Data Analytics Journey Phase 4

After the data platform and machine-learning parts have been well-developed, your data is ready to do more for getting more valued insights, such as:

1. Footfall & Walk-path Analysis
2. People Counting
3. Mall Shop Analysis
4. Travel Behavior Analysis
5. Recommendation for trip planning

Footfall & Walk-Path Analysis

Case Reference: A Large Transportation Service Provider

Footfall & Walk-Path Analysis are important location data that help further study visitors’ journey and foot traffic on each area/ad location.

At its most basic level footfall analysis means counting the people who visit your location.

The old-fashioned way was a manual clicker that they used to keep track of the number of visitors. Newer systems use cameras, beacons, and Wi-fi with corporate applications to gain a better understanding of how people move in the area.

Shop Conversion Rate & Leasing KPI

– Analysis of the number of visitors entering the store, combine with POS data to evaluate the conversion rate.

– Walk-Path analysis to identify the higher and lower visit potential. Venue owner can provide promotion/signage to help shops which have less footfall.

Recommendation Engine for Travelers

Provide tailor-made recommendations or coupons of shops, events, and campaigns by the mobile application. The recommendation can be based on the machine learning of the customer purchase pattern and location. Attract customers from facility A to B.

Coupon with shops advisement can be pushed to the target visitors.

Case Reference – A Transportation Service Provider

Monitor and analysis their shopping malls performance, collect data by using BLE Beacon with their applications.

Analysis

• Customer Demographics and Behaviors
• Dwell time in the mall
• Walk-path analysis to support the rental & advertising price

Example usages of Predictive Analysis:

• Customer Behavior Analysis
• Recommendation Engine
• Inventory Prediction
• Cost Optimization
• Location Analysis
• Real-time Predictive Analysis
• Budget Planning

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