Arpit Verma
Turning data into actionable insights for business growth.
✨ My Journey So Far
2018 – MBA – IIM Ahmedabad
Specialized in Business Analytics and Strategic Decision Making.
2021 – Microsoft Power BI Certification
Mastered Power BI tools to visualize and interpret data for decision making.
2023 – Customer Segmentation Model
Created behavior-based customer clusters to personalize marketing strategies.
🧩 Proof of Work
Customer Segmentation Model
Arpit Verma, a Data Scientist at Flipkart, developed a customer segmentation model to improve targeted marketing efforts. Using machine learning techniques, Arpit segmented customers based on purchasing behavior, demographics, and interactions with the brand. The model I was designed to help businesses better understand their customer base, optimize their marketing strategies, and deliver personalized experiences. Arpit applied advanced clustering algorithms to categorize customers into different groups, each exhibiting unique behaviors. By analyzing these groups, the company can target customers more effectively with personalized promotions, product recommendations, and tailored messaging. This model demonstrated how data-driven insights can drive business decisions, improve customer satisfaction, and ultimately increase sales.
Read more →Predictive Sales Forecasting
Arpit Verma also developed a predictive sales forecasting model as part of his work at Flipkart. The project aimed to forecast sales using historical data, providing the company with accurate predictions for inventory planning, marketing strategies, and revenue projections. Arpit’s model used time-series analysis and regression techniques to predict future sales trends, taking into account factors like seasonality, past sales data, and market conditions. The goal of the predictive sales forecasting model I was to help businesses anticipate demand more effectively, minimize stockouts, and reduce overstocking. By leveraging the power of data science, Arpit I was able to create a tool that not only provides accurate sales predictions but also empowers businesses to make better, data-driven decisions for future growth. The model has already shown potential in streamlining inventory management and enhancing operational efficiency.
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