Digital Marketing Strategy : Runs Food Court, Bangalore
Client: Runs Food Court, SG Palya, Bangalore
Duration: January – March 2025
As part of a strategic consultancy initiative, I designed and executed a full-spectrum digital marketing campaign for Runs Food Court—a multi-branch, family-run restaurant brand seeking to strengthen its digital identity and increase customer engagement in a highly competitive, student-driven locality.
Project Objectives:
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Build brand awareness and reputation in the digital space
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Drive footfall and customer engagement through targeted outreach
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Position the brand competitively against hyperlocal food businesses
My Contribution:
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Strategic Planning: Developed a detailed social media calendar spanning Instagram, Facebook, Pinterest, and YouTube, aligning content with the lifestyle and preferences of the target demographic (students and budget-conscious customers).
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Creative Development: Produced original content including memes, reels, and animated carousels to foster relatability, shareability, and engagement.
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Performance Marketing: Executed Google Ads campaigns (Search and Display) to promote high-conversion menu items.
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Trend & Market Analysis: Leveraged Google Alerts and Google Trends for keyword optimization, competitor insights, and content strategy refinement.
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Campaign Execution: Oversaw scheduling, analytics review, and agile content iterations to ensure timely delivery and maximum ROI within the project timeline.
Outcome:
The campaign delivered measurable improvements in digital engagement, brand visibility, and audience interaction. It also created a strong, scalable digital foundation for the restaurant’s future marketing efforts.
This project reflects my ability to combine creativity with analytics, strategy with execution, and business goals with audience psychology—resulting in engaging and performance-driven campaigns.
Web-Based Machine Learning Prediction Platform
I developed and launched a production-ready web application that enables users to utilize machine learning for real-time predictions, regardless of their technical expertise. Using Python and Streamlit, I created a platform where users can quickly obtain results by entering data or uploading CSV files for batch processing. The application manages all preprocessing tasks, including handling missing values and encoding categories, ensuring accurate outcomes with ease. Key features include automatic result visualization, confidence scoring, and immediate downloads for further analysis, all presented through a user-friendly interface. I oversaw the entire project lifecycle, from data sourcing and preparation to model training, UI design, and cloud deployment for worldwide access. This project showcases my ability to connect advanced data science with practical software engineering, emphasizing my dedication to delivering scalable and impactful solutions for innovative, data-driven teams and organizations.