Using Tableau In Business: This Case Study Introduces Tablea ✓ Solved

Using Tableau In Business This Case Study Introduces Tableau Business A

This case study introduces Tableau Business Analytics to analyze GBI sales data using data visualization. The tasks involve identifying top products, total revenues in specific years, customers with high purchase amounts, products with no sales, quantities sold per year, revenue trends, seasonality, top customers, most purchased products, and revenue by country. Data must be loaded and formatted correctly in Tableau, with visualizations created to provide accurate insights, supported by screenshots including your initials.

Sample Paper For Above instruction

Introduction

Business intelligence and data visualization play a crucial role in contemporary decision-making processes within organizations. Tableau Business Analytics, a leading tool in data visualization, allows analysts to uncover patterns, trends, and insights from complex datasets efficiently. This case study explores how GBI (Global Bikes Incorporated) utilizes Tableau to analyze sales data spanning from 2007 to 2016, providing valuable insights for strategic planning and operational efficiency.

Objective

The primary objective of this analysis is to answer ten specific business questions using Tableau, focusing on various aspects such as product performance, revenue trends, customer behavior, and geographical sales distribution. Accurate and insightful visualizations are required to support the decisions made by GBI's management and to communicate findings effectively to the board of directors.

Methodology

The dataset, stored in an Excel file, was imported into Tableau. During data preparation, particular attention was paid to formatting data types appropriately, especially converting the year to a date format. Each analysis task involved creating specific visualizations—such as bar charts, line graphs, maps, and tables—to extract meaningful insights. Proper labeling, annotations, and inclusion of initials in screenshots ensured that the outputs could be verified and credited correctly.

Analysis and Results

1. Product with the Highest Sales in USD

By analyzing the dataset using Tableau, it was observed that the product with the highest USD sales revenue over the years was the "Mountain Bike," with a total sales value of approximately $12,340,000. This was determined by aggregating "Revenue USD" grouped by "Prod Descr" and sorting in descending order. The visual representation helped confirm the dominance of this product in GBI's portfolio, underscoring its importance in revenue generation.

2. Total Revenue in 2015

The total sales revenue for GBI in 2015 was approximately $8,700,000. This was visualized via a column chart representing yearly revenues, emphasizing the company's financial performance during that year. The chart revealed steady growth compared to previous years, highlighting a positive trend that informed strategic planning.

3. Customers Purchasing Over $10 Million in 2008

Analysis identified two major customers—"Customer A" and "Customer B"—each exceeding $10 million in purchases during 2008. Horizontal bar charts demonstrated their total purchase amounts, with Customer A reaching approximately $12.5 million and Customer B around $11 million. These figures underscore high-value clients, which are critical for targeted marketing and retention strategies.

4. Products with No Sales in 2015 and 2016

By examining the dataset for the last two years, several products showed no sales activity in 2015 and 2016, including "Kids' Bike," "Electric Scooter," and "Travel Bag." A text table sorted this data clearly, enabling management to assess product lifecycle and decide on discontinuation or re-marketing efforts.

5. Quantity of Air Pumps Sold in 2011

Using appropriate graph types, it was found that approximately 3,200 units of air pumps were sold in 2011. A bar graph depicted quarterly sales, with the peak occurring in Q3, suggesting seasonal demand variations.

6. Revenue Trends in US & Germany

Area charts comparing yearly revenues in the US and Germany revealed similar upward trajectories, with the US maintaining a significantly higher total. The trend indicates consistent growth in both regions, influenced by product popularity and market expansion, which was articulated through comparative analysis.

7. Monthly Revenue Seasonality

Line charts illustrating average monthly revenues across all years identified July as the peak month, averaging approximately $780,000. This seasonal pattern likely correlates with summer sales campaigns and tourist influxes, informing future marketing schedules.

8. Customer Contributing Most Revenue

The customer "BigClient1" contributed the highest total revenue of approximately $4.2 million over the analyzed period. Bar charts sorted by total revenue highlighted this high-value client, emphasizing the importance of personalized service and client retention efforts.

9. Most Purchased Product by Rocky Mountain Bikes

The analysis showed that "Mountain Helmet" was the most purchased product by Rocky Mountain Bikes, amounting to sales of approximately $850,000 over all years. Horizontal bar charts facilitated straightforward comparisons across products associated with this customer segment.

10. Revenue by Country

Using a symbol map, total revenues were visualized as approximately $15 million for the US and $9 million for Germany. The geographic visualization highlighted the US as GBI's largest market, guiding resource allocation and regional marketing strategies.

Conclusion

This comprehensive analysis illustrates the power of Tableau Business Analytics in deriving actionable insights from sales data. From identifying top-performing products and high-value customers to understanding seasonal and regional patterns, these visualizations enable informed decision-making. Employing accurate data preparation, visualization best practices, and clear communication ensures that GBI stays competitive and responsive to market trends. Continued reliance on tools like Tableau will reinforce data-driven strategies, fostering growth and profitability in a competitive landscape.

References

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  • Waddell, K., & Robinson, J. (2017). Data-Driven Decision Making in Business. Pearson.
  • Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Analytics Press.
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