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Salla Call Center Dashboard

A seven-page Power BI report that turns three months of call-center records into a structured view of demand, service, abandonment, answer speed, projects, and agents.

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Context
Portfolio dataset — Salla call center operations
Role
Data analyst and BI developer
Data window
Feb–Apr 2022
Deliverable
Seven-page Power BI dashboard
Exhibit 01 — Finished Power BI report cover and navigation

What the business learned

Forecasted Calls

2,437,071

Calls Offered

1,744,885

Calls Handled

1,721,842

Answer Rate

98.68%

Abandon Rate

1.32%

ASA

9.09 sec dashboard summary

Call flow across the reporting period

Offered: 1,744,885. Handled: 1,721,842. Abandoned: 23,043.

Most offered calls were handled, while abandonment remained a small but operationally important share.

Forecast and actual call demand

Forecasted: 2,437,071. Offered: 1,744,885.

Actual offered calls reached 71.60% of the supplied forecast, creating a clear planning comparison.

Business Insights

Forecast and actual demand need to be read together

Actual calls were below the supplied forecast across the period, making the forecast comparison a planning input rather than a decorative benchmark.

Answer rate does not replace service-level analysis

A high share of offered calls were answered, but service level, abandonment, and ASA are still needed to understand the customer waiting experience.

Project and agent views need the same measure definitions

A shared DAX layer keeps comparisons consistent when the report moves from executive summary to operational detail.

Project Evidence

  • Centralized 1,744,885 offered calls across February–April 2022 into one report.
  • Made call demand, service, abandonment, ASA, forecast, project, and agent comparisons accessible through dedicated views.
  • Created a reusable data model, target table, theme, and DAX measure set behind the dashboard.

The Business Problem

The project combines daily call-center records from February, March, and April 2022 into a Power BI model and seven-page report.

The finished report includes an executive summary plus dedicated views for call performance, agent performance, project analysis, business insights, and detailed exploration.

The source data was distributed across monthly CSV files and mixed call volume, service, abandonment, answer-time, project, and agent fields at daily grain.

A reusable model and consistent measure layer were needed to compare performance across time, projects, and agents without manually rebuilding calculations.

Objectives

  • Compare forecasted and offered call demand
  • Track handled calls, service level, answer rate, abandonment, and ASA
  • Analyze project and agent performance
  • Create a clear report path from executive overview to detailed records

The Approach

Dataset

  • Three monthly CSV source files covering February–April 2022
  • Daily records by project and agent
  • Fields include forecasted, offered, handled, within-threshold, abandoned, ASA, and total answer time
  • Modeled as a fact table with date, project, agent, and target dimensions

Data Cleaning

  • Standardized source columns into a consistent fact-table schema
  • Created stable date, project, and agent keys
  • Preserved the source-file field for traceability
  • Kept KPI targets in a separate disconnected target table

Analysis Process

  • Loaded and combined the monthly call-center sources
  • Built fact and dimension tables at daily project-agent grain
  • Defined reusable DAX measures for volume, service, abandonment, ASA, and forecast comparison
  • Applied a consistent Power BI theme and seven-page navigation structure
  • Validated report totals against the modeled source data

Dashboard Screenshots

Executive Summary — demand, handling, SLA, ASA, abandonment, and project performance
Call Performance — volume, answer speed, abandonment, and service-level trends
Agent Performance — productivity and service comparisons across 16 agents
Project Analysis — operational KPI comparison for Projects A, B, and C
Business Insights — findings, KPI definitions, and documented recommendations
Details — filterable project-level operational KPI table
Microsoft Power BIPower QueryDAXData ModelingCSV

Field Notes

Lessons from this project you can use in your own work:

  1. Define service level, answer rate, abandonment, and ASA separately before building visuals; similar-sounding call-center KPIs answer different questions.

  2. Keep the source filename in the fact table when combining monthly exports. It makes reconciliation and exception tracing much easier.

  3. Build measures once and reuse them across executive, project, and agent views so every page speaks the same analytical language.

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