Research and sources
The evidence behind The Salecopy Method.
40 published papers, reports and official statistics, and 7 of our own case studies. Each outside source was opened at its original publisher by one researcher, who recorded the exact figure and where it appears, and then checked again independently by a second. Anything that could not be confirmed twice was left out.
These studies describe what was measured, in the setting where it was measured. They are reasons for the method, not promises about your results. If you find an error, email neel@salecopy.io and we will post a dated correction.
Negotiation research
Tversky and Kahneman described 'adjustment from an anchor' as one of three mental shortcuts people use when judging under uncertainty: when a relevant number is available, estimates start from it. They noted these shortcuts are usually effective but lead to systematic and predictable errors.
Tversky, A., & Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science, 185(4157), 1124-1131.
- 3 experiments
Across three experiments, whichever party made the first offer, buyer or seller, obtained a better outcome, and first offers strongly predicted final settlement prices. The advantage disappeared when the other side focused on information inconsistent with the first offer, such as their own target.
Galinsky, A. D., & Mussweiler, T. (2001). First offers as anchors: The role of perspective-taking and negotiator focus. Journal of Personality and Social Psychology, 81(4), 657-669.
In a series of studies, people who received a round first offer adjusted further away from it in their counteroffers than people who received a precise first offer. Negotiators who made precise first offers were assumed to be better informed.
Mason, M. F., Lee, A. J., Wiley, E. A., & Ames, D. R. (2013). Precise offers are potent anchors: Conciliatory counteroffers and attributions of knowledge in negotiations. Journal of Experimental Social Psychology, 49(4), 759-763.
- 80% of list price (range 33% to 95%); 26 million negotiations
An analysis of more than 26 million eBay negotiations found that lower buyer offers pulled the sale price down but, past a point, raised the risk of no deal. Weighing the two, the authors put the ideal buyer offer at 80% of the seller's list price on average, ranging from 33% to 95% by product, demand and risk preference.
Schweinsberg, M., Petrowsky, H. M., Funk, B., & Loschelder, D. D. (2023). Understanding the first-offer conundrum: How buyer offers impact sale price and impasse risk in 26 million eBay negotiations. Proceedings of the National Academy of Sciences, 120(32), e2218582120.
In a set of experiments, negotiators with a weak alternative made lower first offers and got worse outcomes than negotiators with no alternative at all, because the weak alternative acted as a low anchor. The effect went away when negotiators focused on a countervailing anchor.
Schaerer, M., Swaab, R. I., & Galinsky, A. D. (2015). Anchors weigh more than power: Why absolute powerlessness liberates negotiators to achieve better outcomes. Psychological Science, 26(2), 170-181.
- 3 experiments
In three experiments, negotiators who focused on their target price achieved objectively better outcomes than negotiators who focused on their lower bound, such as a reservation price. They were, however, less satisfied with those better outcomes.
Galinsky, A. D., Mussweiler, T., & Medvec, V. H. (2002). Disconnecting outcomes and evaluations: The role of negotiator focus. Journal of Personality and Social Psychology, 83(5), 1131-1140.
Getting to Yes, based on the work of the Harvard Negotiation Project, sets out a method of negotiation that separates the people from the problem, focuses on interests rather than positions, and has the parties work together to find creative and fair options.
Fisher, R., Ury, W. L., & Patton, B. (2011). Getting to Yes: Negotiating Agreement Without Giving In. Penguin Books (revised edition, ISBN 9780143118756).
- 28 studies
A meta-analysis of 28 studies found that negotiators with a prosocial rather than self-interested motive were less contentious, did more problem solving and reached higher joint outcomes, but only when they also held firm on their own interests (high or unknown resistance to yielding).
De Dreu, C. K. W., Weingart, L. R., & Kwon, S. (2000). Influence of social motives on integrative negotiation: A meta-analytic review and test of two theories. Journal of Personality and Social Psychology, 78(5), 889-905.
A direct replication of Cialdini and colleagues' 1975 door-in-the-face study found that people were more likely to agree to a request after turning down a larger one, with compliance rates similar to the original. The authors read this as support for the role of reciprocity norms.
Genschow, O., Westfal, M., Crusius, J., Bartosch, L., Feikes, K. I., Pallasch, N., & Wozniak, M. (2021). Does social psychology persist over half a century? A direct replication of Cialdini et al.'s (1975) classic door-in-the-face technique. Journal of Personality and Social Psychology, 120(2), e1-e7.
Reviewing research on deadlines, Gino and Moore reported that the common advice to keep a final deadline secret, especially from a weak position, had been shown to be incorrect by studies of revealing deadlines in negotiation.
Gino, F., & Moore, D. A. (2008). Using Final Deadlines Strategically in Negotiation. Negotiation and Conflict Management Research, 1(4), 371-388.
- d = .65 across 94 tests
A meta-analysis of 94 independent tests found that forming an implementation intention, an if-then plan fixing when, where and how to act, had a medium-to-large positive effect on goal attainment (d = .65). The studies covered goals in general, not negotiation specifically.
Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Social Psychology, 38, 69-119.
Gollwitzer summarized a program of research showing that if-then plans, which link an anticipated situation to a specific response, helped people attain their goals when they had trouble getting started or staying on track.
Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493-503.
- nearly 3 times more likely
In one experiment, graduate management students who compared two training cases and drew out the shared principle were nearly three times more likely to use that strategy in a later negotiation than students who studied the same cases separately.
Loewenstein, J., Thompson, L., & Gentner, D. (1999). Analogical encoding facilitates knowledge transfer in negotiation. Psychonomic Bulletin & Review, 6(4), 586-597.
Prospect theory proposed that people assign value to gains and losses rather than to final assets, and that the value function is generally steeper for losses than for gains.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-292.
Tversky and Kahneman showed that people's preferences shifted predictably, and sometimes reversed, when the same decision problem was framed in different ways, in choices about money and about the loss of human lives.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458.
Kahneman, Knetsch and Thaler reviewed evidence for the endowment effect, where people often demand much more to give up an object than they would pay to get it, and for status quo bias, a preference for the current state. They linked both to loss aversion.
Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias. Journal of Economic Perspectives, 5(1), 193-206.
How B2B buying and selling actually runs
- 37% within an hour; 23% never responded
In an audit of 2,241 U.S. companies published in 2011, Oldroyd, McElheran and Elkington found that 37% responded to a web-generated test lead within an hour, 24% took more than 24 hours and 23% never responded at all.
James B. Oldroyd, Kristina McElheran, and David Elkington (2011). The Short Life of Online Sales Leads. Harvard Business Review.
- nearly 7x
In a study reported in Harvard Business Review in March 2011, covering 1.25 million sales leads received by 29 B2C and 13 B2B companies in the U.S., Oldroyd, McElheran and Elkington found that firms that tried to contact a lead within an hour of the query were nearly seven times as likely to qualify it as firms that tried even an hour later. 'Qualify' meant having a meaningful conversation with a key decision maker. The article does not state when the study was run.
James B. Oldroyd, Kristina McElheran, and David Elkington (2011). The Short Life of Online Sales Leads. Harvard Business Review.
- 1% price = 11.1% operating profit
Marn and Rosiello calculated in 1992 that, for a company with the average economics of 2,463 companies in the Compustat aggregate, a 1% improvement in price with no loss of volume raises operating profit by 11.1%, against 3.3% for a 1% rise in unit volume.
Michael V. Marn and Robert L. Rosiello (1992). Managing Price, Gaining Profit. Harvard Business Review.
- 40% to 60% of deals
In a Harvard Business Review article published in June 2022, Matthew Dixon and Ted McKenna reported that their own study of more than 2.5 million recorded sales conversations found between 40% and 60% of deals were lost to customers who said they intended to buy but did not act. The authors are partners in a sales consultancy, and the article does not state the study dates or how the range was calculated.
Matthew Dixon and Ted McKenna (2022). Stop Losing Sales to Customer Indecision. Harvard Business Review.
- 28% of the week
Salesforce's State of Sales report, fifth edition (2022), a survey with 7,775 responses from sales professionals, found that reps spend 28% of their week actually selling. This is a vendor-run survey of self-reported time use.
Salesforce (2022). New Research Reveals Sales Reps Need a Productivity Overhaul – Spend Less than 30% Of Their Time Actually Selling. Salesforce.
- 2x buyer contacts on won deals
Vendor data: Gong, a sales software company, reported from its own analysis of 1.8 million opportunities closed in 2024 that 77% of deals were multi-threaded and that won deals had twice as many buyer contacts as lost ones. This is a correlation in one vendor's data, and the post does not describe the method in detail.
Dan Morgese, Gong (2025). Data shows top reps don't just sell — they orchestrate (with AI). Gong.
- 6.2 months
The Bridge Group's 2026 Account Executive report, based on survey responses from 158 B2B companies, put average ramp time for a new account executive at 6.2 months, the highest in the study's history. Average experience required at hire was 3.7 years.
Matt Bertuzzi, The Bridge Group (2026). State of Sales: 2026 AE Models, Motions, & Metrics Research. The Bridge Group.
CRM data and forecasting
- 31% report losing 20%+ of revenue
Validity's report 'The State of CRM Data Management in 2024', based on its own survey of more than 600 CRM administrators worldwide, says 31% reported that poor-quality data costs them at least 20% of annual revenue and 24% said less than half of their CRM data is accurate and complete. Validity sells data-quality software, these are self-reported estimates, and the report's public page does not state when the survey was run.
Validity (2024). The State of CRM Data Management in 2024. Validity.
AI at work
- 14% average productivity gain; 34% for novices
In a study of 5,179 customer support agents, access to a generative AI assistant raised issues resolved per hour by 14% on average, with a 34% improvement for novice and low-skilled workers and minimal impact on experienced, highly skilled workers.
Erik Brynjolfsson, Danielle Li, Lindsey R. Raymond (2023). Generative AI at Work. NBER.
- 12.2% more tasks, 25.1% faster
In a preregistered experiment with 758 knowledge workers at Boston Consulting Group, those using GPT-4 on 18 realistic consulting tasks within AI's capabilities completed 12.2% more tasks, 25.1% more quickly on average, than those without AI.
Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, Karim R. Lakhani (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. Harvard Business School.
- 19% less likely to be correct
In the same experiment with 758 Boston Consulting Group knowledge workers, on a complex managerial task chosen to be outside AI's capabilities, those using AI were 19% less likely to produce correct solutions than those working without AI.
Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, Karim R. Lakhani (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. Harvard Business School.
- 23% weekly use at work; 9% daily
In nationally representative US surveys, 23% of employed respondents had used generative AI for work at least once in the previous week as of late 2024, and 9% used it every work day.
Alexander Bick, Adam Blandin, David J. Deming (2024). The Rapid Adoption of Generative AI. NBER.
- 54% use AI agents now; 34% expect to within 2 years
In Salesforce's survey of 4,050 sales professionals in 22 countries, conducted August through September 2025, 54% said their sales team uses AI agents now and a further 34% expect to within two years. This is a vendor survey of self-reported use, not a measure of results.
Salesforce (2026). State of Sales, Seventh Edition. Salesforce.
- 117 studies
A meta-analysis of 117 studies of behavior modeling training (watching a modeled skill, then practising it) found the largest effects on learning, smaller effects on job behavior and smaller still on business results. Effects on skills and job behavior held or grew over time, and transfer was greater when practice used scenarios generated by the trainees.
Paul J. Taylor, Darlene F. Russ-Eft, Daniel W. L. Chan (2005). A Meta-Analytic Review of Behavior Modeling Training. Journal of Applied Psychology (American Psychological Association).
Sector facts
- 1,905,400 jobs in 2025; 10% projected growth 2025-35
The US Bureau of Labor Statistics counted about 1.9 million jobs for software developers, quality assurance analysts and testers in the United States in 2025, and projects employment to grow 10% from 2025 to 2035.
US Bureau of Labor Statistics (2026). Occupational Outlook Handbook: Software Developers, Quality Assurance Analysts, and Testers. US Bureau of Labor Statistics.
- 2,354.1 thousand employees, October 2026 (preliminary)
About 2.35 million people worked in computer systems design and related services in the United States in October 2026 (seasonally adjusted, preliminary), according to the Bureau of Labor Statistics payroll survey.
US Bureau of Labor Statistics (2026). Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail. US Bureau of Labor Statistics.
- 72.7% of US freight by weight; $906 billion revenue (2024)
The American Trucking Associations estimates that trucks moved roughly 72.7% of US freight by weight in 2024, and that the trucking freight bill was $906 billion in gross freight revenues (primary shipments only).
American Trucking Associations (2024). Economics and Industry Data. American Trucking Associations.
- 1,471.3 thousand employees, October 2026 (preliminary)
About 1.47 million people worked in truck transportation (NAICS 484) in the United States in October 2026 (seasonally adjusted, preliminary), according to the Bureau of Labor Statistics payroll survey.
US Bureau of Labor Statistics (2026). Industries at a Glance: Truck Transportation: NAICS 484. US Bureau of Labor Statistics.
- Nearly 2.0 million per week; 9.5 million over 2025
The American Staffing Association reports that nearly 2.0 million temporary and contract employees worked for US staffing companies during an average week in 2025, and that staffing companies hired 9.5 million temporary and contract employees over the year.
American Staffing Association (2025). Staffing Industry Statistics. American Staffing Association.
- 2,526.0 thousand employees, October 2026 (preliminary)
About 2.53 million people were employed in temporary help services in the United States in October 2026 (seasonally adjusted, preliminary), according to the Bureau of Labor Statistics payroll survey.
US Bureau of Labor Statistics (2026). Table B-1. Employees on nonfarm payrolls by industry sector and selected industry detail. US Bureau of Labor Statistics.
- 7.5 million job openings, September 2026
There were 7.5 million job openings in the United States in September 2026, with 5.4 million hires and 5.3 million total separations, according to the Bureau of Labor Statistics Job Openings and Labor Turnover Survey (preliminary).
US Bureau of Labor Statistics (2026). Job Openings and Labor Turnover Summary. US Bureau of Labor Statistics.
- 10,881.3 thousand employees, October 2026 (preliminary)
About 10.9 million people worked in professional, scientific and technical services (NAICS 54) in the United States in October 2026 (seasonally adjusted, preliminary), according to the Bureau of Labor Statistics payroll survey.
US Bureau of Labor Statistics (2026). Industries at a Glance: Professional, Scientific, and Technical Services: NAICS 54. US Bureau of Labor Statistics.
- $668.5 billion shipments, September 2026
US manufacturers' shipments were $668.5 billion in September 2026, up 1.3% from August, and new orders for manufactured goods were $677.2 billion, according to the Census Bureau's monthly M3 survey.
US Census Bureau (2026). Monthly Full Report on Manufacturers' Shipments, Inventories, & Orders (September 2026, release CB26-170). US Census Bureau.
- $840.1 billion sales, September 2026
Sales of US merchant wholesalers (excluding manufacturers' sales branches and offices) were $840.1 billion in September 2026, seasonally adjusted and not adjusted for price changes, according to the Census Bureau. The inventories-to-sales ratio was 1.21.
US Census Bureau (2026). Monthly Wholesale Trade Report: September 2026 (release CB26-168). US Census Bureau.
Our own work
Numbers from engagements we ran. Client names are left out; the figures are exact.
- 173 tables · 128 routes
From our own work: we built the platform a four-department revenue org now runs on. The figure: 173 tables · 128 routes, built in 15 months.
Salecopy.io. We built the platform a four-department revenue org now runs on. Salecopy.io case study.
- $13.78M → $11.21M
From our own work: the booked revenue that was counted twice. The figure: $13.78M → $11.21M, corrected booked revenue.
Salecopy.io. The booked revenue that was counted twice. Salecopy.io case study.
- 78% of AI spend
From our own work: the flagship AI deal score was worse than a coin flip. The figure: 78% of AI spend, re-scoped to three bands.
Salecopy.io. The flagship AI deal score was worse than a coin flip. Salecopy.io case study.
- 24.7% → 9.1%
From our own work: the win-rate analysis nobody could reproduce. The figure: 24.7% → 9.1%, real new-business win rate.
Salecopy.io. The win-rate analysis nobody could reproduce. Salecopy.io case study.
- 763 company-years
From our own work: the churn signal every CRM encodes, that had no signal. The figure: 763 company-years, back-tested.
Salecopy.io. The churn signal every CRM encodes, that had no signal. Salecopy.io case study.
- 56% of spend
From our own work: Treating model spend like cost of goods. The figure: 56% of spend, in two workflows, re-tiered.
Salecopy.io. Treating model spend like cost of goods. Salecopy.io case study.
- 83%
From our own work: Cut one platform's spend 83% and got five times the coverage. The figure: 83%, annual spend cut.
Salecopy.io. Cut one platform's spend 83% and got five times the coverage. Salecopy.io case study.