Process

1. Audience

Introduction

The GoodNews National Bookstore dashboard has been developed to assist managerial decision-making by providing a consolidated view of sales performance across states, product categories, and sales channels. This section identifies the target audience, the questions they seek to answer, the decisions supported, and the user experience the dashboard aims to deliver.

Audience Description

The primary audience for this dashboard is the Regional Sales Manager. This role involves oversight of both physical store operations and online sales, requiring access to timely information on profitability, promotions, and customer engagement. Secondary audiences include marketing and operations analysts who rely on data-driven insights for campaign design and inventory management.

Key Questions

The audience is expected to ask:

• Which states and cities are leading in sales and profitability?
• How do online and in-store channels compare in contribution to overall sales?
• Which categories are the most profitable, and which underperform?
• How do loyalty program members differ from non-members in purchasing behaviour?
• Do discounts increase sales while maintaining profitability?
• What trends over time can inform marketing campaigns and forecasting?

Supported Actions

The dashboard enables managers to:

• Pinpoint underperforming locations and categories for corrective action.
• Allocate resources between online and in-store channels.
• Evaluate promotions and pricing strategies.
• Enhance loyalty program effectiveness.
• Plan campaigns and stocking decisions based on observed seasonal patterns.

Intended Experience

The dashboard is designed to be intuitive, interactive, and empowering, allowing managers to move beyond static reporting into dynamic exploration. The aim is to create confidence in decision-making and a sense of control over complex data.

2. Data Elements

The dataset (T22025A3.xlsx) contains 220 transactions capturing details of customers, products, financials, and transactions. Each field was reviewed, classified, and its role in analysis identified as shown below:

Field Name	Description	Data Type	Role in Analysis
TransactionID	Unique identifier for each transaction	Categorical (Nominal)	Used to count distinct orders; not visualised directly
OrderDate	Date of purchase	Date/Time	Supports time series analysis and trend identification
CustomerState	State or territory of the customer	Categorical (Geographic)	Mapped to Australian states for regional performance analysis
Store	Bookstore branch	Categorical (Nominal)	Location-level segmentation; supports comparison of outlets
Channel	Online or In-Store purchase	Categorical (Nominal)	Channel comparison in bar charts and trend lines
Membership	Customer loyalty status	Categorical (Binary)	Segmentation of member vs non-member spending behaviour
Category	Book type (e.g., Fiction, Textbook)	Categorical (Nominal)	Category-level performance in bar/treemap charts
Format	Book format (Hardcover, Paperback, eBook)	Categorical (Nominal)	Optional segmentation; filter for deeper analysis
GrossSales	Total sales before discounts	Numeric (Continuous)	Base measure; informs discount and revenue calculations
DiscountAmount / DiscountRate	Discount value and percentage	Numeric (Continuous)	Analysed in scatterplot to test impact on profitability
ShippingFee / GiftWrapFee	Additional charges applied	Numeric (Continuous)	Contribute to overall customer spend; included in Net Sales
NetSalesAfterReturns	Revenue after discounts and returns	Numeric (Continuous)	Core measure for KPIs and all major charts
COGS	Cost of Goods Sold	Numeric (Continuous)	Used with Net Sales to calculate Gross Margin
GrossMargin / GrossMarginAfterReturns	Profit before/after returns	Numeric (Continuous)	Supports profitability analysis; used in KPI and charts
ReturnQty / ReturnAmount	Quantity and value of returns	Numeric (Discrete/Continuous)	Feeds into Return Rate KPI; supports quality insights
PaymentMethod	Method used (Cash, Card, Digital)	Categorical (Nominal)	Optional segmentation; can reveal channel preferences

Calculated Fields

Field Name	Formula	Purpose
Profit Margin %	GrossMarginAfterReturns ÷ NetSalesAfterReturns	Measures profitability as a percentage of sales after returns
Return Rate %	ReturnQty ÷ Quantity	Shows percentage of products returned relative to sold quantity

Classification Summary

Categorical fields: TransactionID, Store, Channel, Membership, CustomerState, CustomerSegment, Category, Format, PaymentMethod.

Numerical fields: GrossSales, NetSalesAfterReturns, GrossMarginAfterReturns, COGS, DiscountAmount, DiscountRate, ShippingFee, GiftWrapFee, ReturnQty, ReturnAmount.

Date fields: OrderDate.

Geographic fields: CustomerState (mapped to Australian states).

Conclusion

By classifying fields, it becomes clear which lend themselves to aggregation, segmentation, and trend analysis, ensuring accurate and meaningful visualisation.

3. The Right Fit

Introduction

Visualisations must be carefully selected to ensure they effectively communicate insights aligned with the needs of the Regional Sales Manager. This section justifies the chart types chosen for the dashboard.

KPI Tiles

Measures: Net Sales, Profit, Profit Margin %, Return Rate %.
Purpose: Provide an immediate, high-level view of organisational health.

Geographic Map

Measure: Net Sales by State.
Why: Maps are intuitive for highlighting regional strengths and weaknesses.

Category Analysis

Measure: Net Sales and Margin by Category.
Why: Bar charts and treemaps clearly compare categories, helping managers prioritise high-margin book types.

Channel Comparison

Measure: Online vs In-Store performance.
Why: Side-by-side bar charts highlight relative contributions and profitability of channels.

Promotions Impact

Measure: Discount % vs Profit Margin %.
Why: Scatterplots reveal whether discounts improve or erode profitability.

Trend Analysis

Measure: Monthly Sales Trends.
Why: Line charts communicate seasonality, spikes, and long-term growth or decline.

Conclusion

Each visualisation type was chosen not only for clarity but for its ability to answer a specific managerial question, ensuring alignment between persona needs and dashboard design.

4. Ethics

Introduction

Data visualisation is not only a technical task but also an ethical responsibility. Insights can influence significant business decisions, so accuracy, fairness, and transparency must be prioritised.

Privacy

The dataset does not include personally identifiable information. Nevertheless, careful handling ensures no individual customer can be traced or profiled unfairly.

Bias and Representation

Care was taken to avoid emphasising certain categories, regions, or channels disproportionately. All data points were treated with equal importance to prevent skewed interpretations.

Transparency in Data Processing

Minor data cleaning steps (e.g., formatting states, correcting nulls, standardising percentages) were documented to ensure transparency and reproducibility.

Responsible Use of Insights

Promotional and pricing insights should not encourage exploitative practices. For example, targeting discounts should be aimed at enhancing customer value, not exploiting vulnerable segments.

Academic Integrity

The dashboard is produced as part of an academic assignment and is not intended for commercial deployment. All visualisations are original and based solely on the dataset provided.

Conclusion

By addressing privacy, bias, transparency, and responsible use, the dashboard maintains integrity and ensures it is a reliable decision-support tool.