預測分析

數據分析 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. 步行量和人行道分析
  2. 人數盤點
  3. 商場分析
  4. 出行行為分析
  5. 旅行計劃建議

步行量和步行路徑分析

案例參考:大型交通服務提供商

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

從根本上講,人流分析意味著對訪問您位置的人進行計數。

老式的方式是手動點擊器,他們用來跟踪訪問者的數量。 較新的系統將攝像頭,信標和Wi-fi與公司應用程序配合使用,以更好地了解人們在該地區的活動方式。

Footfall & Walk-path Analysis 步行量和步行路徑分析

店鋪轉換率和租賃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.

Shop Conversion Rate & Leasing KPI Formula 店鋪轉換率和租賃KPI方程式
Shop Conversion Rate & Leasing KPI 店鋪轉換率和租賃KPI

遊客推薦引擎

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.

帶有商店建議的優惠券可以推給目標訪客。

Recommendation Engine for Travelers 遊客推薦引擎

Case Reference – A Transportation Service Provider

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

分析

  • 客戶統計和行為
  • 在商場的停留時間
  • 步行路徑分析以支持租賃和廣告價格
Case Reference - Railway Service Provider 案例參考–運輸服務提供商

預測分析的示例用法:

  • 客戶行為分析
  • 推薦引擎
  • 庫存預測
  • 成本優化
  • 位置分析
  • 實時預測分析
  • 預算計劃

 

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