Nagaharsha H A

Customer Composition: Studios spend billions of dollars in marketing, not knowing who to address since the population is diverse. This composition helps to organize customers based on demographics. Accordingly, promotions can be set to have better upticks in growth.

RFM Segmentation: Do all customers in an organization need to be treated similarly? The answer is no. RFM segmentation helps segment customers based on how recent they are, how frequent, and how much he/she has spent. Segmentation is done based on these values and helps businesses retain their best customers, bring back customers, and cross-sell.

Recommendation: Do businesses need to promote new products? If so, using the product clustering approach based on product characteristics helps to identify the right target of customers and make a personalized recommendation based on his/her interest.

Data Quality: Sometimes, it's critical for businesses to understand how customer data is captured for analytical use cases. Data quality metrics help shed light on the quality of data and plan the next steps.

Day-to-day operational reports were built across various functions such as planning, merchandising, marketing, HR, finance, and compliance to understand business health, some of which are explained here.

An HR-driven initiative for incentive report/dashboard were created for store staff to motivate them and achieve even more.

The marketing team could do ad-hoc composition in the reports, enabling them to customize their approach to each customer.

The compliance team was always informed through Power BI reports, which allowed them to monitor the daily routine that a store manager has to meet.

Product-level performance reports (Power BI/report connected to Excel) were available for sales/stock mix and helped inform their buying decisions.

With the latest Power BI features, the leadership team was provided with mobile reports/dashboards with near real-time operational reporting to assist them in making key decisions.

I built several warehouses and datamarts in industries such as retail, manufacturing, entertainment, healthcare, and finance.

I was also involved in building a massive financial consolidation DWH and customized the reporting based on finance. Due to heavy gigabytes of data, I chose the best infrastructure and implemented the specific indexes (such as Columnar) to improve performance.

A customer-centric DWH was built to provide a 360 analysis for the customer. This DWH had the ability to analyze POS-related metrics as well as consolidated levels (MDM match and merge process). I was the key decision-maker on data modeling and architecture for the project.