Sr. Specialist, Data Science – Global Advanced Analytics Group
Bain & Company, Bangalore
M.A (Economics), Delhi School Of Economics, Delhi University
B.A. (Economics Hons.), St. Stephen’s College, Delhi University
Over 7 years of experience in Business Analytics & Data Science. Core competency in Predictive Analytics & Statistical Modeling with a keen interest in designing, developing and deploying analytical solutions for organizations. Deployed advanced algorithms across different verticals; CRM (Customer Analytics), Marketing, Space planning, Merchandising, Pricing, Assortment planning, Supply Chain (Demand Planning), Category Management & Web Analytics.
Currently working at Bain & Company in the Global AAG to develop data science solutions for different practice verticals and industries. Prior to this, lead a team of data scientists to develop advanced analytics solutions and products for multiple businesses and brands of a Retail Organization.
Keywork Areas
Design, development and deployment of machine learning, advanced analytics algorithms in business verticals.
Collaborate with a team of consultants, analysts, data engineers, product/account managers & business managers to develop state of the art analytics products and solutions.
Research and provide point of view on existing practice areas and determine ways to improve capabilities. Collaborate with external vendors for the same.
Manage & mentor juniors in all aspects of project management.
Topic Title: The Price is Right?
Abstract: A multitude of challenges occur when considering pricing in B2B and B2C. The presentation would cover two applications of analytical algorithms in B2B and B2C space, Deal guidance and Markdown management, respectively and provide a contrasting view from a business & application standpoint.
Deal Guidance in B2B requires a pricing management software that integrates data, machine learning algorithms, and a pricing mechanism that can be fed into existing CRM/ CPQ systems, whereby sales reps can feel confident about the guidance on pricing for a new deal, react to deliver quotes for customers and significantly improve the chances of the deal getting approved. Being able to determine the right price for every quoting solution is the goal in B2B. The lack of loss data (deals lost) however makes the problem challenging as one does not exactly know the factors that contribute to it. There is hence, a requirement for a hybrid solution that uses win data of customers to understand and develop a mechanism to determine the target price for a deal. Such a system was developed using analytical algorithms and augmented to a pricing software that is used by most clients for B2B pricing.
On the other hand, Markdown Pricing in B2C is a common problem for fast-fashion retailers in the apparel industry. They have to cater to continuously changing assortment and consumer-style needs based on seasonal variation. Given the low amount of history of data points of price changes for a new item in a retail store, it is hence challenging to predict a discount for unsold or low selling inventory on the store floor. Instead of a manual and informal decision making process, a markdown management system was developed to accurately determine the discount % that achieves the objectives of margin maximization & sell-through rate maximization by keeping customer willingness to buy, and demand elasticity at the heart of the solution. A controlled experiment delivered significant uplift results in margin accrued from discounted items and a full deployment was carried for all brands for a major retail organization. The system is currently used by the retailer for its brands to make markdown decisions.