Quantitative Modeling for Arbitrage Positions in Ad KeyWords Internet Marketing

Assume you treat an ad keyword as an equity stock. There are slight differences in the cost for advertising for that keyword across various locations (Zurich vs Delhi) and various channels (Facebook vs Google) . You get revenue if your website ranks naturally in organic search for the keyword, and you have to pay costs for getting traffic to your website for that keyword.
An arbitrage position is defined as a riskless profit when cost of keyword is less than revenue from keyword. We take examples of Adsense  and Adwords primarily.
There are primarily two types of economic curves on the foundation of which commerce of the  internet  resides-
1) Cost Curve- Cost of Advertising to drive traffic into the website  (Google Adwords, Twitter Ads, Facebook , LinkedIn ads)
2) Revenue Curve – Revenue from ads clicked by the incoming traffic on website (like Adsense, LinkAds, Banner Ads, Ad Sharing Programs , In Game Ads)
The cost and revenue curves are primarily dependent on two things
1) Type of KeyWord-Also subdependent on
a) Location of Prospective Customer, and
b) Net Present Value of Good and Service to be eventually purchased
For example , keyword for targeting sales of enterprise “business intelligence software” should ideally be costing say X times as much as keywords for “flower shop for birthdays” where X is the multiple of the expected payoffs from sales of business intelligence software divided by expected payoff from sales of flowers (say in Location, Daytona Beach ,Florida or Austin, Texas)
2) Traffic Volume – Also sub-dependent on Time Series and
a) Seasonality -Annual Shoppping Cycle
b) Cyclicality– Macro economic shifts in time series
The cost and revenue curves are not linear and ideally should be continuous in a definitive exponential or polynomial manner, but in actual reality they may have sharp inflections , due to location, time, as well as web traffic volume thresholds
Type of Keyword – For example ,keywords for targeting sales for Eminem Albums may shoot up in a non linear manner after the musician dies.
The third and not so publicly known component of both the cost and revenue curves is factoring in internet industry dynamics , including relative market share of internet advertising platforms, as well as percentage splits between content creator and ad providing platforms.
For example, based on internet advertising spend, people belive that the internet advertising is currently heading for a duo-poly with Google and Facebook are the top two players, while Microsoft/Skype/Yahoo and LinkedIn/Twitter offer niche options, but primarily depend on price setting from Google/Bing/Facebook.
It is difficut to quantify  the elasticity and efficiency of market curves as most literature and research on this is by in-house corporate teams , or advisors or mentors or consultants to the primary leaders in a kind of incesteous fraternal hold on public academic research on this.
It is recommended that-
1) a balance be found in the need for corporate secrecy to protest shareholder value /stakeholder value maximization versus the need for data liberation for innovation and grow the internet ad pie faster-
2) Cost and Revenue Curves between different keywords, time,location, service providers, be studied by quants for hedging inetrent ad inventory or /and choose arbitrage positions This kind of analysis is done for groups of stocks and commodities in the financial world, but as commerce grows on the internet this may need more specific and independent quants.
3) attention be made to how cost and revenue curves mature as per level of sophistication of underlying economy like Brazil, Russia, China, Korea, US, Sweden may be in different stages of internet ad market evolution.
For example-
A study in cost and revenue curves for certain keywords across domains across various ad providers across various locations from 2003-2008 can help academia and research (much more than top ten lists of popular terms like non quantitative reports) as well as ensure that current algorithmic wightings are not inadvertently given away.
Part 2- of this series will explore the ways to create third party re-sellers of keywords and measuring impacts of search and ad engine optimization based on keywords.

2011 Analytics Recap

Events in the field of data that impacted us in 2011

1) Oracle unveiled plans for R Enterprise. This is one of the strongest statements of its focus on in-database analytics. Oracle also unveiled plans for a Public Cloud

2) SAS Institute released version 9.3 , a major analytics software in industry use.

3) IBM acquired many companies in analytics and high tech. Again.However the expected benefits from Cognos-SPSS integration are yet to show a spectacular change in market share.

2011 Selected acquisitions

Emptoris Inc. December 2011

Cúram Software Ltd. December 2011

DemandTec December 2011

Platform Computing October 2011

 Q1 Labs October 2011

Algorithmics September 2011

 i2 August 2011

Tririga March 2011

 

4) SAP promised a lot with SAP HANA- again no major oohs and ahs in terms of market share fluctuations within analytics.

http://www.sap.com/india/news-reader/index.epx?articleID=17619

5) Amazon continued to lower prices of cloud computing and offer more options.

http://aws.amazon.com/about-aws/whats-new/2011/12/21/amazon-elastic-mapreduce-announces-support-for-cc2-8xlarge-instances/

6) Google continues to dilly -dally with its analytics and cloud based APIs. I do not expect all the APIs in the Google APIs suit to survive and be viable in the enterprise software space.  This includes Google Cloud Storage, Cloud SQL, Prediction API at https://code.google.com/apis/console/b/0/ Some of the location based , translation based APIs may have interesting spin offs that may be very very commercially lucrative.

7) Microsoft -did- hmm- I forgot. Except for its investment in Revolution Analytics round 1 many seasons ago- very little excitement has come from MS plans in data mining- The plugins for cloud based data mining from Excel remain promising yet , while Azure remains a stealth mode starter.

8) Revolution Analytics promised us a GUI and didnt deliver (till yet 🙂 ) . But it did reveal a much better Enterprise software Revolution R 5.0 is one of the strongest enterprise software in the R /Stat Computing space and R’s memory handling problem is now an issue of perception than actual stuff thanks to newer advances in how it is used.

9) More conferences, more books and more news on analytics startups in 2011. Big Data analytics remained a strong buzzword. Expect more from this space including creative uses of Hadoop based infrastructure.

10) Data privacy issues continue to hamper and impede effective analytics usage. So does rational and balanced regulation in some of the most advanced economies. We expect more regulation and better guidelines in 2012.

JMP and R – #rstats

An amazing example of R being used sucessfully in combination (and not is isolation) with other enterprise software is the add-ins functionality of JMP and it’s R integration.

See the following JMP add-ins which use R

http://support.sas.com/demosdownloads/downarea_t4.jsp?productID=110454&jmpflag=Y

JMP Add-in: Multidimensional Scaling using R

This add-in creates a new menu command under the Add-Ins Menu in the submenu R Add-ins. The script will launch a custom dialog (or prompt for a JMP data table is one is not already open) where you can cast columns into roles for performing MDS on the data table. The analysis results in a data table of MDS dimensions and associated output graphics. MDS is a dimension reduction method that produces coordinates in Euclidean space (usually 2D, 3D) that best represent the structure of a full distance/dissimilarity matrix. MDS requires that input be a symmetric dissimilarity matrix. Input to this application can be data that is already in the form of a symmetric dissimilarity matrix or the dissimilarity matrix can be computed based on the input data (where dissimilarity measures are calculated between rows of the input data table in R).

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis

Chernoff Faces Add-in

One way to plot multivariate data is to use Chernoff faces. For each observation in your data table, a face is drawn such that each variable in your data set is represented by a feature in the face. This add-in uses JMP’s R integration functionality to create Chernoff faces. An R install and the TeachingDemos R package are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Data Visualization

Support Vector Machine for Classification

By simply opening a data table, specifying X, Y variables, selecting a kernel function, and specifying its parameters on the user-friendly dialog, you can build a classification model using Support Vector Machine. Please note that R package ‘e1071’ should be installed before running this dialog. The package can be found from http://cran.r-project.org/web/packages/e1071/index.html.

Submitted by: Jong-Seok Lee SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis/Mining

Penalized Regression Add-in

This add-in uses JMP’s R integration functionality to provide access to several penalized regression methods. Methods included are the LASSO (least absolutee shrinkage and selection operator, LARS (least angle regression), Forward Stagewise, and the Elastic Net. An R install and the “lars” and “elasticnet” R packages are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Regression

MP Addin: Univariate Nonparametric Bootstrapping

This script performs simple univariate, nonparametric bootstrap sampling by using the JMP to R Project integration. A JMP Dialog is built by the script where the variable you wish to perform bootstrapping over can be specified. A statistic to compute for each bootstrap sample is chosen and the data are sent to R using new JSL functionality available in JMP 9. The boot package in R is used to call the boot() function and the boot.ci() function to calculate the sample statistic for each bootstrap sample and the basic bootstrap confidence interval. The results are brought back to JMP and displayed using the JMP Distribution platform.

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Basic Statistics

Revolution Webinar Series #Rstats

Revolution Analytics Webinar-

 

Featured Webinar
David Champagne REGISTER NOW
Presenter David Champagne
CTO, Revolution Analytics
Date Tuesday, December 20th
Time 11:00AM – 11:30AM Pacific 
Click here for the webinar time in your local time zone

Big Data Starts with R

Traditional IT infrastructure is simply unable to meet

the demands of the new “Big Data Analytics” landscape.   Many enterprises are turning to the “R” statistical programming language and Hadoop (both open source projects) as a potential solution. This webinar will introduce the statistical capabilities of R within the Hadoop ecosystem.  We’ll cover:

  • An introduction to new packages developed by Revolution Analytics to facilitate interaction with the data stores HDFS and HBase so that they can be leveraged from the R environment
  • An overview of how to write Map Reduce jobs in R using Hadoop
  • Special considerations that need to be made when working with R and Hadoop.

We’ll also provide additional resources that are available to people interested in integrating R and Hadoop.

 

Upcoming Webinars
Wed, Dec 14th
11:00AM – 11:30AM PT
Revolution R Enterprise – 100% R and MoreR users already know why the R language is the lingua franca of statisticians today: because it’s the most powerful statistical language in the world. Revolution Analytics builds on the power of open source R, and adds performance, productivity and integration features to create Revolution R Enterprise. In this webinar, author and blogger David Smith will introduce the additional capabilities of Revolution R Enterprise.
 Archived Webinars-
Revolution Webinar: New Features in Revolution R Enterprise 5.0 (including RevoScaleR) to Support Scalable Data AnalysisRevolution R Enterprise 5.0 is Revolution Analytics’ scalable analytics platform.  At its core is Revolution Analytics’ enhanced Distribution of R, the world’s most widely-used project for statistical computing.  In this webinar, Dr. Ranney will discuss new features and show examples of the new functionality, which extend the platform’s usability, integration and scalability

 

Graphs in Statistical Analysis

One of the seminal papers establishing the importance of data visualization (as it is now called) was the 1973 paper by F J Anscombe in http://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf

It has probably the most elegant introduction to an advanced statistical analysis paper that I have ever seen-

1. Usefulness of graphs

Most textbooks on statistical methods, and most statistical computer programs, pay too little attention to graphs. Few of us escape being indoctrinated with these notions:

(1) numerical calculations are exact, but graphs are rough;

(2) for any particular kind of statistical data there is just one set of calculations constituting a correct statistical analysis;

(3) performing intricate calculations is virtuous, whereas actually looking at the data is cheating.

A computer should make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.

Of course the dataset makes it very very interesting for people who dont like graphical analysis too much.

From http://en.wikipedia.org/wiki/Anscombe%27s_quartet

 The x values are the same for the first three datasets.

Anscombe’s Quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

For all four datasets:

Property Value
Mean of x in each case 9 exact
Variance of x in each case 11 exact
Mean of y in each case 7.50 (to 2 decimal places)
Variance of y in each case 4.122 or 4.127 (to 3 d.p.)
Correlation between x and y in each case 0.816 (to 3 d.p.)
Linear regression line in each case y = 3.00 + 0.500x (to 2 d.p. and 3 d.p. resp.)
But see the graphical analysis –
While R has always been great in emphasizing graphical analysis, thanks in part due to work by H Wickham and others, SAS products and  language has also modified its approach at http://www.sas.com/technologies/analytics/statistics/datadiscovery/
 SAS Visual Data Discovery combines top-selling SAS products (Base SASSAS/STAT® and SAS/GRAPH®), along with two interfaces (SAS® Enterprise Guide® for guided tasks and batch analysis and JMP® software for discovery and exploratory analysis).
 and  ODS Statistical Graphs at
While ODS Statistical graphs is still not as smooth as say R’s GGPLOT2 http://tinyurl.com/ggplot2-book, it still is a progressive step
Pretty graphs make for better decisions too !

 

 

Interview Zach Goldberg, Google Prediction API

Here is an interview with Zach Goldberg, who is the product manager of Google Prediction API, the next generation machine learning analytics-as-an-api service state of the art cloud computing model building browser app.
Ajay- Describe your journey in science and technology from high school to your current job at Google.

Zach- First, thanks so much for the opportunity to do this interview Ajay!  My personal journey started in college where I worked at a startup named Invite Media.   From there I transferred to the Associate Product Manager (APM) program at Google.  The APM program is a two year rotational program.  I did my first year working in display advertising.  After that I rotated to work on the Prediction API.

Ajay- How does the Google Prediction API help an average business analytics customer who is already using enterprise software , servers to generate his business forecasts. How does Google Prediction API fit in or complement other APIs in the Google API suite.

Zach- The Google Prediction API is a cloud based machine learning API.  We offer the ability for anybody to sign up and within a few minutes have their data uploaded to the cloud, a model built and an API to make predictions from anywhere. Traditionally the task of implementing predictive analytics inside an application required a fair amount of domain knowledge; you had to know a fair bit about machine learning to make it work.  With the Google Prediction API you only need to know how to use an online REST API to get started.

You can learn more about how we help businesses by watching our video and going to our project website.

Ajay-  What are the additional use cases of Google Prediction API that you think traditional enterprise software in business analytics ignore, or are not so strong on.  What use cases would you suggest NOT using Google Prediction API for an enterprise.

Zach- We are living in a world that is changing rapidly thanks to technology.  Storing, accessing, and managing information is much easier and more affordable than it was even a few years ago.  That creates exciting opportunities for companies, and we hope the Prediction API will help them derive value from their data.

The Prediction API focuses on providing predictive solutions to two types of problems: regression and classification. Businesses facing problems where there is sufficient data to describe an underlying pattern in either of these two areas can expect to derive value from using the Prediction API.

Ajay- What are your separate incentives to teach about Google APIs  to academic or researchers in universities globally.

Zach- I’d refer you to our university relations page

Google thrives on academic curiosity. While we do significant in-house research and engineering, we also maintain strong relations with leading academic institutions world-wide pursuing research in areas of common interest. As part of our mission to build the most advanced and usable methods for information access, we support university research, technological innovation and the teaching and learning experience through a variety of programs.

Ajay- What is the biggest challenge you face while communicating about Google Prediction API to traditional users of enterprise software.

Zach- Businesses often expect that implementing predictive analytics is going to be very expensive and require a lot of resources.  Many have already begun investing heavily in this area.  Quite often we’re faced with surprise, and even skepticism, when they see the simplicity of the Google Prediction API.  We work really hard to provide a very powerful solution and take care of the complexity of building high quality models behind the scenes so businesses can focus more on building their business and less on machine learning.

 

 

Webinar: Using R within Oracle #rstats

Webinar: Using R within Oracle — Nov 30, noon EST

==========================================
Oracle now supports the R open source statistical programming language. Come to this webinar to learn more about using R within an Oracle environment.

— URL for TechCast: https://stbeehive.oracle.com/bconf/confDetails?confID=334B:3BF0:owch:38893C00F42F38A1E0404498C8A6612B0004075AECF7&guest=true&confKey=608880
— Web Conference ID: 303397
— Web Conference Key: 608880
— Dialup:             1-866-682-4770      , ID 5548204, passcode 1234

After a steady rise in the past few years, in 2010 the open source data mining software R overtook other tools to become the tool used by more data miners (43%) than any other (http://www.rexeranalytics.com/Data-Miner-Survey-Results-2010.html).

Several analytic tool vendors have added R-integration to their software. However, Oracle is the largest company to throw their weight behind R. On October 3, Oracle unveiled their integration of R: Oracle R Enterprise (http://www.oracle.com/us/corporate/features/features-oracle-r-enterprise-498732.html) as part of their Oracle Big Data Appliance announcement (http://www.oracle.com/us/corporate/press/512001).

Oracle R Enterprise allows users to perform statistical analysis with advanced visualization on data stored in Oracle Database. Oracle R Enterprise enables scalable R solutions, while facilitating production deployment of R scripts and Hadoop based solutions, as well as integration of R results with Oracle BI Publisher and OBIEE dashboards.

This TechCast introduces the various Oracle R Enterprise components and features, along with R script demonstrations that interface with Oracle Database.

TechCast presenter: Mark Hornick, Senior Manager, Oracle Advanced Analytics Development.
This TechCast is part of the ongoing TechCasts series coordinated by Oracle BIWA: The BI, Warehousing and Analytics SIG (http://www.oracleBIWA.org).