Shankar Lal

SAS To EAP Transition:

• Spearheaded the transition from SAS to EAP (PySpark/Python cloud-based), pioneering the development of a comprehensive framework for code migration within the workstream.
• Equipped the team with essential templates and training on data manipulation and modeling techniques, facilitating a smooth transition and ensuring proficiency in handling data in the new environment.

Sentiment Score Targeting:

• Engineered tailored sentiment analysis thresholds to strategically elevate PTP rates, fostering superior customer satisfaction outcomes.
• Demonstrated significant enhancements in sentiment score performance for top and bottom performing agents, both Offshore and Onshore

Shift Scheduling Optimization:

• Implemented a data-driven approach resulting in significant Opex savings of $1 million while maximizing agent productivity and maintaining seamless customer support during high-demand periods.
• Utilized segment analysis to recommend an optimized shift schedule for calling agents, ensuring sufficient capacity on the floor during peak demand periods.

Staffing Strategy:

• Engineered and executed staffing strategies by constructing comprehensive simulations utilizing the Erlang C model to determine optimal buffer capacities.
• By employing various levers such as hiring and overtime, optimized buffer capacity recommendations derived from Erlang C simulations directly contributed to significant cost reductions $5 million.

Call Forecasting:

• Created a driver-based call volume forecasting system integrating time series and linear regression models for effective capacity planning.
• Enabled the Workforce Management (WFM) team to make efficient, data-driven decisions by automated call volume forecasting framework

IVR Call Journey:

• Designed and implemented an advanced IVR call journey, enabling precise tagging for seamless tracking and analysis of each customer interaction.
• Utilized Customer Effort Score data to pinpoint pain points and bottlenecks in the IVR system, facilitating targeted improvements for enhanced customer satisfaction.

Separate Entity Migration:

• Devised and executed a campaign strategy to encourage customer information updates
• Optimized communication channels to boost update rates and minimize contact frequency using driver analysis
• Successfully reduced contact rate from 23% to 13% through targeted customer engagement tactics

Techniques/Tools used - Pyspark, Python, Hive, SQL, SAS, Segmentation, Bagging & Boosting algorithms, Linear regression, Text Mining

• Developed a geographically viewed dashboard for comparing different geographical constraints at the same level, enhancing data visualization.
• Conceptualized an end-to-end data collection pipeline within a two-month timeframe and incorporated drill-down functionality from country to hotel level for comparing business metrics.

• Implemented automation for generating post-call disposition summary notes, significantly reducing agents' manual effort by 20-30%.
• Utilized unigram ROUGE as an evaluation metric, achieving a validation interaction score of 0.53, indicating effective summarization accuracy and system performance.