Project Overview
The objective of the project was to design and implement a comprehensive data analytics solution that accurately measures and analyzes store visit patterns with the help of Access Point Data. By delivering advanced analytics and visualization capabilities, the solution is aimed to link foot traffic data to sales performance metrics. This helps the organization to achieve data-driven decision-making, operational optimization, and strategic planning.
Scope:
- Data Integration: Consolidate and integrate data from several Access Point sources to achieve accurate tracking of store visits.
- Advanced Analytics: Develop models that help to identify trends, peak hours, and customer behaviours that are linked to foot traffic.
- Correlation with Sales: Implement algorithms and visualization tools in order to correlate footfall data with sales performance, leading to actionable insights.
- BI Dashboards: Develop interactive and user-friendly dashboards with real-time data visualization. This helps various stakeholders to customize views and reports for improved decision-making across the entire organization.
Key Challenges
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Data Gathering & Feature Selection The availability of key features for modeling was limited, and Wi-Fi data, though available, led to privacy concerns.
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Data Masking The devices mask the real MAC address and pseudo-randomize the addresses for privacy. This makes it extremely difficult to accurately measure data.
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Time Series Complexity The data was influenced by external factors, such as weather, calendar events, and business cycles. This made both prediction and forecasting challenging.
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Data Transformation Structuring the unprocessed data into a format suitable for analysis was a time-consuming process that required extensive validation.
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Complex Metrics Developing metrics and algorithms that help to link footfall patterns to sales performance across varying data volumes and store locations was a complex task.
Our Solution
Unified Data Platform
We integrated multiple data sources into a unified platform, which led to the standardization of footfall and sales performance indicators. As a result, the client was able to get consistent analysis.
Time Series Analysis
Applied a time series analysis approach to address the challenge of underfitting with routine machine learning algorithms. This led to accurate trend predictions.
Power BI Dashboards
Developed dynamic and user-friendly dashboards in Power BI while effectively presenting KPIs like store traffic, sales growth, and operational efficiency. These dashboards provided real-time data visualization and flexibility for customization based on the needs of the stakeholders.
Automation & Scalability
Delivered a ready-to-use automated pipeline that processed data, generated predictions, and provided prediction intervals. This helped the client to allocate resources effectively and manage uncertainties.
Key Results
Real-time Visualization
Enabled real-time updates on trends and performance metrics, leading to quicker and more informed decision-making.
Interactive Dashboards
Provided dynamic exploration of datasets, offering deeper insights into patterns and relationships between foot traffic and sales.
Standardized Reporting
The creation of standardized indicators and customizable dashboards led to consistent performance benchmarking across multiple locations and timeframes.
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