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Corporate Brochure:

Predictive Research has corporate offices in San Francisco, CA, Charlotte, NC, USA and Bangalore, India, is a leading data modeling focused firm led by highly experienced and qualified Data Scientists. We are company with strong predictive modeling focus, built on collective experience of the team in building successful quantitative and analytic products. Predictive Research has comprehensive product portfolio and solutions road map that is designed to leverage on the research and innovation abilities of data scientists.

We have developed expertise in multiple domain verticals thus offering our customers various options that best suit their requirements. The competent and highly skilled team at Predictive Research pride itself in delivering the best-in-class business solutions working with various open source technologies, tools and methodologies.

Management Team

The executive management team of Predictive Research comprises of hard-core Quant Professionals, Analytic Professionals with deep understanding of current trends in Predictive Modeling and Business Development Leaders spread across geographical locations.  The management team includes industry veterans with strong business acumen who have been at senior management positions at various reputed multinationals and have managed global operations and were part of the management of large scale businesses.


Mrs. Ashvini B Patil, CEO & Founder



She has more than 10 years of Experience in  Data Mining and holds a Master's Degree in Software Engineering.  She has lead number of analytic projects in Retail, Pharma and Healthcare. Now, She looks at business development, sales and responsible for whole analytics operations at Predictive Research. She can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it.




Mr. Hemanth Kumar (Ph.D.,) Senior Data Scientist 


He has more than 6 years of experience in  Data Mining and holds a Master's Degree in Electronics Engineering from NIT and writing Ph.D, Thesis. He has 6 years of experience in Analytics worked in client locations like Indonesia and is responsible for Data Science business. He takes care of the Quantitative Finance business at Predictive Research. He can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it.




Dr.S.Basavaraj Patil, Ph.D, Chief Data Scientist & Co-Founder


He  has more than 15 years of Experience in AI and Data Mining. He has earned his Ph.D, for Thesis work titled “Machine Learning Techniques for Data Mining” in 2004. He worked as Technical Architect for designing a Pharma Analytics Product with ArisGlobal ( in the year 2003 and worked until mid of 2004.  Next, he practiced as an Independent Consultant, mainly consulting Manthan Systems ( helping them in their efforts to build Analytic Product for Retail Enterprises until the end of 2005.  After that he joined as Assistant Vice President with HSBC in 2005, Bangalore and took the responsibility to set up the Equity quantitative Research Business for Asia in their Capital Markets Research Division. Here he spent his quality time in building Predictive Models for financial markets.  Finally chasing the old dreams, quit this  role in 2010, and joined  Predictive Research in 2011. He also works as a Ph.D, Supervisor mentoring 3 Ph.D. Scholars, all of them are employees of Predictive Research. Most of the times, he is excited by some of the bio-inspired algorithms, like Neural Networks, Fuzzy Systems, Genetic Algorithms and their applications for Big Data research to solve real-world problems.   He can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it.

Mrs. Latha Mallikarjun, Analytics Consultant, Sydney, Australia

She has  experience in Mathematical Modeling, Business Development and E-mail marketing techniques.  She  believes that Big-Data is revolutionizing the businesses and Predictive Research as started by Data Scientists who have put in several decades of experience will definitely play a major role in this revolution. She can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it. .

Mrs. Latha Sirigere,  Business Development, Perth, Australia

She has several years of experience in diversified businesses. Of course, I was also involved in data processing in one or the other way, in most of  assignments. Now she carry out market research for Predictive Research and proud to be part of it.   She can be reached at This email address is being protected from spambots. You need JavaScript enabled to view it. .








The following is a sample list of types of Industries we have supported and have in-depth experience. However the list is not exhaustive, please contact us if you feel there is a component of predictive analytics or predictive modeling is necessary.

Financial Markets

Predictive Research is Quant focused company and has several products and services for Financial Markets.  Strengths of company include the rich experience in building products that  can be used by Analysts for Data Analysis and Prediction.

Banking Industry

Predictive Research offers variety of products and services for Banking Data Analytics, starting from identifying the high value banking customers ( Fuzzy Clustering Tool) and services like partnering in building risk models, fraud detection, etc.

Retail Industry

Our offerings include the products like Cause and Effect Finder and services like customer segmentation based on RFM modeling, customer data analysis and life time value, etc.

Pharma Industry

Predictive Research specializes in the open source tools like R, Scilab, Octave which are fast replacing the highly expensive Commercial Analytics tools in this Industry.  We can provide chemo-informatics data analysis, and predictive models for reducing the time for drug discovery (virtual screening).

Web log Data Analytics

We have capability to process the click-through data collected through web and build the models for browser behavior and browsers buying intent.


Case Studies


1. Case Studies For Predictive Analytics

1. Cause and Effect Relationships :

2. Customer Churn Analysis:

3. Feature Selection Process In R:

4. Fuzzy Based Classification Technique For Time Series Data:

5. Forecasting of a Time Series Signal Using GARCH Techniques:

6. Actionable Insights of the Consumer Sales Data using MongoDB - Big Data Case Study:


2. Case Studies For Quant

1. Implementing a Trading Strategy:

2. Back testing of  a Trading Strategy:

3. Predicting of Stock Trends using Neuro Fuzzy based Techniques:

4. Stock Market Price Predictions by different Data Mining Techniques:


Predictive Research is a leading venture in Quantitative Financial Business Services, Business Intelligence, Big-Data Mining Techniques, and Predictive Modeling. We offer wide variety of services, Product customizations to our customers across the globe, in this arena.


Data Scientists at Quantitative Analytics group specializes in advanced Statistical Techniques of Estimation, Prediction and Artificial Intelligence based Machine Learning Techniques. Our Quant Offerings include Simple data gathering services and Analytic Services, Regression and Simulation Services, Portfolio Optimization Services, Quantitative Modeling Techniques for financial data and Third Party Model Validation services.


Data Scientists at Predictive Analytics group works hand in hand with customers in building complex predictive models and solving challenges in Consumer Analytics, Automotive Analytics, Retail Analytics and Web Mining techniques in diversified sectors. We have capabilities to process Big Data.



In this technologically challenging scenario, organizations have to rapidly customize themselves to be more responsive towards changing customer needs. We are well versed in delivering these solutions using open source technologies like R, Scilab, Octave, Python and Java. Predictive Research is well positioned to be a partner with Clients in Big-Data Analytics, co-Innovator to businesses who have realized the importance of data, and co-Creator of products in their transformation journey.

Predictive Analytics

Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model which has, in turn, been trained over your data, learning from the experience of your organization.

Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn. Each customer's predictive score informs actions to be taken with that customer — business intelligence just doesn't get more actionable than that.

Predictive Modeling

A predictive model is simply an equation used to predict something.

Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends.

A predictive model is made up of a number of predictors, which are variable factors that are likely to influence future behavior or results.


Why Predictive Modeling?


Nearly every business in competitive markets will eventually need to do predictive modeling to remain ahead of the curve.

  • Predict prospects likely to buy
  • Automatically predict risk and pricing at an individual-level
  • Use unstructured text sources to predict market performance
  • The chance a prospect will respond to an Ad
  • When a customer is likely to churn

Regression Analysis

Statistical approach to forecasting change in a dependent variable (sales revenue, for example) on the basis of change in one or more independent variables (population and income, for example).  It is widely used for forecasting and prediction. 

In finance, linear regression is used for quantifying and analyzing the investment risk. 

We Know How

The Predictive Models help our clients assess the implications for market growth several quarters into the future based on a formal quantification of the relationship between the various indicator series and market growth.

Separate analyses are conducted for the product and service sub-markets of focus, in each of the countries of interest, using the various indicators most closely associated                             with each of them.