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Services sales representatives

Official title Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel (SOC 41-3091)

Also called Service sales rep, B2B sales representative, Account executive, Account manager, Inside sales rep

In brief

In May 2025, BLS counted 1,256,010 US services sales representatives, with median pay of $69,990 a year. Of the BLS counts since 2021, the May 2025 count is the highest. The earliest figure here is a Census count of 93,000 from 1900, not measured the same way as today's count. The last full technology era for services sales representatives: Sales engagement platforms, 2010 to 2022.

What the sources say

Official outlook

BLS projects employment to grow 2.7% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Services sales representatives: jobs and pay since 1900 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/41-3091

Chicago

Future History. "Services sales representatives: jobs and pay since 1900." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/41-3091.

BibTeX

@misc{futurehistory_41_3091, author = {{Future History}}, title = {Services sales representatives: jobs and pay since 1900}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/41-3091}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
1850187519001925195019752000now
2026
Known then as Drummer / Commercial Traveler (wholesale goods agent era)
Known then as Traveling Salesman / Commercial Traveler (systematic selling era)
Known then as Sales Representative / Field Sales Representative (telephone and territory era)
Known then as Sales Representative / Account Executive (professional services era)
Known then as Account Executive / Business Development Representative (CRM and SFA era)
Known then as Sales Representatives of Services (BLS SOC 41-3091)
Known today as Sales Representatives of Services (BLS SOC 41-3091)
Projected to still be called Sales Representatives of Services (BLS SOC 41-3091)
Data from the May 2025 survey
US Employment
1.26M
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$69,990
Est.

≈ $68,196 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1884

John H. Patterson acquires the National Cash Register Company and builds the first modern sales management system: exclusive territories, scripted presentations (the NCR Primer), daily activity reports, annual sales conventions, and quota-based compensation. Patterson is selling a physical product (a cash register) but the ongoing service relationship -- installation, maintenance, repair, upgrades -- is central to the value proposition. NCR alumni who carry this model forward include Thomas Watson Sr. (IBM), Charles Kettering (who went into industrial research), and Charles Schwab (US Steel). The "Patterson alumni club" seeded the modern sales organization across the Fortune 500.

Beat · 1916

First World Salesmanship Congress, Detroit, 1916: more than three thousand salespeople attend; U.S. President Woodrow Wilson keynotes. The event, organized by D.M. Barrett (Salesmanship magazine) and sponsored by NCR's Patterson, declares the intent to "promote the dignity of salesmanship by the elevation of the rank of the salesman to a higher plane." The Congress marks the moment selling achieves formal professional status in American business culture: a craft with its own body of knowledge, its own training curricula, and its own institutions.

Beat · 1920

The Great Salesperson Purge of 1920-1922: the post-World War I economic collapse triggered mass layoffs of the approximately 400,000 traveling salesmen in the US. Sales executives "discharged practically all their salesmen" as demand collapsed. The Bureau of Personnel Research estimated per-hire training costs of $200-$2,000 against a workforce with 50% annual turnover, placing total excess hiring costs at $40 million annually. The purge forced systematic thinking about sales force management, selection, and training that would mature into the professional selling curricula of the 1930s and 1940s.

Beat · 1936

Dale Carnegie publishes "How to Win Friends and Influence People" (November 1936), which becomes one of the best-selling books of the 20th century. Carnegie's core insight -- that people buy from people they like, that understanding the other person's perspective is more persuasive than presenting features -- formed the intellectual backbone of relationship-based service selling for the following half-century. The book is still in print and still assigned in sales training programs.

Beat · 1999

Marc Benioff founds Salesforce.com on March 8, 1999 with Parker Harris, Dave Moellenhoff, and Frank Dominguez. The product is a web-browser-accessible CRM at $99/month, delivered without enterprise software installation. The company's implicit product is a proof-of-concept for the SaaS model itself: if CRM can be delivered as a subscription, anything can be. Over the following decade, the SaaS model produces thousands of software companies each requiring a service sales force to sell annual contracts. Salesforce is the most consequential structural driver of employment growth in SOC 41-3091 from 2000 to 2024: not because of the company itself, but because the model it proved out created an entirely new category of service to sell.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace services sales representatives? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+2.7%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 41-3091 (Sales representatives of services, except advertising, insurance, financial services, and travel): 1,272.7 thousand jobs in 2025, projected 1,306.7 thousand in 2035, a change of +33.9 thousand (+2.7%). Matrix employment counts all jobs in the occupation, including self-employed workers (1.2% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
56.7%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 41-3091.00 Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel. Shown: human-rated β = 0.567, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.333 and γ (all LLM-assisted) 0.8; GPT-4-rated β 0.633. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for services sales representatives

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is taking this onManage the CRM pipeline, update deal stages, log activity notes, and maintain accurate account records: use Salesforce Agentforce or HubSpot Breeze AI to automatically log call summaries, update deal stages based on engagement signals, and generate pipeline health scores: eliminating the 30–40 minutes of manual CRM hygiene per rep per day that previously followed every customer interaction.

Manage the CRM pipeline, update deal stages, log activity notes, and maintain accurate account records: use Salesforce Agentforce or HubSpot Breeze AI to automatically log call summaries, update deal stages based on engagement signals, and generate pipeline health scores: eliminating the 30–40 minutes of manual CRM hygiene per rep per day that previously followed every customer interaction.[4],[9],[2]

Where your edge is

CRM hygiene is being substantially automated: accept this shift and stop treating manual CRM entry as a proxy for productivity. Redirect that time to a weekly pipeline review habit: use AI-generated deal health scores not just to update fields but to identify which deals have gone cold and why, and make explicit decisions about which accounts to re-engage vs. deprioritize. The reps who extract the most value from AI-automated pipelines are those who treat the data as a coaching input, not just an administrative output.

AI is sitting alongside you hereCompute, compare, and present costs of services for buyers evaluating options: use AI-assisted CPQ (Configure-Price-Quote) tools embedded in Salesforce or HubSpot to generate accurate, brand-consistent price quotes from service configuration inputs in seconds

Compute, compare, and present costs of services for buyers evaluating options: use AI-assisted CPQ (Configure-Price-Quote) tools embedded in Salesforce or HubSpot to generate accurate, brand-consistent price quotes from service configuration inputs in seconds; ensure quotes reflect current pricing tiers, volume discounts, and promotional terms without manual calculation error; follow up with credit-term and financing-option explanations when buyer cash-flow or budget-cycle constraints surface.[2],[10]

Where your edge is

Automated CPQ removes the mechanical cost-comparison task from the rep's plate: accept that and invest the freed time in understanding the buyer's total cost of ownership framing, not just the contract price. Service buyers compare your quote against the cost of staying with the current vendor (including switching costs, retraining, and integration effort). Build fluency in articulating the full-cost case for switching vs. staying, including the hidden costs of incumbent inertia that buyers systematically underweight. That economic framing conversation is where you win deals that a pure price comparison would lose.

Get started with these tools
AI is sitting alongside you hereProspect new service accounts using AI-enriched lead intelligence: use ZoomInfo Copilot or Apollo.io to surface buyer-intent signals (job-change triggers, technology-adoption signals, company-growth indicators, competitor-contract renewal windows) and build prioritized target lists ranked by likelihood-to-buy score: replacing the manual business-directory search and cold-list dialing that previously consumed 30–40% of rep time.

Prospect new service accounts using AI-enriched lead intelligence: use ZoomInfo Copilot or Apollo.io to surface buyer-intent signals (job-change triggers, technology-adoption signals, company-growth indicators, competitor-contract renewal windows) and build prioritized target lists ranked by likelihood-to-buy score: replacing the manual business-directory search and cold-list dialing that previously consumed 30–40% of rep time.[7],[2],[5]

Where your edge is

AI surfaces the right prospect at the right moment; shift your time from list-building to signal interpretation. For every AI-identified account, develop one specific hypothesis about why this service fits their situation right now: a pain point surfaced by a recent news event, a technology gap evident in job postings, or a contract expiry inferred from LinkedIn tenure data. That hypothesis layer is what separates a credible opening conversation from a generic outreach blast, and it is what AI cannot yet generate reliably.

Where services sales representatives go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Sales Managers

Top-performing service sales reps with documented quota attainment and account-development track records are the natural pipeline for sales management: overseeing a territory team, managing pipeline health, forecasting revenue, and coaching junior reps on discovery and negotiation techniques. The transition trades individual contributor selling for team leverage; it is the most common upward career path in service sales organizations across SaaS, managed services, and staffing. AI is changing the sales manager role (Salesforce Agentforce and Gong generate call analytics and pipeline forecasts automatically), but quota accountability, rep development, and strategic account oversight remain irreducibly human. Slightly lower CRI reflects that sales management layers face their own automation pressure on administrative and reporting functions.

What you'd add
  • · Pipeline management: Salesforce or HubSpot pipeline analytics, forecast accuracy, rep productivity dashboards
  • · Coaching methodology using Gong call reviews: structured feedback frameworks, performance improvement plans, and call-methodology calibration
  • · Hiring and onboarding: building a service-sales hiring scorecard, role-play evaluation rubric, and 90-day ramp structure
  • · Incentive compensation design: commission structures, accelerators, territory carve-out principles, and retention arrangements
  • · Revenue forecasting: weighted-pipeline methodology, deal-stage qualification criteria, and bias-correction techniques for rep forecast sandbagging
What it takesSome new skills to pick up
Tools of the era

How has the work of services sales representatives changed since 1840?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Sample case + order book + railroad (the drummer era)

    The foundational technology of service-and-goods sales was portable: the drummer carried a trunk of product samples, a printed price list, and a blank order book. The railroad was the enabling infrastructure. A commercial traveler departing New York could cover the Ohio River valley in a week, calling on merchants in Cleveland, Columbus, Cincinnati, and Louisville, leaving orders to be fulfilled by a warehouse hundreds of miles away. The sample case made the product tangible; the order book made the promise binding; the railroad made scale possible. Every subsequent tool era built on the same basic architecture: make the product legible, close the agreement, maintain the relationship.

    Effect on the work

    The railroad-and-sample-case combination allowed a single salesman to cover territories that previously required a physical storefront or a network of local agents. One drummer could replace several local wholesalers.

  • NCR scripted sales system + quota + territory (Patterson model)

    John H. Patterson acquired the National Cash Register Company in 1884 and built the first modern sales management system: each salesman received an exclusive territory, a scripted presentation (the NCR Primer), a defined quota, a requirement to file daily activity reports, and mandatory attendance at annual sales conventions. Patterson was selling not just a machine but an ongoing service contract: installation, training, repair, and periodic upgrades. His model proved that systematic service-selling could outperform relationship-based informal selling, and NCR alumni carried the template to IBM, Burroughs, and dozens of other companies. The "canned pitch" plus the territory plus the quota became the enduring structural skeleton of B2B service sales.

    Effect on the work

    Patterson grew NCR through 1897 using this system. The model created the professional sales force as an organizational concept: trained, managed, measured, compensated on outcomes.

  • Telephone cold calling and inside service selling

    The first documented use of telephone cold calling for B2B service sales dates to August 1914, when an ice machine sales office in Kansas City made outbound calls to prospect serum plants. By the 1920s, the telephone had become a standard tool for appointment setting, qualifying prospects, and following up on field sales visits. For service sellers, the telephone was transformative in a different way than for product sellers: a service -- telecom, maintenance contracts, subscription data services -- could be fully sold and contracted by phone without a physical demonstration of goods. This enabled inside service-selling teams (later called "inside sales") to operate at scale, expanding the addressable market for lower-value service contracts beyond what field sales economics could justify.

    Effect on the work

    Telephone-based prospecting and closing allowed service sales teams to cover more accounts per rep-day than field-only models. The inside sales channel began bifurcating from field sales in the 1950s-1960s as service businesses scaled.

  • Contact management software: ACT! (1986), Siebel SFA (1993)

    ACT! (launched 1986 by Conductor Software; later Pat Sullivan and Mike Muhney) was the first commercial contact management software for PCs, allowing a service sales rep to store complete customer histories, schedule follow-up calls, and track pipeline status in a digital format for the first time. Before ACT!, the territory rep kept contacts in a Rolodex and notes on paper or in a Day-Timer. Siebel Systems (founded 1993) brought enterprise-grade Sales Force Automation to large organizations: pipeline management, quota tracking, and contact management at the organizational level. For service sales, the contact database was uniquely valuable: a managed-services contract or a staffing agreement ran for years, and the rep needed to track dozens of open issues, contacts, renewal dates, and account histories. ACT! was the first tool to make that feasible without a personal assistant.

    Effect on the work

    Contact management software let a single service rep manage as many active accounts as was practical with paper systems. Territory size expanded; the marginal cost of maintaining a relationship dropped.

  • Cloud CRM: Salesforce.com (1999) + SaaS economy boom

    For the service sales representative, the cloud CRM changed the job in two ways. First, the administrative burden of managing pipeline, contacts, and forecasts moved from a personal tool (ACT!, paper) to an organizational platform visible to management, which raised accountability and changed how performance was measured. Second, the SaaS business model that Salesforce pioneered created a massive new category of service to sell: software subscriptions. By 2010, thousands of SaaS companies had emerged, each needing a service sales force to sell recurring annual contracts. The SaaS boom is the single largest driver of employment growth for SOC 41-3091 from 2000 to 2024.

    Effect on the work

    The broader SaaS sector drove massive expansion in service sales headcount throughout the 2000s and 2010s.

  • Sales engagement platforms: LinkedIn Sales Navigator (2014), Outreach (2014), Salesloft (2011)

    The 2010s produced a new category of software specifically for the service sales workflow: sales engagement platforms that orchestrated multi-channel outreach sequences (email, phone, LinkedIn, text), tracked prospect engagement, and fed signal data back to the rep. LinkedIn Sales Navigator (launched 2014) gave service reps access to professional network data for prospect research in a way that had no prior equivalent. Outreach and Salesloft built workflow automation that let a single rep manage hundreds of simultaneous prospecting sequences. These tools made inside service sales radically more productive and created the SDR (Sales Development Representative) as a specialized role, splitting pipeline generation from pipeline closure. The SDR model became the dominant go-to-market structure for SaaS and services companies by 2015.

  • AI SDR agents + conversation intelligence: Salesforce Agentforce, Gong, ZoomInfo Copilot

    Generative AI entered the service sales workflow in force in 2023-2024 with three converging tool categories: autonomous AI SDR agents (Salesforce Agentforce, which handled 3,200 prospect qualification sessions in several months at Salesforce itself), conversation intelligence platforms (Gong, which eliminated manual post-call documentation and generated actionable call summaries), and AI-enriched prospect databases (ZoomInfo Copilot, whose users reported 60% more demos booked and 90% improvement in email response rates). These tools are shifting the service rep's time from administrative overhead (CRM updates, sequence management, call notes) toward higher-value activities: live discovery conversations, multi-stakeholder negotiation, and post-sale account development. The McKinsey (2024) analysis of generative AI in B2B sales found that AI could reduce time spent on preparation tasks by up to 80% and increase customer-facing time by up to 50%. The net effect is augmentation, not displacement, for the consultative service seller: AI tools make high performers more productive but do not yet replicate the human capacity for needs discovery, SLA negotiation, and relationship accountability that enterprise service contracts require.

    Effect on the work

    AI SDR agents are beginning to handle initial qualification and meeting scheduling for high-volume, lower-complexity service contracts (commodity SaaS, SMB telecom), creating displacement pressure on transactional inside-sales roles while the consultative and enterprise segment expands.

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Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel employment and pay by year

On record since1840
Latest US employment1,256,010 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$69,990 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+2.7% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 11 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
190093,000n/aDecennial Census 1904 Researched contextReport
1925400,000$2,200Est., Todd Caponi Researched contextReport
1960700,000n/aEst., HBS Working Knowledge Researched contextReport
2000870,000$35,000Est., O*NET 41-3091.00 Researched contextReport
20191,039,670$56,130BLS OEWS May 2019 Official statisticReport
2020977,070$58,770BLS OEWS May 2020 Official statisticReport
20211,026,390$60,550BLS OEWS May 2021 Official statisticReport
20221,075,750$62,400BLS OEWS May 2022 Official statisticReport
20231,142,020$64,600BLS OEWS May 2023 Official statisticReport
20241,189,330$66,260BLS OEWS May 2024 Official statisticReport
20251,256,010$69,990BLS OEWS May 2025 Official statisticReport
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