Job Market Health score
35/ 100
Weak
Stable · -1
- At least 90% of required inputs and occupation-industry mapping are covered.
Job Market Health scores are derived indicators based on published data, informational only, not a forecast or guarantee.
Customer service representatives
SOC 43-4051 · 43-0000 · Score reference month August 2026
Interact with customers to provide basic or scripted information in response to routine inquiries about products and services. May handle and resolve general complaints. Excludes individuals whose duties are primarily installation, sales, repair, and technical support.
In short
The national Job Market Health score is in the Weak band for August 2026.
$44,770Median annual wageOEWS May 2025
289,500Projected annual openingsBLS Employment Projections 2025-2035
-5.3%Projected growth2025–2035
2,595,750Total employmentOEWS May 2025
Latest interpretation
What stands out for customer service representatives
The clearest signals from the latest validated score, outlook and metro data.
Long-term outlook is the strongest component
Long-term outlook scores 39/100, compared with 28/100 for occupation fundamentals, a 11-point difference.
JobMarketHealth score componentsLong-term employment is projected to contract
BLS projects 289,500 average annual openings and -5.3% employment growth from 2025 to 2035. Projected openings are a modeled annual average, not vacancies open today.
BLS Employment ProjectionsLargest published metro market
New York-Newark-Jersey City, NY-NJ has the largest published metro employment count for customer service representatives, at 125,570 jobs. The next-largest published metro is Dallas-Fort Worth-Arlington, TX with 96,930 jobs. This measures employment scale, not market quality.
BLS OEWSLocal market context
QCEW industry conditions for this occupation’s employment mix
Choose a state to see how the industries that employ this occupation are performing locally.
Choose a state above to explore its available industry context.
How to read this section
QCEW measures employment and wages by industry, not by occupation. We weight those industry conditions using the published industry mix for this occupation. These figures describe the industries that employ this occupation; they are not direct counts of people working in the occupation or jobs currently open.
Source: BLS QCEW. This section is not part of the national 0 to 100 Job Market Health score. See the methodology.
Compare states for customer service representativesSide-by-side published industry conditions for two or three states. There is no state-level health score; the 0 to 100 score is national.
How to read this page
The score is a national composite from 0 to 100 that weights near term hiring demand at 50 percent, the occupation's own employment fundamentals at 30 percent, and long-term outlook at 20 percent. Wages and occupation profile details are context shown alongside it, not score inputs. See the full methodology.
Median annual wage and metro employment come from BLS OEWS, which is annual. Projected annual openings and projected growth come from BLS Employment Projections and describe the published projection horizon, so they are not a count of jobs open today. Values shown as not published are suppressed by the source rather than filled in here.
Job Market Health trend
Published composite score · 0–100 · by reference month
Latest published values
- Aug 2635 / 100
View values: Job Market Health history
| Reference month | Score / 100 |
|---|---|
| Jan 25 | 36 |
| Feb 25 | 36 |
| Mar 25 | 36 |
| Apr 25 | 34 |
| May 25 | 35 |
| Jun 25 | 33 |
| Jul 25 | 32 |
| Aug 25 | 29 |
| Sep 25 | 30 |
| Oct 25 | 29 |
| Nov 25 | 31 |
| Dec 25 | 29 |
| Jan 26 | 29 |
| Feb 26 | 30 |
| Mar 26 | 32 |
| Apr 26 | 33 |
| May 26 | 36 |
| Jun 26 | 37 |
| Jul 26 | 36 |
| Aug 26 | 35 |
Score components
The three published inputs behind the composite · each 0–100
Largest metro markets by employment
OEWS employment count, where the jobs are, not a quality ranking
OEWS May 2025. These occupation counts describe the survey reference period.
Linked metro names open overall job-market profiles.
| Metro area | Employment | Jobs per 1,000 | Median annual wage |
|---|---|---|---|
| New York-Newark-Jersey City, NY-NJ | 125,570 | 13.23 | $49,590 |
| Dallas-Fort Worth-Arlington, TX | 96,930 | 23.93 | $44,990 |
| Chicago-Naperville-Elgin, IL-IN | 75,240 | 16.67 | $47,100 |
| Phoenix-Mesa-Chandler, AZ | 68,930 | 29.01 | $46,410 |
| Los Angeles-Long Beach-Anaheim, CA | 66,590 | 10.62 | $48,840 |
| Houston-Pasadena-The Woodlands, TX | 65,510 | 19.91 | $40,380 |
| Miami-Fort Lauderdale-West Palm Beach, FL | 63,440 | 22.50 | $39,560 |
| Atlanta-Sandy Springs-Roswell, GA | 59,990 | 20.76 | $42,710 |
| Philadelphia-Camden-Wilmington, PA-NJ-DE-MD | 41,740 | 14.40 | $47,070 |
| Minneapolis-St. Paul-Bloomington, MN-WI | 38,630 | 19.79 | $49,480 |
Education & training
Typical entry requirements · BLS Employment Projections 2025-2035
Top skills
Occupation profile · O*NET 31.0 Database
- Active Listening
- Service Orientation
- Speaking
- Reading Comprehension
- Critical Thinking
- Complex Problem Solving
- Time Management
- Monitoring
- Negotiation
- Persuasion
Also known as: Account Representative, Call Center Representative, Client Services Representative, Customer Care Representative (CCR), Customer Service Agent, Customer Service Representative (CSR)
AI & this occupation
How AI relates to Customer service representatives work
Across 4 research exposure measures, Customer service representatives is in the higher technical AI exposure band based on the mean of the available source-specific percentile ranks. Exposure describes how much of the work the studied AI capabilities could technically assist with; it is not a forecast of job loss.
Observed use of Claude for this occupation's tasks is in the higher third of occupations in the Anthropic Economic Index, a dated observed task-exposure benchmark, not conversation share or the share of workers using AI. Its observation window is not recorded in this saved release.
In the task mix derived from the March 27, 2025 Anthropic release, 66.4% followed augmentation patterns (people iterating, validating or learning with AI), based on 9 matched tasks.
JobMarketHealth has not measured an AI effect on this occupation's employment or wages, and these figures do not enter its scores; there is no clear evidence of displacement in the data shown here.
Exposure measures (kept separate)
- GPTs are GPTs exposure (β, GPT-4 rated): 0.57 of 1, national percentile 89 (higher third)
- Microsoft AI applicability: 0.41 of 1, national percentile 99 (higher third)
- AIOE (Felten, Raj & Seamans): 0.96 (standardized), national percentile 73 (higher third)
- OECD AI Capability Gap (lower gap = higher potential exposure): gap 1.4 of 23, national percentile 81 (higher third)
OECD capability gap by domain
As published by the OECD: the gap between what the occupation demands (0-5) and current AI capability in each domain. A lower gap means AI is closer to the requirement (higher potential exposure); 0 means AI already meets it or the domain is not relevant.
- Language: gap 0.15 (demand 3.15 of 5)
- Social interaction: gap 1.15 (demand 3.15 of 5)
- Problem solving: gap 1.00 (demand 3.00 of 5)
- Creativity: gap 0.00 (demand 1.70 of 5)
- Metacognition & critical thinking: gap 0.50 (demand 2.50 of 5)
- Knowledge, learning & memory: gap 0.00 (demand 2.50 of 5)
- Vision: gap 0.00 (demand 0.45 of 5)
- Manipulation: gap 0.00 (demand 0.10 of 5)
- Robotic intelligence: gap 0.00 (demand 0.00 of 5)
National context: what businesses used generative AI for
From the Census 2025-26 BTOS AI Supplement (all industries; businesses whose employees used generative AI). Census did not measure Customer service representatives specifically.
- Writing or editing documents, emails, or communications: 85.4%
- Searching for information or technical help: 49.9%
- Interpreting, analyzing, translating, or summarizing documents: 44.6%
- Information processing, paperwork, or filing: 34.7%
Exposure and observed usage are separate measures and neither equals job loss. Census BTOS figures are business-level surveys and are not occupation-specific. Sources: GPTs are GPTs occupation exposure; Microsoft Working with AI: AI applicability; AI Occupational Exposure (AIOE); OECD AI Exposure Measure (AI Capability Gap); Anthropic Economic Index; Census BTOS Core AI Use; Census BTOS AI Supplement 2025-26. These figures do not affect Job Market Health scores. Explore AI & Jobs · How we use AI data.
Sources and methodology
The datasets behind the figures on this page
Values marked “Not published” are suppressed or not published by the source. We leave them blank rather than estimating them. Pipeline status for every source is on Data Status.
Related occupations
O*NET® lists these occupations as related. Each link opens the same published view of score, wages and outlook.
Evidence record
JobMarketHealth derived indicator; not a government statistic. The composite score is national and occupation level.
- Occupation
- Customer service representatives
- SOC code
- 43-4051
- Geography
- United States
- Reference month
- August 2026
- Published Job Market Health score
- 35 / 100
- Restated score
- 35 / 100
- Band
- Weak
- Current Demand component
- 37 / 10050% weight
- Occupation Fundamentals component
- 28 / 10030% weight
- Long-Term Outlook component
- 39 / 10020% weight
- Confidence
- HighAt least 90% of required inputs and occupation-industry mapping are covered.
- Methodology version
- v1.0
- Source vintage: Annual
- EP-2025-2035|CPS-25b-2025|OEWS-2020-2025
- Source vintage: Monthly calculated at
- 2026-10-02T15:32:37.772Z
- First published at
- Sep 29, 2026
- Recalculated at
- Oct 2, 2026
- Canonical record URL
- https://jobmarkethealth.com/occupations/customer-service-representatives#evidence-record
- Derived by
- JobMarketHealth
MethodologyBLS JOLTSBLS CESBLS Employment ProjectionsHow to citeNational Job Market Health release, August 2026
Keep going
Learn how to judge a market from these numbers in how to tell if a job market is healthy, or why projected growth and annual openings often disagree in projected growth vs projected annual openings. Coverage, revisions and limits are explained on about the data.
The datasets behind these figures, and what each one can and cannot show, are set out on data sources.