Customer Support Analytics Training Course

Participants can calculate and interpret balanced support metrics for operational and customer decisions.

• Distinguish demand, service, quality, productivity and customer-outcome measures
• Calculate contact rate per customer, transaction and active account
• Use service level, response time and abandonment rate correctly
• Interpret average handle time alongside transfer, hold and after-call work
• Separate first contact resolution from repeat-contact suppression effects
• Build balanced scorecards linking operational drivers to customer outcomes
• Set metric targets using baseline variance and capacity constraints

Participants can test support data quality and draw sound conclusions from segmented performance results.

• Profile distributions using median, percentiles, range and interquartile range
• Detect outliers, missing values, duplicates and impossible timestamp sequences
• Calculate confidence intervals for survey scores and resolution rates
• Compare cohorts with rate normalization and statistically valid denominators
• Use control charts to distinguish common-cause from special-cause variation
• Segment results by channel, issue, customer value and tenure
• Prevent Simpson's paradox when aggregating queue and channel results

Participants can design, analyse and qualify customer feedback measures from support interactions.

• Design post-contact surveys with sampling rules and response-window controls
• Calculate NPS, Customer Effort Score and customer satisfaction scores correctly
• Separate transactional feedback from relationship research and complaint records
• Weight survey results when respondent profiles differ from customer populations
• Code verbatim comments using a controlled theme and sentiment taxonomy
• Link survey responses to interaction metadata without exposing personal data
• Identify nonresponse bias, acquiescence bias and questionnaire wording effects

Participants can use interaction content to identify customer needs, service failures and emerging contact drivers.

• Prepare chat transcripts, email text and call recordings for analysis
• Apply topic coding to identify contact drivers and emerging issues
• Use sentiment analysis cautiously against manually coded validation samples
• Measure keyword frequency, co-occurrence and theme movement over time
• Score quality behaviours from calibrated speech and interaction evaluations
• Combine qualitative themes with volume, effort and resolution metrics
• Protect personally identifiable information during transcript extraction and storage

Participants can trace customer journeys across channels and identify sources of avoidable support demand.

• Compare channel mix using arrival patterns, complexity and containment rates
• Measure digital deflection without misclassifying customer abandonment or failure
• Trace repeat contacts across channels using customer and case identifiers
• Analyse handoffs, transfers and escalation paths for avoidable effort
• Build journey funnels from self-service search through assisted resolution
• Quantify contact drivers following product releases, outages and policy changes
• Prioritise friction points using volume, customer impact and fix feasibility

Participants can connect quality assurance findings to customer outcomes and process improvement priorities.

• Create quality assurance forms aligned to critical customer and compliance behaviours
• Calibrate evaluators using blind scoring, discussion and agreement thresholds
• Calculate inter-rater agreement before publishing quality performance results
• Link quality scores to resolution, effort, complaints and repeat contacts
• Separate agent-controlled behaviours from policy, process and system failures
• Use root cause analysis with five whys and fishbone diagrams
• Turn monitoring findings into targeted coaching and process improvement actions

Participants can relate support demand and service performance to staffing, cost and automation decisions.

• Forecast contact demand using seasonal patterns, events and channel shifts
• Apply Erlang C assumptions when interpreting service-level staffing requirements
• Relate occupancy, shrinkage and schedule adherence to customer waiting times
• Assess cost per contact alongside resolution quality and customer effort
• Model automation savings after accounting for containment, rework and exceptions
• Identify demand-reduction opportunities through policy, product and journey fixes
• Present investment cases with benefits, costs, risks and measurement methods

Participants can create dashboard specifications and communicate support insights for timely decisions.

• Specify executive, operational and team views for distinct decision cycles
• Design dashboards with metric hierarchies, thresholds and drill-down paths
• Choose charts that reveal trends, variation, distributions and relationships
• Write analytical commentary separating facts, interpretations, assumptions and actions
• Set data refresh, access control and exception-alerting requirements
• Audit dashboard calculations against source extracts and metric definitions
• Use storytelling structure to recommend decisions without overstating evidence

Participants can turn analytical findings into prioritised, measurable service improvement actions.

• Frame improvement questions using problem statements, hypotheses and success measures
• Prioritise initiatives through impact-effort matrices and root-cause evidence
• Design controlled pilots with comparison groups, guardrails and evaluation criteria
• Apply PDCA cycles to service process and knowledge-base improvements
• Create action plans assigning owners, milestones, measures and review cadences
• Establish benefits tracking for customer, operational and financial outcomes
• Prepare a 90-day support analytics application plan for workplace use

Participants should understand basic customer support or contact centre operations and be comfortable with percentages, rates and charts. Advanced statistics, SQL, programming and data science experience are not required.

Participants should bring a laptop for live online delivery and are encouraged to bring examples of current support reports, metrics or service challenges. Confidential data is not needed; anonymised examples or descriptions are sufficient.

The course is designed for support managers, contact centre leaders, CX managers, quality leaders, service analysts and service delivery managers. It is most useful for professionals responsible for interpreting service performance and recommending action.

Contact centre management courses typically focus on operations, workforce management and leadership, while customer experience courses focus more broadly on journeys and experience strategy. This course concentrates on the analytical methods needed to measure, diagnose and improve support performance across channels.

Participants can use the metric dictionary, scorecard model, dashboard specification and root-cause framework with existing support reports. The 90-day application plan helps convert a current service issue into defined measures, actions and review points.

The five days contain approximately 32.5 contact hours, organised through ten modules of about three hours each, with time for integration and review. Sessions combine briefings, case analysis, group work, simulations and individual application planning.