Simulating real consulting work: turning messy business data into executive-ready Power BI dashboards
This project is a simulation of real analyst work at PwC Switzerland, completed via the Forage virtual experience program. Across 4 tasks, I built 6 interactive Power BI dashboards to help a telecom client (PhoneNow) understand three critical business challenges: call center inefficiency, high customer churn, and workforce diversity gaps.
Key headline finding: The company had a 34.1% churn rate — significantly above industry benchmarks — with 1,869 customers actively at risk, generating $2.86M in yearly charges that could be lost.
PhoneNow faced three interconnected challenges with no unified visibility into any of them:
1. Call Center Performance — No clear tracking of agent efficiency, resolution rates, or satisfaction trends across 5,000 monthly calls.
2. Customer Retention — A 34.1% churn rate with no proactive identification of at-risk customers, leaving revenue vulnerable. Month-to-month contracts dominated at 88.55%, making churn easier than ever for customers.
3. Diversity & Inclusion — Leadership (executive level) was 87.5% male, with no structured tracking of hiring, promotion, or turnover equity across job levels.
Without a data-driven system, the business was reacting to problems after they occurred rather than preventing them.
Raw Data → Power Query (Clean) → Data Model → DAX Measures → Dashboard → Insights → Stakeholder Report
Defined the business questions each dashboard needed to answer before touching any data. Identified KPIs for each domain: operations, retention, and workforce.
- Imported and transformed the Call Center dataset in Power Query
- Fixed datatypes for
TimeandAvgTalkDurationfields - Built 2 dashboards: Operational Overview and Agent Performance & Satisfaction
- KPIs tracked: Total Calls, Answered/Abandoned, Resolution Rate, Avg Speed of Answer, Avg Rating
- Imported and validated the Churn dataset (checked for nulls and type errors)
- Built 3 dashboards: Welcome Page (with KPI framing), Churn Dashboard, Customer Risk Analysis
- Segmented churn by contract type, payment method, internet service, and subscription duration
- Wrote a stakeholder email to the engagement partner summarizing findings and retention recommendations
- Imported and validated the Diversity-Inclusion dataset (Pharma Group AG)
- Built 2 dashboards tracking 6 KPIs: Hiring, Promotions, Turnover Rate, Performance Rating, Executive Gender Diversity, Age Group
- Identified structural imbalances in promotions and executive hiring pipelines
| Category | Skills |
|---|---|
| BI & Visualization | Power BI Desktop, multi-page dashboard design, slicers & filters, KPI cards, donut/bar/line charts |
| Data Preparation | Power Query, data type correction, null handling, Close & Apply workflow |
| Analytics | Churn analysis, workforce analytics, call center KPI analysis, cohort segmentation |
| Business Communication | Stakeholder email writing, insight storytelling, executive-level recommendations |
| Soft Skills | Problem framing, translating data into business language, consulting mindset |
| KPI | Value |
|---|---|
| Total Calls | 5,000 |
| Answered Calls | 4,054 (81.1%) |
| Abandoned Calls | 946 (18.9%) |
| Resolved Calls | 3,646 (72.9%) |
| Avg Speed of Answer | 01:08 min |
| Avg Satisfaction Rating | 3.40 / 5 |
💡 Satisfaction declined month-over-month (5,026 → 4,388 sum of ratings from Jan to Mar), signaling a deteriorating customer experience trend that needed immediate attention.
💡 Joe had the slowest avg answer speed (01:11) and the lowest satisfaction rating (3.33) — a clear candidate for targeted coaching.
| KPI | Value |
|---|---|
| Total Customers | 7,043 |
| Churn Rate | 26.54% (risk pool: 34.1%) |
| Customers at Risk | 1,869 |
| Yearly Charges at Risk | $2.86M |
| Monthly Charges at Risk | $139.1K |
| Tech Tickets (at-risk customers) | 2,173 |
💡 88.55% of churning customers were on month-to-month contracts — the highest-risk segment. Encouraging annual or two-year contracts is the single highest-leverage retention action.
💡 Fiber optic customers had a disproportionately high churn rate and generated 62.11% of monthly charges — high value, high risk. Service quality improvement here would have outsized revenue impact.
💡 53%+ of at-risk customers subscribed less than 1 year — early churn is the dominant pattern, pointing to onboarding and first-90-days experience as the root cause.
| KPI | Insight |
|---|---|
| Executive Hiring (FY20) | 100% Male |
| Promotion to Executive (FY20) | 100% Male |
| Executive Split (FY20) | 87.5% Male / 12.5% Female |
| Avg Performance Rating | Men: 2.41 / Women: 2.42 (essentially equal) |
| Female Hires (all levels) | 41% |
💡 Despite nearly equal performance ratings, women are consistently underrepresented in promotions to senior and executive roles — suggesting structural bias in promotion decisions, not a performance gap.
💡 The workforce skews young (20–29 age group dominates), which creates a long-term pipeline opportunity if D&I practices are embedded early.
- Implement targeted coaching for agents with below-average satisfaction ratings and slower response times
- Investigate the Jan→Mar satisfaction decline — check for staffing, process, or volume changes in that period
- Reduce the 18.9% call abandonment rate through better queue management and callback options
- Priority action: Launch proactive outreach to the 1,869 at-risk customers — especially those on month-to-month contracts within their first year
- Offer discounted annual/two-year contracts to month-to-month customers at key churn risk periods (6-month and 12-month marks)
- Improve fiber optic service quality — this segment pays the most and churns the most
- Introduce automated payment (currently underused) incentives, as electronic check users show higher churn
- Establish transparent, structured promotion criteria to address the gender gap at senior and executive levels
- Track and report D&I metrics quarterly — what gets measured gets managed
- Introduce mentorship programs targeting high-performing female employees at Manager and Senior Officer levels to build the executive pipeline
| Dashboard | Task |
|---|---|
| Operational Overview | Call Center Trends |
| Agent Performance & Satisfaction | Call Center Trends |
| Welcome Page (KPIs) | Customer Retention |
| Churn Dashboard | Customer Retention |
| Customer Risk Analysis | Customer Retention |
| Diversity & Inclusion (2 pages) | Diversity & Inclusion |
Dashboard screenshots are included in the project PDF presentation.
- Predictive churn model — Use historical churn patterns to build a risk score for each customer, enabling proactive outreach before they decide to leave
- Agent performance trend tracking — Build month-over-month views to detect performance deterioration early
- D&I promotion pipeline dashboard — Track the gender composition at each stage of the promotion process to identify where drop-off occurs
- Connect live data — Replace static dataset with a live connection for ongoing monitoring
- NPS integration — Add customer satisfaction survey data to correlate service quality with churn risk
Completed as part of the PwC Switzerland virtual experience on Forage — a simulation designed to mirror real analyst work at a Big 4 consulting firm. All data is simulated for learning purposes.
Built with 📊 Power BI · Analyzed with a consulting mindset · Presented with clarity