Verge Mobile - Datazeb

Power Bi

Client: Verge Mobile
Duration: 2 Years
Website Link: www.vergemobile.com

Category: Power Bi

Project overview :

Verge Mobile, a retail company with over 50 locations across 10 states, faced a challenge: they lacked visibility into key performance metrics for their retail locations. Store managers relied on basic Excel reports for sales data, leading to a decentralized and inefficient tracking system. To address this issue, Verge Mobile initiated a project with the following goals:
  • Build an automated analytics platform with interactive dashboards for real-time visibility into retail operations.
  • Enable daily tracking of 100+ Key Performance Indicators (KPIs) for each store, covering sales, conversion rates, foot traffic, average ticket size, and Net Promoter Scores.
  • Monitor service metrics such as First Call Resolution, Wait Times, and Support Ticket Aging to enhance the customer experience.
  • Rank stores by overall performance and individual metrics to identify high and low performers.
  • Drill down into trends by product, segment, geography, and store manager to uncover insights.
  • Establish dynamic targets tailored for each store based on trends, seasonality, and peer benchmarks.
  • Provide actionable insights to optimize staffing, merchandising, promotions, and pricing for sales growth.
  • Aim to reduce support call waiting times by 20% and increase first call resolution by 30%.
  • Strive for a 15%+ increase in sales conversion rates across the retail chain in the first year.

What we did for this project :

To achieve these objectives, we adopted the following technical approach:
  • Implemented Extract, Transform, Load (ETL) processes to extract daily data from source systems and load it into a cloud data warehouse.
  • Designed a star schema Online Analytical Processing (OLAP) model optimized for slicing and dicing operations data.
  • Built aggregated metrics, KPIs, and advanced analytics using Data Analysis Expressions (DAX) calculations.
  • Created interactive Power BI dashboards with drill-down capabilities, synchronized filtering, and conditional formatting.
  • Embedded dashboards into an internal portal with row-level security and access controls.
  • Automated the delivery of reports to store managers daily via email.
  • Leveraged artificial intelligence (AI) and machine learning (ML) for forecasting, anomaly detection, and predictive insights.
The project’s timeline included the following phases:
  • Data Warehouse Build: 4 weeks
  • ETL & Data Modeling: 3 weeks
  • Dashboard Design: 4 weeks
  • User Acceptance Testing: 2 weeks
  • Training & Change Management: 1 week

Project results :

The project yielded significant outcomes, including:

  • Establishment of a centralized, real-time analytics platform providing visibility into daily store operations.
  • Monitoring and tracking of over 100 KPIs, improving performance evaluation and decision-making.
  • Enhanced customer service metrics through the optimization
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