Accelerating Decisions Self‑Service Analytics with Power BI
Reduction in Reporting Time
0
%
Increase in Decision Making
0
%
Increase in Revenue
0
%
Problem Statement
- Lack of Detailed Sales Data: The client, an IT services company, had only monthly total sales figures without critical details like client information, sale type (new sale, upsell, cross-sale), or currency variations.
- Unclear Sales Performance Metrics: There was no structured approach to track sales against targets, making it difficult to assess progress towards yearly and quarterly goals.
- Limited Forecasting Ability: The company lacked mechanisms to estimate whether the team was on track to achieve sales goals, hindering proactive decision-making.
- Lack of Client-Based Insights: There was no way to assess which clients contributed most to revenue growth, limiting targeted sales strategies.
Our Solution
- Data Ingestion: Source data was extracted from various internal systems and ingested into Azure Blob Storage.
- Data Processing & Transformation: Azure Data Factory was used to process and clean raw data. A dimensional data model was designed to support advanced analytics.
- Data pipelines enabled automated updates, keeping reports up to date with the latest insights.
- Data Visualization & Reporting: Power BI was used to develop interactive dashboards with drill-down capabilities and intuitive navigation features, providing a web-like experience for users.
Impact
- Increased Sales Visibility: The company gained real-time insights into sales performance across clients, sale types, and time periods.
- More Accurate Forecasting:With the target remainder analysis, the company could proactively adjust sales strategies to close gaps and achieve goals.
- Revenue Growth Focus: : Leadership identified high-value clients and refined sales strategies accordingly.
- Better Decision-Making: Executives could seamlessly analyze key metrics and intervene when necessary.
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