Project: Supervalu Retail Business (April 2017 to Till date):

Overview of the Project: Gathered user requirements, analyzed and designed software solution based on the requirements using Tableau and SAP WEBI.

As a Tableau Developer performed the following responsibilities:

• Design, document and build Service Management Tableau reports providing IT stakeholders critical information displayed in creative charts, tables, maps
• Created & Designed scorecards and trend graphs for governance dashboards.
• As a team member, design and build Tableau and CABI (CA Service Desk Business Intelligence) reports for IT support teams to manage incidents, requests, change orders and problems.
• Create data extracts, work with Tableau admin for scheduling, refreshing extract.
• Involved in creating database views in SQL Server as backend for tableau reports.
• Developed Tableau workbooks from multiple data sources using Data Blending.

Analyze Google Merchandise Store's (also known as GStore) customer dataset to predict revenue per customer. The outcome will have more actionable operational changes which will help making better use of marketing budgets for Google.
The objective of this problem is to predict the “transactionRevenue” generated by each unique Customer ID. We are predicting the natural log of the transactions revenue with respect to the full visitor ID. There are multiple attributes in the dataset which will help in the prediction of the “transactionRevenue”.

Analytics for an Online Retailer: Demand Forecasting and Sales Prediction – April 2018
Rue La La, as an example of how a retailer can use its wealth of data to optimize pricing decisions on a daily basis. It is in the online fashion sample sales industry, where they offer extremely limited-time discounts on designer apparel and accessories.
One of the retailer’s main challenges is pricing and predicting demand for products that it has never sold before, which account for the majority of sales and revenue. To tackle this challenge, machine learning techniques is used to estimate historical lost sales and predict future demand of new products. The nonparametric structure of our demand prediction model, along with the dependence of a product’s demand on the price of competing products, pose new challenges on translating the demand forecasts into a pricing policy. Developing an algorithm to efficiently solve the subsequent multi-product price optimization that incorporates reference price effects, and creation and implementation of this algorithm into a pricing decision support tool for Rue La La’s daily use. We conduct a field experiment and find that sales does not decrease due to implementing tool recommended price increases for medium and high price point products. Finally, we estimate an increase in revenue of the test group by approximately 9.7% with an associated 90% confidence interval of [2.3%, 17.8%].