TP Commercials Dashboard 

This overview provides a comprehensive look at the data analysis process for TP Commercial Sales Company, beginning with data cleaning and culminating in actionable insights. 

LINK TO PROJECT

Role Played in Project, Insights, and Recommendations

1. Data Collection first step involves gathering data from various sources within the company, which may include:

• Sales Data: Transactions, product sales, customer purchases.

• Customer Data: Demographics, behavior, preferences, location.

• Operational Data: Inventory levels, supply chain efficiency, production metrics.

• Financial Data: Revenue, profit margins.

These datasets were obtained from a boot camp with Data Science Nigeria 

2. Data Cleaning

Ensuring data accuracy and consistency is crucial for meaningful analysis. The data cleaning process includes:

• Data Integration: Combining data from multiple sources into a unified format. This often requires resolving discrepancies in data formats and aligning timeframes.

• Handling Missing Values: Identifying missing data and deciding whether to impute these values using statistical methods, fill them based on business rules, or remove the affected records.

• Removing Duplicates: Detecting and eliminating duplicate records to avoid skewed analysis and ensure that each data point is unique.

• Data Validation: Cross-checking data to ensure consistency and accuracy. This step involves verifying that all data points fall within expected ranges and formats.

• Normalization: Standardizing data to ensure consistency, such as converting all currencies to a single standard or adjusting time zones to a common reference point.

• Outlier Detection and Management: Identifying outliers that could distort analysis. These could be errors or valid but extreme values; the approach to handling them depends on the business context.

• Data Enrichment: Adding value to the existing dataset by integrating external data or deriving new variables, such as customer segments or product categories.

3. Data Analysis

Once the data is cleaned, the analysis process begins. This typically involves:

• Descriptive Analytics: Summarizing the data to understand basic trends and patterns. For example, analyzing sales trends over time or customer demographics.

• Segmentation Analysis: Grouping customers or products based on shared characteristics. This involved segmenting customers by purchasing behavior, demographics, or product preferences.

• Correlation Analysis: Identifying relationships between different variables, such as the product to sales or the relationship between product pricing and customer demand.

4. Insights and Recommendations

The analysis leads to valuable insights that inform business decisions. Examples of insights and corresponding recommendations might include:

• Customer Behavior:

  • Insight: High-value customers tend to purchase premium products and respond well to personalized marketing.

  • Recommendation: Focus on personalized marketing campaigns targeting high-value customers, with an emphasis on premium offerings.

• Sales Trends:

  • Insight: Sales peak at certain locations or in response to specific marketing campaigns.

  • Recommendation: Increase inventory and marketing efforts at specific locations, and replicate successful campaigns.

• Operational Efficiency:

  • Insight: Certain products have high demand but frequently experience stockouts, leading to lost sales.

  • Recommendation: Improve inventory management for these products by adjusting reorder levels or working with suppliers to reduce lead times.

• Product Performance:

  • Insight: Product amarila, montana and corretara line have seen a decline in sales, possibly due to increased competition or changes in consumer preferences.

  • Recommendation: Reevaluate the product line, considering either innovation to differentiate or repositioning in the market.

• Financial Health:

  • Insight: Profit margins are eroding due to increasing costs and stagnant pricing.

  • Recommendation: Explore cost reduction strategies, renegotiate supplier contracts, or consider value-based pricing to improve margins.

5. Implementation and Monitoring

The final step is implementing the recommended actions and continuously monitoring their impact. This involves:

• Action Plans: Develop detailed action plans to implement recommendations, with assigned responsibilities and timelines.

• Tracking KPIs: Monitoring key performance indicators to assess the effectiveness of the implemented changes.

• Continuous Improvement: Regularly revisiting the data to identify new opportunities for optimization and adjust strategies as needed.