arxiv: 2604.06759 · v1 · submitted 2026-04-08 · 💻 cs.CR · cs.CY· cs.HC

Recognition: no theorem link

Understanding Data Collection, Brokerage, and Spam in the Lead Marketing Ecosystem

Yash Vekaria , Nurullah Demir , Konrad Kollnig , Zubair Shafiq

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:43 UTC · model grok-4.3

classification 💻 cs.CR cs.CYcs.HC

keywords lead marketingdata brokeragespamprivacyhealth datatelemarketingmeasurement studyconsumer data

0 comments

The pith

Health lead websites share sensitive data with over 70 third parties, generating thousands of spam calls.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tracks how personal information entered on health lead websites flows through brokers to marketers. Researchers used controlled phone numbers and emails to monitor what happens after submitting forms on more than one hundred sites. They also bought leads themselves to see brokerage practices. The study finds widespread sharing of health details, deceptive data augmentation, and high volumes of calls and texts that start almost immediately. Opt-out attempts only partially reduce the volume, leaving consumers exposed.

Core claim

By instrumenting over 100 health lead-generation websites and monitoring 200 controlled contacts, the authors trace data flows showing sharing of highly personal health information to more than 70 distinct third parties. Purchasing leads reveals deceptive practices including selling to unvetted buyers and fabricating attributes like health status. Consumers received over 8,000 telemarketing calls, 600 texts, and 200 emails, with calls often using VoIP spoofing, and opt-outs proving ineffective at stopping all communications. Analysis of BBB complaints supports these observations from the consumer side.

What carries the argument

An end-to-end measurement framework that instruments lead websites and uses controlled contact points to trace data sharing and downstream spam practices.

If this is right

Sensitive health data is monetized by being sold across many parties without strong compliance.
Lead platforms engage in deceptive practices by augmenting or fabricating consumer attributes.
High-frequency dialing and neighbor spoofing make phones unusable for many consumers.
Existing opt-out mechanisms fail to fully protect consumers from marketing.
Consumer complaints to BBB align with the measured spam volumes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar measurement approaches could help identify and regulate data brokers in other industries.
The rapid start of calls suggests automated systems that could be targeted by enforcement.
Consumers may need stronger rights to control data after submission to prevent fabrication.
This ecosystem's non-compliance highlights gaps in current privacy laws enforcement.

Load-bearing premise

The chosen health-related websites and controlled contacts accurately reflect the typical experiences and data flows for average consumers in the lead marketing ecosystem.

What would settle it

Finding that similar lead generation sites share data with few or no third parties and generate minimal spam after form submissions would contradict the reported scale of sharing and aggressive marketing.

Figures

Figures reproduced from arXiv: 2604.06759 by Konrad Kollnig, Nurullah Demir, Yash Vekaria, Zubair Shafiq.

**Figure 1.** Figure 1: An Overview of Lead Marketing Ecosystem its marketing partners, including through automated calls or texts, as required by the TCPA [23]. This notice, known as the TCPA consent string, authorizes certain marketing practices using an automatic dialing system or an artificial or pre-recorded voice [31]. Many lead generation websites also integrate third-party certification services (e.g., ActiveProspect, Jo… view at source ↗

**Figure 2.** Figure 2: An overview of methodology of our study. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Distributions of (a) age, (b) gender, (c) height, and (d) weight of leads bought from the 3 lead marketing platforms. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: Stacked bar distributions of (a) income and (b) [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Number of retry attempts per caller–receiver pair 1 2 3 4 5 5 10 11 20 21 50 >50 Total calls (binned) 0 100 200 300 400 500 600 Number of callers 42% 22% 12% 11% 7% 4% 2% [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

**Figure 8.** Figure 8: Marketing communication trends over time across opt-out and control groups. Opt-out was performed on Day 4. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 9.** Figure 9: Consumer Marketing Communication Intensity as [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

**Figure 10.** Figure 10: Distribution of call volume by days. Each cell represents the number of calls received on a given day. [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗

**Figure 11.** Figure 11: Distribution of email volume by days. Each cell represents the number emails received on a given day. [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗

read the original abstract

The lead marketing ecosystem enables collection, sale, and use of personal data submitted via web forms to deliver personalized quotes in high-value verticals such as insurance. Despite its scale and sensitivity of the collected data, this ecosystem remains largely unexplored by the research community. We present the first empirical study of privacy and spam risks in lead marketing, developing an end-to-end measurement framework to trace data flows from data collection to consumer contact. Our setup instruments over 100 health-related lead-generation websites and monitors 200 controlled phone numbers and email addresses to understand downstream marketing practices. We observe sharing of highly personal and sensitive health information to more than 70 distinct third parties on these lead generation websites. By purchasing our own and other organic leads from three major lead platforms, we uncover deceptive brokerage practices, where consumer data is sold to unvetted buyers and often augmented or fabricated with attributes such as health status and weight. We received a total of over 8,000 telemarketing phone calls, 600 text messages, and 200 emails, where calls often began within seconds of form submission. Many campaigns relied on VoIP-based neighbor spoofing and high-frequency dialing, at times rendering phones unusable. Our experiments with phone and email opt-outs suggest phone-based opt-outs to help the most, although all were ineffective at completely stopping marketing communications. Analysis of 7,432 Better Business Bureau (BBB) complaints and reviews corroborates these findings from the consumer perspective. Overall, our results reveal a highly interconnected and non-compliant lead marketing ecosystem that aggressively monetizes sensitive consumer data.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper gives the first end-to-end trace of health lead data into spam and third-party sharing, but its broad claims about the whole ecosystem rest only on health measurements.

read the letter

The main takeaway is that the authors built a measurement pipeline that follows data from over 100 health lead-gen sites through purchased leads and into thousands of calls, texts, and emails. They document sharing with more than 70 third parties, cases where leads get augmented with fabricated health details, and rapid spam that starts within seconds. The BBB complaint analysis adds a consumer-side check. These concrete observations on brokerage practices and opt-out failures are new for this domain and give privacy researchers something specific to work with instead of just high-level descriptions of data brokers. The controlled-contact setup and lead-purchase experiments are a practical way to see downstream effects that earlier studies missed. That part of the work is solid and worth having in the literature. The soft spot is scope. Every primary measurement—site instrumentation, lead buys from three platforms, contact monitoring—stays inside health-related verticals. The abstract and claims talk about the lead marketing ecosystem across insurance and other high-value areas, yet no cross-vertical data or sampling argument appears to justify treating health results as representative. The 7,432 complaints help corroborate consumer impact but do not measure data flows outside health. Without that link, the “highly interconnected and non-compliant” description for the full ecosystem is an over-extension. Details on how the 100+ sites were chosen and any bias checks would also help readers judge how far the numbers travel. This paper is for researchers studying online privacy, data brokerage, and marketing spam. Anyone tracking consumer protection in web forms or looking for empirical baselines on lead gen would find the numbers and framework useful. It deserves peer review. The empirical core is sharp enough that referees can usefully push on scope and methods rather than starting from scratch.

Referee Report

1 major / 2 minor

Summary. The paper presents the first empirical study of privacy and spam risks in the lead marketing ecosystem. It instruments over 100 health-related lead-generation websites, monitors 200 controlled phone numbers and email addresses, purchases leads from three major platforms, and analyzes 7,432 BBB complaints. Key observations include sharing of sensitive health data to more than 70 third parties, deceptive brokerage practices involving fabricated attributes, over 8,000 telemarketing calls (often starting within seconds and using VoIP spoofing), 600 texts, and 200 emails, with opt-outs proving largely ineffective. The authors conclude that the ecosystem is highly interconnected, non-compliant, and aggressively monetizes sensitive consumer data across high-value verticals.

Significance. If the empirical observations hold and the scope limitations are addressed, this would be a significant contribution as the first end-to-end measurement of data flows from collection through brokerage to consumer contact in lead marketing. The multi-method design, including real lead purchases and controlled contact monitoring, provides concrete evidence of privacy harms and spam tactics that could inform policy and future research on data brokerage.

major comments (1)

[Abstract] Abstract: The central claim that the results reveal a 'highly interconnected and non-compliant lead marketing ecosystem' operating across 'high-value verticals such as insurance' rests on an untested uniformity assumption. All primary measurements (instrumentation of 100+ sites, lead purchases, contact monitoring, and third-party observations) are confined to health-related websites, with no data, overlap statistics, or sampling argument provided for other verticals despite the abstract's broader framing.

minor comments (2)

The methodology lacks explicit details on site selection criteria for the 100+ health websites and potential selection biases.
Discussion of how the 200 controlled contacts and purchased leads were chosen to represent typical consumer experiences would improve clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for highlighting the need to align the abstract's claims more closely with the empirical scope of the study. We address the major comment below and have revised the manuscript to improve precision without altering the core contributions.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that the results reveal a 'highly interconnected and non-compliant lead marketing ecosystem' operating across 'high-value verticals such as insurance' rests on an untested uniformity assumption. All primary measurements (instrumentation of 100+ sites, lead purchases, contact monitoring, and third-party observations) are confined to health-related websites, with no data, overlap statistics, or sampling argument provided for other verticals despite the abstract's broader framing.

Authors: We agree that the abstract's phrasing risks implying uniform findings across all verticals without direct empirical support from non-health sites. Our instrumentation, lead purchases, and contact monitoring were performed exclusively on health-related lead-generation websites, as stated in the methods and results sections. While the lead marketing ecosystem exhibits structural similarities across verticals (e.g., form-based collection, brokerage platforms, and aggressive outbound contact), we did not collect or analyze data from insurance or other non-health sites and therefore cannot claim direct evidence of uniformity. To address this, we will revise the abstract to explicitly scope the claims to the health vertical studied, qualify the broader ecosystem description as contextual rather than empirically demonstrated across verticals, and add a limitations paragraph discussing the absence of cross-vertical sampling. This revision preserves the paper's focus on the measured health data flows while correcting the overgeneralization. revision: yes

Circularity Check

0 steps flagged

No circularity: purely observational empirical study with no derivations or self-referential predictions

full rationale

The paper reports direct measurements from instrumenting 100+ health websites, purchasing leads, monitoring 200 contacts, and analyzing 7,432 BBB complaints. No equations, fitted parameters, predictions, or mathematical derivations appear in the abstract or described methodology. Claims about interconnection and non-compliance rest on observed data flows and consumer reports rather than any reduction to inputs by construction or self-citation chains. Generalization from health vertical to broader ecosystem is a representativeness issue, not circularity per the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a purely empirical measurement study relying on standard web instrumentation and controlled experiments; it introduces no free parameters, mathematical axioms, or new postulated entities.

pith-pipeline@v0.9.0 · 5595 in / 1152 out tokens · 33153 ms · 2026-05-10T17:43:06.104110+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Tracking Conversations: Measuring Content and Identity Exposure on AI Chatbots
cs.CR 2026-04 unverdicted novelty 7.0

17 of 20 AI chatbots share conversation content or identifiers with third parties, including plaintext text sent to Microsoft Clarity via session replay in three cases.
Tracking Conversations: Measuring Content and Identity Exposure on AI Chatbots
cs.CR 2026-04 accept novelty 7.0

17 of 20 AI chatbots share conversation content or identifiers with third parties, including plaintext prompt and response text with Microsoft Clarity in three cases.

Reference graph

Works this paper leans on

102 extracted references · 8 canonical work pages · cited by 1 Pith paper · 1 internal anchor

[1]

Public Law 63–203, 38 Stat

Federal trade commission act. Public Law 63–203, 38 Stat. 717,
[2]

§§ 41-58

Codified at 15 U.S.C. §§ 41-58
[3]

Technical report, U.S

The belmont report: Ethical principles and guidelines for the protec- tion of human subjects of research. Technical report, U.S. Department of Health, Education, and Welfare, 1979

1979
[4]

Technical report, U.S

The menlo report: Ethical principles guiding information and com- munication technology research. Technical report, U.S. Department of Homeland Security, 2012

2012
[5]

https://leginfo.legislature.ca

California consumer privacy act of 2018. https://leginfo.legislature.ca. gov/faces/codes displayText.xhtml?division=3.&part=4.&lawCode= CIV&title=1.81.5, 2018. California Civil Code §1798.100–1798.199

2018
[6]

No bound- aries: data exfiltration by third parties embedded on web pages

Gunes Acar, Steven Englehardt, and Arvind Narayanan. No bound- aries: data exfiltration by third parties embedded on web pages. Proceedings on Privacy Enhancing Technologies, 2020(4):220–238

2020
[7]

Leadconduit’s ping post

ActiveProspect. Leadconduit’s ping post. https://activeprospect.com/ blog/leadconduit-now-offers-real-time-ping-post-bidding/, 2020

2020
[8]

Discover leadconduit & automate your lead flows

ActiveProspect. Discover leadconduit & automate your lead flows. https://activeprospect.com/leadconduit/, 2025. Accessed: 2025-09-22

2025
[9]

How does Ping-post work in lead generation? Ac- tiveProspect Glossary, 2025

ActiveProspect. How does Ping-post work in lead generation? Ac- tiveProspect Glossary, 2025. Accessed: 2025-09-22

2025
[10]

J ¨ager: Automated telephone call traceback

David Adei, Varun Madathil, Sathvik Prasad, Bradley Reaves, and Alessandra Scafuro. J ¨ager: Automated telephone call traceback. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security, pages 2042–2056, 2024

2024
[11]

Advances in lead generation and mar- keting efficiency through predictive campaign analytics.IJMRGE, 3(1):1143–1154, 2022

Oluwademilade Aderemi Agboola, Jeffrey Chidera Ogeawuchi, Abra- ham Ayodeji Abayomi, Abiodun Yusuf Onifade, OO George, and Remolekun Enitan Dosumu. Advances in lead generation and mar- keting efficiency through predictive campaign analytics.IJMRGE, 3(1):1143–1154, 2022

2022
[12]

Machine learning techniques for spam detection in email and iot platforms: analysis and research challenges

Naeem Ahmed, Rashid Amin, Hamza Aldabbas, Deepika Koundal, Bader Alouffi, and Tariq Shah. Machine learning techniques for spam detection in email and iot platforms: analysis and research challenges. Security and Communication Networks, 2022(1):1862888, 2022

2022
[13]

10 top states where health insurers dominate: New study

American Medical Association. 10 top states where health insurers dominate: New study. American Medical Association, 2021

2021
[14]

Educational data brokers: Using the walkthrough method to identify data brokering by edtech platforms.Learning, Media and Technology, 49(2):320–333, 2024

Janine Arantes. Educational data brokers: Using the walkthrough method to identify data brokering by edtech platforms.Learning, Media and Technology, 49(2):320–333, 2024

2024
[15]

How to verify leads: A guide for lead buyers, 2025

Andrew Bailey. How to verify leads: A guide for lead buyers, 2025

2025
[16]

Modern lead generation in internet marketing for the development of enter- prise potential

Svitlana Bondarenko, Olena Laburtseva, Olena V Sadchenko, Vira Lebedieva, Oleksandra Haidukova, and Tetyana Kharchenko. Modern lead generation in internet marketing for the development of enter- prise potential. 2019

2019
[17]

Using thematic analysis in psychology.Qualitative research in psychology, 3(2):77–101, 2006

Virginia Braun and Victoria Clarke. Using thematic analysis in psychology.Qualitative research in psychology, 3(2):77–101, 2006

2006
[18]

A survey on web tracking: Mechanisms, implications, and defenses.Proceedings of the IEEE, 105(8):1476–1510, 2017

Tomasz Bujlow, Valent´ın Carela-Espa˜nol, Josep Sole-Pareta, and Pere Barlet-Ros. A survey on web tracking: Mechanisms, implications, and defenses.Proceedings of the IEEE, 105(8):1476–1510, 2017

2017
[19]

9 trends driving historic aca enrollment growth

Terry Burke, Shyam Vichare, Travis Kistler, and Ali Mar. 9 trends driving historic aca enrollment growth. Oliver Wyman, 2024. Ac- cessed: 2025-09-24

2024
[20]

Reflexive thematic analysis for applied qualitative health research.The Qualitative Report, 26(6):2011–2028, 2021

Karen A Campbell, Elizabeth Orr, Pamela Durepos, Linda Nguyen, Lin Li, Carly Whitmore, Paige Gehrke, Leslie Graham, and Susan M Jack. Reflexive thematic analysis for applied qualitative health research.The Qualitative Report, 26(6):2011–2028, 2021

2011
[21]

Optimizing insurance sales through automated lead management.NAJER, 4(4), 2023

Rahul Deb Chakladar. Optimizing insurance sales through automated lead management.NAJER, 4(4), 2023

2023
[22]

You shall not register! detecting privacy leaks across registration forms

Manolis Chatzimpyrros, Konstantinos Solomos, and Sotiris Ioannidis. You shall not register! detecting privacy leaks across registration forms. InInternational Workshop on Information and Operational Technology Security Systems, pages 91–104. Springer, 2019

2019
[23]

Lead generation m&a fall 2024

COLADV . Lead generation m&a fall 2024. COLADV Report, 2024

2024
[24]

Rules and regulations imple- menting the telephone consumer protection act of 1991 (tcpa)

Federal Communications Commission. Rules and regulations imple- menting the telephone consumer protection act of 1991 (tcpa). Federal Register document, November 2024

1991
[25]

Strengthening the abil- ity of consumers to stop robocalls.Federal Register, 89(2024- 04587):15756–15763, 2024

Federal Communications Commission. Strengthening the abil- ity of consumers to stop robocalls.Federal Register, 89(2024- 04587):15756–15763, 2024. Rule; CG Docket No. 02-278; FCC 24- 24; 47 CFR 64

2024
[26]

Lead generation: When the “product” is personal data

Federal Trade Commission. Lead generation: When the “product” is personal data. Business Guidance / FTC blog, July 2017

2017
[27]

California-based lead generator agrees to settlement banning it from making or assisting others in making telemarketing calls, including robocalls

Federal Trade Commission. California-based lead generator agrees to settlement banning it from making or assisting others in making telemarketing calls, including robocalls. Press release, January 2024

2024
[28]

The limits of transparency: Data brokers and com- modification.New media & society, 20(1):88–104, 2018

Matthew Crain. The limits of transparency: Data brokers and com- modification.New media & society, 20(1):88–104, 2018

2018
[29]

Un- derstanding privacy norms through web forms.arXiv preprint arXiv:2408.16304, 2024

Hao Cui, Rahmadi Trimananda, and Athina Markopoulou. Un- derstanding privacy norms through web forms.arXiv preprint arXiv:2408.16304, 2024

work page arXiv 2024
[30]

Reproducibility and replicability of web measurement studies

Nurullah Demir, Matteo Große-Kampmann, Tobias Urban, Christian Wressnegger, Thorsten Holz, and Norbert Pohlmann. Reproducibility and replicability of web measurement studies. InProceedings of the ACM Web Conference 2022, pages 533–544, 2022

2022
[31]

A study on the opportunities and challenges of digital insurance marketing.Turkish Online Journal of Qualitative Inquiry, 12(7), 2021

Uma Dubey and Madhu Dixit. A study on the opportunities and challenges of digital insurance marketing.Turkish Online Journal of Qualitative Inquiry, 12(7), 2021

2021
[32]

Fcc confirms that tcpa applies to ai technologies that generate human voices, 2024

Federal Communications Commission. Fcc confirms that tcpa applies to ai technologies that generate human voices, 2024

2024
[33]

In the matter of rules and regulations implementing the telephone consumer protection act of 1991

Federal Communications Commission. In the matter of rules and regulations implementing the telephone consumer protection act of 1991. https://docs.fcc.gov/public/attachments/FCC-24-24A1.pdf,

1991
[34]

Rules and regulations im- plementing the tcpa act of 1991

Federal Communications Commission. Rules and regulations im- plementing the tcpa act of 1991. https://www.fcc.gov/document/ rules-and-regulations-implementing-tcpa-act-1991-0, 2025

1991
[35]

National do not call registry data book: Fiscal year 2024

Federal Trade Commission. National do not call registry data book: Fiscal year 2024. Technical report, Federal Trade Commission, Washington, DC, November 2024

2024
[36]

2021 florida statutes (including 2021b session)

Florida Senate. 2021 florida statutes (including 2021b session). https: //www.flsenate.gov/laws/statutes/2021/501.616, 2021

2021
[37]

Leaky autofill: An empirical study on the privacy threat of password managers’ autofill functionality

Yanduo Fu and Ding Wang. Leaky autofill: An empirical study on the privacy threat of password managers’ autofill functionality. In 2024 Annual Computer Security Applications Conference (ACSAC), pages 288–303. IEEE, 2024

2024
[38]

Pixelconfig: Longitudinal measurement and reverse-engineering of meta pixel configurations.arXiv preprint arXiv:2603.09380, 2026

Abdullah Ghani, Yash Vekaria, and Zubair Shafiq. Pixelconfig: Longitudinal measurement and reverse-engineering of meta pixel configurations.arXiv preprint arXiv:2603.09380, 2026

work page arXiv 2026
[39]

Measuring the accuracy and effectiveness of pii removal services.arXiv preprint arXiv:2505.06989, 2025

Jiahui He, Pete Snyder, Hamed Haddadi, Fabi ´an E Bustamante, and Gareth Tyson. Measuring the accuracy and effectiveness of pii removal services.arXiv preprint arXiv:2505.06989, 2025

work page arXiv 2025
[40]

Hanging up too early: remedies to reduce robocalls

Maria G Hibbard. Hanging up too early: remedies to reduce robocalls. Case W. Res. JL Tech. & Internet, 5:79, 2014

2014
[41]

Cloak of Visibility: Detecting When Machines Browse a Different Web

Luca Invernizzi, Kurt Thomas, Alexandros Kapravelos, Oxana Co- manescu, Jean-Michel Picod, and Elie Bursztein. Cloak of Visibility: Detecting When Machines Browse a Different Web. S&P, 2016

2016
[42]

A review of spam email detection: analysis of spammer strategies and the dataset shift problem.Artificial Intelligence Review, 56(2):1145–1173, 2023

Francisco J ´a˜nez-Martino, Roc ´ıo Alaiz-Rodr´ıguez, V´ıctor Gonz´alez- Castro, Eduardo Fidalgo, and Enrique Alegre. A review of spam email detection: analysis of spammer strategies and the dataset shift problem.Artificial Intelligence Review, 56(2):1145–1173, 2023

2023
[43]

What are the largest lead generation niches? MonetizePros, 2021

Michael Johnston. What are the largest lead generation niches? MonetizePros, 2021

2021
[44]

Spamalytics: An empirical analysis of spam marketing conversion

Chris Kanich, Christian Kreibich, Kirill Levchenko, Brandon Enright, Geoffrey M V oelker, Vern Paxson, and Stefan Savage. Spamalytics: An empirical analysis of spam marketing conversion. InProceed- ings of the 15th ACM conference on Computer and communications security, pages 3–14, 2008

2008
[45]

Tracking the surveillance and information practices of data brokers: A report

Rahul Kanwal and Kevin Walby. Tracking the surveillance and information practices of data brokers: A report. 2024

2024
[46]

Measuring biases in a data broker’s coverage

Levi Kaplan, Alan Mislove, and Piotr Sapie ˙zy´nski. Measuring biases in a data broker’s coverage. InAMC IMC, 2021

2021
[47]

Tracker installations are not created equal: Understanding tracker configuration of form data collection

Julia B Kieserman, Athanasios Andreou, Chris Geeng, Tobias Lauinger, and Damon McCoy. Tracker installations are not created equal: Understanding tracker configuration of form data collection. arXiv preprint arXiv:2506.16891, 2025

work page arXiv 2025
[48]

Data brokers and the sale of americans’ mental health data.Durham: Duke Sanford School of Public Policy, 2023

Joanne Kim. Data brokers and the sale of americans’ mental health data.Durham: Duke Sanford School of Public Policy, 2023

2023
[49]

Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda, 2024

Nir Kshetri, Yogesh K Dwivedi, Thomas H Davenport, and Niki Panteli. Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda, 2024

2024
[50]

Fill in the blanks: Empirical analysis of the privacy threats of browser form autofill

Xu Lin, Panagiotis Ilia, and Jason Polakis. Fill in the blanks: Empirical analysis of the privacy threats of browser form autofill. InProceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, pages 507–519, 2020

2020
[51]

Selling consumers not lists: The new world of digital decision-making and the role of the fair credit reporting act.Suffolk UL Rev., 46:845, 2013

Ed Mierzwinski and Jeff Chester. Selling consumers not lists: The new world of digital decision-making and the role of the fair credit reporting act.Suffolk UL Rev., 46:845, 2013

2013
[52]

E-mail marketing at the crossroads: A stakeholder analysis of unsolicited commercial e-mail (spam).Internet research, 16(1):38–52, 2006

Evangelos Moustakas, C Ranganathan, and Penny Duquenoy. E-mail marketing at the crossroads: A stakeholder analysis of unsolicited commercial e-mail (spam).Internet research, 16(1):38–52, 2006

2006
[53]

Every keystroke you make: A tech-law measurement and analysis of event listeners for wiretapping,

Shaoor Munir, Nurullah Demir, Qian Li, Konrad Kollnig, and Zubair Shafiq. Every keystroke you make: A tech-law measurement and analysis of event listeners for wiretapping.arXiv preprint arXiv:2508.19825, 2025

work page arXiv 2025
[54]

Data deserts and black boxes: The impact of socio-economic status on consumer profiling.Management Science, 70(11):8003–8029, 2024

Nico Neumann, Catherine E Tucker, Levi Kaplan, Alan Mislove, and Piotr Sapiezynski. Data deserts and black boxes: The impact of socio-economic status on consumer profiling.Management Science, 70(11):8003–8029, 2024

2024
[55]

Fron- tiers: How effective is third-party consumer profiling? evidence from field studies.Marketing Science, 38(6):918–926, 2019

Nico Neumann, Catherine E Tucker, and Timothy Whitfield. Fron- tiers: How effective is third-party consumer profiling? evidence from field studies.Marketing Science, 38(6):918–926, 2019

2019
[56]

Nathan Sosa, Rep

Offices of Rep. Nathan Sosa, Rep. Farrah Chaichi, and Rep. David Gomberg. Oregon house passes telemarketing modernization act. Press release, April 2025. HB 3865 A

2025
[57]

Why Johnny Can’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns

Lukasz Olejnik, Claude Castelluccia, and Artur Janc. Why Johnny Can’t Browse in Peace: On the Uniqueness of Web Browsing History Patterns. PETS, 2012

2012
[58]

Who’s calling? characterizing robocalls through audio and metadata analysis

Sathvik Prasad, Elijah Bouma-Sims, Athishay Kiran Mylappan, and Bradley Reaves. Who’s calling? characterizing robocalls through audio and metadata analysis. In29th USENIX Security Symposium (USENIX Security 20), pages 397–414, 2020

2020
[59]

Diving into robocall content with{SnorCall}

Sathvik Prasad, Trevor Dunlap, Alexander Ross, and Bradley Reaves. Diving into robocall content with{SnorCall}. In32nd USENIX Security Symposium (USENIX Security 23), pages 427–444, 2023

2023
[60]

Char- acterizing robocalls with multiple vantage points

Sathvik Prasad, Aleksandr Nahapetyan, and Bradley Reaves. Char- acterizing robocalls with multiple vantage points. In2025 IEEE Symposium on Security and Privacy (SP), pages 19–36. IEEE, 2025

2025
[61]

A study on utility and feasibility of digital marketing tools with lead acquisition, lead nurturing and client engagement

U Prasanna Kumar and R Arthi. A study on utility and feasibility of digital marketing tools with lead acquisition, lead nurturing and client engagement. InInternational conference on economics, business and sustainability, pages 198–204. Springer, 2023

2023
[62]

Wiley, 2014

Dayna Rothman.Lead generation for dummies. Wiley, 2014

2014
[63]

The sales lead black hole: On sales reps’ follow-up of marketing leads.Journal of marketing, 77(1):52–67, 2013

Gaurav Sabnis, Sharmila C Chatterjee, Rajdeep Grewal, and Gary L Lilien. The sales lead black hole: On sales reps’ follow-up of marketing leads.Journal of marketing, 77(1):52–67, 2013

2013
[64]

Using chatbots against voice spam: Analyzing{Lenny’s}effectiveness

Merve Sahin, Marc Relieu, and Aur ´elien Francillon. Using chatbots against voice spam: Analyzing{Lenny’s}effectiveness. InSOUPS 2017, pages 319–337, 2017

2017
[65]

What is lead generation? guide & best practices

Salesforce. What is lead generation? guide & best practices. https: //www.salesforce.com/marketing/lead-generation-guide/, 2025

2025
[66]

AMACOM Div AMA, 2013

David T Scott.The new rules of Lead Generation: proven strategies to maximize marketing ROI. AMACOM Div AMA, 2013

2013
[67]

Leaky forms: A study of email and password exfiltration before form submission

Asuman Senol, Gunes Acar, Mathias Humbert, and Fred- erik Zuiderveen Borgesius. Leaky forms: A study of email and password exfiltration before form submission. In31st USENIX Security Symposium (USENIX Security 22), pages 1813–1830, 2022

2022
[68]

Advertising in health insurance markets.Market- ing Science, 39(3):587–611, 2020

Bradley T Shapiro. Advertising in health insurance markets.Market- ing Science, 39(3):587–611, 2020

2020
[69]

Lead generation market overview

Ronit Sharma and Neha Kashyap. Lead generation market overview. Technical report, Roots Analysis, November 2024

2024
[70]

Data brokers and sensitive data on us individuals

Justin Sherman. Data brokers and sensitive data on us individuals. Duke University Sanford Cyber Policy Program, 9, 2021

2021
[71]

Data brokers and the sale of data on us military personnel, 2023

Justin Sherman, Hayley Barton, Aden Klein, Brady Kruse, and Anushka Srinivasan. Data brokers and the sale of data on us military personnel, 2023

2023
[72]

Are you sure you want to contact us? quantifying the leakage of pii via website contact forms.Proceedings on Privacy Enhancing Technologies, 2016

Oleksii Starov, Phillipa Gill, and Nick Nikiforakis. Are you sure you want to contact us? quantifying the leakage of pii via website contact forms.Proceedings on Privacy Enhancing Technologies, 2016

2016
[73]

Pearson Education, 2012

Ruth Stevens.Maximizing lead generation: The complete guide for B2B marketers. Pearson Education, 2012

2012
[74]

Online lead generation: An emerging industry.Mar- keting Review St

Simon Stolz, Kilian C Wisskirchen, Christian Schlereth, and Alexan- der Hoffmann. Online lead generation: An emerging industry.Mar- keting Review St. Gallen, 38(4):32–39, 2021

2021
[75]

a how website owners face privacy issues: Thematic analysis of responses from a covert notification study reveals diverse circumstances and challenges

Alina St ¨over, Nina Gerber, Henning Prid ¨ohl, Max Maass, Sebastian Bretthauer, I Spiecker, M Hollick, and D Herrmann. a how website owners face privacy issues: Thematic analysis of responses from a covert notification study reveals diverse circumstances and challenges. Proc Priv Enhanc Technol, 2023

2023
[76]

Lead generation strategies for financial services and institutions, June 4 2025

Martyna Targosz. Lead generation strategies for financial services and institutions, June 4 2025. Accessed 2025-11-04

2025
[77]

Quickstart for number lookup

Telnyx LLC. Quickstart for number lookup. https://developers.telnyx. com/docs/identity/number-lookup/quickstart, 2025

2025
[78]

Sok: Everyone hates robocalls: A survey of techniques against telephone spam

Huahong Tu, Adam Doup ´e, Ziming Zhao, and Gail-Joon Ahn. Sok: Everyone hates robocalls: A survey of techniques against telephone spam. In2016 IEEE Symposium on Security and Privacy (SP), pages 320–338. IEEE, 2016

2016
[79]

Beyond the Front Page: Measuring Third Party Dynamics in the Field

Tobias Urban, Martin Degeling, Thorsten Holz, and Norbert Pohlmann. Beyond the Front Page: Measuring Third Party Dynamics in the Field. Inwww, TheWebConf, 2020

2020
[80]

Census Bureau

U.S. Census Bureau. Income in the united states: 2023. Technical Report P60-282, U.S. Department of Commerce, Economics and Statistics Administration, 2024

2023

Showing first 80 references.