Tableau Interactive "Viz" Contest

The Las Vegas Sign.
Image via Wikipedia
One more contest- open only for US though
but the prizes are hmm okay. The catch is you have to use the software Tableau created 
not R or J or ggobi or ggplot or java

Check out http://www.tableausoftware.com/public/biz-viz-contest/?=decisionstats

Tableau Interactive “Viz” Contest

AS FEATURED AT

Win a trip to Vegas and a chance for $2,000 & an iPad2

Are you a business, finance or real estate geek? This contest is for you! In cooperation with The Economist Ideas Economy conference, the Tableau Software Interactive “Viz” Contest will focus on business, finance and real estate data… Find some data then use Tableau Public to analyze and visualize it. That’s all it takes.

What you’ll win

A 3-day trip to Las Vegas and a chance to win $2,000 & an iPad2

The winner chosen by our judges will also take away a free roundtrip ticket to attend the2011 Tableau Customer Conference. This includes 3 night’s accommodations at theEncore and a chance to compete in the Iron Viz championship with the winners of two other contests. The winner of Iron Viz will take away a new iPad2, and $2,000.

Cash for the crowd favorite

After entering you’ll receive a custom bit.ly link to your viz. Tweet, Facebook and e-mail that link to everyone you can! Whoever gets the most clicks through their link will become our Crowd Favorite and receive a $250 debit card.

Recognition from The Economist Ideas Economy

Your winning entry will be announced live on stage at The Economist Ideas Economy conference, and Tableau will issue a national press release naming the winner.

Everyone who enters gets a t-shirt!

Everyone who enters will get a very cool Tableau t-shirt. The winner will also receive increased Tableau Public limits and a free copy of Tableau Desktop (a $1999 value)!

How it works

(Click on the steps to expand and get the details.)
 Check the box to view all steps and details.

  • Step 1

    Download the FREE Tableau Public tool


  • Step 2

    Create and publish your “viz” to your blog or website


  • Step 3

    Submit your entry formFill out the entry form and submit by June 3, 2011. A panel of judges will evaluate all submissions based on overall appeal, design elements, and data analysis/findings.

Contest Rules Summary

The following contest is open to legal residents of the United Sates only. You must publish your “viz” on your blog or website to be qualified. Submission form must be submitted by June 3, 2011. Winners will be notified by June 7, 2010. Incomplete applications will not be accepted.

Please read all the rules in their entirety before entering.

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.

—————————————————————————————————-

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.

KXEN Case Studies : Financial Sector

Here are the summaries of some excellent success stories that KXEN has achieved working with partners in the financial world over the years.

Fraud Modeling- Disbank (acquired by Fortis) Turkey

1. Dısbank increased the number of identified fraudulent applications by 200% from 7 to 21 per day.

2.More than 50 fraudsters using counterfeit cards at merchant locations or fraudulent applications have been arrested after April 2004 when the fraud modeling system was set.


A large Bank on the U.S. East Coast

1.Response Modeling

Previously it took the modeling group four weeks to build one model with several hundred variables, using traditional modeling tools. KXEN took one hour for the same problem and doubled the lift in the top decile because it included variables that had not been used for this business question before.

2.Data Quality

Building a Cross/Up-sell Model for a direct marketing campaign to high net worth customers, the modelers needed four weeks using 1500 variables. Again it took one hour with KXEN, which uncovered significant problems with some of the top predictive variables. Further investigation proved that these problems were created in the data merge of the mail file and response file, creating several “perfect” predictors. The model was re-run, removing these variables, and immediately put into production.

Le Crédit Lyonnais

1.Around 160 score models now built annually – compared to around 10 previously – for 130 direct marketing campaigns.
2.KXEN software has allowed LCL to drive up response rates, leading to more value-added services for customers.

Finansbank, Turkey

1.Within 4 months of starting the project to combat dormancy using KXEN’s solution, the bank had successfully reactivated half its previously dormant customers as per Kunter Kutluay, Finansbank Director of Marketing and Risk Analytics.

Bank Austria Creditanstalt , Austria

1.Some 4.5 terabytes of data are held in the bank’s operational systems, with a further 2 terabytes archived. Analytical models created in KXEN are automatically fed through the bank’s scoring engine in batches weekly
or monthly depending on the schema.

“But we are looking at a success rate of target customer deals in the area of three to five per cent with KXEN.
Before that, it was one per cent or less. ”
Werner Widhalm, Head of the Customer Knowledge Management Unit.

Barclays

1.Barclays’ Teradata warehouse holds information on some 14 million active customers, with data
on many different aspects of customer behaviour. Previously, analysts had to manually whittle down several thousand fields of data to a core of only a few hundred to fit the limitations of the modelling process. Now, all of the variables can be fed straight into the predictive model.

Summary– KXEN has achieved tremendous response in all aspects of data modelling in financial sector where time in building, deploying and analyzing model is much more crucial than many other sectors. I would be following this with other case studies on other KXEN successes across multiple domains.

Source – http://www.kxen.com/index.php?option=com_content&task=view&id=220&Itemid=786

Disclaimer- I am a social media consultant for KXEN.