Every marketer has a version of the same problem. A customer taps an ad on their phone, installs on their tablet, and buys on their desktop, and the reporting shows three unique, disconnected interactions. New paid and organic AI channels have added even more places for those journeys to start and break. Branch solves this by connecting fragmented signals into unified device personas when the necessary signals are available and their use is permitted.
Branch linking and measurement both run on the same signal foundation, which makes link routing more reliable and attribution more accurate. I sat down with Justin Furstoss from Branch’s product team to talk about what this looks like at the device level and why it matters for marketers running campaigns.
1. When people talk about connecting customer data, what do they usually picture, and how does that differ from what Branch does?
Most people picture the marketing-database version, which merges emails, customer relationship management (CRM) records, and loyalty profiles into one master customer record, mainly for personalization and CRM. Linking and measurement solve a different problem. In our world, the work is connecting related interactions across devices, like a click in a mobile browser, an app install on a tablet, and a purchase on desktop, when an available identifier or permitted attribution method supports the connection. One cleans up known customer data, and the other connects touchpoints across devices and platforms.
2. And why does connecting those touchpoints matter for a marketer running campaigns across phones, tablets, and desktops?
Without that connection, each device looks like a different user. In practice, that fragmentation means your data represents a string of disconnected touchpoints instead of one customer journey. So you can end up overcounting users, undercounting conversions, and giving credit to the wrong channels, depending on the signals available and your configuration. Budget then flows to the channel that happened to engage the device, rather than the channel that actually drove the customer.
3. How does Branch actually connect touchpoints across devices? Walk us through the process, including where deterministic matching and Branch’s proprietary models come in and how you decide a match is confident enough to act on.
Branch uses deterministic matching when an available known identifier directly links a marketing interaction and a conversion, such as a permitted advertising identifier or a developer-provided user identifier.
When a deterministic signal isn’t available, our models can estimate whether a link click and later conversion came from the same device, where platform policies, customer configuration, and user consent status permit its use. Every match gets a confidence score, and the threshold to act depends on the use case. You might, for example, set a lower bar for a deep linking decision than an attribution decision, or require more scrutiny for a channel prone to fraud, like affiliates, than for a major social network.
4. People often misconstrue fingerprinting for probabilistic matching. What’s the difference, and what is Branch’s approach?
Fingerprinting builds a persistent device ID out of hardware and network attributes so you can track someone over time without consent. It’s brittle and it violates platform policy. Probabilistic matching is different in that it’s a one-time, confidence-scored prediction that determines whether two specific touchpoints belong to the same person. As a rule, we use proprietary probabilistic models designed to compare a limited set of signals from a link click and a later conversion for attribution, only where permitted.
5. We’ve talked about the challenges of disconnected touchpoints, but what use cases and user journeys does connecting them actually help with?
This work shows up in practically everything we do at Branch. It lets deferred deep linking survive the install, so someone who taps a link before they have the app still lands on the right content after downloading it. The same goes for web-to-app, email-to-app, and QR code journeys. It underpins cross-device and cross-platform attribution by reducing duplicated user counts and improving lifetime value (LTV) calculations. And it powers reengagement, audience building, and fraud detection. Basically, it plays a part in any journey where the customer switches surfaces mid-funnel, and that’s most journeys now.
6. To make this more concrete, let’s imagine a customer who taps an ad on a phone, installs on a tablet, and buys on a desktop. What does the marketer see with and without connected measurement, and what does that gap cost them?
Without that connection, you’d see three anonymous, disconnected users: one ad click that never converted, another organic install, and a desktop purchase without a source. The ad channel then looks like a failure because it doesn’t appear to generate any revenue. With the touchpoints connected, you’d see one customer and one journey where the ad drove the purchase, so the ad channel gets credit for the value it actually provided. That gap drains real money from marketing budgets, because teams may cut spend on the channel that worked and move it to whichever channel happened to touch the customer last.
7. Marketers may never “see” the signals and models behind their linking and attribution. Why should they care about them?
Because it impacts every number they trust and rely on. Match rates, deferred deep link accuracy, and deduplicated return on ad spend (ROAS) all sit on top of it. Weak matching means links that drop people on a homepage and reporting that credits installs to the wrong campaign. A broader, deeper signal foundation gives you measurement you can reliably act on.
8. How does Branch’s approach differ from how others in the market handle it?
Branch has more than a decade of experience building linking products and its graph is built on over 3 billion daily devices of observed linking activity. Because Branch runs the linking infrastructure itself, it sees the actual web-to-app handoff moments that others have to infer. Most of the legacy mobile measurement partners (MMPs) grew up as attribution SDKs, so their matching is mostly scoped to install attribution inside a single app’s context. Branch’s signal foundation supports linking and measurement across eligible web and app touchpoints and is continuously refreshed by live link traffic.
9. As signal availability keeps shrinking and new channels like AI-driven search complicate user journeys, what happens to this kind of measurement?
As AI search and agentic surfaces fragment journeys even more, the core question of “is this the same customer?” only gets harder to answer. With the decline of user-level signals, we relies more on aggregated, privacy-preserving measurement, such as SKAdNetwork and AdAttributionKit, our proprietary solution Predictive Aggregate Measurement, and on context carried through our own links.
Want to see what connected journeys look like on your own campaigns? Talk to our team.
