AI Operations Research & Data

Missed Calls & Speed to Lead

THE COST OF A SLOW RESPONSE

When your phone rings and nobody answers, or when a lead waits hours for a reply, the data shows exactly what happens next.

~80%

Approximately 80% of callers sent to voicemail won't leave a message — they simply hang up.

This figure has been reported by Forbes and corroborated by multiple answering service industry studies, with estimates ranging from 75–85% depending on the sector. The remaining callers who do leave a message often wait days for a response — voicemails sit unplayed for an average of 3 days. Note: This is an industry-wide estimate from multiple independent sources, not a single controlled study.

SellCell — Voicemail Statistics (2025) AnswerFirst — Why Voicemail Is Killing Your Business Forbes via DestinationCRM

78%

Research suggests that the majority of customers — up to 78% — buy from the first company that responds to their inquiry.

This finding originates from a Lead Connect survey and has been widely cited across the sales industry by Chili Piper, Salesforce, and others. While the precise methodology of the original survey is not publicly documented, the directional finding is consistent across multiple independent speed-to-lead studies: being first matters more than being best. Note: Original survey methodology not publicly available. We cite the range because multiple studies corroborate the direction, even if the exact figure varies.

Lead Connect Survey via CallPage Chili Piper — Speed to Lead Vendasta — Lead Response Time

21×

Leads contacted within 5 minutes are 21× more likely to be qualified than those contacted after 30 minutes (2011, n=2.24M leads).

The original Harvard Business Review study by James Oldroyd (Kellogg) analyzed over 2.24 million sales leads and 100,000+ call attempts across multiple industries. They also found that firms responding within one hour were 7× more likely to qualify leads than those who waited 60 minutes, and 60× more likely than those who waited 24 hours. Note: This 2011 study remains the gold standard for speed-to-lead research — no comparable study at this scale has been conducted since. Its findings continue to be cited by Salesforce, HubSpot, and HBR.

HBR Study via Workato Revenue.io — HBR Lead Response LeadAngel — Speed to Lead Statistics

391%

Responding to a lead within 60 seconds yields a 391% increase in conversion rates.

Research by Velocify (now part of ICE Mortgage Technology) found that immediate response doesn't just improve outcomes incrementally — it produces exponential conversion gains. This is why our Intake Coordinator agent is designed for near-instant response.

Velocify Study via Voiso Kixie — Speed to Lead Statistics

85%

Up to 85% of callers who don't reach a live person will never call back.

Research from PATLive confirms that the window for capturing a caller is essentially one shot. Furthermore, approximately 62% of unanswered callers will immediately call a competitor. For service businesses, a single missed call can represent $500–$1,200 in lost revenue.

PATLive via Dialzara SkipCalls — Voicemail Is Dead

Growth & Marketing

WHAT ACTUALLY BRINGS IN NEW CUSTOMERS

The channels the Campaign Manager runs aren't guesswork. Decades of marketing data show exactly where the return is — here's the evidence behind email, SMS, paid ads, and fast websites.

$36

Email marketing returns an average of $36 for every $1 spent — the highest ROI of any marketing channel.

Litmus's research across thousands of marketers found an average return of about $36 per $1 on email (roughly a 3,600% ROI), consistently the top-performing channel year after year. For a business with an existing customer list in its CRM, email is the lowest-cost, highest-leverage growth lever available. Note: Returns vary by industry and list quality — some sectors such as retail and e-commerce report figures above $40, others lower.

Litmus — The ROI of Email Marketing Litmus — Email ROI Infographic

98%

SMS messages are opened about 98% of the time, the vast majority within minutes — far above email's roughly 20% open rate.

Across multiple 2024 industry analyses, SMS open rates land around 98%, with about 90% of texts read within three minutes. Click-through rates average 19–20% (versus low single digits for email), making SMS the channel of choice for time-sensitive offers and reminders. Note: These are aggregate benchmarks; performance depends on opt-in list quality and message relevance, and all SMS marketing must follow consent rules.

Constant Contact — SMS Statistics OptiMonk — SMS Marketing Statistics

~2:1

Businesses earn an average of about $2 in revenue for every $1 spent on Google Search ads — and high-intent campaigns often return far more.

Google's Economic Impact analysis cites an average ~2:1 return as a general baseline, while real-world data varies widely by campaign type: high-intent Search campaigns commonly return $5+ per $1, broader awareness campaigns less. We optimize toward booked customers, not clicks. Note: ROAS is highly variable by industry, offer, and targeting — published averages are directional, not guarantees.

WordStream — Google Ads Benchmarks Triple Whale — Google Ads ROAS Data

+32%

As a mobile page's load time grows from 1 second to 3 seconds, the probability a visitor bounces rises about 32%.

Google's analysis of over 900,000 mobile landing pages found bounce probability climbs sharply with load time — up 32% from 1s to 3s, and over 90% by 5s. Conversions can fall meaningfully for each additional second of load time. This is why the Campaign Manager builds fast pages: the traffic you pay for only converts if the page keeps up. Note: Figures come from Google's 2016–2017 mobile speed research and remain the most-cited benchmark for page-speed impact.

Google — Mobile Page Speed Benchmarks Website Speed & Conversion Statistics

Revenue Leakage

THE MONEY YOUR BUSINESS ALREADY EARNED

Past-due invoices, failed payments, expired contracts, and billing errors silently drain revenue. Multiple studies quantify exactly how much.

1–5%

Companies lose approximately 1–5% of EBITDA annually to revenue leakage, with some sectors losing up to 8%.

EY research established the 1–5% EBITDA benchmark, while DealHub found that subscription and professional services businesses with complex billing can leak 5–8% of total revenue. For a company generating $10M annually, even 2% leakage means $200,000 disappearing each year.

DealHub — What Is Revenue Leakage EY Study via Zone & Co Revenue Grid — Revenue Leakage

42%

42% of companies experience some form of revenue leakage, and most don't realize it.

MGI Research found that nearly half of all companies are losing money they've already earned. A separate Boston Consulting Group survey found that 45% of executives believe revenue leakage is a systematic problem at their companies.

MGI Research via Revenue Grid BCG Survey via DealHub

~9%

Across subscription businesses studied, the average revenue loss from leakage is approximately 9% of annual revenue.

Chargebee's research across subscription businesses found that failed payments, missed renewals, billing errors, and unbilled services collectively drain nearly a tenth of total revenue. xfactrs research found that invoice disputes alone can reduce invoice value by 20–30% through write-offs and credits. Scope note: The 9% figure is from Chargebee's analysis of subscription-model businesses specifically. Non-subscription businesses may experience different rates depending on billing complexity.

Chargebee via HubiFi xfactrs — Revenue Leakage Statistics

AI & Automation

THE AUTOMATION OPPORTUNITY

The world's leading research firms agree: the majority of repetitive business tasks can now be handled by AI — and the companies that adopt first are pulling ahead.

57%

57% of U.S. work hours are technically automatable by AI agents and robotics today (McKinsey, November 2025).

McKinsey Global Institute's November 2025 report, "Agents, Robots, and Us," nearly doubled their previous 2023 estimate of 30% automation potential. This doesn't mean 57% of jobs disappear — it means the repetitive tasks within most jobs can be offloaded, freeing humans for higher-value work. This is the core thesis behind Untapped Agents.

McKinsey — Agents, Robots, and Us (2025)

$4.4T

McKinsey sizes the long-term AI opportunity at $2.6–$4.4 trillion in added annual productivity growth potential (2023, 63 use cases across 16 functions).

Their 2023 analysis examined 63 use cases across 16 business functions, 850+ occupations, and 2,100+ work activities in 47 countries representing over 80% of the global workforce. They found that 60–70% of worker activities could be automated (up from 50% before gen AI), with the biggest impact in knowledge-work industries like banking (up to 5% of revenue), pharmaceuticals (5%), and education (4%). Customer operations was identified as the function most likely to see immediate productivity gains.

McKinsey — Economic Potential of GenAI (2023) McKinsey — Superagency in the Workplace (2025)

92%

92% of companies plan to increase AI investments over the next three years — but only 1% describe themselves as "mature" (2025, n=3,600+).

McKinsey's Superagency in the Workplace report (Jan 2025) surveyed 3,613 employees and 238 C-suite executives across six countries in Oct–Nov 2024. The gap between investment intent and deployment maturity underscores that most companies are still early in their AI journey. AI high performers — approximately 6% of respondents — are 3× more likely than peers to be scaling AI agents across multiple business functions.

McKinsey — Superagency in the Workplace (2025)

65%

65% of organizations now regularly use generative AI in at least one business function — nearly double from 33% in 2023 (McKinsey, 2025).

McKinsey's 2024 Global Survey on AI found that adoption is accelerating rapidly. Half of all respondents use AI across two or more business functions, with marketing, sales, and customer operations seeing the greatest returns. Important caveat: Only 39% of companies report that AI has improved their EBIT at all, and in most cases the impact is less than 5% of revenue. Adoption is outpacing measurable ROI for many organizations.

McKinsey — State of AI (2025)

People Operations

THE HR BOTTLENECK

HR teams are stretched thin. Research consistently shows the majority of employee interactions are repetitive, informational requests that don't require human judgment — and they're consuming the time HR needs for strategic work.

60–70%

McKinsey found that 60–70% of employee work activities could be automated with current generative AI technology (2023, 850+ occupations analyzed).

This finding applies broadly across knowledge work, but HR is among the functions most affected because of its high proportion of routine informational tasks — answering policy questions, processing paperwork, onboarding coordination, and benefits administration. Deloitte's Global Human Capital Trends series has consistently found that organizations automating these functions free HR to focus on talent strategy, culture, and retention. Note: The 60–70% refers to activities, not jobs. Most HR roles involve a mix of automatable tasks and high-judgment work that requires human involvement.

McKinsey — Economic Potential of GenAI (2023) Deloitte — Global Human Capital Trends

44d

The average time-to-fill for an open position is 44 days, with screening as the most time-intensive stage (SHRM, 2023).

SHRM's annual HR benchmarking report tracks time-to-fill across industries and company sizes. At 44 days average, every unfilled role represents lost productivity and revenue. The screening and initial candidate communication stages — reviewing resumes, scheduling interviews, sending status updates — are the most amenable to automation. LinkedIn's Global Talent Trends research confirms that recruiters spend the majority of their time on these administrative tasks rather than assessment and relationship-building.

SHRM — Talent Acquisition Benchmarking (2023) LinkedIn — Global Talent Trends

15–20%

McKinsey estimates AI can reduce HR operating costs by 15–20% by identifying key factors behind employee attraction, turnover, and performance.

McKinsey's research across multiple reports has consistently found that HR is one of the functions with the highest automation potential. By analyzing patterns in employee data — including attrition signals, performance drivers, and hiring pipeline bottlenecks — AI can both reduce administrative overhead and improve the quality of people decisions. The savings come primarily from automating recruiting workflows, onboarding logistics, and routine employee inquiries.

McKinsey — Economic Potential of GenAI (2023)

Customer Success & Retention

THE MATH OF KEEPING CUSTOMERS

Acquiring customers is expensive. Losing them is worse. Decades of research from Bain & Company, Harvard Business Review, and Accenture quantify the compounding value of retention.

5–25×

Acquiring a new customer costs 5 to 25 times more than retaining an existing one.

This range was established by Frederick Reichheld at Bain & Company and popularized by Amy Gallo's 2014 Harvard Business Review article, "The Value of Keeping the Right Customers." The wide range reflects industry variation — B2B companies with long sales cycles typically fall at the higher end (15–25×), while consumer businesses with low switching costs trend lower. Note: The original Bain research dates to the 1990s–2000s but has been validated across industries over two decades and remains the most-cited acquisition-vs-retention benchmark in business.

HBR — The Value of Keeping the Right Customers (2014) Bain & Company — Retaining Customers

5%→95%

Increasing customer retention rates by just 5% can boost profits by 25% to 95%.

Originally from Frederick Reichheld's work at Bain & Company, published in "The Loyalty Effect" (1996). The compounding effect is driven by three forces: retained customers spend more over time, cost less to serve, and generate referrals. The 25–95% range reflects industry variation — subscription and financial services businesses typically see the highest impact. Note: This foundational research is from the 1990s. The wide range (25–95%) reflects genuine differences across industries, not imprecision in the research itself.

Bain & Company / Reichheld

$1.6T

U.S. businesses lose an estimated $1.6 trillion per year due to customers switching providers.

From Accenture's "Digital Disconnect in Customer Engagement" study. Primary drivers include poor customer service, feeling unappreciated, and lack of personalization. Scope note: This figure includes all brand-switching, not just churn from poor service. It represents the total economic cost of customer mobility in the U.S. market, including voluntary switches to competitors offering better value.

Accenture — Digital Disconnect in Customer Engagement

~65%

Approximately 65% of a company's revenue typically comes from existing customers.

This widely cited benchmark is consistent across multiple industry sources, with estimates ranging from 60–70% depending on business model. McKinsey research adds that existing customers spend approximately 31% more per transaction on average compared to new customers. The implication: your current customer base is your most valuable revenue engine, and even small improvements in retention compound significantly.

SmallBizTrends — Customer Retention Statistics

2×+

On average, an industry's Net Promoter Score leader outgrows competitors by a factor of more than 2× (Bain & Company).

Bain's research found that Net Promoter Scores explain roughly 20–60% of variation in organic growth rates among competitors. NPS leaders consistently outpace their peers in revenue growth. Important counter-stat: However, Gainsight/ProfitWell research found that NPS does NOT reliably predict churn or renewal rates for approximately 75% of companies studied. NPS correlates more strongly with growth and expansion revenue than with retention. Use NPS as one input, not the only measure of customer health.

Bain & Company — NPS & Loyalty Research Gainsight/ProfitWell — Customer Success Index

5–9%

A one-star increase in a business's online rating can lift revenue by 5–9%.

Harvard Business School economist Michael Luca, combining Yelp ratings with Washington State revenue data, found that a one-star rise in rating drove a 5–9% increase in revenue — an effect concentrated among independent businesses rather than established chains. It is one of the most rigorous causal studies of reviews and revenue, and it's the research behind our Customer Success Guardian's review engine. Note: The original study analyzed restaurants (2003–2009); the directional effect — better ratings drive measurably more business — has been corroborated across local-business categories since.

Harvard Business School — Reviews, Reputation & Revenue (Luca) HBS Working Knowledge — The Value of Reviews

97%

97% of consumers read reviews before choosing a local business, and 88% are more likely to use one that replies to its reviews (vs. 47% for a business that doesn't).

BrightLocal's 2024 Local Consumer Review Survey found reviews are now a primary decision factor: 98% read reviews at least occasionally, star rating is the number-one factor consumers judge a business on, and responding to reviews nearly doubles the share of consumers willing to use you. Both review volume and owner responses matter — which is why the Guardian both requests reviews and prompts you to reply to them. Note: Survey of ~1,100 US consumers; self-reported behavior, consistent with prior years' findings.

BrightLocal — Local Consumer Review Survey (2024) BrightLocal — Online Review Statistics

How we use this research. Every statistic referenced on our homepage, pricing page, and marketing materials links back to this page. We cite original studies wherever possible and note when data is sourced through secondary reporting. Where a precise figure comes from an industry estimate rather than a single controlled study, we say so. Where research is older but still foundational, we disclose the date and note continued relevance. Statistics are presented as industry benchmarks — your actual results will depend on your business, industry, and implementation.

We include counter-statistics and limitations where they exist — because credibility requires acknowledging what the data doesn't say, not just what it does. If a stat has a narrow scope, an old vintage, or a competing finding, we note it.

We update this page as new research becomes available. Our commitment: if a study is retracted, updated, or contradicted by newer evidence, we update both this page and any marketing materials that reference it. If you find an error or have questions about our sourcing, contact us at team@mail.untappedagents.ai.

Last updated: February 2026

EVERY CLAIM WE MAKE,
SOURCED

THE COST OF A SLOW RESPONSE

WHAT ACTUALLY BRINGS IN NEW CUSTOMERS

THE MONEY YOUR BUSINESS ALREADY EARNED

THE AUTOMATION OPPORTUNITY

THE HR BOTTLENECK

THE MATH OF KEEPING CUSTOMERS

The data is clear.
Let's put it to work.

EVERY CLAIM WE MAKE,SOURCED

THE COST OF A SLOW RESPONSE

WHAT ACTUALLY BRINGS IN NEW CUSTOMERS

THE MONEY YOUR BUSINESS ALREADY EARNED

THE AUTOMATION OPPORTUNITY

THE HR BOTTLENECK

THE MATH OF KEEPING CUSTOMERS

The data is clear.Let's put it to work.

EVERY CLAIM WE MAKE,
SOURCED

The data is clear.
Let's put it to work.