Hiya: State of the Call 2025

Keypoints

• Typical report structure — Introduction: frames the importance of voice and high-level findings; Understanding Unwanted Calls: definitions, behavioral impacts, and how metrics are tracked; Top Voice Call Trends: headline trends with supporting survey and telemetry data; Individual Trend sections: deep dives (e.g., criticality of voice, security concerns, deepfake threat, financial fallout, under-investment); Spotlight sections: focused challenges like BYOD; Recommendations: consumer and enterprise actions; Conclusion and Methodology: summary, sample sizes, data sources and limitations.
• Data sources and scope — Hiya analyzed 262.8 billion calls and included surveys of 12,003 consumers, 1,802 workers, and 600 senior IT/security professionals across the US, UK, Canada, France, Germany and Spain; market research conducted by Censuswide (Dec 24, 2024–Jan 7, 2025).
• Voice remains essential — Survey shows broad and in many cases growing preference for voice in high-stakes domains (banking, healthcare, retail) and daily business activities like resolving customer issues and closing sales.
• Key adoption metrics — 40% of businesses are looking to add a branded calling solution in 2025; 29% of workers increased voice use on the job year-over-year, while 57% stayed the same.
• Unidentified-call behavior — 48% of consumers never answer unidentified calls, 33% rarely answer, and only 20% always answer, producing the headline that ~80% of unidentified calls are likely never answered.
• Unwanted call volumes rising — Average consumer receives 8 unwanted calls per week (up from 4.8 in 2024), and a majority report “a lot more” spam calls in the past 12 months.
• Deepfake prevalence and impact — 39% of consumers and roughly half of workers reported experiencing a deepfake communication during a recent three-month period; voice calls, texts and video are all used as attack vectors with voice accounting for a sizable share.
• AI-generated voice in spam — Hiya’s Q1 honeypot analysis found 25% of analyzed spam calls contained AI-generated audio; among those, 55% were fraudulent, 35% nuisance, and the rest unclear.
• Direct financial harm — 15% of consumers reported losing money to a phone scam in the prior year; businesses: ~33% of workers say their company lost money because they couldn’t reach customers by phone; 60% of sales pros report losing deals due to call-related issues.
• Country-level consumer loss averages — Reported mean losses among victims over 12 months: US $539; UK £595; Canada $1,479; France €1,089; Germany €723; Spain €556.
• Carrier and customer loyalty effects — 38% of consumers have considered or actually switched mobile providers due to how carriers handle spam/fraud; 27% say carrier handling greatly improved satisfaction, while 10% have left or plan to leave their carrier.
• Enterprise risk signals — High rates of impersonation and spoofing: 28% of workers report their outbound calls are sometimes labeled spam, 26% have seen their brand used by fraud callers, and 27% report call spoofing incidents; 93% of IT pros are concerned about impersonation risks.
• Protection gaps — Two-thirds of consumers use no call-protection apps; many companies only adopt call confirmation or branded calling after incidents; only a third of employees use personal spam detection apps even when available.
• Effective mitigation techniques — Top effectiveness and adoption signals: branded caller ID and adding business identity to calls (30% most effective), ensuring calls avoid spam labels (19%), increasing agent coaching (12%).
• Anti-impersonation and anti-deepfake adoption — IT leader adoption rates reported: customer education 61%, call confirmations 57%, spoof protection 56%, branded calling 49%; for deepfakes: AI detection tools 71%, enhanced policies 69%, employee training 67%, third-party collaboration 52%.
• BYOD complicates protection — ~68% of businesses have BYOD policies covering smartphones; common BYOD protections include MFA (65%), required security training (63%), but only ~56% use inbound anti-fraud authentication, leaving an enterprise exposure vector on unmanaged devices.
• Common fraudulent call types using AI voices — Examples captured: Google business listing scams, cable-TV discount scams, debt-management scams, and tax-relief scams—AI audio is being used across familiar fraud playbooks to increase believability.
• Recurring themes — The report emphasizes: (1) voice’s persistent centrality for high-stakes communication; (2) evolving attacker sophistication (AI deepfakes layered on top of spoofing and robocalls); (3) measurable economic and operational harm; and (4) a persistent gap between available protections and real-world adoption.
• Strategic recommendations — Consumers: install call-protection apps, choose carriers/devices with strong voice security, report unwanted calls; Businesses: register business numbers, adopt branded caller ID and anti-spoofing, deploy inbound protection and call-confirmation workflows, coach agents, and use a layered approach combining identity, blocking, and AI detection.
• Operational takeaway — A layered defense that combines network-level protections, device/app-based AI detection, business identity and consumer-friendly calling practices is the highest-impact path to preserve call deliverability, trust, and reduce fraud losses.