🤔How do we do it?

Data Sources:

We collect data from more than 650 government and trusted public data sources such as national surveys and ministry reports, satellite data, etc.

Data Transformation:

The data collected from various sources in various types and formats is transformed into consumable location data variables characterised as socio-economic, demographic, infrastructure, affluence, and many more. This data is also used in the machine learning models built for predictive analytics.

ML Modelling:

The location data trains ML models to identify patterns that depict success scenarios for a retail store.

Data Privacy:

We do not source or use any kind of user data and personal information for our prediction models, hence there is no privacy breach. We're ISO-27001, HIPAA, and SOC-2 (Type 2) compliant while maintaining the highest standards of security, privacy, and integrity in our operations.

Data Validation and Quality Assurance:

Data accuracy is paramount to us. We implement stringent validation processes, including cross-referencing datasets, identifying inconsistencies, and using automated tools to clean and standardize data. This ensures that our location-based insights are not only reliable but also actionable for decision-makers. Regular audits and quality checks are conducted to maintain the highest level of data integrity.

Scalability:

RetailIQ is built to scale. Whether you're analyzing a single store location or hundreds across the country, our platform can handle large-scale data processing without compromising on speed or accuracy. The system is designed to expand with your needs, ensuring that as your business grows, RetailIQ remains a trusted partner in your expansion strategy.

Real-Time Data Updates:

We understand that retail landscapes can change rapidly. That’s why RetailIQ offers timely data updates, ensuring that you always have the latest insights at your fingertips. Whether it's a shift in footfall patterns or changes in local demographics, our platform keeps you informed, allowing for agile decision-making.

Performance Monitoring and Improvement:

Our commitment to excellence extends beyond deployment. RetailIQ continuously monitors the performance of its ML models, tracking key metrics to ensure accuracy and relevance. Feedback loops and regular updates are part of our process, allowing us to refine and improve our models based on real-world results and customer feedback.