AccAnalysisAccAnalysis
Professional Services & Staffing

Branch performance and rep commissions from one pipeline

A staffing group tracked commissions and branch financial performance in disconnected systems, so comparing offices was guesswork. We built the pipeline and the reporting layer that made a single view possible.

At a glance
Status
Delivered
Region
United States
Engagement model
Dedicated team

Status: Delivered. Described from our own delivery records. Client identity, brand and commercial terms are withheld. Figures, where given, cover the period stated and nothing beyond it.

The organisation

Context

A staffing and recruitment group operating multiple branch offices, each with its own placements, revenue, margin and sales team.

Performance data lived in the operational staffing platform, commissions were tracked separately, and financial results arrived through a third route.

The brief

The problem

  • Benchmarking one office against another required assembling three sources by hand.
  • Commission calculation was disconnected from the placement data that drives it.
  • Leadership decisions on targets and resource allocation waited on a manual consolidation.
  • There was no agreed definition of branch profitability, so different people produced different answers.
The work

What we built

An ingestion pipeline

Pulling operational data from the staffing platform's API into a cloud data store on a schedule, with monitoring and failure alerting.

A modelled reporting layer

With documented definitions for placements, revenue, margin and profitability, so each measure has one meaning.

Dashboards

Covering sales-representative commissions and branch-level financial performance, with drill-through to the underlying records.

Method

How we delivered it

  1. 1

    Agree the measures first

    The dozen or so numbers leadership actually decides on, defined before any pipeline was written.

  2. 2

    Build ingestion

    From the operational platform, scheduled and monitored.

  3. 3

    Model

    The warehouse layer with tested, documented business logic.

  4. 4

    Visualise for the decision rather than for the demo

    Commission views for managers, financial views for leadership.

  5. 5

    Govern

    Ownership and a change process per measure, so definitions don't drift.

Sequence

How it was phased

PhaseDurationWhat happens
1Define the measures
2 wks

Agreed definitions, ownership, drill-through requirements

2Ingestion
3–4 wks

API pipeline into cloud storage, scheduling, alerting

3Modelling
3–4 wks

Warehouse layer, business logic, tests

4Dashboards
3–4 wks

Commission and branch financial views

5Governance & hand-over
2 wks

Metric dictionary, change process, documentation

Indicative phasing for work of this shape. Actual duration varies with data quality, access and decision speed.

Hand-over

What the client keeps

  • Pipeline code in their own repository
  • The warehouse schema and models
  • The metric dictionary
  • Dashboards they can edit
  • The data-quality test suite
Stack
PythonFlask API integrationAzureSQLPower BI
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