The 2026 Phone Intent Benchmark Report

What more than 2.3 million B2B contact records submitted for scoring reveal about who answers cold calls.

Executive summary

By Joey Gilkey, CEO at TitanX
‍

For the last decade, outbound teams have chased growth with one playbook: more. Add more SDRs, buy more data, require AEs to build more of their own pipeline, run more sequences. Rip more dials.

That playbook has failed.

Why? Because the problem was never that there wasn’t enough. So “more” cannot be the solution.

The real problem is a lack of precision.

In almost every B2B business, pipeline can only be created once there’s a conversation between a prospect and a seller. And the fastest route to a conversation is the phone.

But there’s a problem.

Most people on any list will never answer a cold call. That’s a hard behavioral rule of human nature. Across more than 100 million phone numbers, in every market, roughly 20% of people answer cold calls. The other 80% never pick up, regardless of when reps call, or how many times.

~20%

of any market answers cold calls, across more than 100 million phone numbers

Calling every contact the same is imprecise and wasteful. What if you could identify which contacts are in the 20% and which are not?

We call that property reachability, and we use Phone Intent™ to score prospects for their propensity to answer. We identify the 20% of their market who are reachable, with high Phone Intent scores, so reps can prioritize calling them. They don’t waste their time dialing every prospect the same.

When you know the Phone Intent score of your contacts, and you prioritize dialing those who are known to pick up, your team can get 20–30%+ connect rates. That’s one conversation every four dials. Without Phone Intent scores, dialing the full list of contacts, connect rates average about 3–7%. It takes up to 30 dials to have just one conversation.

So the problem is not that there aren’t enough dials. It’s that too many dials are wasted, and they’re wasted because reps can’t tell the contacts who answer cold calls from those who don’t.

2,305,366

scored records

data vendors

industries

months, Oct 2025–Oct 2026

For this report, we studied over 2.3 million contact records that TitanX customers submitted to us for Phone Intent scoring between October 2025 and October 2026, from prospecting lists they bought from a variety of data vendors.

We wanted to know who is most likely to answer, how that changes by data vendor, title, and industry, and whether Phone Intent behavior changes over time.

We’re publishing our findings because if you’ve been trying to hit your targets by just doing more, you deserve to see exactly why it’s not working, and why a more precision-based approach to outbound is the only real path to unlocking growth.

Joey Gilkey
CEO

What is Phone Intent and how is it measured?

Phone Intent scores every record on three questions: is this number active, does it belong to the right contact, and is that contact likely to answer a cold call? Each record goes into one of four tiers.

P1 High Intent

An active number, confirmed to belong to the named person, who shows a high propensity to answer.

P2 Low Intent

A number confirmed to belong to the right contact, but not verified that they answer cold calls.

P3 Unverified

An active line that nobody has yet confirmed belongs to this person.

Bad Data

Disconnected lines and wrong people.

Phone Intent is a proprietary scoring model we developed that measures the behavioral propensity of a specific person to answer a cold call. We built it from observed individual behavior across more than 100 million phone numbers, in every market and business segment. If your team has bought buyer-intent signals, you’ve learned that a company may be in a buying window. Phone Intent is different: it tells you who you can actually speak to on the phone.

Contacts with a score of P1 represent the minimum we expect to answer cold calls at some point during your outreach. Phone Intent does not predict who picks up on the first attempt. Some P2 contacts answer too, just less often. So when we say roughly 20% of any market is reachable, we’re including the proportion of P1-scored contacts and the P2-scored contacts who typically do answer.

A few other definitions before we dig in

‍Connect: a rep spoke with the prospect they meant to speak to, for any amount of time. Not a gatekeeper, voicemail or wrong contact.Connect Rate:
Connect Rate: the percentage of dials that result in a connect.Conversation:
Conversation: a rep spoke with the prospect they meant to speak to, and was able to state a reason for calling.Reach Rate:
Reach Rate: the percentage of your total list you’re able to have a conversation with.Reachability:
Reachability: Describes how likely it is you'll be able to speak to a given contact. Phone Intent gives distinct scores (P1, P2, P3): P1 contacts are highly reachable. P3 contacts are not reachable. It can also describe a list or a campaign.

Where the data comes from

All 2,305,366 contact records in this analysis were submitted for scoring by TitanX customers between October 2025 and October 2026. We traced 40.8% of the records to 10 contact data vendors, which we anonymized as Source A through Source J. We couldn’t confirm the source of the other 59.2%, so we used every record for the overall figures and only the 10 identified vendors for the vendor comparisons. Title and industry labels are normalized. You’ll find the full method at the end of this report.

Key findings

Phone Intent describes a rule of human behavior that varies only slightly across industry, geography, job title or contact data vendor.

15%

Only 15% of contacts are likely to answer a cold call. Across the entire dataset, an average of 15.68% of contacts scored High Intent (P1): their number was verified as theirs, and they showed a strong habit of answering cold calls.

22% less

Leaders are 22% less likely to answer cold calls than managers or individual contributors. 13.63% of directors, VPs and C-level contacts scored High Intent, compared with 17.41% of managers and individual contributors.

48% more

Contacts in some industries are 48% more likely to answer cold calls than others. Across 21 industries, 18.96% of construction contacts scored High Intent, compared with 12.81% of contacts at hospitals and physicians clinics. Software contacts are among the hardest to reach: they scored High Intent just 14.29% of the time.

1 in 5

Even the best data vendor delivered only about one in five contacts likely to answer cold calls. Across the 10 contact data vendors we analyzed, customers got between 5.08% and 21.84% High Intent contacts, depending on the vendor.

Clean ≠ reachable

Clean data and reachable contacts are two different things. Even the data vendor with the lowest percentage of bad data (just 6.23%) only provided 14.14% contacts with High Phone Intent scores — below the 15.68% average. Across all vendors, we found no significant link between how clean a vendor's records were and how many of its contacts were highly likely to answer.

~5 pp

The proportion of a given market that is likely to answer cold calls doesn't change over time. From November 2025 through October 2026, the share of contacts scored as High Intent moved by only about 5 percentage points, from a low of 13.08% to 18.18% at its highest, with no trend up or down.

Over 4x

Some data vendors delivered over 4x more bad data than others. Customers got between 6.23% and 27.29% bad data, meaning disconnected lines or the wrong person, depending on the vendor. Across all records we scored in the last 12 months, 15.55% were bad data.

Most contacts on any list won't answer a cold call, whatever the source, title, or industry. Across 2,305,366 records, 15.68% of contacts scored P1, 18.18% P2, 50.59% P3, and 15.55% Bad Data. While the proportions do vary slightly, we found that same four-way split in the records from every one of the 10 vendors, in every title band, and in every one of the 21 industries we looked at.

There is no industry, seniority level, job title, function, or data vendor where more than 23% of the market consistently scored P1: high Phone Intent.

We also saw little change in the proportion of any market with a high Phone Intent over time.

From November 2025 through October 2026, between 13.08% and 18.18% of contacts scored P1 each month, with no trend up or down. Over 12 months, the share of contacts likely to answer moved by only about 5 percentage points. The number of prospects who do or don't answer cold calls stayed steady.

Over 12 months, the share of contacts likely to answer moved by only about 5 percentage points. Between 13.08% and 18.18% of contacts scored P1 each month from November 2025 through October 2026.

When your reps dial blindly through their lists without knowing beforehand who'll pick up and who won't, they're destined to waste the vast majority of their time and energy trying to reach people who will never answer.

Most sales leaders we've talked to have reacted to that problem with a seemingly common-sense question: could I fix this with a better list or contact data vendor?

Finding 1: Only about 15% of contacts are likely to answer a cold call

And that doesn’t change over time.

Could I fix this with a better list or contact data vendor?

Switching data vendors does not drastically improve the likelihood of increasing connect rate. Across the 10 vendors from whom our customers sourced data in this analysis, between 5.08% and 21.84% of contacts scored P1. Even from the vendor that provided the highest proportion of high Phone Intent contacts, 78.16% of records were not high intent, couldn't be verified at all, or were bad data.

Reachability is a third measure, separate from market coverage and data accuracy

The three most common questions people ask to evaluate a data vendor (aside from price):

How big is the database?
What's the overlap with our market?
How clean are the records?
‍

All smart questions. But if you stop there, you still won't know how many of those contacts are reachable, and your team will just have to find out the hard way.

78.16%

of records from the best vendor were not high intent, couldn’t be verified, or were bad data

How much bad data a vendor gives you doesn’t matter much for impacting likelihood of conversations: teams that bought cleaner records got about the same share of P1 contacts as everyone else. Across the 10 vendors, we saw no statistically significant relationship between a vendor’s bad-data share and its P1 share.

Take the anonymized Source E, for example: it provided records that were just 6.23% bad data, the lowest of the 10, and 14.14% of its records scored P1. That was below the 15.68% average, and far short of the best vendor’s 21.84%. The same was true of all vendors with lower than 9% bad data: they all provided between 8.46% and 14.14% of records with P1 scores.

There is also no statistically significant relationship between a vendor’s size (the records customers submitted from it) and either its P1 share or its bad-data share. When your team buys a big supply of records, you still don’t know who on the list will answer.

“Contact data is a commodity. The question to ask is not how much, or even how cheaply can I get my data. It’s, ‘how do I put my data to work?’”

Joey Gilkey

CEO, TitanX

Some vendors delivered over 4x as much bad data as others. Customers got between 6.23% and 27.29% bad data, depending on the vendor. Only one vendor topped 20%.

Plan to enrich records before your reps dial.

Some data vendors supply nearly 60% more high Phone Intent contacts, but without visibility into exactly which contacts those are, most outreach to the list is still wasted

Our customers used the same four vendors for almost 90% of their data. Of those four, the share of contacts scored P1 ranged from 12.88% to 20.38%. So if your team moved from the bottom of that range to the top, you’d get nearly 60% more P1 contacts, and your reps would likely reach more people.

Still, without Phone Intent scoring, your team can’t know which contacts answer cold calls. So even if they call a list of 1,000 prospects with a high proportion of P1 contacts, they’ll still have wasted about 800 dials, in a best-case scenario.

So don’t rely on a contact data vendor (or even changing from your current one) to deliver a meaningfully higher connect rate or more conversations. Just pick the vendor whose data fits your market best. Then score the list for Phone Intent and prioritize speaking to the contacts you can speak to: not just the ones you’d most like to speak to.

(The TitanX Precision Dialer does this automatically, by the way: Smart Stream gives reps a prioritized dial queue that takes Phone Intent scores, conversion likelihood, time zone, and other factors into account.)

‍

Regardless of contact data vendor, most contacts do not answer cold calls. Even from the highest-scoring vendor, customers got 21.84% of records in P1, and 78.16% in P2, P3, or Bad Data.

Finding 2: Even the best data vendor delivers only about one in five contacts likely to answer

And the list doesn’t show which ones.

Even on the best vendor’s list, about 800 of every 1,000 dials are wasted without scoring.

Finding 2

Across all vendors, 13.76% of C-level records scored P1, along with 13.32% of VP and 13.79% of Director records, compared with 17.59% of manager and 17.24% of staff or IC records. Leaders (directors, VPs and C-level) cluster around 13.63% P1, and managers and individual contributors around 17.41%.

The proportion of contacts with P2 scores had the opposite trend. As a reminder, P2 designates a contact whose number could be confirmed to belong to exactly the right contact. It's not just a "verified live" number — confirmation that a number is in use by somebody — which is the level of verification most contact data vendors or enrichment tools can provide.

It's much easier to confirm a phone number belongs to the right leader: 23.22% of VP records scored P2, along with 21.25% of C-level, 19.57% of director, 15.49% of manager, and 11.54% of staff or IC records.

Junior contacts are the reverse: a little harder to verify, but easier to reach. Staff or IC records had the highest bad-data share of any band, at 18.95%, and 17.24% of them still scored P1, more than any leader band.

Leaders were about 22% less likely to answer cold calls than managers or ICs. 13.63% of director, VP and C-level records scored P1, compared with 17.41% of manager and staff or IC records.

Prioritize outreach based on who you can talk to, not just who you want to talk to

94,000

high-intent (P1) C-level or VP leaders in this dataset

Don’t limit your team to calling only executives just because they’re the decision-makers

The data set we analyzed had a total of 94,000 high-intent (P1) C-level or VP leaders, and about 161,000 P1 directors, managers or individual contributors. If your reps dial only contacts with executive titles on the basis that they're more likely to be a decision-maker, they'll spend most of their dials on people who won't answer, when they could be having conversations with others in your target market.

Instead, use Phone Intent scores to inform who you reach out to and when. If an executive at a target account scores P1, great: call them. If no executive at a target account has a score of P1, but managers or operators on their team do, your reps can call those people to gather intel and find a referral path to the decision-maker.

Two other findings to note:

Marketing Heads also scored high, at 17.50% P1.
Staff or IC records had the highest bad-data share of any band, at 18.95%, so expect the most cleanup on your most junior contacts.

Finding 3: Leaders are 22% less likely to answer than managers or individual contributors

Leaders cluster around 13.63% P1; managers and individual contributors around 17.41%.

Across the 21 industries with at least 1,000 records each, the share of contacts scored P1 ranged from 12.81% to 18.96%.

The top three industries were Construction (18.96% P1), Telecommunications (18.19%), and Law Firms and Legal Services (18.02%).

Hospitals and Physicians Clinics are the hardest industry to cold call when it comes to likelihood of a prospect picking up the phone: just 12.81% of their contacts scored P1. Software ranked 16th of 21, at 14.29%.

Construction contacts were 48% more likely than contacts at hospitals and physicians clinics to score P1: 18.96% compared with 12.81%, the widest gap across 21 industries.

Finding 4: Contacts in some industries are 48% more likely to answer cold calls

Across the 21 industries with at least 1,000 records each, the share of contacts scored P1 ranged from 12.81% to 18.96%.

No matter which industry or industries you sell into, you will waste the majority of your time, effort and dials unless you have Phone Intent. While differences look stark when you compare just the P1 proportion, looking at the share of all scores by industry makes it clear that they all share the same general pattern we've described time and time again so far in this report. Most people just will not answer. The only way to avoid wasting dials on those people is to know who they are before reps dial.

The playbook for Phone Intent: how to work a scored list

Once your lists are scored, you’ll know who to call first. Turning that knowledge into pipeline takes discipline.

P1: call only

Dial the Best Phone, single-line, and never leave a voicemail. Make seven attempts, every other day, over 13 days.

P2: full sequence

Work the full multi-channel sequence: call, voicemail, email, and LinkedIn. Call anyone who replies by email.

P3: email and LinkedIn

Use email and LinkedIn only until the number is verified. Add the phone once there’s a signal.

Bad Data: enrich or remove

We treat 12–18% as an acceptable bad-data share, and 10% or less as the standard to aim for after enrichment.

We get asked this a lot. After all, what you do with your data is much more important than what data we can give you.

Once your lists are scored, you'll know who to call first. Turning that knowledge into pipeline takes discipline.

Plug: Our customers use Phone Intent data built into our Precision Dialer. It scores contacts before they dial, serves up a prioritized call queue in Smart Stream that prioritizes dials to contacts most likely to answer and convert, protects numbers from spam flags with Titan Defense, and more.

Our platform aside, assume you can know who is likely to pick up. We're often asked what to do with that information. Here's our answer:

P1 High Intent. Call only. Dial the Best Phone, single-line, and never leave a voicemail. Make seven attempts, every other day, over 13 days.
P2 Low Intent. Work the full multi-channel sequence: call, voicemail, email, and LinkedIn. Call anyone who replies by email.
P3 Unverified. Use email and LinkedIn only until the number is verified. Add the phone once there's a signal.
Bad Data. Enrich or remove. We treat 12–18% as an acceptable bad-data share, and 10% or less as the standard to aim for after enrichment.

Conversation velocity. When your reps work a block of P1 contacts, they'll get roughly 10 conversations for every 40–50 dials.

~10

conversations for every 40–50 dials when reps work a block of P1 contacts

What this means for the mandate

Whether you're giving the mandate or receiving it, you've heard the same words from board rooms to sales floors, from enterprise orgs all the way down to emerging startups: scale growth without scaling headcount, or "do more with less." The playbook of more can't meet it: more dials won't help, because most people on every list won't answer however often a rep calls. Your team can sell only to the people in your market who are reachable. On every list your reps have worked, the same four-way split was there before the first dial. If you switch to a new data vendor, you'll change the size of the slices, and your reps still won't see which contact belongs in which one. The teams we've seen meet the mandate have known which records are which before anyone picks up the phone. They've met it with precision outbound: they've known who's reachable before the first dial, and their reps have worked those people in the Precision Dialer, single-line, from numbers prospects trust, with every outcome feeding the next call.

“We have quite literally doubled SDR production over the last six months, and I believe TitanX has played a huge role in that.”

Kevin “KD” Dorsey

Chief Revenue Officer • Finally

Conversations are the atomic unit of a sale. The answer was never more. The answer was always precision.

How we built this report

Here’s exactly what the dataset covered and where its limits were.

2,305,366

records submitted for scoring by customers between October 2025 and October 2026. We included only records scored with our advanced Phone Intent models and left out Lightning, our now-deprecated standard model.

40.8%

of records (940,564) traced to 10 vendors, anonymized as Source A to J and lettered by P1 share. Read the gaps between vendors as directional, never as a verdict on a vendor.

24.41%

of records had title labels too vague to use. We built the industry view on the 1,822,288 records with a usable industry label.

1,000+

records before we ranked a group. A gap counted only at 2.0 percentage points or more, with a 95% confidence range on every share.

746

head-to-head comparisons, each with a raised bar, so no finding passed on the luck of running many tests.

October 2025

was a ramp-up month (21,618 records) with unusual results. We kept it in the overall figures and left it off the monthly trend.

Tier split by data source

Shares of scored records. We anonymized the sources and lettered them by P1 share, from highest to lowest.

We have only small samples for Sources H and J, so read their figures as directional.

Tier split by title band

All sources combined. We excluded the 24.41% of records with unusable title labels.

Expected ranges for the scoring models in this report

We found an overall bad-data share of 15.55%, inside the expected 12–18% range. We couldn't see how much of each source's data a customer had enriched before scoring.

How we did this

We analyzed 2,305,366 contact records that TitanX customers submitted for Phone Intent scoring between October 2025 and October 2026, using only records scored with our advanced Phone Intent models. Each record fell into one of four tiers: P1 High Intent, P2 Low Intent, P3 Unverified, or Bad Data (disconnected lines or the wrong person: 346,108 records, or 15.55%). We traced 940,564 records (40.8%) to 10 contact data vendors, anonymized as Source A through Source J. We couldn't confirm the source of the other 59.2%, so we used every record for overall figures and only the 10 identified vendors for vendor comparisons. We set aside the 24.41% of records whose title labels were too vague to use, and built the industry view on the 1,822,288 records with a usable industry label, covering the 21 industries with at least 1,000 records each. Read the gaps between vendors as directional, not as a verdict on any vendor.

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What is the average cold call connect rate?

What percent of prospects answer cold calls?

Do executives answer cold calls?

Which industries have the most High Intent phone records?

Will switching contact-data providers get more prospects to answer?

What share of B2B contact data is bad?

Average connect rates for cold callers across industries is about 3-5% without Phone Intent data to prioritize calling high-intent contacts first. Dialing with TitanX, outbound teams typically get 20-30% connect rates at scale.

We've consistently seen roughly 20% of any market as reachable, meaning those people will answer a cold call, across TitanX's work with more than 100 million phone numbers. In this report's 2,305,366 records, 15.68% of contacts scored P1 High Intent: we were able to verify they have the behavioral propensity to answer calls from unknown numbers. Some P2 contacts answer too; their score just means our model didn't verify it. So if you call a full list, you'll reach more people than the P1 share shows.

Yes, they do, but less often than managers and individual contributors. 13.63% of Director, VP and C-level records scored P1, compared with 17.41% of managers and individual contributors. If you're deciding which of your prospects to call, prioritize dials to contacts who are confirmed to pick up the phone.

Construction, Telecommunications, and Law Firms and Legal Services were the top three, at 18.96%, 18.19% and 18.02% P1. Contacts at Hospitals and Physicians Clinics scored lowest, at 12.81%, so construction contacts were 48% more likely to score P1. The ranking covered the 21 industries with at least 1,000 scored records.

It could, but not by a significant margin. Regardless of which contact data vendor you use to source leads, you'll be dialing them blindly (without knowing who answers cold calls) without Phone Intent scoring.

On average, 15.55% of the data we scored was bad data: we could confirm the number was wrong, wasn't active, or didn't belong to the right person. How much bad data is in any given B2B sales team's CRM depends heavily on the data source, though: some vendors delivered over 4x as much bad data as others. Across 10 anonymized contact data vendors, customers got between 6.23% and 27.29% bad data.

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