TurnB Approach


Client Discussion to Understand Objective:

Discussions with stakeholders were held to understand client requirements and grasp the company's data assets.

 

Brainstorming:

  • Possible factors contributing to the sales decline were listed, providing direction for analysis. Main factors included:
  • Customer Behavior
  • Inventory Management
  • Brand Competitors
  • lations, enabling prompt interventions to avert cancellations.
  • Purchase Returns from Customers
  • Positioning of Products in Retail Stores
  • Poor Marketing Practices

Dataset Creation and EDA:

  • Identified data sources required for analysis in consultation with stakeholders.
  • Checked for missing values, data discrepancies, outliers, etc.
  • Extracted, treated, and maintained data to ensure correctness.
  • Employed automated data transformation techniques for proper data quality and reduced ETL time.

Root Cause Analysis:

  • Conducted exhaustive analysis on factors such as:
  • Time Period Analysis: Analyzed four years of data for revenue, customer base, product prices, margins, and portfolio changes.
  • Product Analysis: Evaluated SKU portfolio, and product performance, and explored correlations between inventory and purchasing activities.
  • Customer Analysis: Utilized multiple customer segmentation models to understand purchase behavior, performance, and engagement.
  • Sales Executive Analysis: Scrutinized performance and key indicators of sales executives, identifying poor performers.

Story Design and Document Creation:

  • Crafted a narrative conveying insights, transformed into a document with compelling visualizations.

Client Discussion & Recommendation:

  • Communicated insights and recommendations to improve sales revenue to the leadership team.
Approaches background
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Implications

  • Root cause analysis enabled correlation and analysis of key sales-driving factors.
  • Developed a Customer Analysis Framework for patterns, retention, churn, and purchase portfolio.
  • Created a product analysis framework for performance, lifecycle, and inventory monitoring.
  • Developed models for analyzing and optimizing product and SKU performance.
  • Established a salesperson framework based on multiple KPIs.
  • Implemented a process to capture additional data, enhancing the company's data footprint.
  • Improved customer engagement and retention.
  • Enabled proactive measures by identifying levers for maximizing sales.
  • Streamlined operations and automated reporting for client stakeholders.
  • Instilled an analytical DNA in the client.
  • Enhanced the company's data assets.