Interview Pranay Agrawal Co-Founder Fractal Analytics

Here is an interview with Pranay Agrawal, Executive Vice President- Global Client Development, Fractal Analytics – one of India’s leading analytics services providers and one of the pioneers in analytics services delivery.

Ajay- Describe Fractal Analytics’ journey as a startup to a pioneer in the Predictive Analytics Services industry. What were some of the key turning points in the field of analytics that you have noticed during these times?


Pranay- In 2000, Fractal Analytics started as a pure-play analytics services company in India with a focus on financial services. Five years later, we spread our operation to the United States and opened new verticals. Today, we have the widest global footprint among analytics providers and have experience handling data and deep understanding of consumer behavior in over 150 counties. We have matured from an analytics service organization to a productized analytics services firm, specializing in consumer goods, retail, financial services, insurance and technology verticals.
We are on the fore-front of a massive inflection point with Big Data Analytics at the center. We have witnessed the transformation of analytics within our clients from a cost center to the most critical division that drives competitive advantage.  Advances are quickly converging in computer science, artificial intelligence, machine learning and game theory, changing the way how analytics is consumed by B2B and B2C companies. Companies that use analytics well are poised to excel in innovation, customer engagement and business performance.

Ajay- What are analytical tools that you use at Fractal Analytics? Are there any trends in analytical software usage that you have observed?

Pranay- We are tools agnostic to serve our clients using whatever platforms they need to ensure they can quickly and effectively operationalize the results we deliver.  We use R, SAS, SPSS, SpotFire, Tableau, Xcelsius, Webfocus, Microstrategy and Qlikview. We are seeing an increase in adoption of open source platform such as R, and specialize tools for dashboard like Tableau/Qlikview, plus an entire spectrum of emerging tools to process manage and extract information from Big Data that support Hadoop and NoSQL data structures

Ajay- What are Fractal Analytics plans for Big Data Analytics?

Pranay- We see our clients being overwhelmed by the increasing complexity of the data. While they are all excited by the possibilities of Big Data, on-the-ground struggle continues to realize its full potential. The analytics paradigm is changing in the context of Big Data. Our solutions focus on how to make it super-simple for our clients combined with analytics sophistication possible with Big Data.
Let’s take our Customer Genomics solution for retailers as an example. Retailers are collecting information about Shopper behaviors through every transaction. Retailers want to transform their business to make it more customer-centric but do not know how to go about it. Our Customer Genomics solution uses advanced machine learning algorithm to label every shopper across more than 80 different dimensions. Retailers use these to identify which products it should deep-discount depending on what price-sensitive shoppers buy. They are transforming the way they plan their assortment, planogram and targeted promotions armed with this intelligence.

We are also building harmonization engines using Concordia to enable real-time update of Customer Genomics based on every direct, social, or shopping transaction. This will further bridge the gap between marketing actions and consumer behavior to drive loyalty, market share and profitability.

Ajay- What are some of the key things that differentiate Fractal Analytics from the rest of the industry? How are you different?

Pranay- We are one of the pioneer pure-play analytics firm with over a decade of experience consulting with Fortune 500 companies. What clients most appreciate about working with us includes:

  • Experience managing structured and unstructured Big Data (volume, variety) with a deep understanding of consumer behavior in more than 150 counties
  • Advanced analytics leveraging supervised machine-learning platforms
  • Proprietary products for example: Concordia for data harmonization, Customer Genomics for consumer insights and personalized marketing, Pincer for pricing optimization, Eavesdrop for social media listening,  Medley for assortment optimization in retail industry and Known Value Item for retail stores
  • Deep industry expertise enables us to leverage cross-industry knowledge to solve a wide range of marketing problems
  • Lowest attrition rates in the industry and very selective hiring process makes us a great place to work

Ajay- What are some of the initiatives that you have taken to ensure employee satisfaction and happiness?

Pranay- We believe happy employees create happy customers. We are building a great place to work by taking a personal interest in grooming people. Our people are highly engaged as evidenced by 33% new hire referrals and the highest Glassdoor ratings in our industry.
We recognize the accomplishments and contributions made through many programs such as:

  1. FractElite – where peers nominate and defend the best of us
  2. Recognition board – where anyone can write a visible thank you
  3. Value cards – where anyone can acknowledge great role model behavior in one or more values
  4. Townhall – a quarterly all hands where we announce anniversaries and FractElite awards, with an open forum to ask questions
  5. Employee engagement surveys – to measure and report out on satisfaction programs
  6. Open access to managers and leadership team – to ensure we understand and appreciate each person’s unique goals and ambitions, coach for high performance, and laud their success

Ajay- How happy are Fractal Analytics customers quantitatively?  What is your retention rate- and what plans do you have for 2013?

Pranay- As consultants, delivering value with great service is critical to our growth, which has nearly doubled in the last year. Most of our clients have been with us for over five years and we are typically considered a strategic partner.
We conduct client satisfaction surveys during and after each project to measure our performance and identify opportunities to serve our clients better. In 2013, we will continue partnering with our clients to define additional process improvements from applying best practice in engagement management to building more advanced analytics and automated services to put high-impact decisions into our clients’ hands faster.


Pranay Agrawal -Pranay co-founded Fractal Analytics in 2000 and heads client engagement worldwide. He has a MBA from India Institute of Management (IIM) Ahmedabad, Bachelors in Accounting from Bangalore University, and Certified Financial Risk Manager from GARP. He is is also available online on

Fractal Analytics is a provider of predictive analytics and decision sciences to financial services, insurance, consumer goods, retail, technology, pharma and telecommunication industries. Fractal Analytics helps companies compete on analytics and in understanding, predicting and influencing consumer behavior. Over 20 fortune 500 financial services, consumer packaged goods, retail and insurance companies partner with Fractal to make better data driven decisions and institutionalize analytics inside their organizations.

Fractal sets up analytical centers of excellence for its clients to tackle tough big data challenges, improve decision management, help understand, predict & influence consumer behavior, increase marketing effectiveness, reduce risk and optimize business results.


Interview Dean Abbott Abbott Analytics

Here is an interview with noted Analytics Consultant and trainer Dean Abbott. Dean is scheduled to take a workshop on Predictive Analytics at PAW (Predictive Analytics World Conference)  Oct 18 , 2010 in Washington D.C

Ajay-  Describe your upcoming hands on workshop at Predictive Analytics World and how it can help people learn more predictive modeling.


Dean- The hands-on workshop is geared toward individuals who know something about predictive analytics but would like to experience the process. It will help people in two regards. First, by going through the data assessment, preparation, modeling and model assessment stages in one day, the attendees will see how predictive analytics works in reality, including some of the pain associated with false starts and mistakes. At the same time, they will experience success with building reasonable models to solve a problem in a single day. I have found that for many, having to actually build the predictive analytics solution if an eye-opener. Seeing demonstrations show the capabilities of a tool, but greater value for an end-user is the development of intuition of what to do at each each stage of the process that makes the theory of predictive analytics real.

Second, they will gain experience using a top-tier predictive analytics software tool, Enterprise Miner (EM). This is especially helpful for those who are considering purchasing EM, but also for those who have used open source tools and have never experienced the additional power and efficiencies that come with a tool that is well thought out from a business solutions standpoint (as opposed to an algorithm workbench).

Ajay-  You are an instructor with software ranging from SPSS, S Plus, SAS Enterprise Miner, Statistica and CART. What features of each software do you like best and are more suited for application in data cases.

Dean- I’ll add Tibco Spotfire Miner, Polyanalyst and Unica’s Predictive Insight to the list of tools I’ve taught “hands-on” courses around, and there are at least a half dozen more I demonstrate in lecture courses (JMP, Matlab, Wizwhy, R, Ggobi, RapidMiner, Orange, Weka, RandomForests and TreeNet to name a few). The development of software is a fascinating undertaking, and each tools has its own strengths and weaknesses.

I personally gravitate toward tools with data flow / icon interface because I think more that way, and I’ve tired of learning more programming languages.

Since the predictive analytics algorithms are roughly the same (backdrop is backdrop no matter which tool you use), the key differentiators are

(1) how data can be loaded in and how tightly integrated can the tool be with the database,

(2) how well big data can be handled,

(3) how extensive are the data manipulation options,

(4) how flexible are the model reporting options, and

(5) how can you get the models and/or predictions out.

There are vast differences in the tools on these matters, so when I recommend tools for customers, I usually interview them quite extensively to understand better how they use data and how the models will be integrated into their business practice.

A final consideration is related to the efficiency of using the tool: how much automation can one introduce so that user-interaction is minimized once the analytics process has been defined. While I don’t like new programming languages, scripting and programming often helps here, though some tools have a way to run the visual programming data diagram itself without converting it to code.

Ajay- What are your views on the increasing trend of consolidation and mergers and acquisitions in the predictive analytics space. Does this increase the need for vendor neutral analysts and consultants as well as conferences.

Dean- When companies buy a predictive analytics software package, it’s a mixed bag. SPSS purchasing of Clementine was ultimately good for the predictive analytics, though it took several years for SPSS to figure out what they wanted to do with it. Darwin ultimately disappeared after being purchased by Oracle, but the newer Oracle data mining tool, ODM, integrates better with the database than Darwin did or even would have been able to.

The biggest trend and pressure for the commercial vendors is the improvements in the Open Source and GNU tools. These are becoming more viable for enterprise-level customers with big data, though from what I’ve seen, they haven’t caught up with the big commercial players yet. There is great value in bringing both commercial and open source tools to the attention of end-users in the context of solutions (rather than sales) in a conference setting, which is I think an advantage that Predictive Analytics World has.

As a vendor-neutral consultant, flux is always a good thing because I have to be proficient in a variety of tools, and it is the breadth that brings value for customers entering into the predictive analytics space. But it is very difficult to keep up with the rapidly-changing market and that is something I am weighing myself: how many tools should I keep in my active toolbox.

Ajay-  Describe your career and how you came into the Predictive Analytics space. What are your views on various MS Analytics offered by Universities.

Dean- After getting a masters degree in Applied Mathematics, my first job was at a small aerospace engineering company in Charlottesville, VA called Barron Associates, Inc. (BAI); it is still in existence and doing quite well! I was working on optimal guidance algorithms for some developmental missile systems, and statistical learning was a key part of the process, so I but my teeth on pattern recognition techniques there, and frankly, that was the most interesting part of the job. In fact, most of us agreed that this was the most interesting part: John Elder (Elder Research) was the first employee at BAI, and was there at that time. Gerry Montgomery and Paul Hess were there as well and left to form a data mining company called AbTech and are still in analytics space.

After working at BAI, I had short stints at Martin Marietta Corp. and PAR Government Systems were I worked on analytics solutions in DoD, primarily radar and sonar applications. It was while at Elder Research in the 90s that began working in the commercial space more in financial and risk modeling, and then in 1999 I began working as an independent consultant.

One thing I love about this field is that the same techniques can be applied broadly, and therefore I can work on CRM, web analytics, tax and financial risk, credit scoring, survey analysis, and many more application, and cross-fertilize ideas from one domain into other domains.

Regarding MS degrees, let me first write that I am very encouraged that data mining and predictive analytics are being taught in specific class and programs rather than as just an add-on to an advanced statistics or business class. That stated, I have mixed feelings about analytics offerings at Universities.

I find that most provide a good theoretical foundation in the algorithms, but are weak in describing the entire process in a business context. For those building predictive models, the model-building stage nearly always takes much less time than getting the data ready for modeling and reporting results. These are cross-discipline tasks, requiring some understanding of the database world and the business world for us to define the target variable(s) properly and clean up the data so that the predictive analytics algorithms to work well.

The programs that have a practicum of some kind are the most useful, in my opinion. There are some certificate programs out there that have more of a business-oriented framework, and the NC State program builds an internship into the degree itself. These are positive steps in the field that I’m sure will continue as predictive analytics graduates become more in demand.


DEAN ABBOTT is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, response modeling, survey analysis, planned giving, predictive toxicology, signal process, and missile guidance. In addition, he has developed and evaluated algorithms for use in commercial data mining and pattern recognition products, including polynomial networks, neural networks, radial basis functions, and clustering algorithms, and has consulted with data mining software companies to provide critiques and assessments of their current features and future enhancements.

Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including DAMA, KDD, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught both applied and hands-on data mining courses for major software vendors, including Clementine (SPSS, an IBM Company), Affinium Model (Unica Corporation), Statistica (StatSoft, Inc.), S-Plus and Insightful Miner (Insightful Corporation), Enterprise Miner (SAS), Tibco Spitfire Miner (Tibco), and CART (Salford Systems).

So what happened to S Plus

Splus – The corporate version of S ( the predecessor of R) is still being marketed by Tibco corporation- again rumoured to be an acquisition target of  (???)

  • SAS ( who have desired R like capabilties especially in their IML  product to be released soon
  • SAP who lost out to IBM in the SPSS acquisition
  • Oracle
  • Microsoft
  • Rogue Wave (acquirer of Visual Numerics)
  • etc etc.

Anyways S Plus is still alive and kicking-

“The S language and the S+ application have been critical to our ability to manage big data objects intrinsic to wind analytics and wind energy development,” said Brad Horn, Director of Wind Analytics at NextEra Energy.  “We credit our long-term interface and Spotfire consulting with unlocking new ideas and sources of value.  Joint dialogue on configuration alternatives and our recent efforts to restructure legacy code is allowing us to transition from simple interactive use of S+ to a customized S+ configuration with integrated batch processing, server load balancing, and parallel processing.  S+ has a central role in supporting internal decisions and our group emphasis on scale, speed, and quality.”

  • Wavelets, Spatial Stats, EnvironmentalStats: Apply statistics for advanced analysis of signal and image data, spatially correlated data, and environmental data.
  • Resampling: Apply resampling techniques, such as bootstrap and permutation tests, to enable the use of standard statistics on smaller data sets.
  • Association Rules: Uncover relationships between variables in large data sets, most commonly to detect purchase patterns (Market Basket Analysis), or in many other areas like web site usage analysis.
  • Recode Values: Easily handle and prepare data from multiple sources by changing the values in a column to a new value.
  • Deployment and Integration:

    • Spotfire Integration: Read and write Spotfire Text Data files, and leverage examples of using Spotfire Professional to visualize, explore and share model results.
    • Custom Java & C++ nodes: Extend Spotfire Miner by writing custom nodes in Java and C++.
    • Remote Script Execution: Execute S+ scripts remotely on S+ Server to offload and distribute intensive jobs.
    • Global Worksheet Parameters: Make workflows more flexible and reusable to interactive and batch applications.
    • FlexBayes: Create more realistic models, provide a natural way to address missing data, and take advantage of prior analysis.

    Data Access and Preparation:

    • New Data File Types: Unlock more data sources by reading new formats including Spotfire Text Data, Microsoft Excel 2007, Microsoft Access 2007, and Matlab 7.
    • JDBC Access: Access new data sources for analysis with data import and export via the sjdbc library in Spotfire S+ 8.1.