A government department approached Marketways because policy intent was not translating consistently into the citizen experience. Early discovery showed that service demand, applicant journeys, operational capacity and management information had to be treated as one delivery problem. We built the engagement around the points where policy, demand and execution were drifting apart.
The engagement objective
Through the initial discovery, Marketways defined the objective: connect policy intent, service demand, citizen journeys and operational capacity.
How Marketways translated the problem
We began with a practical question: Did a policy change the intended outcome, and for whom? The first analysis used administrative outcomes, timing, comparison groups and contextual indicators.
That evidence could not be read in isolation. Average effects can hide gains and losses across income, age, nationality, location or vulnerability. Demand can shift with eligibility, population, seasonality, policy and channel adoption.
A single service-level average would have hidden which applicants, journeys and operating conditions were creating the failure. We kept demand, capacity and citizen experience connected before interpreting performance.
We did not judge each component by its isolated KPI. We examined how people, assets, decisions and constraints affected one another, then used the evidence to test whether an apparent improvement would strengthen the complete system or merely move cost, pressure or risk elsewhere.
How the engagement developed
The initial work on policy impact and distributional assessment exposed dependencies with public-service demand and capacity plan, citizen and resident journey redesign, government data and management-information design. Treating them as separate recommendations would have left the operating trade-offs unresolved.
- Policy impact and distributional assessment: Distinguish real effects from changes that would have happened anyway.
- Public-service demand and capacity plan: Align service locations, staffing and digital capacity with public need.
- Citizen and resident journey redesign: Remove avoidable effort while protecting necessary controls.
- Government data and management-information design: Create a shared view of outcomes, operations and emerging risk.
Evidence we examined
- Administrative outcomes, timing, comparison groups and contextual indicators.
- Transactions, applications, waiting times, population, geography and channel use.
- Journey events, complaints, contact reasons, completion and rework.
- Definitions, source systems, ownership, quality checks and decision cadence.
Industry conditions we accounted for
- Average effects can hide gains and losses across income, age, nationality, location or vulnerability.
- Demand can shift with eligibility, population, seasonality, policy and channel adoption.
- A faster step in one agency may simply move delay to another agency or to the applicant.
- Different agencies may use similar words for different populations, events and completion states.
How our engagement contributed to business impact
We connected every method to a decision and a business measure. The organisation could assess the engagement through operating results as well as model performance.
- Policy impact and distributional assessment
- Method: Statistics & Econometrics, Research & Evidence Collection.
- Evidence: Administrative outcomes, timing, comparison groups and contextual indicators.
- Decision supported: Distinguish real effects from changes that would have happened anyway.
- Impact measure: Outcome change, reach, unintended effects and uncertainty.
- Public-service demand and capacity plan
- Method: Forecasting, Risk & Optimisation, Statistics & Econometrics.
- Evidence: Transactions, applications, waiting times, population, geography and channel use.
- Decision supported: Align service locations, staffing and digital capacity with public need.
- Impact measure: Access, waiting time, utilisation and cost per completed service.
- Citizen and resident journey redesign
- Method: Process, Workflow & Systems, Market, Customer & Behavioural Analytics.
- Evidence: Journey events, complaints, contact reasons, completion and rework.
- Decision supported: Remove avoidable effort while protecting necessary controls.
- Impact measure: End-to-end completion, repeat contact, effort and equitable access.
- Government data and management-information design
- Method: Data Foundations & Business Intelligence, Process, Workflow & Systems.
- Evidence: Definitions, source systems, ownership, quality checks and decision cadence.
- Decision supported: Create a shared view of outcomes, operations and emerging risk.
- Impact measure: Measure consistency, decision lead time and data exceptions.
Implementation
The engagement was structured as multi-phase transformation. We connected the analysis to the decisions, operating constraints and measures that the organisation would continue to use.
How success was assessed
The overall assessment considered public outcome, access, completion and sustainable operating capacity. The supporting measures included:
- Outcome change, reach, unintended effects and uncertainty.
- Access, waiting time, utilisation and cost per completed service.
- End-to-end completion, repeat contact, effort and equitable access.
- Measure consistency, decision lead time and data exceptions.
Services and methods used
Services: Decision Assurance, Risk & Uncertainty Modelling, Sales Forecasting & Demand Planning, Workforce Planning & Capacity, Process & Workflow Analysis & Redesign, Customer Satisfaction & Experience Research, Business Systems Design & Architecture, Operational Performance Diagnostic.
Methods: Statistics & Econometrics, Research & Evidence Collection, Forecasting, Risk & Optimisation, Process, Workflow & Systems, Market, Customer & Behavioural Analytics, Data Foundations & Business Intelligence.