DATA AUDIT AND ASSESSMENT; DATA INTEGRATION

For data science to provide competitive advantage you need the following:

•Data that is unique to the enterprise about its customers, products, pricing, manufacturing, operations, logistics, finance etc.,

•Top management support of data science and analytics and a continuous culture of establishing and proving use cases at a corporate level that will provide competitive advantage across the organization

•Data sources that are stable, reliable and have near infinite availability

These data sources have to be documented (tables; ERDs; fields; missing data), then integrated using match keys, checked and cleaned to be input into the analytical, data visualization and modeling stages. We take pride in our clarity of understanding this crucial phase and working with our clients to arrive at leading edge technical solutions.

Pricing and Promotional Analysis

Pricing and promotions are key to a firm’s profitability. Pricing analytics and strategy is one of the most challenging areas as you need to account for thousands of SKUs (for a retailer) to product costs, strategic needs, customer segment’s price sensitivity, competitive reactions, channels, and time among many drivers. We have worked on over 50 pricing projects using survey data, experimental design and customer transaction data to solve pricing problems for our clients.

•Creating and operationalizing price elasticity models for a multi-department, multi-product retail company across channels, time and across major customer segments using the idea of key value items and key value segments.

•Pricing models and optimization algorithms for a major cruise line

•Credit card pricing optimization models using experimental design, discrete choice modeling and transaction data.

•Optimizing price bundling for a major telecommunications company

•Price optimization and demand forecasting for in-line pharma products

•Determine price setting mechanisms for various oncology and infectious disease products.

Business Intelligence and Data Visualization

We have deep expertise in using data to create compelling business intelligence and data visualization products. We use our expertise here for exploratory analysis prior to building machine learning models as well as create stand alone products. Data visualization is complex and requires asking many questions and building the end product in an iterative fashion. We focus on using R base graphics, ggplot2 and Tableau. For interactive visualizations we use RShiny.

Customer segmentation and valuation

Customer segmentation is a crucial construct in creating and driving marketing strategy. We have worked on over 100 segmentation projects across multiple industries using a variety of data. Examples of data sources that we have worked with are survey data, customer transaction data, loyalty program data and social media data Our approaches use unsupervised learning approaches such as principal components analysis, Factor and cluster analysis and latent class analysis. We also have deep expertise in developing scoring models to classify customers in the data base into segments for developing targeting and cross selling campaigns.

•Multiple projects in physician segmentation for various markets such as for pain management, CNS products, Dermatology, Physician and Parent Segmentation in the ADHD marketplace. (Algorithm used was Finite Mixture Models)

•Customer segmentation for  multiple retail banks and mutual fund companies

•Segmentation and customer valuation for a major US based footwear retail chain.

•Develop customer segmentation and product design using choice models for a streaming music service for a major entertainment company

•Segmentation using social media data to develop a audience attribution platform

Strategic marketing science

Our foundational expertise is in marketing science. We have industrial grade expertise across industries to solve most marketing, pricing, branding and channel problems. Our key methodologies are experimental design, supervised and unsupervised machine learning, A / B testing. Some illustrative thumbnail descriptions of our projects are:

•Customer segmentation, promotional analysis, pricing analysis for a major retailer using point of sale and loyalty card data

•Creating social media data-based products to develop an audience attribution and research platform for major entertainment and media companies.

•Brand equity modeling using experimental designs and supervised machine learning

•Customer lifetime valuation models for the subscribers of a major US cable TV company (Used Multinomial Logistic Regression)

•Customer churn models for wireless service providers using telecom transaction data. (Used Logistic regression and Survival Modeling)

Training

We are currently designing training programs for the following analytics and data sciences topics:

•Foundational course in statistical methods inference

•Data visualization using R Software – ggplot and R Shiny

•Data visualization using Tableau

•Foundational program in using R for data analysis

•Python data manipulation using Pandas

•Supervised learning modeling using R software

•Unsupervised learning modeling using R software

•Supervised learning in Python using SciKit Learn

•Machine learning in Python using Tensor Flow

•Analytics for the Internet of Things (IOT)