Tag: marketing

Choosing R for business – What to consider?

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Additional features in R over other analytical packages-

1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

2) Wide literature of training material in the form of books is available for the R analytical platform.

3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

 

6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

Specific to the following domains R has the following costs and benefits

  • Business Analytics
    • R is free per license and for download
    • It is one of the few analytical platforms that work on Mac OS
    • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
    • It has open source code for customization as per GPL
    • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
    • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
    • Huge library of packages for regression, time series, finance and modeling
    • High quality data visualization packages
    • Data Mining
      • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
      • Flexibility in tweaking a standard algorithm by seeing the source code
      • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
      • Business Dashboards and Reporting
      • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
        • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
        • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

Additional factors to consider in your R installation-

There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

3) Operating system sub choice- 32- bit or 64 bit.

4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

Commercial Vendors of R Language Products-
1) Revolution Analytics http://www.revolutionanalytics.com/
2) XL Solutions- http://www.experience-rplus.com/
3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

  1. Choosing Operating System
      1. Windows

 

Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

        1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
      2. MacOS

 

Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

      1. Linux

 

        1. Ubuntu
        2. Red Hat Enterprise Linux
        3. Other versions of Linux

 

Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

      1. Multiple operating systems-
        1. Virtualization vs Dual Boot-

 

You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

  1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

 

  1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

    1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
      1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
      2. Workstation
    2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
      1. Amazon
      2. Google
      3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
    3. How much resources
      1. RAM-Hard Disk-Processors- for workstation computing
      2. Instances or API calls for cloud computing
  2. Interface Choices
    1. Command Line
    2. GUI
    3. Web Interfaces
  3. Software Component Choices
    1. R dependencies
    2. Packages to install
    3. Recommended Packages
  4. Additional software choices
    1. Additional legacy software
    2. Optimizing your R based computing
    3. Code Editors
      1. Code Analyzers
      2. Libraries to speed up R

citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

(Note- this is a draft in progress)

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2011 Forecast-ying

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I had recently asked some friends from my Twitter lists for their take on 2011, atleast 3 of them responded back with the answer, 1 said they were still on it, and 1 claimed a recent office event.

Anyways- I take note of the view of forecasting from

http://www.uiah.fi/projekti/metodi/190.htm

The most primitive method of forecasting is guessing. The result may be rated acceptable if the person making the guess is an expert in the matter.

Ajay- people will forecast in end 2010 and 2011. many of them will get forecasts wrong, some very wrong, but by Dec 2011 most of them would be writing forecasts on 2012. almost no one will get called on by irate users-readers- (hey you got 4 out of 7 wrong last years forecast!) just wont happen. people thrive on hope. so does marketing. in 2011- and before

and some forecasts from Tom Davenport’s The International Institute for Analytics (IIA) at

http://iianalytics.com/2010/12/2011-predictions-for-the-analytics-industry/

Regulatory and privacy constraints will continue to hamper growth of marketing analytics.

(I wonder how privacy and analytics can co exist in peace forever- one view is that model building can use anonymized data suppose your IP address was anonymized using a standard secret Coco-Cola formula- then whatever model does get built would not be of concern to you individually as your privacy is protected by the anonymization formula)

Anyway- back to the question I asked-

What are the top 5 events in your industry (events as in things that occured not conferences) and what are the top 3 trends in 2011.

I define my industry as being online technology writing- research (with a heavy skew on stat computing)

My top 5 events for 2010 were-

1) Consolidation- Big 5 software providers in BI and Analytics bought more, sued more, and consolidated more.  The valuations rose. and rose. leading to even more smaller players entering. Thus consolidation proved an oxy moron as total number of influential AND disruptive players grew.

 

2) Cloudy Computing- Computing shifted from the desktop but to the mobile and more to the tablet than to the cloud. Ipad front end with Amazon Ec2 backend- yup it happened.

3) Open Source grew louder- yes it got more clients. and more revenue. did it get more market share. depends on if you define market share by revenues or by users.

Both Open Source and Closed Source had a good year- the pie grew faster and bigger so no one minded as long their slices grew bigger.

4) We didnt see that coming –

Technology continued to surprise with events (thats what we love! the surprises)

Revolution Analytics broke through R’s Big Data Barrier, Tableau Software created a big Buzz,  Wikileaks and Chinese FireWalls gave technology an entire new dimension (though not universally popular one).

people fought wars on emails and servers and social media- unfortunately the ones fighting real wars in 2009 continued to fight them in 2010 too

5) Money-

SAP,SAS,IBM,Oracle,Google,Microsoft made more money than ever before. Only Facebook got a movie named on itself. Venture Capitalists pumped in money in promising startups- really as if in a hurry to park money before tax cuts expired in some countries.

 

2011 Top Three Forecasts

1) Surprises- Expect to get surprised atleast 10 % of the time in business events. As internet grows the communication cycle shortens, the hype cycle amplifies buzz-

more unstructured data  is created (esp for marketing analytics) leading to enhanced volatility

2) Growth- Yes we predict technology will grow faster than the automobile industry. Game changers may happen in the form of Chrome OS- really its Linux guys-and customer adaptability to new USER INTERFACES. Design will matter much more in technology on your phone, on your desktop and on your internet. Packaging sells.

False Top Trend 3) I will write a book on business analytics in 2011. yes it is true and I am working with A publisher. No it is not really going to be a top 3 event for anyone except me,publisher and lucky guys who read it.

3) Creating technology and technically enabling creativity will converge at an accelerated rate. use of widgets, guis, snippets, ide will ensure creative left brains can code easier. and right brains can design faster and better due to a global supply chain of techie and artsy professionals.

 

 

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R is Ready for Business™

A new 5 page brochure from Revolution Analytics. Not that slick and some marketing under-kill (which frankly is a surprise)- but I guess Revolution Analytics does not have a full time graphics designer to help with it’s collateral.

Take a look if you are curious how and why R is getting more and more ready for business.

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Sugar CRM: Forrester Webinar

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https://sugarcrmevents.webex.com/mw0306lb/mywebex/default.do?nomenu=true&siteurl=sugarcrmevents&service=6&main_url=https://sugarcrmevents.webex.com/ec0605lb/eventcenter/event/eventAction.do%3FtheAction%3Ddetail%26confViewID%3D279191911%26siteurl%3Dsugarcrmevents%26%26%26

Date and time:

Thursday, December 2, 2010 11:00 am 
Pacific Standard Time (San Francisco, GMT-08:00) 
Change time zone
Thursday, December 2, 2010 2:00 pm 
Eastern Standard Time (New York, GMT-05:00)
Thursday, December 2, 2010 7:00 pm
Western European Time (London, GMT)
Thursday, December 2, 2010 8:00 pm
Europe Time (Berlin, GMT+01:00)
Duration: 1 hour
Description:
Every organization wants to improve the way they manage their customer relationships. But until recently, adding robust CRM tools to your organization was a time consuming and cost prohibitive endeavor for many resources-constrained organizations. Until Now. On December 2 join us to learn how new developments in technology like open source, cloud computing and web 2.0 – are making it easier than ever to add a top notch CRM system to your operations. 

 

This live webinar hosted by SugarCRM will feature Forrester Research, Inc. Vice President William Band, named one CRM Magazine’s 2007 Influential Leaders. Mr. Band will discuss the current state of the market, review the major trends affecting the CRM landscape, and discuss some criteria you can use to ensure your next CRM decision is the right one.

In addition, all attendees of the live webinar will receive a complimentary download a recent Forrester Wave™ Report! Register today!

Speakers:

William Band, Vice President, Forrester Research
Martin Schneider, Sr. Director Communications, SugarCRM

Who Should Attend:
VP Sales, VP Marketing, CIO’s, Head of Customer Support and other technical decision makers
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Increasing views to Youtube Videos

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The Youtube Promoted Videos (basically a video form of Adsense) can really help companies like Oracle, SAP, IBM, Netezza, SAS Insititute, AsterData, Rapid Miner, Pentaho,  JasperSoft, Teradata, Revolution who create

either corporate videos/training videos or upload their seminar, webinar,conference videos to Youtube.

Making a video is hard work in itself- doing an A/ B test with Youtube Promoted videos might just get a better ROI for your video marketing budget and IMHO embeddable videos from Youtube are much better and easier to share than Videos that can be seen only after registration on a company web site. You want to get the word out for your software, or you want to get website views?

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Predictive Analytics World March2011 SF

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Message from PAWCON-

 

Predictive Analytics World, Mar 14-15 2011, San Francisco, CA

More info: pawcon.com/sanfrancisco

Agenda at-a-glance: pawcon.com/sanfrancisco/2011/agenda_overview.php

PAW’s San Francisco 2011 program is the richest and most diverse yet, including over 30 sessions across two tracks – an “All Audiences” and an “Expert/Practitioner” track — so you can witness how predictive analytics is applied at Bank of America, Bank of the West, Best Buy, CA State Automobile Association, Cerebellum Capital, Chessmetrics, Fidelity, Gaia Interactive, GE Capital, Google, HealthMedia, Hewlett Packard, ICICI Bank (India), MetLife, Monster.com, Orbitz, PayPal/eBay, Richmond, VA Police Dept, U. of Melbourne, Yahoo!, YMCA, and a major N. American telecom, plus insights from projects for Anheiser-Busch, the SSA, and Netflix.

PAW’s agenda covers hot topics and advanced methods such as uplift modeling (net lift), ensemble models, social data (6 sessions on this), search marketing, crowdsourcing, blackbox trading, fraud detection, risk management, survey analysis, and other innovative applications that benefit organizations in new and creative ways.

Predictive Analytics World is the only conference of its kind, delivering vendor-neutral sessions across verticals such as banking, financial services, e-commerce, education, government, healthcare, high technology, insurance, non-profits, publishing, social gaming, retail and telecommunications

And PAW covers the gamut of commercial applications of predictive analytics, including response modeling, customer retention with churn modeling, product recommendations, fraud detection, online marketing optimization, human resource decision-making, law enforcement, sales forecasting, and credit scoring.

WORKSHOPS. PAW also features pre- and post-conference workshops that complement the core conference program. Workshop agendas include advanced predictive modeling methods, hands-on training and enterprise decision management.

More info: pawcon.com/sanfrancisco

Agenda at-a-glance: pawcon.com/sanfrancisco/2011/agenda_overview.php

Be sure to register by Dec 7 for the Super Early Bird rate (save $400):
pawcon.com/sanfrancisco/register.php

If you’d like our informative event updates, sign up at:
pawcon.com/signup-us.php

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Quantifying Analytics ROI

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I had a brief twitter exchange with Jim Davis, Chief Marketing Officer, SAS Institute on Return of Investment on Business Analytics Projects for customers. I have interviewed Jim Davis before last year https://decisionstats.com/2009/06/05/interview-jim-davis-sas-institute/

Now Jim Davis is a big guy, and he is rushing from the launch of SAS Institute’s Social Media Analytics in Japan- to some arguably difficult flying conditions in time to be home in America for Thanksgiving. That and and I have not been much of a good Blog Boy recently, more swayed by love of open source, than love of software per se. I love equally, given I am bad at both equally.

Anyways, Jim’s contention  ( http://twitter.com/Davis_Jim ) was customers should go in business analytics only if there is Positive Return on Investment.  I am quoting him here-

What is important is that there be a positive ROI on each and every BA project. Otherwise don’t do it.

That’s not the marketing I was taught in my business school- basically it was sell, sell, sell.

However I see most BI sales vendors also go through -let me meet my sales quota for this quarter- and quantifying customer ROI is simple maths than predictive analytics but there seems to be some information assymetry in it.

Here is a paper from North Western University on ROI in IT projects-.

but overall it would be in the interest of customers and Business Analytics Vendors to publish aggregated ROI.

The opponents to this transparency in ROI would be market leaders in market share, who have trapped their customers by high migration costs (due to complexity) or contractually.

A recent study listed Oracle having a large percentage of unhappy customers who would still renew!, SAP had problems when it raised prices for licensing arbitrarily (that CEO is now CEO of HP and dodging legal notices from Oracle).

Indeed Jim Davis’s famous unsettling call for focusing on Business Analytics,as Business Intelligence is dead- that call has been implemented more aggressively by IBM in analytical acquisitions than even SAS itself which has been conservative about inorganic growth. Quantifying ROI, should theoretically aid open source software the most (since they are cheapest in up front licensing) or newer technologies like MapReduce /Hadoop (since they are quite so fast)- but I think that market has a way of factoring in these things- and customers are not as foolish neither as unaware of costs versus benefits of migration.

The contrary to this is Business Analytics and Business Intelligence are imperfect markets with duo-poly  or big players thriving in absence of customer regulation.

You get more protection as a customer of $20 bag of potato chips, than as a customer of a $200,000 software. Regulators are wary to step in to ensure ROI fairness (since most bright techies are qither working for private sector, have their own startup or invested in startups)- who in Govt understands Analytics and Intelligence strong enough to ensure vendor lock-ins are not done, and market flexibility is done. It is also a lower choice for embattled regulators to ensure ROI on enterprise software unlike the aggressiveness they have showed in retail or online software.

Who will Analyze the Analysts and who can quantify the value of quants (or penalize them for shoddy quantitative analytics)- is an interesting phenomenon we expect to see more of.

 

 

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