Adithya Shankar Chandrasekaran

In WW ACES CI Expert role i was tasked to work on world wide contact efficiency program (CEP).

• In Amazon there are thousands of customer executives who are trained in various specializations to assist varying customer query. However to reach to those specialized skills would be determined by multiple factors. In this scenario . The main mission of the program is to connect customers to the right customer executive first time, every time.
• The contact efficiency program is mandated to scale transfer and consult reduction programs globally.
• Flash chat bot program - 1. On WW ACES contact efficiency program behalf i partnered with operations to repurpose a chat bot for reducing consults. 2. The frequently contacted issues were text mined and analyzed. The solutions for frequently contacted issue were added to chat bot 3. The program piloted with a site that handles US market using double technique method. The pilot program resulted in reduction of 16,000 consults . 4. The program is now a prototype for other sites to replicate and ACES is partnering with operations to scale the program.
• New security architecture migration - 1. Amazon customer service is moving to a new security architecture in line with its policy of safeguarding customer information. 2. The migration would require large tech resource to add current list of specialty skill to new architecture. 3. To mitigate this problem tech team is partnering with ACES CEP team to validate various skills and de-specialize them. 4. As a research member in this program i extracted the data and applied various statistical techniques on the data. This resulted in bucketing each skills volume,agents involved, unique policies etc. 5. The projects primary analysis phase is completed and post completion of this project entire specialization space would be simplified.

In IN ACES CI Expert role i was tasked to work on CS India transfers program.

• In Amazon there are thousands of customer executives who are trained in various specializations to assist customers. However to reach to those specialized skills would be determined by multiple factors. In this scenario . The main mission of the program is to connect customers to the right customer executive first time, every time.
• To achieve this mission we analyzed historical data using pareto analysis technique and tabulated top problem areas. Then we partnered with operational stakeholders to perform root cause analysis (RCA) on major pain points.
• The root cause analysis gave us an insight into knowledge and procedural gaps in the system. We used these insights to partner with tech team to create new routing logic for top specialized skill.
• This new routing technique enabled 1.25 million transfer call reduction or $500,000 dollar savings against 2020 goal of 750,000.
• Achievements -
• The new routing helped amazon india achieve low transfer % among amazon marketplaces.
• In Q2,2020 i was awarded "Business Enabler" for transfer program.
• In 2020 performance was marked under "Exceeded expectation" category.

• Lennox is a leading climate control provide in USA.
• In lennox i was tasked with demand forecast of steel by factoring in steel price, order demand, storage capacity etc.
• In my short stint I data visualized steel demand forecast for year 2020 that could potentially reduce average purchase by 5 % ( Inflation adjusted)

Process Lead of an analytics team whose primary role is to provide actionable insights for business

• Warehouse selector - The warehouse selector model built with multiple factor selection method using inputs like shipment volume, transportation cost, location and traffic. This helped reduce shipping cost by $100,000 for client Shelter-logic.
• Metrics dashboard - Created metrics for client to track real time shipping data with baseline value. This enabled to track anomaly data easily
• Anomaly Dashboard - Conceptualized and provided structural framework for dashboard which compares real time data with last 52 week average on various parameters
• Carrier optimization - 1. The carrier optimization model performed with two predictor variable cheapest carrier rates and change of discount % with increase in volume 2. This enabled to mix and match carriers and reduce the shipping cost by 10 %- 15 % year on year for existing clients and 25 30 % for new clients
• Process Analytics - Created a continuous improvement metrics system that would monitor wastages and send report to operational managers.