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Michelin Mobility Intelligence: Turning Road Data into Smarter Mobility and Logistics Decisions Michelin is widely recognised for its tyres, but the company has been steadily expanding far beyond manufacturing into data-driven services.  One of its most notable digital ventures is Michelin Mobility Intelligence, a business unit focused on transforming road and mobility data into actionable insights for logistics, transport operators and public sector planning. As global transport systems become more complex and efficiency-focused, Michelin Mobility Intelligence positions itself at the intersection of physical infrastructure and digital analytics, using real-world driving data to improve how goods and people move. From Tyres to Data: The Strategic Shift Michelin’s move into mobility intelligence reflects a broader transformation within the automotive and logistics industries.  Modern vehicles generate vast quantities of movement and performance data, and companies operating lar...

Google and Meta don’t “magically” know your phone and laptop are yours.  They build that link by stitching together multiple overlapping signals until the probability becomes very high. Here we’ll break down how each company typically does it in practice. 1) The core idea: identity graphs Both Google and Meta Platforms build what’s called an identity graph. Instead of: “This is user X” They store: • devices • browsers • app sessions • login events • IP history • behavioural patterns ..and connect them with confidence scores like: “These devices likely belong to the same person or household” 2) The strongest link: login accounts Google If you’re signed into: • Gmail • YouTube • Chrome • Android …you’ve essentially provided a hard identity anchor. Once logged in: • laptop browser ID • phone app ID • tablet session ..all get tied to the same Google Account automatically. Even if you switch devices, the login connects them instantly. Meta Same idea with: • Facebook • Instagram • WhatsA...

SKAdNetwork (StoreKit Ad Network) is Apple’s system for measuring ad performance on iOS in a privacy-preserving way—introduced as part of the shift away from user-level tracking after App Tracking Transparency. It’s basically Apple’s answer to: “How do advertisers know an ad worked without tracking the person who saw it?” 1) The core idea Instead of telling advertisers: “User X saw ad Y and installed app Z” SKAdNetwork only tells them: “An install happened, attributed to ad campaign Y” …but: • no personal identity • no device ID (like IDFA) • no cross-app tracking data So it’s aggregated, delayed, and anonymised attribution. 2) How it works (step-by-step) Step 1: Ad impression / click A user sees or clicks an ad inside an app on iOS. The ad network registers: • campaign ID • ad network ID • basic metadata But not the user identity. Step 2: App install happens If the user installs the advertised app within a time window, iOS records: “This install is associated with campaign X” But agai...

Ad networks don’t usually “know you” in a single, direct way. They assemble a stitched-together identity from many weak signals collected across different websites.  Think of it less like a passport and more like reconstructing the same person from repeated patterns of behaviour and device traits. Here’s how that identity merging actually works. 1) The shared tracking script ecosystem (the backbone) Most websites don’t run their own tracking from scratch. They embed third-party scripts from large ad platforms and analytics providers. When you visit Site A and Site B, both might load the same tracking code from the same company. That means: • The same tracking infrastructure sees both visits • It can compare signals across millions of sites • It builds a cross-site activity graph So even if the websites are unrelated, the tracker is the common observer linking them. 2) Cookie syncing (classic cross-site identity linking) Historically, ad networks used third-party cookies to recognis...

Leave the Phone at Home: Covert Tracking and Surveillance in Modern Apps Smartphones have become indispensable tools for communication, navigation, banking, and entertainment.  Yet beneath their convenience lies a complex and often poorly understood ecosystem of data collection.  Many apps quietly gather far more information than users realise, creating detailed behavioural profiles that can be used for advertising, analytics, and sometimes surveillance. The phrase “leave the phone at home” captures a growing concern: in certain contexts, a smartphone is not just a device—it is a tracking beacon. This article explores how covert tracking works in mobile apps, what data is collected, who uses it, and why it matters. The Hidden Economy of App Data Most free apps are not truly free.  Instead, they are funded through data-driven business models, primarily advertising technology but also data collection.  To serve targeted ads, apps and their partners need to collect exte...

IP-based geolocation is basically a lookup system that maps an internet connection to an approximate physical area.  It’s widely used, but it’s important to understand what it can and can’t do. How it works When you connect to a service, your device uses an IP address (e.g. something like 82.xxx.xxx.xxx).  That IP is assigned by your internet provider (ISP). Geolocation databases (run by companies like MaxMind, IP2Location, etc.) map IP ranges to locations using: • ISP registration data (who owns that block of IPs) • Routing information (where traffic enters major internet hubs) • Data from Wi-Fi/mobile networks (crowdsourced or licensed) • Previous user/device location signals (in some cases) So instead of “tracking you,” it’s more like: “This block of IPs is usually used in or around Manchester.” How accurate it is It varies a lot: • City-level (like Manchester): often fairly accurate, but not guaranteed • Wrong city but correct country: common • Completely wrong region: ha...