Prajwal R

Data Science Enthusiast helping fortune companies setup center of analytics

Bengaluru Area, India

Summary
analytic Regression Models Business Intelligence Projects Ecommerce Analytics Predective analytics Decision Sciences R Programming SAS Programming Data Science Machine Learning
Decision science enthusiast with over 6 years of experience across various industries like Financial Services, sports media, fashion and Retail.

Has helped various customers make better decisions using analytics for business problems related to pricing of the products, price optimization, lift measurement, customer behavioral analysis, sales effectiveness, customer segmentation, attrition, loyalty measurement and life time value assessment.

Specialties: Data Analytics and Statistics, Modelling and decision making, Market Research, Consumer behavior, Account Management, Project Management

Technologies: SAS, SQL, R, Python, Tableau, Adobe SiteCatalyst, Adobe Insight
Experience

Partner at TheMathCompany

Aug 2016 - Present

Apprentice Delivery Leader at Mu Sigma

Mar 2016 - Sep 2016

Apprentice Leader at Mu Sigma

Nov 2014 - Feb 2016

Client – One of the largest online retailers Drop Off Analysis: (Oct 2014 to Dec 2014) - Customers would enroll for various services offered by the client - The conversion rate of such customers was very low due to customers dropping off during the process - Analyzed the website features and ease of enrollment to find out reasons behind incomplete completions - Role played – Worked with a team of 3 in data extraction from Adobe Insight and Sitecatalyst. Performed path analysis using structural equation modeling to identify reasons behind drop offs

Senior Business Analyst at Mu Sigma

Dec 2012 - Oct 2014

Client – Largest Insurance provider in USA Sales Attribution: (June 2014 to Sep 2014) - Identify the impact of online marketing on offline sales - Sales though happens offline is influenced by online content and channel of marketing - Derive the effect of online marketing and website on driving sales - Role played – Worked with a team of 3 in data extraction from Adobe Insight and Sitecatalyst. Performed regression analysis to quantify effect of marketing channel and website content on sales Client – Largest membership based wholesaler in USA Loyalty & Attrition: (Oct 2013 to May 2014) - Develop a framework to evaluate as well as understand loyalty and map the leading paths, in order to make a customer loyal using behavioral and transactional information - Benchmark the existing customers across the developed framework and design marketing initiatives to move customers up the loyalty chain - Additionally, leverage the customer purchasing patterns to identify potential at-risk customers and design time bound win back campaigns - Role played – Design and ensures that the framework evolves in an agile fashion. Incorporate feedbacks from the exploratory data analysis to restructure the framework if necessary. Synthesize the insights generated from modeling exercise to actionable recommendations

Business Analyst at Mu Sigma

Oct 2011 - Nov 2012

Client – One of the largest home improvement retailers in USA Clearance SKU Pricing: (Aug 2012 to June 2013) - 40% of the inventory was leftover at the end of the year which had to be discarded - Find out optimum markdowns to be given over a span of 26 weeks in order to increase the revenue and reduce leftover inventory - Brute Force optimization technique was used to identify the set of markdowns that gives the maximum revenue - Left over inventory was reduced by 26% and the overall revenue from Clearance products was increased by 43% - Role Played – Worked independently to create an automated mechanism that calculates the optimum revenue and delivers new prices for the products on a weekly basis. These prices were dispatched to the stores and implemented. The algorithm enhanced the results based on previous price performance. - Substitute SKU identification: (Apr 2012 to July 2012) - Develop a reusable, extendable and white box method for systematically identifying substitute SKUs within a 200K+ assortment - This framework considers parameters like SKU attributes, price responsiveness, customer choice patterns among similar SKUs to determine pairs of SKUs that can be identified as substitutes - The decisions from this method can be utilized to determine alternative SKUs during out of stock scenarios, pricing point determination, promotions and recommendations during online purchases - Role played – Develop an end to end automated framework using SAS with a team of 3 analysts. Create a feedback loop to readjust based on customers’ business inputs and change in SKU behavior over time periods

Education

BE at PESIT

Sep 2007 - May 2011

User Testimonials
Mibin

The call has been extremely helpful. I was advised as to how I should prepare for an analytics transition. We discussed about how should I prepare for the interview, what role could be suitable for me at this point of my career, different analytics companies, what aspects the companies look for in an interview etc.. I take this opportunity to thank him for his time.

Lohith

It was a really good session and Prajwal is a really nice person. He gave me a lot of clarity about a career in Data Analytics and Data Science, what internship positions should I go for and what skill set to acquire.

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