Excel 2022 Project Guide Chapter 6 Prepare Part B

excel 2022 Projectyo22 Excel Ch06 Prepare Partb

Analyze sales data for the Red Bluff Pro Shop to develop a marketing strategy. Create and format data tables and ranges, utilize advanced Excel functions such as FILTER and database functions, and build PivotTables and PivotCharts to explore and visualize sales patterns. Incorporate slicers for interactive filtering and update the analysis with new data entries. Finalize the report with comprehensive summaries, percentage calculations, and visualizations to support strategic decision-making.

Paper For Above instruction

The purpose of this research is to thoroughly analyze sales data for the Red Bluff Pro Shop, focusing on understanding customer behavior, transaction patterns, and revenue streams to inform strategic marketing efforts. Using sophisticated Excel techniques, the analysis encompasses data organization, application of advanced functions, creation of PivotTables and PivotCharts, and interactive filtering, all aimed at providing actionable insights.

Introduction

Effective data analysis is critical for businesses aiming to refine their marketing strategies and improve customer engagement. The Red Bluff Pro Shop management, led by Aleeta Herriott, seeks to leverage sales data to increase patronage and enhance customer loyalty. This study uses Excel 2022 tools to analyze sales records, revealing valuable insights into customer preferences, purchase patterns, and revenue sources. The comprehensive approach includes data organization, filtering, summarizing, and visualizing transactions over several years, with additional focus on recent data entries and specific transaction types such as Apple Pay and discounts.

Data Organization and Initial Analysis

The first step involves opening the provided Excel file, titled 'Excel_Ch06_Prepare_SalesAnalysis.xlsx', and saving it in the local storage. The sales data, stored as a table on the 'SalesData' worksheet, is converted into an Excel Table named 'SalesData' with headers for structured data management. Utilizing Excel's Table feature allows for dynamic data management, where new records can be effortlessly added and automatically included in analyses.

To facilitate targeted data exploration, a range named 'SalesDatabase' is created encompassing all data in the table, including headers. An essential filter operation employs the FILTER function in cell N4, which retrieves sales records with payment method 'cash' and quantity greater than 2, or transactions recorded as 'Club Member' sales. Proper formatting of date columns as ShortDate and autofitting columns ensures data visibility and clarity.

Furthermore, the 'SalesData' worksheet's column headers are duplicated onto the 'DatabaseTotals' worksheet starting at cell A1 to set up criteria cells. These criteria are applied using database functions—SUM, AVERAGE, COUNT, MAX, and MIN—to the 'NetRevenue' and 'TotalDiscounts' fields, based on criteria such as transaction dates after 11/15/2025 and payment methods like Apple Pay. The flexibility of database functions underpins the detailed analysis of revenue performance and discount utilization, which are vital for marketing insights.

PivotTable Analysis

PivotTables are instrumental in summarizing large datasets with interactive functionalities. Utilizing the built-in 'Recommended PivotTables' feature, an initial PivotTable is created based on the 'SalesData' table to analyze overall revenues by time and payment method for club members only. Adjustments include deselecting unnecessary fields such as 'TransDate', 'NetRevenue', and 'EMP-ID', and reorganizing the PivotFields: placing 'Quantity' in rows, 'ClubMember?' in columns, and 'CashDisc' in values. Setting the 'Summarize Value Field By' to 'Average' highlights average discounts given per transaction.

To improve interpretability, the pivot options are configured such that error values display as zero. The labels are customized by replacing 'Row Labels' with 'Quantity Sold' and 'Column Labels' with 'ClubMember?'. The 'CashDisc' field is replaced with 'TotalDiscounts' to focus on total discounts granted, and the worksheet is renamed 'TotalDiscountsByQtySold'.

Additionally, a second PivotTable on a new worksheet named 'PivotAnalysis' examines the breakdown of 'NetRevenue' over different time frames—grouped into Years, Quarters, and Months—using the 'TransDate' field. The 'PaymentType' serves as a column field, and 'ClubMember?' as a filter. Calculating 'Total Net Revenue' as a custom sum, formatted in the Accounting style, encapsulates revenue contributions across time periods. Applying a percentage of grand total reveals the relative contribution of each 'PaymentType'.

To enhance interactivity, an 'EMP-ID' slicer is added, positioned at cell G3, enabling dynamic filtering of data by employee. After entering a new transaction (TransID P000121 on 01/01/2026, EMP-00024, with specific item details), the PivotTable is refreshed to include this record. Clearing filters provides a comprehensive view of the data, which supports detailed drill-downs such as isolating December 2025 Apple Pay transactions onto a new worksheet titled 'ApplePayTransactions'.

PivotChart Creation and Final Presentation

A visual analysis is achieved through PivotCharts. A Pie Chart, depicting the proportion of 'NetRevenue' by 'PaymentType', is created based on the 'SalesData' table on a dedicated worksheet named 'RevenueByPaymentType'. A filter by 'Years' allows viewing data from specific years, with the chart titled 'Proportion of Revenue by Payment Type'. The chart is repositioned onto its own worksheet for clarity, and styled with 'White, Pivot Style Light 23' for aesthetics.

Slicers are added for user-friendly filtering, including an 'EMP-ID' slicer set to filter for 'EMP-00024'. The slicer is styled as 'White, Slicer Style Other 2' and positioned at the specified location. This interactivity facilitates focused analysis, especially when new transaction data is entered and the PivotTables are refreshed accordingly.

Throughout the process, attention is paid to proper formatting, data integrity, and presentation, ensuring that each analytical step seamlessly integrates into a comprehensive report. The use of dynamic Excel functions, PivotTables, PivotCharts, and slicers collectively enables a detailed, interactive, and visually appealing analysis suitable for strategic decision-making by Aleeta Herriott and the board of directors.

Conclusion

This detailed analysis exemplifies how advanced Excel features can be harnessed to assess retail sales data effectively. By converting data to tables, applying database functions, creating and customizing PivotTables and PivotCharts, and utilizing slicers for interactivity, the Red Bluff Pro Shop can better understand its customer base, revenue streams, and transaction trends. These insights enhance the capability to develop targeted marketing strategies, optimize sales performance, and ultimately increase patronage.

References

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  • Microsoft Support. (2023). Create and use PivotTables. https://support.microsoft.com/en-us/excel
  • Microsoft Support. (2023). Create a PivotChart. https://support.microsoft.com/en-us/excel
  • Walkenbach, J. (2019). Excel 2019 power programming with VBA. Wiley.
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  • Myers, S. (2022). Excel for business analysts: PivotTables and dashboards. Business Publishing.
  • Office Support. (2023). Using slicers in PivotTables. https://support.microsoft.com/en-us/excel

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