MAC 隨機化影響與數據完整性模擬器

模擬裝置 MAC 輪替如何影響訪客 WiFi 分析，並建立數據協調模型。

模擬 MAC 隨機化影響

1. Venue profile type

Choose a template that matches your venue footprint.

Large physical footprint, high pass-by count from adjacent streets, long dwell times, high return rate.

2. Ground truth footfall

Actual number of unique human visitors entering the venue per day.

3. Visitor return rate

40%

The percentage of daily visitors who have visited this venue previously.

4. Private address rate

80%

Percentage of visitor mobile devices running iOS 14+ or Android 10+ with MAC randomization enabled.

Data distortion simulation

+62%Raw Device Count Inflation

Without deduplication, raw probe sensors count rotated MAC addresses as new unique devices. In this scenario, raw logs record 24,240 devices, inflating your actual footfall of 15,000 guests.

Raw Probe Data (Skewed)24,240

Purple Reconciled (Corrected)14,775

Ground Truth (Actual Visitors)15,000

MetricUnique VisitorsReturn RateAvg Dwell

Raw Logs24,2405%58m

Purple14,77539%89m

Operational impact of data skew

Over-budgeting staff: Planning shift patterns based on raw device counts leads to over-staffing due to duplicate counts of rotated MAC addresses.

Skewed conversion rates: If visitor counts are artificially high, retail metrics like sales conversions (transactions / footfall) appear artificially low.

Undervalued loyalty: True returning visitors are masked, leading to poor customer loyalty tracking and inaccurate lifetime value modeling.

How Purple resolves address rotation and restores data integrity

Purple mitigates MAC randomization errors at the software layer by correlating signals across multiple touchpoints. When a user logs in via the captive portal, their account is anchored to their profile. Even if the device rotates its MAC address, subsequent logins are stitched together using session variables, browser cookies, and passive RF signatures. This cross-referencing corrects the database inflation, providing venue operators with accurate footfall, return frequencies, and dwell analytics.

深入了解隱私輪替與數據準確性

現代行動作業系統會定期隨機化 MAC 位址以保護使用者隱私。雖然這有利於安全，但它會因虛報訪客人數和縮短停留時間，進而扭曲線下場域分析。此模擬器展示了原始數據如何偏差，以及 Purple 如何協調這些訊號。

模擬的關鍵參數

因位址輪替導致的原始裝置數量虛報率。
回訪客被重複計算為首次到訪訪客。
使用強制入口登入與 Cookie 拼接進行數據協調。

Struggling with data accuracy?

Address rotation on iOS and Android can inflate raw footfall metrics. Purple reconciles guest signals through multi-source correlation, restoring analytics integrity.

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