Friday, June 28, 2013

Big Data Analytics : R and SAS programming

Big Data Analytics : Statistical R and SAS programming
When we discuss cost we can't avoid the constant bickering over the choice of the right statistical Software environment and its pros and cons - namely R or SAS. With respect to statistical analytics capability both SAS and R share the same respectability and we must agree on some occasions one leads the other. However, it is argued that some of the cutting edge latest techniques available in R are not available in SAS. Unfortunately, we are not going to add to the huge amount of information already available on this topic but would rather stick to providing clients worldwide a choice in choosing the software they are already invested in terms of time or money.
Market Equations India offers clients a combination of rich Industry experience and a committed group of intellectuals from business, science and mathematics disciplines that are passionate about analytics and are comfortable and current with programming using different statistical software including R, SAS, SPSS and MATLAB.

Our expertise with the techniques used in R programming includes:
  • Reading data from various source files
  • Evaluate the cumulative distribution function, the probability density function and the quintile function
  • Examining the distribution of a set of data: stem and leaf plot
  • One or two sample tests: box plot, t-test, F-test, two-sample Wilcoxon test, Two-sample Kolmogorov-Smirnov test
  • Grouping, loops and conditional execution: if statements, for loops, repeat, and while loops
  • Writing R functions
  • Statistical modelling: regression analysis and the analysis of variance, generalized linear models, nonlinear regression models
  • Creating data graphics: High-level plotting functions, Low-level plotting functions, Interactive graphics functions
  • Accessing and installing R packages
  • Debugging
  • Organizing and commenting R code
Our knowledge in R programming extends to its comprehensive list of concepts including:
Accessing built-in datasets, Additive models, Analysis of variance, Arithmetic functions and operators, Arrays, Binary operators, Box plots, Character vectors, Concatenating lists, Control statements, Customizing the environment, Data frames, Density estimation, Determinants, Diverting input and output, Dynamic graphics, Eigenvalues and eigenvectors, Empirical CDFs, Generalized linear models, Generalized transpose of an array, Generic functions, Graphics device drivers, Graphics parameters, Grouped expressions, Indexing of and by arrays, Indexing vectors, Kolmogorov-Smirnov test, Least squares fitting, Linear equations, Linear models, Lists, Local approximating regressions, Loops and conditional execution, Matrices, Matrix multiplication, Maximum likelihood, Missing values, Mixed models, Named arguments, Namespace, Nonlinear least squares, One- and two-sample tests, Ordered factors, Outer products of arrays, Probability distributions, QR decomposition, Quantile-quantile plots, Reading data from files, Regular sequences, Removing objects, Robust regression, Search path, Shapiro-Wilk test, Singular value decomposition, Statistical models, Student's t test, Tabulation, Tree-based models, Updating fitted models, Wilcoxon test, Workspace, Writing functions.
Case Study : Statistical R Programming:
Market Equations helps a United Kingdom (UK) based E-Retailer institutionalize Sales and Marketing Analytics by building a correlation model linking Facebook "likes" and "fan" growth to Sales, helping them allocate their marketing spends effectively into channels that maximize returns and reduce costs incurred in holding excess inventory and retain clients by eliminating the possibility of stock outs.
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Thursday, June 27, 2013

Customer Preferences : Outsourcing Max Diff Analysis

Outsourcing Max-Differential Analysis to understand customer preferences

Maximum Difference Scaling (MaxDiff) is a statistical exercise wherein respondents score multiple features and attributes such as product features, product preference and usage etc based on the most important and least important feature or attribute to help obtain importance scores. This exercise provides different preference scores showing the relative importance of attributes  compared to a standard rating scale exercise. Hierarchical Bayesian technique is used to derive the importance scores at the respondent level.

Case Study : Max Differential Analysis on survey data for a large utility vehicle manufacturer in the US

Objective : To collect opinions from current or potential consumers on several new product concepts and to better understand customer needs in a utility vehicle product.

Process & Methodology: Each respondent was given a set of questions containing some utility vehicle attributes (below) and asked to indicate the most and least important attribute.


  • Analysis and summary plots were obtained for responses from EACH of the questions.
  • Primary analysis was performed using multinomial logit model to obtain the Importance Value of each attribute in percent-shared utility scale (add up to 100). These are the easiest to interpret and were obtained by probability based rescaling procedure of the raw utility scores.
  • Count analysis starting from simple proportions of least and most important attributes was also presented as a supportive analysis to the primary model based analysis.

Outcome: Our analysis helped the Utility Vehicle manufacturer better understand their target audience and helped them devise need based strategies based on customer feedback on the features the had the highest importance value for the customer. 

Customer Analytics

Customer Analytics Outsourcing Services

"Data today is being termed as the "new currency", the "new oil", the new "natural resource" and yet it is surprising that most organizations do not use this huge arsenal of data available to improve decision making and drive results." 

Embracing data in any size, shape or form helps organizations transform their huge customer data inventory into actionable insights through the use of extensive data analytics and predictive modeling services. 

Organizations that institutionalize extensive Customer Analytics into their decision management and reporting systems stand out and stay ahead of their competition as they have a clear and precise understanding of their customer base and treat data as a business asset that needs to be nurtured and worked on by applying Analytics driven data transformation that deliver actionable insights and impact based results. 

Customer Analytics & Reporting outsourcing services include:

·         Campaign Design & Tracking 
·         Customer Segmentation &Profiling
·         Life Time Value Modeling
·         Propensity Modeling
·         Customer Churn Analytics
·         Customer Loyalty Analytics
·         Customer Satisfaction Analytics
·         Spend Optimization Analytics
·         Retention Prediction scorecards
·         Revival Scorecards and Segmentation
·         Early warning churn prediction model
·         Market Basket Analysis
·         Cross sell- Up sell
·         New/Inline product Forecasts
·         Cross Channel Effectiveness
·         Demand, Supply and Inventory Planning 

You may find the below Case Studies worth a read.

Case Study: Customer Churn Analytics for a large Telecommunications provider in the United States
Market Equations India developed a Customer Churn Analysis Scorecard services for a large Telecom service provider in the United States to identify key churn drivers and helping them retain subscribers by implementing churn prevention strategies. 
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Case Study: Cross Sell Analytics strategies on a financial portfolio
Market Equations India helps a leading financial services group leverage its huge customer database to attract customers towards its various other financial products using innovative and smartly designed cross sell strategies. 
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Case Study: Customer Portfolio analytics and Loan performance optimization services
Market Equations India helps one of the largest Car rental dealerships in the US build incisive and comprehensive predictive models to help the dealership predict profitable future loans while avoiding unprofitable loans, design optimum pricing strategies and optimize portfolio performance to maximize revenue. 
Read More.