role

Technology Customer Success Manager

Source-cited RoleMath page about Technology Customer Success Manager.

Build my personalized career plan

Researched by RoleMath Research. Every figure on this page traces to the official source shown next to it.

What the numbers say about this work

Government occupation data for the role this maps to Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products (SOC 41-4011). This is planning context for the occupation, not a salary or a job this role guarantees you.

Median pay (occupation)
$104,920 / yr · $52,600 to $200,440 (10th–90th percentile)
Projected change (2024–34)
+1.9% · ~27.2k openings/yr
Typical entry education
Bachelor's degree

BLS OEWS — occupation-level, national BLS Employment Projections 2024–34 This role uses a broad O*NET-SOC/BLS occupation mapping. Treat salary, outlook, and task data as occupation-level evidence, not a guarantee for this exact job title.

What it pays by metro

The national median hides a wide geographic spread. Below is the occupation’s median in some of the highest-paying and largest-employment metros, adjusted for local prices — regional price-level context, not take-home pay or a salary this role guarantees you.

MetroNominal medianCost-adjusted
San Jose, CA$167,630$151,807
Kansas City, MO$130,850$141,394
Des Moines, IA$126,830$138,308
Durham, NC$131,120$134,383
Richmond, VA$129,880$132,723
Detroit, MI$129,070$128,687

See all metros and how this is calculated → Sources: BLS OEWS (May 2025), occupation-level metro median ÷ BEA Regional Price Parities (2024, US=100).

What this work involves

The tasks the U.S. Department of Labor’s O*NET lists most central to this occupation — role-fit evidence to weigh against your background, not a measure of employer demand.

  • Negotiate prices or terms of sales or service agreements.
  • Prepare and submit sales contracts for orders.
  • Visit establishments to evaluate needs or to promote product or service sales.
  • Maintain customer records, using automated systems.
  • Answer customers' questions about products, prices, availability, or credit terms.
  • Quote prices, credit terms, or other bid specifications.

O*NET — occupation-level

Skills that matter

The skills O*NET rates most important for this occupation. A starting map for what to build — weigh it against the specific job you’re targeting.

  • Speaking
  • Active Listening
  • Reading Comprehension
  • Active Learning
  • Critical Thinking
  • Writing
  • Monitoring
  • Mathematics

O*NET — occupation-level

What employers ask for right now

The skills and certifications employers most often name in a sample of 407public job postings for this role. Treat it as a to-learn list — it’s dated hiring language, not a count of open jobs, demand, or salary.

Most-named skills

  • Python 86
  • Cybersecurity 80
  • Problem solving 74
  • Excel 52
  • AWS 51
  • Azure 49
  • API 48
  • Project Management 45
  • SQL 44
  • GCP 32
  • Software development 32
  • Agile 31

Certifications named

  • CCNA 7
  • Network+ 7
  • Security+ 7
  • PMP 2

Compare what employers ask across roles → Qualitative employer-language sample only; do not use as official demand, market-size, salary, or certification ROI evidence.

Technology Customer Success Manager

Quick Verdict

Technology Customer Success Manager maps to the BLS occupation Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products (SOC 41-4011), which has a national median of $104,920. Pay is occupation-level and location-driven - not caused by the job title or a certification. Below are the full cited labor-market context, the skills the role draws on, and the certification paths that map to it. This role uses a broad O*NET-SOC/BLS occupation mapping. Treat salary, outlook, and task data as occupation-level evidence, not a guarantee for this exact job title.

Fit Signals

  • Enterprising (5.27)
  • Conventional (5.14)
  • Investigative (3.15)

Skills & Tools

*Tools and technologies ONET associates with this occupation* - role-specific examples with ONET hot/in-demand flags, not employer requirements:

  • Microsoft Excel (hot technology, in demand)
  • Microsoft Office software (hot technology, in demand)
  • Microsoft Outlook (hot technology, in demand)
  • Salesforce software (hot technology, in demand)
  • Amazon Web Services AWS software (hot technology)
  • Apache Hadoop (hot technology)
  • Google Analytics (hot technology)
  • Google Workspace software (hot technology)

*Foundational ONET skills** (broadly shared across occupations, not unique to this role): Speaking, Active Listening, Reading Comprehension, Active Learning, Critical Thinking, Writing.

AI & this career

What we can — and can’t — tell you about AI and this role

Cited context only: an occupation-level outlook, descriptive usage data, an employer-language sample, and attributed research — kept separate. No RoleMath AI score, no automation timeline, no job-loss prediction. How we source this →

Occupation outlook · BLS

Where the occupation is projected to go

BLS projects Sales representatives, wholesale and manufacturing, technical and scientific products at 1.9% employment change for 2024-2034, with 27.2 thousand annual openings. U.S. Bureau of Labor Statistics

A forecast, not a guarantee; occupation-level, not about you - and BLS does not model rapid AI adoption, so this is never an AI prediction.

How AI shows up in the work

Descriptive usage, not demand or loss

For this shared SOC, the May 2026 usage sample reports 51.85% augmentation-labeled and 48.15% automation-labeled Claude conversations. Anthropic Anthropic Economic Index dataset, CC-BY.

Across all occupations the same dataset splits 51.4% augmentation / 48.6% automation (May 2026) — shown so a single role’s number is never read as an outlier.

Descriptive Claude usage data, not employment demand, not job loss, and not a personal forecast; CC-BY attribution required.

Employer language · sample

What a posting sample mentions

a sample of 14 postings (as of 2026-06-11) mentions these AI-related terms RoleMath public ATS employer-language pilot

Employer-language sample only; not official demand, market-size, salary, or certification ROI evidence.

Published research · attributed

What independent research says (not RoleMath’s claim)

  • Eloundou et al. estimate that about 80% of U.S. workers have at least 10% of their work tasks exposed to large language model capabilities (Science 2024). American Association for the Advancement of Science exposure = task overlap, not job loss.
  • Eloundou et al. estimate that about 19% of U.S. workers have at least 50% of their work tasks exposed to large language model capabilities (Science 2024). American Association for the Advancement of Science exposure = task overlap, not job loss.
  • Eloundou et al. explicitly disclaim any forecast of AI adoption or timing, describing their measure as capability overlap with tasks rather than a prediction of job loss (Science 2024). American Association for the Advancement of Science exposure = task overlap, not job loss.
  • OECD reports that high-skill occupations are the most exposed to AI on task-overlap measures (OECD Employment Outlook 2023). Organisation for Economic Co-operation and Development exposure = task overlap, not job loss.
  • OECD reports that, as of 2023, there is little empirical evidence of negative employment effects from AI (OECD Employment Outlook 2023). Organisation for Economic Co-operation and Development exposure = task overlap, not job loss.
  • OECD and the AIOE research find that AI exposure and automation risk often run in opposite directions, with the most-exposed high-skill occupations tending to be the least at risk of automation. Organisation for Economic Co-operation and Development exposure = task overlap, not job loss.
  • Felten, Raj and Seamans construct an occupation-level AI Occupational Exposure index by linking AI capabilities to O*NET occupational abilities (Strategic Management Journal). Strategic Management Journal (Wiley) exposure = task overlap, not job loss.
  • Stanford Digital Economy Lab researchers find a roughly 16% relative decline in employment for workers ages 22-25 in the most AI-exposed occupations, based on high-frequency ADP payroll data (Canaries in the Coal Mine, working paper). Stanford Digital Economy Lab correlational usage data, not proof.
  • The ILO notes that AI-exposure indicators measure potential task overlap and cannot by themselves establish job loss (Workers' exposure to AI). International Labour Organization exposure = task overlap, not job loss.
  • The Anthropic Economic Index reports no measured systematic rise in unemployment attributable to AI in its usage data. Anthropic correlational usage data, not proof.

Tier A research stays attributed and separate from BLS outlook and employer-language samples.

Every figure on this page, sourced

The claims above trace to these records — the source, and when it was last checked. If a figure has no row here, we did not publish it.

IDSupportsSourceChecked
SCHEMA-CIT-1Schema citationTechnology Customer Success Manager BLS OEWS wage sourceLogged in source packet
SCHEMA-CIT-2Schema citationTechnology Customer Success Manager BLS Employment Projections sourceLogged in source packet
SCHEMA-CIT-3Schema citationTechnology Customer Success Manager O*NET sourceLogged in source packet

Ready to see how this fits your background?

Get my fit score