If you've noticed your connect rates cratering over the past year, you're not imagining things. The cold calling landscape underwent a seismic shift with iOS 26, and most sales teams are still playing by rules that no longer exist.

The average cold calling success rate dropped from 4.82% in 2024 to just 2.3% in 2025—nearly a 50% decline in just twelve months. For sales development teams already struggling with the daily grind of 60+ dials to book a single meeting, this collapse represents an existential threat to pipeline generation.

But here's what most SDR leaders are missing: the drop isn't happening evenly. While average performers watch their numbers plummet, top teams using intent-based calling are actually achieving 20-30% connect rates—roughly 10x the industry average.

The difference? They've stopped trying to outrun the spam filters and started working with the system instead.

This guide breaks down exactly what changed with iOS 26, how carrier spam analytics actually work, and the specific strategies that separate the teams drowning in "Spam Likely" labels from those booking meetings consistently.

Table of Contents

1. The iOS 26 Shift: Why Everything Changed in 2025
2. How Carrier Spam Analytics Actually Work
3. Decoding the Labels: Likely Sales vs. Spam Likely vs. Scam Likely
4. STIR/SHAKEN Attestation: What It Actually Means for Your Calls
5. Why "Burning and Churning" Numbers No Longer Works
6. The Parallel Dialer Problem: How Volume Became a Liability
7. The Phone Intent Solution: Calling the Right People at the Right Time
8. Your Complete Remediation Action Plan
9. Free Resources and Tools

The iOS 26 Shift: Why Everything Changed in 2025

For years, the cat-and-mouse game between sales teams and carrier spam filters followed a predictable pattern. You'd get flagged, buy new numbers, "warm them up," and start the cycle again. This worked because spam detection happened primarily at the carrier network level, with relatively slow feedback loops.

iOS 26 fundamentally rewired this dynamic by moving spam intelligence onto the device itself, creating a faster, more aggressive, and harder-to-evade filtering system.

The Three-Layer Defense System

Modern call screening now operates on three simultaneous layers:

Layer 1: On-Device AI Screening

iOS 26 introduced enhanced call screening that uses on-device processing to display live transcripts of unknown calls without your phone ever ringing. The system analyzes audio fingerprints, calling patterns, and behavioral signals to make instant judgments about incoming calls.

The "Screen Unknown Callers" feature now offers three modes: Never (calls ring normally), Ask Reason for Calling (callers must state their purpose before your phone rings), and Silence (calls go directly to voicemail without notification).

For sales teams, the "Ask Reason for Calling" mode is particularly devastating. When your call triggers this screen, the prospect hears an automated message asking why you're calling—before they ever see your name or number. If you hang up (as most auto-dialers do when hitting voicemail or screening), that behavior is logged and shared with the broader system.

Layer 2: Carrier Analytics Integration

The on-device intelligence doesn't operate in isolation. iOS 26 maintains faster, deeper integration with carrier analytics engines than any previous version. Behavioral data flows from device to carrier in near real-time, meaning your calling patterns can trigger flags within hours rather than days.

Each major carrier partners with a specific analytics engine:

• AT&T uses Hiya
• Verizon uses TNS (Transaction Network Services)
• T-Mobile uses First Orion

These analytics engines use machine learning trained on billions of call detail records to identify patterns associated with spam and fraud. When their algorithms see a number making 50,000 calls on a Monday morning, they flag it immediately—regardless of whether the calls are "legitimate" by legal standards.

Layer 3: User Feedback Loops

iOS 26 also expanded the mechanisms for users to report and flag calls directly. Calls identified as spam can be sent to a dedicated "Spam" folder, and Voicemail Spam Reporting gives users one-tap flagging capability.

This creates a compounding problem: even a small number of user reports can accelerate the algorithmic flagging, which increases the likelihood of calls being screened, which increases the likelihood of hang-ups and reports.

The Silent Delivery Problem

Perhaps the most insidious change in iOS 26 is "silent delivery" for calls flagged as "Likely Sales" or "Nuisance Likely."

With Silence Unknown Callers enabled (which Apple actively promotes during device setup), flagged calls don't ring at all. The phone doesn't vibrate. The screen doesn't light up. The call simply appears as a missed call after the fact—if it appears at all. Some users have their Unknown Callers list filtered entirely from their main call log.

This means your dials aren't just going unanswered—they're going unnoticed entirely. Your prospect doesn't make a conscious choice to decline; they never knew you called.

How Carrier Spam Analytics Actually Work

Understanding how you get flagged is the first step to avoiding it. Most sales leaders have a vague sense that "high volume = spam flags," but the actual mechanics are more nuanced—and more punishing—than commonly understood.

The Analytics Engine Ecosystem

The three major analytics engines (Hiya, First Orion, and TNS) each use proprietary algorithms that consider multiple factors when scoring calls. While the exact formulas are trade secrets, industry research has identified the key signals:

Call Detail Records (CDRs)

These are the metadata associated with every call: timing, duration, frequency, geographic patterns, and routing information. The analytics engines analyze CDRs looking for patterns that match known spam profiles:

• High call frequency from a single number or number pool
• Very short call durations (indicating hang-ups on voicemail or screening)
• High ratios of unanswered to answered calls
• Burst patterns (many calls in short windows followed by silence)
• Unusual routing through multiple carriers

Audio Fingerprinting

This is a relatively new and particularly sophisticated approach. Analytics engines record brief audio samples from calls and use machine learning to identify patterns:

• Automated voice detection (IVR systems, recorded messages)
• Background noise profiles associated with call centers
• Speech patterns that indicate scripted pitches
• The distinctive "delay" that occurs when parallel dialers connect a human agent

If your calls consistently start with a 1-2 second pause before an agent speaks—the telltale sign of a parallel dialer—the audio fingerprint identifies this as "bot behavior" even if a human eventually speaks.

User Reports and Blocking Data

When users block a number or report it as spam through their device or carrier app, that feedback enters the analytics engine's training data. Multiple reports from unrelated users accelerate flagging dramatically.

What many sales teams don't realize: when a contact blocks your number, subsequent calls from that number to any user on the same carrier get weighted more heavily for spam scoring. You're not just losing that one prospect—you're potentially burning that number across the entire carrier network.

The Inconsistency Problem

One of the most frustrating aspects of the current system is inconsistency across carriers. Because AT&T, Verizon, and T-Mobile each use different analytics partners with different algorithms, the same number can show clean on one carrier and flagged as "Spam Likely" on another.

Research from Numeracle found that when testing a newly allocated block of 100 phone numbers (never previously assigned to any customer), Hiya flagged 32% as spam and TNS flagged 24% as potential spam—before a single call had been made from those numbers.

This means you can purchase "clean" numbers that are already considered suspicious by some carriers simply based on the number block they came from.

The Feedback Latency Challenge

Analytics engines don't update instantaneously. When you register a number with the Free Caller Registry or submit a remediation request, changes can take days or weeks to propagate across all carrier networks.

This creates a painful catch-22: by the time you realize a number is flagged, fix it, and get the fix propagated, you've likely already burned through significant prospect lists with calls that never connected.

Decoding the Labels: Likely Sales vs. Spam Likely vs. Scam Likely

Not all negative labels are created equal. Understanding the hierarchy of spam classifications helps you prioritize remediation and adjust your strategy accordingly.

"Likely Sales" / "Telemarketer" / "Nuisance Likely"

Severity: Warning Level

This is the first stage of flagging. Your calling patterns match what the algorithms associate with call centers—high volume, short duration, commercial intent—but you haven't accumulated enough negative signals to be classified as actual spam.

What triggers it:

• Using parallel dialers that result in high abandonment rates
• Making 100+ calls per day from a single number
• Low answer rates (under 5%) combined with short call durations
• Calling patterns that suggest automated dialing rather than human judgment

What it does to your calls: On iOS 26 with default settings, these calls may be delivered silently (no ring, no notification) or sent directly to the Unknown Callers folder. The prospect sees a missed call—maybe—hours later.

Recovery difficulty: Moderate

Numbers at this stage can often be rehabilitated through behavioral changes and carrier registration, though recovery typically takes 2-4 weeks.

"Spam Likely" / "Spam Risk" / "Potential Spam"

Severity: Critical

Your number has been flagged by carrier algorithms and likely reinforced by user reports. You're now in the category of calls that carriers actively discourage users from answering.

What triggers it:

• Progression from "Likely Sales" without behavioral changes
• Direct user reports of spam
• Dialing low-intent data where under 3% of calls result in conversations
• Numbers associated with high complaint rates to carrier customer service
• Purchasing numbers from blocks with negative reputation history

What it does to your calls: These calls are explicitly labeled on the incoming call screen. Most users won't answer a call tagged "Spam Likely." On some devices and carrier configurations, these calls may be automatically diverted to a junk folder that users rarely check.

Recovery difficulty: Hard

Remediation requires formal submissions to carrier analytics providers, plus sustained good behavior over 4-8 weeks. Many teams find it more efficient to quarantine flagged numbers and start fresh.

"Scam Likely" / "Fraud Risk"

Severity: Terminal

Your number is associated with fraud, illegal robocalling, or scam operations. Connect rates at this level are effectively zero.

What triggers it:

• Association with known fraud campaigns
• Spoofing caller ID numbers
• Patterns consistent with illegal robocalling operations
• FTC/FCC enforcement actions against associated entities

What it does to your calls: Calls may be blocked outright before reaching the device, or displayed with maximum-severity warnings. Some carriers auto-reject these calls entirely.

Recovery difficulty: Near Impossible

Numbers flagged as "Scam Likely" are typically unrecoverable. Attempting to remediate them wastes time that should be spent on fresh numbers and improved processes.

STIR/SHAKEN Attestation: What It Actually Means for Your Calls

You've probably heard that STIR/SHAKEN compliance is essential for call deliverability. This is true—but widely misunderstood. Many sales leaders believe that STIR/SHAKEN "A attestation" is a silver bullet against spam flags. It isn't.

What STIR/SHAKEN Actually Does

STIR/SHAKEN (Secure Telephony Identity Revisited / Signature-based Handling of Asserted information using toKENs) is a caller ID authentication protocol mandated by the FCC under the TRACED Act. It creates a digital certificate system that verifies whether the calling party is authorized to use the phone number displayed as their caller ID.

In practical terms: STIR/SHAKEN proves you're not spoofing your number. It does not prove you're not spam.

The Three Attestation Levels

When your carrier signs a call under STIR/SHAKEN, they assign one of three attestation levels:

Full Attestation (A)

The carrier knows who you are, verifies that you're authorized to use the calling number, and confirms the call originated on their network. This is the highest trust level—the carrier is vouching for your identity.

Partial Attestation (B)

The carrier knows who you are but cannot fully verify you're authorized to use the specific calling number. This typically happens when you're using numbers from a third-party provider or operating behind an enterprise PBX system.

Gateway Attestation (C)

The carrier only knows where they received the call from, not who actually originated it. This is the lowest trust level and is most commonly applied to calls entering from external networks.

Why A Attestation Doesn't Protect You

Here's the critical point most vendors won't tell you: attestation is authentication, not reputation.

A Full Attestation means "this caller is who they say they are." It does not mean "this caller makes calls people want to receive."

Spam analytics engines—the same Hiya, First Orion, and TNS systems that actually apply "Spam Likely" labels—operate independently of STIR/SHAKEN verification. An A-attested call can absolutely be labeled as spam if the calling patterns trigger algorithmic flags.

As the FCC has clarified, STIR/SHAKEN authenticates the identity of the calling party. Call labeling decisions are made by analytics engines based on behavioral patterns, user reports, and proprietary scoring algorithms—regardless of attestation level.

What STIR/SHAKEN Does Provide

That said, STIR/SHAKEN compliance is table stakes for legitimate outbound calling in 2025:

1. Deliverability baseline: Calls without any STIR/SHAKEN signature may be blocked outright by some carriers, never reaching the spam analytics layer at all.
   
   
2. Visual trust indicators: Some Android devices display a green checkmark for verified calls, and iOS shows a gray checkmark in call logs. This doesn't prevent spam labeling but does provide some positive signal to users who investigate.
   
   
3. Traceback protection: The STIR/SHAKEN certificate chain makes your calls traceable to your originating carrier. This is actually protective—it distinguishes you from spammers who rely on untraceable routing.
   
   
4. Regulatory compliance: Failure to implement STIR/SHAKEN can result in having your calls blocked by other carriers under FCC rules.
   
   

The Bottom Line on STIR/SHAKEN

Ensure your calling infrastructure uses A attestation wherever possible. Register your numbers properly. But don't mistake authentication for reputation. STIR/SHAKEN gets your calls into the system—what happens next depends entirely on your calling behavior and the data you're calling.

Why "Burning and Churning" Numbers No Longer Works

The traditional playbook for dealing with spam flags was simple: when a number gets flagged, quarantine it, buy fresh numbers, warm them up with low-volume calling, and rotate them into your pool.

In the iOS 26 era, this strategy has a fatal flaw: the feedback loop is now faster than your rotation cycle.

The Math Has Changed

Consider the traditional number rotation approach:

1. Purchase 10 new numbers
2. "Warm" them with 20-30 calls per day for 2-3 weeks
3. Gradually increase volume over 4-6 weeks
4. Use numbers at full volume until flagged (typically 60-90 days)
5. Replace with fresh numbers, repeat

This worked when spam detection was slow and carriers relied primarily on volume thresholds. You could stay ahead of the system by never letting any single number accumulate enough calls to trigger flags.

Now consider what actually happens:

1. Purchase 10 new numbers
2. Numbers may already show spam risk on some carriers (pre-flagged number blocks)
3. First day of "warming" calls: 25 calls, 2 answers, 23 voicemails and declines
4. iOS 26 devices report the instant hang-ups when your dialer hits call screening
5. Analytics engines see 8% answer rate, short call duration, pattern matching to known spam
6. Numbers flagged within 1-2 weeks, before "warming" period completes
7. Purchase new numbers, repeat the cycle faster

The fundamental problem: your calling behavior triggers flags regardless of how "fresh" the number is. If you're calling data where 95% of people don't want to hear from you, the algorithms will identify that pattern—and they'll identify it faster with each iOS update.

The Reputation Inheritance Problem

There's also a less-known issue: number block reputation.

Phone numbers aren't random. They come from allocated blocks, and those blocks have histories. When Numeracle tested brand-new numbers from a fresh block, they found that 24-32% were already flagged before making a single call.

Why? The analytics engines track patterns at the block level, not just the individual number level. If previous users of numbers from that block exhibited spam behavior, the entire block carries negative reputation weight. Your "clean" numbers may be dirty from the moment you acquire them.

The Real Cost of Number Churn

Beyond effectiveness, number churn is expensive:

• Direct costs: Number acquisition, STIR/SHAKEN registration, carrier registration fees
• Operational costs: Time spent managing number pools, monitoring for flags, coordinating rotations
• Opportunity costs: Prospects you've already called with flagged numbers are unlikely to answer a new number from your organization
• Data destruction: When you burn through your prospect list with flagged numbers, you've wasted those leads—they won't be more receptive when you call again from a different number

The most successful sales organizations have stopped trying to outrun the system and started fixing the root cause: calling people who actually want to talk.

The Parallel Dialer Problem: How Volume Became a Liability

Parallel dialers were supposed to solve the efficiency problem. By dialing multiple lines simultaneously and connecting reps only when a human answers, they promised 5x, 10x, even 20x more conversations per hour.

In practice, parallel dialers have become one of the primary drivers of spam flagging in B2B sales.

How Parallel Dialers Trigger Spam Flags

The Abandonment Problem

When a parallel dialer calls 5 lines simultaneously and one person answers, the other 4 calls are abandoned. On iOS 26 with call screening enabled, this creates a specific pattern that analytics engines recognize instantly:

• Call connects to voicemail or screening
• Caller immediately hangs up
• Pattern repeats across hundreds of calls per day

This isn't speculative. Industry research confirms that parallel dialers carry the highest risk of call abandonment and create the telltale "telemarketer delay"—a 1-2 second pause after the prospect answers before an agent connects. This pause is now audio-fingerprinted and used as a spam signal.

The Volume Trigger

Parallel dialers encourage a specific behavior: maximum calls per hour. Teams measure success by dials, and dialers compete on how many lines they can fire simultaneously.

The problem is that carrier analytics engines are specifically designed to identify high-volume calling patterns. The exact behavior that parallel dialers encourage—hundreds of calls per number per day, rapid-fire dialing, consistent abandonment rates—matches the profile of illegal robocalling operations.

The TAM Exhaustion Problem

Here's the strategic cost that rarely appears in parallel dialer ROI calculations:

When you dial your entire TAM (Total Addressable Market) 5x in a month with no personalization and flagged caller IDs, you've:

1. Burned through every phone number those prospects are likely to answer
2. Trained those prospects to associate your area codes with spam
3. Eliminated any second-chance opportunities with warm leads
4. Destroyed your ability to sequence phone into multi-channel cadences

You haven't generated pipeline faster—you've accelerated its destruction.

The Industry Data

The numbers tell a stark story about parallel dialing's diminishing returns:

• Phone numbers get flagged as "Spam Likely" within 2-3 weeks of high-volume parallel dialing
• Teams report 50% reductions in connect rates (from 4% to 2%) despite making 5x more dials
• The "spray and pray" approach burns through prospect lists in months instead of years

Meanwhile, teams using intent-based calling with standard dialers report:

• Sustained 20-30% connect rates
• Numbers remaining clean for 6+ months
• Higher conversion rates per conversation (because they're reaching people who want to talk)

When Parallel Dialers Make Sense

Parallel dialers aren't universally bad. They can work effectively in specific scenarios:

• High-intent inbound callbacks: When prospects have requested contact, parallel dialing the queue is efficient
• Time-sensitive notifications: Appointment reminders, delivery confirmations, urgent account updates
• Verified contact data: When you have confirmed, opted-in mobile numbers with recent engagement
• Lower volume implementations: Some teams use parallel dialers at 2-3 lines rather than 5-10, reducing abandonment rates

The common thread: parallel dialers work when you're calling people who want to hear from you. They fail spectacularly when applied to cold, unqualified lists.

The Phone Intent Solution: Calling the Right People at the Right Time

If volume-based calling has become a liability, what's the alternative? The answer lies in a fundamental reversal of the cold calling workflow.

Traditional outbound calling follows this logic:

1. Acquire a list of prospects matching your ICP
2. Load the list into a dialer
3. Call everyone, hope for the best
4. Repeat until list is exhausted

Intent-based calling inverts this:

1. Acquire a list of prospects matching your ICP
2. Analyze which prospects are actually reachable by phone right now
3. Call only the high-intent subset
4. Achieve dramatically higher connect rates with dramatically fewer dials

The 80/20 Reality of Cold Calling

Here's a data point that should reshape how you think about outbound: only about 20% of any prospect list will ever answer a cold call, regardless of how many times you try.

The other 80% either:

• Don't answer unknown numbers as a matter of policy
• Have silencing features enabled
• Are in contexts where they can't take calls (meetings, commute, etc.)
• Have already flagged or blocked sales calls from your area codes

Volume-based calling treats all prospects as equally likely to answer. It burns through that unreachable 80% at exactly the same rate as the reachable 20%, wasting effort and flagging your numbers in the process.

Intent-based calling identifies the 20% first, then focuses exclusively on reaching them.

How Phone Intent Works

Phone Intent platforms like TitanX analyze billions of signals to determine who is actually picking up the phone—right now:

Behavioral Signals

• Recent call pickup patterns
• Historical answer rates by time of day
• Device activity indicators
• Previous engagement with sales outreach

Contextual Signals

• Time zone and likely availability windows
• Meeting schedule analysis (via calendar integrations)
• Out-of-office indicators
• Business hours for specific roles and industries

Data Quality Signals

• Phone number verification and validity
• Mobile vs. landline classification
• Number ownership confirmation
• Recency of last successful contact

When you upload a list to TitanX, the Phone Intent Engine scores each prospect and identifies those with "High Phone Intent"—the subset most likely to answer if you call them within the next window.

The Math That Actually Works

Compare two approaches for a 10-person SDR team over one month:

Volume Approach (Parallel Dialer)

• 500 dials per rep per day
• 100,000 total dials per month
• 3% connect rate = 3,000 conversations
• Numbers flagged within 2-3 weeks
• TAM exhausted in months

Precision Approach (Phone Intent)

• 100 High Intent dials per rep per day
• 20,000 total dials per month
• 25% connect rate = 5,000 conversations
• Numbers stay clean (high answer rates signal legitimacy)
• Sustainable pace preserves TAM

The precision team has 67% more conversations with 80% fewer dials, while maintaining clean numbers and sustainable pipeline.

Why Carriers Reward Intent-Based Calling

Here's the mechanism that makes Phone Intent work at the systems level:

Spam analytics engines are fundamentally pattern matchers. They identify spam by looking for calling patterns that match known bad actors:

• Low answer rates
• High abandonment
• Short call durations
• User reports and blocks

When you use Phone Intent to call only prospects likely to answer, you flip every one of these signals:

• High connect rates (25%+) signal legitimate business calls
• Low abandonment because you're making single-line calls to available prospects
• Longer call durations because you're reaching people who can talk
• Minimal user reports because you're not annoying the 80% who don't want calls

The carriers' systems see your calling patterns and classify your numbers as "Legitimate Business" rather than "Nuisance." You stop fighting the system and start working with it.

Your Complete Remediation Action Plan

If your team is currently dealing with "Spam Likely" labels and cratering connect rates, here's the step-by-step protocol to recover.

Phase 1: Triage (Days 1-3)

1.1: Audit Your Current Numbers

Before implementing any fixes, you need to know which numbers are salvageable and which are terminal.

Use a multi-carrier spam check tool to test your entire number pool against all three major carrier networks (AT&T/Hiya, Verizon/TNS, T-Mobile/First Orion).

Categorize each number:

• Clean: No flags on any carrier (continue using, monitor weekly)
• Warning: "Likely Sales" or "Nuisance" on 1-2 carriers (remediate immediately)
• Flagged: "Spam Likely" on 2+ carriers (quarantine, begin remediation)
• Burned: "Scam Likely" on any carrier (retire permanently)

1.2: Immediate Behavior Changes

Stop any activities that accelerate flagging:

• Halt parallel dialing immediately (switch to single-line or power dialer)
• Reduce per-number call volume to under 100 calls/day
• Eliminate hang-ups on voicemail—leave messages or let calls complete
• Stop calling numbers that have previously declined or blocked you

1.3: Register Your Numbers

Submit all active numbers to the Free Caller Registry (FreeCallerRegistry.com), which feeds data to all three major analytics providers simultaneously.

Also register directly with individual carriers if you're seeing flags on specific networks:

• AT&T: att.com/reviewmycalllabel
• T-Mobile: callreporting.t-mobile.com
• Verizon: voicespamfeedback.com

Note: Registration doesn't guarantee flag removal—it establishes a record of your business identity and opens communication channels with the analytics providers.

Phase 2: Infrastructure Fixes (Days 4-14)

2.1: Verify STIR/SHAKEN Implementation

Confirm with your telephony provider that all outbound calls are being signed with Full Attestation (A level). Request documentation showing your STIR/SHAKEN certificate status.

If you're using a CCaaS or UCaaS platform, verify they're passing attestation properly through their systems.

2.2: Implement Branded Caller ID

Register for branded calling through one of the major providers:

• Hiya Connect
• First Orion Branded Calling
• TNS Enterprise Branded Calling

Branded Caller ID displays your business name instead of just a number, increasing answer rates and reducing spam reports. This requires verification of your business identity and typically takes 2-4 weeks to fully propagate.

2.3: Clean Your Data

Bad data creates bad calling patterns. Run your entire prospect database through verification:

• Remove disconnected and invalid numbers
• Flag numbers that have previously blocked or declined your calls
• Identify and prioritize mobile numbers (higher connect rates vs. landlines)
• Remove numbers from your own existing customer base (calling customers from sales numbers triggers complaints)

Phase 3: Implement Phone Intent (Days 15-30)

3.1: Pilot with Phone Intent Scoring

Select a subset of your prospect list (1,000-5,000 contacts) and run them through TitanX's Phone Intent Engine.

Segment the results:

• High Intent: Score indicates strong likelihood of answering
• Medium Intent: Moderate likelihood, good for multi-channel
• Low Intent: Focus email and LinkedIn, deprioritize phone

3.2: Restructure Your Calling Workflow

Train your SDR team on the new approach:

• Begin each call block by refreshing Phone Intent scores
• Call High Intent prospects first, during their optimal windows
• For Medium Intent prospects, send email or LinkedIn before calling
• Remove Low Intent prospects from call lists entirely (email only)

3.3: Measure and Iterate

Track these metrics weekly:

• Connect rate by intent score tier
• Call-to-meeting conversion by tier
• Number health across carriers
• Average call duration (increasing duration = better conversations)

Within 30 days, you should see:

• Connect rates improving toward 20-25% on High Intent dials
• Clean number status maintained or improving
• Higher quality conversations (reaching decision-makers in buying mode)

Phase 4: Sustained Operations (Ongoing)

4.1: Ongoing Number Monitoring

Check your number pool against all carriers weekly. Catch early warning signs before numbers become unfixable.

4.2: New Number Onboarding

When adding numbers to your pool:

• Scan for pre-existing flags before deployment
• Register with Free Caller Registry before first dial
• Start at low volume (50 calls/day) for first 2 weeks
• Monitor for flags during ramp-up period

4.3: Continuous Optimization

Use call analytics to identify patterns:

• Which times of day produce highest connect rates?
• Which industries or personas have highest/lowest Phone Intent accuracy?
• What's the optimal call-to-email ratio for your market?

Free Resources and Tools

Carrier Registration Links

• Free Caller Registry: FreeCallerRegistry.com (registers with all three major analytics providers)
• AT&T Call Label Review: att.com/reviewmycalllabel
• T-Mobile Call Reporting: callreporting.t-mobile.com
• Verizon Voice Spam Feedback: voicespamfeedback.com

Spam Check Tools

• TitanX Phone Intent Scanner: Get a free scan of your prospect list to identify High Intent contacts and flag likely spam issues before they hurt your numbers

Further Reading

• FCC STIR/SHAKEN Implementation Guide
• TRACED Act Compliance Requirements
• Parallel Dialer Comparison Guide

Stop Calling the Void. Start Calling People Who Answer.

The iOS 26 update wasn't designed to kill legitimate sales calls—it was designed to protect consumers from the billions of spam and scam calls flooding the phone network every year.

Sales teams that continue treating every prospect as equally likely to answer are going to keep fighting the spam filters, burning through numbers, and watching connect rates decline.

Teams that embrace intent-based calling are working with the system instead of against it. By calling only the prospects who are actually reachable, they achieve higher connect rates, maintain clean numbers, and build sustainable pipeline.

The technology exists today to identify who will answer the phone before you dial. The only question is whether you'll keep playing a game you can't win—or evolve your approach to match the new reality.

Ready to stop the spam flag cycle?

Get a Phone Intent Scan of Your Prospect List

Discover how many of your contacts are actually reachable by phone, identify High Intent prospects ready for outbound, and see the difference precision calling makes for connect rates.

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