Customer Service Manager
Accountable for a customer service operation: its escalations, capacity, service quality and the team.
4tasks in this job
50skills it uses
9tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 3 of 4 tasks
- 2Setting and reviewing action thresholds for scores and model predictionsNeeded in 1 of 4 tasks
- 3Converting demand into capacity requirementsNeeded in 1 of 4 tasks
- 4Evaluating service performance and quality trade-offsNeeded in 1 of 4 tasks
- 5Analysing queue behaviour, capacity and service constraintsNeeded in 1 of 4 tasks
ToolsCalabrio WFM (formerly Teleopti)Microsoft ExcelWorkdayNICE CXone and NICE IEX WFMZendeskGenesys Cloud CXPuzzelMicrosoft Dynamics 365 Sales and Customer InsightsSalesforce
All 4 tasks, where people get stuck and all 50 skills
Tasks
- Plan service capacity, schedules and routing against service levels
- Produce an accountable operating budget
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Run service quality assurance on sampled cases and calibrate quality scoring
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Evaluating service performance and quality trade-offs
- Escalating unresolved risks with evidence and clear ownership
All skills
- Analysing queue behaviour, capacity and service constraints
- Converting demand into capacity requirements
- Escalating unresolved risks with evidence and clear ownership
- Evaluating service performance and quality trade-offs
- Setting and reviewing action thresholds for scores and model predictions
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Assessing data-driven fairness and bias
- Balancing territories and quotas from account potential
- Building driver-based financial forecasts
- Classifying AI uses against regulatory obligations
- Communicating quantified uncertainty
- Constructing a controlled cash-flow plan
- Designing and comparing decision scenarios
- Designing and weighting rule-based scoring models
- Designing auditable decision models
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Evaluating ML generalisation and fitness
- Evaluating responsible AI use risks
- Exercising human oversight of automated and AI outputs
- Explaining how business events flow into accounting records
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Monitoring model fitness and drift
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Reading business metrics
- Recognising and resolving ethical conflicts in data and AI work
- Reconstructing a record's state at a point in time from history and audit trails
- Sampling and reviewing a team's work against standards
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
Paths to start
Coming soonService Operations Analytics and Capacity PlanningMachine Learning: from Data to Validated Models and Decisions
6tasks in this job
122skills it uses
12tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 5 of 6 tasks
- 2Setting and reviewing action thresholds for scores and model predictionsNeeded in 1 of 6 tasks
- 3Converting demand into capacity requirementsNeeded in 1 of 6 tasks
- 4Evaluating service performance and quality trade-offsNeeded in 1 of 6 tasks
- 5Analysing queue behaviour, capacity and service constraintsNeeded in 1 of 6 tasks
ToolsGenesys Cloud CXZendeskQualtricsMicrosoft Power BICalabrio WFM (formerly Teleopti)Microsoft Excel+ 6 more
All 6 tasks, where people get stuck and all 122 skills
Tasks
- Evaluate an AI use case for suitability, value and risk before adoption
- Investigate customer-experience evidence
- Plan service capacity, schedules and routing against service levels
- Produce an accountable operating budget
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Run service quality assurance on sampled cases and calibrate quality scoring
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Evaluating service performance and quality trade-offs
- Escalating unresolved risks with evidence and clear ownership
All skills
- Analysing queue behaviour, capacity and service constraints
- Converting demand into capacity requirements
- Escalating unresolved risks with evidence and clear ownership
- Evaluating service performance and quality trade-offs
- Setting and reviewing action thresholds for scores and model predictions
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Balancing territories and quotas from account potential
- Bounding and securing AI systems' tool and data access
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and running evaluations of AI systems
- Designing and weighting rule-based scoring models
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing testable data controls
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Making accountable decisions under uncertainty
- Mapping personal data processing and flows
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Reconstructing a record's state at a point in time from history and audit trails
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Training reproducible machine learning models
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
All tools
- Genesys Cloud CX
- Zendesk
- Qualtrics
- Microsoft Power BI
- Calabrio WFM (formerly Teleopti)
- Microsoft Excel
- Workday
- NICE CXone and NICE IEX WFM
- Puzzel
- Microsoft Dynamics 365 Sales and Customer Insights
- Salesforce
- ServiceNow
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonService Operations Analytics and Capacity PlanningMachine Learning: from Data to Validated Models and Decisions
6tasks in this job
140skills it uses
12tools named
The skills this job leans on most
- 1Escalating unresolved risks with evidence and clear ownershipNeeded in 5 of 6 tasks
- 2Converting demand into capacity requirementsNeeded in 2 of 6 tasks
- 3Evaluating service performance and quality trade-offsNeeded in 2 of 6 tasks
- 4Analysing queue behaviour, capacity and service constraintsNeeded in 2 of 6 tasks
- 5Setting and reviewing action thresholds for scores and model predictionsNeeded in 1 of 6 tasks
ToolsServiceNowGenesys Cloud CXZendeskQualtricsMicrosoft Power BICalabrio WFM (formerly Teleopti)+ 6 more
All 6 tasks, where people get stuck and all 140 skills
Tasks
- Diagnose operational and fulfilment exceptions and agree corrective ownership
- Evaluate an AI use case for suitability, value and risk before adoption
- Investigate customer-experience evidence
- Plan service capacity, schedules and routing against service levels
- Oversee an automated or AI system in operational use and correct, restrict or withdraw faulty outputs
- Run service quality assurance on sampled cases and calibrate quality scoring
Where people get stuck
- Setting and reviewing action thresholds for scores and model predictions
- Evaluating service performance and quality trade-offs
- Escalating unresolved risks with evidence and clear ownership
All skills
- Analysing queue behaviour, capacity and service constraints
- Converting demand into capacity requirements
- Escalating unresolved risks with evidence and clear ownership
- Evaluating service performance and quality trade-offs
- Setting and reviewing action thresholds for scores and model predictions
- Analysing business drivers and contributions
- Analysing costs, allocation and margin drivers
- Analysing error patterns and failure modes in models and AI systems
- Analysing event sequences, states and durations
- Analysing pay equity with raw and adjusted pay gaps
- Analysing working-capital drivers and cash conversion
- Applying data classification and handling rules
- Applying research ethics and participant consent
- Assessing data source credibility and fitness for a question
- Assessing data-driven fairness and bias
- Assessing data-service continuity and recoverability
- Assessing lawful basis, purpose compatibility and minimisation for data reuse
- Assessing privacy risk through data protection impact assessments
- Assessing supplier dependency, data-handling and exit risk
- Assigning and operating data ownership and stewardship
- Balancing territories and quotas from account potential
- Bounding and securing AI systems' tool and data access
- Building data protection by design into data products and pipelines
- Building decision-focused reports and dashboards
- Building driver-based financial forecasts
- Calculating and interpreting percentages, rates and ratios
- Choosing an analytical method to fit the question and data
- Choosing and justifying missing-data treatments
- Classifying AI uses against regulatory obligations
- Combining tables safely: cardinality, fan-out and conservation of totals
- Communicating quantified uncertainty
- Communicating sensitive or unwelcome analytical findings
- Comparing groups
- Constructing a controlled cash-flow plan
- Constructing clear and honest charts
- Controlling disclosure risk in shared data and outputs
- Decomposing one's own tasks for AI assistance
- Defining measurable outcomes and leading indicators
- Defining the analysis population: filters, nulls, dates and time zones
- Describing distributions with summaries and plots
- Designing AI-assisted workflows with bounded tools and human authority
- Designing and appraising samples
- Designing and comparing decision scenarios
- Designing and interpreting hypothesis tests
- Designing and quality-checking survey instruments
- Designing and running evaluations of AI systems
- Designing and weighting rule-based scoring models
- Designing auditable decision models
- Designing decision-oriented research with customers, users or employees
- Designing efficient and reproducible investigations
- Designing experiments
- Designing testable data controls
- Diagnosing data quality exceptions
- Diagnosing forecast accuracy and bias and comparing methods fairly
- Diagnosing incorrect query and calculation results
- Diagnosing misleading data visualisations
- Diagnosing operational handoffs and flow
- Disclosing AI contribution to professional work
- Discovering process variants from event logs
- Eliciting needs through interviews, workshops and observation
- Eliciting the decision behind an analytical request
- Enforcing and auditing data access
- Establishing and declaring data grain
- Estimating quantities and differences with intervals
- Evaluating ML generalisation and fitness
- Evaluating and improving chart accessibility
- Evaluating employee experience evidence responsibly
- Evaluating experimental results and integrity
- Evaluating fitness and full cost of AI-assisted work
- Evaluating responsible AI use risks
- Evaluating whether people understood, adopted and sustained a change
- Exercising human oversight of automated and AI outputs
- Explaining control types, dependencies and compensating controls
- Explaining how business events flow into accounting records
- Explaining technical work to accountable non-specialists
- Exploring data to find patterns that survive validation
- Facilitating evidence-led retrospectives and corrective actions
- Fitting, checking and interpreting inferential regression models
- Forecasting business trends and turning points for planning
- Forecasting operational demand at interval and site grain
- Formulating answerable data questions
- Giving specific developmental feedback on data and AI work
- Independently checking analytical conclusions and claims
- Influencing change without formal authority
- Interpreting averages, weighted measures and mix effects
- Interpreting budgets and planning assumptions
- Interpreting correlation without inferring cause
- Interpreting customer feedback and qualitative evidence
- Interpreting policy scope and delegated authority limits
- Interpreting trends and seasonality
- Interpreting workforce metrics and populations
- Investigating anomalies
- Judging the materiality of defects and deficiencies for the intended use
- Judging whether a change exceeds normal variation
- Judging whether and how to use AI for a task
- Judging whether uncertainty changes the decision
- Locating and acting on personal data for individual rights requests
- Maintaining requirements traceability and change control
- Making accountable decisions under uncertainty
- Mapping customer journeys and evidence gaps
- Mapping personal data processing and flows
- Mapping proposed process changes and control impacts
- Mapping stakeholders' decisions, constraints and needs
- Monitoring model fitness and drift
- Preparing analysis-ready datasets reproducibly
- Preparing leakage-resistant ML data
- Presenting and defending evidence-based recommendations to decision-makers
- Profiling data
- Protecting sensitive data in AI-assisted work
- Rating risks against appetite and choosing proportionate treatment
- Reading business metrics
- Reading charts and dashboards correctly, including their state
- Recognising and containing suspected personal data breaches
- Recognising and resolving ethical conflicts in data and AI work
- Recognising cybersecurity threats to data work
- Recognising misleading numerical claims
- Recognising personal data and privacy duties
- Recognising statutory incident reporting duties and deadlines
- Reconciling conflicting reports
- Reconstructing a record's state at a point in time from history and audit trails
- Repairing data defects reversibly under owner authority
- Resolving consent and suppression status across systems
- Sampling and reviewing a team's work against standards
- Selecting charts for evidence and decisions
- Specifying AI transparency and explanation duties to affected people
- Specifying decision-useful reports
- Standardising and cleaning data under agreed rules
- Statistical power and sample size
- Testing competing explanations with discriminating evidence
- Testing investment business cases under uncertainty
- Tracing data lineage upstream and downstream
- Training reproducible machine learning models
- Validating records by matching, recomputation and end-to-end tracing
- Variance analysis
- Verifying AI-assisted outputs against sources
- Verifying reports and dashboards before and after release
- Working through stakeholder conflict over evidence and responsibility
- Writing data quality rules
- Writing evidence-led findings and auditable recommendations with their limits
- Writing verifiable requirements and acceptance criteria
All tools
- ServiceNow
- Genesys Cloud CX
- Zendesk
- Qualtrics
- Microsoft Power BI
- Calabrio WFM (formerly Teleopti)
- Microsoft Excel
- Workday
- NICE CXone and NICE IEX WFM
- Puzzel
- Microsoft Dynamics 365 Sales and Customer Insights
- Salesforce
Paths to start
Skill Path
Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up.
- Modules
- 5 open
- Practice data
- Raket Parts
Tool Path
Excel for Analysis and Decision Models
Build and check Excel workbooks others can trust.
- Modules
- 5 open
- Runs in
- Your browser
Coming soonService Operations Analytics and Capacity PlanningMachine Learning: from Data to Validated Models and Decisions
About the role
Accountable for a customer service operation: its escalations, capacity, service quality and the team. Decides staffing against demand, sets quality checks and resolves escalated cases that frontline staff cannot close. Data and AI judgement sits in reading service measures, checking AI-suggested answers and case summaries and deciding where AI assistance is safe to use.
Also called Contact Centre Manager