways of data, statistics and analytics- I come across people who like to call themselves analysts.

As per me, there are 4 kinds of analysts principally,

1) Corporate Analysts- They work for a particular software company. As per them their product is great and infallible, their code has no bugs, and last zillion customer case studies all got a big benefit by buying their software.

They are very good at writing software code themselves, unfortunately this expertise is restricted to Microsoft Outlook (emails) and MS Powerpoint ( presentations). No they are more like salesmen than analysts, but as Arthur Miller said ” All salesmen (person) are dreamers. When the dream dies, the salesman (person) dies (read transfers to bigger job at a rival company)

2) Third -Party Independent Analsyst- The main reason they are third party is they can not be tolerated in a normal corporate culture, their spouse can barely stand them for more than 2 hours a day, and their Intelligence is not matched by their emotional maturity. Alas, after turning independent analysts, they realize they are actually more dependent to people than before, and they quickly polish their behaviour to praise who ever is sponsoring their webinar,  white paper , newsletter, or flying them to junkets. They are more of boutique consultants, but they used to be quite nifty at writing code, when younger, so they call themselves independent and “Noted Industry Analyst”

3) Researcher Analysts- They mostly scrape info from press releases which are mostly written by a hapless overworked communications team thrown at a task at last moment. They get into one hour call with who ever is the press or industry/analyst  relations honcho is- turn the press release into bullet points, and publish on the blog. They call this as research Analysts and give it away for free (but actually couldnt get anyone to pay for it for last 4 years). Couldnt write code if their life depended on it, but usually will find transformation and expert somehwere in their resume/about me web page. May have co -authored a book, which would have gotten them a F for plagiarism had they submitted it as a thesis.

4) Analytical Analysts- They are mostly buried deep within organizational bureaucracies if corporate, or within partnerships if they are independent. Understand coding, innovation (or creativity). Not very aggressive at networking unless provoked by an absolute idiot belonging to first three classes of industry analyst. Prefer to read Atlas Shrugged than argue on business semantics.

Next time you see an industry expert- you know which cluster to classify them ;)

Image Citation-

http://gapingvoidgallery.com/

Creating 3D Graphs with Data in R

Creating 3D graphs  in a 3d scatterplot is a 2 minute task in R using the woderful R Commander GUI. You can see an example video-

I loaded R,                                                                                                                                               then loaded the GUI,                                                                                                                           inputted data (from an attached package) but you can input data from a csv,           then went to Graphs- 3D ScatterPlot.

Here is the result-

and here is the video.

Not bad for 2 minutes of clicking a GUI.

Here is the auto generated code by R Commander.

> data(iris3, package="datasets")
> iris3 <- as.data.frame(iris3)
> names(iris3) <- make.names(names(iris3))
> library(rgl, pos=4)
> library(mgcv, pos=4)
> scatter3d(iris3$Petal.W..Setosa, iris3$Petal.L..Setosa, +   iris3$Sepal.L..Setosa, fit="linear", residuals=TRUE, bg="black", +   axis.scales=TRUE, grid=TRUE, ellipsoid=FALSE, xlab="Petal.W..Setosa", +   ylab="Petal.L..Setosa", zlab="Sepal.L..Setosa")
> scatter3d(iris3$Petal.L..Versicolor, iris3$Petal.L..Setosa, +   iris3$Petal.L..Virginica, fit="linear", residuals=TRUE, bg="white", +   axis.scales=TRUE, grid=TRUE, ellipsoid=FALSE, xlab="Petal.L..Versicolor", +   ylab="Petal.L..Setosa", zlab="Petal.L..Virginica")
> rgl.snapshot("C:/Documents and Settings/abc/Desktop/RGLGraph.png")