This content originally appeared on HackerNoon and was authored by Andrew
Recently, we worked with two customers who were independently experiencing the same core issue: degraded lead quality due to unreliable data collected via Facebook/Google Ads integrations and website form submissions. Upon investigation, it became clear that the root problem stemmed from insufficient contact validation at the point of ingestion and the gradual degradation of this data. As a result, it's leading to downstream inefficiencies in their CRM and lead scoring systems.
Conventional validation pipelines typically rely on syntactic and superficial checks—such as regex-based email validation, domain existence, and phone number prefix matching. While these methods catch malformed inputs, they do little to confirm whether the contact is reachable, active, or even real. As a result, sales and marketing teams waste resources pursuing leads that are fundamentally non-actionable. In some cases, to avoid fully fake leads, businesses implemented OTP validation methods (one-time password confirmation), but it's not protected when leads use services for a disposable/temporary mailbox or phone number.
In trying to resolve this issue, we began investigating the possibility of leveraging iMessage and FaceTime usage data as a validation signal. By checking whether a phone number is associated with an active iMessage or FaceTime account, we can effectively confirm that the contact is not only valid but also active and reachable. This signal-driven validation process introduces a new dimension to lead scoring, as it allows us to classify phone numbers as high-quality, confirmed leads, reducing the risk of pursuing low-value or unverifiable contacts. As a result, lead scoring becomes more reliable, with a clear focus on contacts that are more likely to engage.
Introduction
Organizations collecting phone numbers and emails for outreach face the two most common issues:
- Data degradation (especially after 12 months)
- Users fill fake or disposable email address/phone number
- Users use unreachable email addresses (registering a separate mailbox that is never checked)
After checking customers' statistics, we discovered that in different segments, between 10–40% of contact records are outdated/unreachable, with an annual decay rate of approximately 30%.
As a result, it needs to spend a lot of resources to recheck all contacts manually. Usually, these attempts include calls to recipients, and if their phone number is unreachable, need to flag them is not more relevant. Each such call may take up to a few minutes. As a result, to process 1000 contacts, it may take 15-20 hours per week.
After checking the internet, we were surprised that degradation may impose high costs. For example, Salesforce reports revenue losses of up to 30% due to poor data quality, while Validity.com, in their 2022 reports, estimates losses of $9.7M–$15M annually for mid- to large-scale organizations.
Of course, to avoid fake data, anyone can try to use traditional validation methods like:
- MX/SPF record lookups (for emails only);
- Carrier prefix verification and phone number validation;
- Validating email/phone by sending OTP code;
But all these methods may be easily bypassed by users. In case of email addresses, many users use services for temporary email addresses, which even allow them to accept any inbound email and could be used to accept text codes. Another issue is that by default, Apple provides to any iCloud+ customer generating a disposable email address, but in most cases, they are unreachable. The same thing with phone numbers, users can just Google any service for temporarily accepting a code on a random number. But if you try to call this phone number, it will be unreachable.
All these things generate the most important question: Is the contact currently active and reachable? As a result, we tried to offer the client to use of client validation based on checking whether the email or phone number uses Apple ecosystem services and sharing the results of this research.
Related researches
In the past, we could already find some additional research on data quality that was described in the books:
- Data and Information Quality: Dimensions, Principles and Techniques (2016) by Carlo Batini and Monica Scannapieco
- Foundations of Data Quality Management (2012) by Wenfei Fan and Floris Geerts
Research in data quality management emphasizes record linkage, deduplication, and syntactic validation. Machine learning approaches have been applied for fraud detection and data enrichment, but most studies lack reliable real-time behavioral indicators. Prior work in communication/telecom/messaging systems has shown the utility of protocol-level registration signals (e.g., SIP presence in VoIP systems) as proxies for user activity. We extend this concept to mobile ecosystems, leveraging Apple’s iMessage and FaceTime infrastructure.
Methodology
Unlike other messaging apps, Apple services allow to reach recipients by phone number (from their active-only SIM cards) and email address that they used as their Apple ID (or as an additional email/alias).
Every iPhone automatically registers its associated phone numbers and/or Apple IDs with iMessage and FaceTime. Deregistration occurs when:
- SIM card is removed (only for phone number);
- User signed out from Apple ID (only for email address);
- User disables iMessage and/or FaceTime in the iPhone settings (less than 1% iPhone users do that);
- A device is inactive for a while (usually from 1 to 7 days).
Thus, the presence of a phone number or email in iMessage/FaceTime serves as a confirmation of contact validity and most likely reachable.
Implementation
Basically, this service was implemented as a part of our main service: Loop Message, but in 2025, it was re-designed and moved to a totally separate product: LoopLookup.com, with a separate API.
We developed a microservice, Loop Lookup, that performs the following:
Queries Apple registration status for phone numbers or emails;
Flags contacts as active (registered) or inactive (unregistered) for each service iMessage/FT;
Return a CSV file with the results or a webhook in case of using API requests.
Deployment
Two customers integrated the Lookup API into their CRM systems and applied their own metric that may sound like “lead scoring”. When they get a confirmation of registering a contact in iMessage/FT, they apply a “high score” for this contact due to this contact has already been validated by the Apple ecosystem.
After that, such confirmation also discovers the next few things on how to interact with leads. They can use iMessage for text or FaceTime for a call. Both these channels work over the internet, and you can easily use them for international calls.
Results
| Metric | Before (Manual Validation) | With LoopLookup (Apple services usage Validation) | |----|----|----| | Percentage of outdated contacts detected | ~5% | ~22% | | Manual verification time (per week) | ~15-20 hours | <1 hour | | Lead response rate | 8–10% | 22–28%* | | International outreach success | Limited (carrier/VoIP/SMS restrictions) | High (via iMessage/FaceTime internet-based delivery) |
* This depends on the lead segment and is related to cases where the contact has been registered in iMessage/FaceTime.
The integration of new validation reduced wasted outreach efforts and improved overall pipeline efficiency.
Discussion
The findings indicate that Apple ecosystem signals provide a reliable proxy for contact authenticity. Compared to other methods, this approach:
- Detects fraudulent or disposable numbers at a higher rate;
- Detects when users use unreachable email addresses or phone numbers;
- Reduces manual labor in lead verification;
- Improves lead conversion rates by prioritizing contacts who use Apple services;
- Enables international communication via internet-based channels: iMessage/FaceTime.
Given Apple’s market share (around 55-60% in the U.S. and Canada), the impact of this validation method is especially pronounced in B2B contexts, where executive adoption of iOS exceeds 80–90%.
Of course, such validation shouldn't be used for user irritation. But if you use it for B2B leads/cooperation, it could be a good approach for resolving issues with contact data degradation.
This experience presents the feasibility and effectiveness of additional validation for contact data quality. By leveraging Apple ecosystem registration signals, organizations can significantly reduce invalid data, enhance lead scoring, and streamline outreach workflows.
\n \n
\n \n \n \n
This content originally appeared on HackerNoon and was authored by Andrew
Andrew | Sciencx (2025-08-28T06:12:06+00:00) Detecting and Mitigating Fake Contact Data: A Case Study with Apple Ecosystem Signals. Retrieved from https://www.scien.cx/2025/08/28/detecting-and-mitigating-fake-contact-data-a-case-study-with-apple-ecosystem-signals/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.