Session 05: Sales Analysis Dashboard

Advanced Dashboards | 1

tableau

In the comming two sessions we are going to build solid dashboards first of all to enrich our portfolio and obtain advanced dashboard development skills.

Dashboard Goal

The purpose of this dashboard is to analyze retail business performance using:

Sales trends
Customer behavior
Product performance
Segmentation
Geographic analysis
Time-based purchasing behavior

The dashboard should help business users answer:

Are sales increasing over time?
Which products generate the highest revenue?
Which countries generate the most sales?
Which weekday and time hour generates the most sales?
Revenue vs Product

The dashboard should feel:

Modern
Minimalistic
Executive-level
Professional
Presentation-ready

Dataset Overview

The Tableau workbook contains multiple analytical worksheets focused on retail performance analysis.

InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	1/12/10 8:26	2,55	17850	United Kingdom
536365	71053	WHITE METAL LANTERN	6	1/12/10 8:26	3,39	17850	United Kingdom
536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	1/12/10 8:26	2,75	17850	United Kingdom
536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	1/12/10 8:26	3,39	17850	United Kingdom
536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	1/12/10 8:26	3,39	17850	United Kingdom

Main analytical areas include:

Analysis Area	Business Purpose
KPI Section	High-level business overview
Sales Trend	Revenue trend analysis
Geographic Analysis	Country-level sales analysis
Product Performance	Best-selling product analysis
Time Analysis	Purchase behavior analysis

Dashboard Structure

Recommended Dashboard Size

Element	Recommendation
Dashboard Size	1400 × 900
Layout	Tiled
Device Layout	Desktop
Outer Padding	20–30 px
Space Between Containers	10–20 px

A fixed dashboard size helps maintain alignment consistency and improves presentation quality.

Dashboard Visual Hierarchy

The dashboard should follow a clear visual hierarchy.

The user should first see:

KPI Section
Sales Trends
Geographic Analysis
Product Analysis

The layout should naturally guide the user from summary insights into deeper analytical exploration.

Step 0 | Calculated Fields and Parameters

`Period`

We need this, order to be able effectively analyzie sales by any predifined period first we need to create a Parameter named Period

Once we have this, we can create the following metrics:

Current Period Orders
Current Period AVG Invoice
Current Period Customers
Current Period Sales

Is Canceled

This is useful because some invoice numbers may be stored as numbers, while cancelled invoices contain a letter such as C536379

C means cancelled and we need to filter out them from further calculation, unless we are explicitly analyzing cancelled orders.

LEFT(STR([Invoice No]),1) = "C"

`Valid Sale`

Filter variable to test:

NOT [Is Cancelled]
AND [Quantity] > 0
AND [Unit Price] > 0
AND NOT ISNULL([Customer ID])

We need this in order to clean the table

`Max Order Date`

We need this in order to figure out the latest invoice date.

{ FIXED : MAX(DATE([Invoice Date])) }

Remember the RFM analysis

`Sales`

Step 1 | KPI Section

Purpose of KPI Section

The KPI section provides an instant overview of overall business health.

The KPI cards should be positioned at the top of the dashboard because they establish the business context before deeper analysis begins.

We need to create 16 new calculated fields in meaning that for Sales, Customers, Orders, Avg Invoice we need

Recommended KPIs

KPI	Business Meaning
Total Revenue	Total generated sales
Total Orders	Number of transactions
Total Customers	Unique customer count
AVG Revenue per Customer	Customer value
Growth %	Performance comparison

Recommended KPI Layout

flowchart LR
    A[Revenue KPI]
    B[Orders KPI]
    C[Customers KPI]
    D[AVG Revenue KPI]

    A --> B
    B --> C
    C --> D

Related Calculated Fields

`Current Period Sales`

IF [Period] = "Last Week" THEN
    IF DATETRUNC('week', DATE([Invoice Date])) =
       DATEADD('week', -1, DATETRUNC('week', [Max Order Date]))
    THEN [Sales]
    END

ELSEIF [Period] = "Last Month" THEN
    IF DATETRUNC('month', DATE([Invoice Date])) =
       DATEADD('month', -1, DATETRUNC('month', [Max Order Date]))
    THEN [Sales]
    END

ELSEIF [Period] = "Last Quarter" THEN
    IF DATETRUNC('quarter', DATE([Invoice Date])) =
       DATEADD('quarter', -1, DATETRUNC('quarter', [Max Order Date]))
    THEN [Sales]
    END

ELSEIF [Period] = "Last Year" THEN
    IF DATETRUNC('year', DATE([Invoice Date])) =
       DATEADD('year', -1, DATETRUNC('year', [Max Order Date]))
    THEN [Sales]
    END
END

`Current Period Orders`

INT(IF [Period] = "Last Week" THEN
    IF DATETRUNC('week', DATE([Invoice Date])) =
       DATEADD('week', -1, DATETRUNC('week', [Max Order Date]))
    THEN [Invoice No]
    END

ELSEIF [Period] = "Last Month" THEN
    IF DATETRUNC('month', DATE([Invoice Date])) =
       DATEADD('month', -1, DATETRUNC('month', [Max Order Date]))
    THEN [Invoice No]
    END

ELSEIF [Period] = "Last Quarter" THEN
    IF DATETRUNC('quarter', DATE([Invoice Date])) =
       DATEADD('quarter', -1, DATETRUNC('quarter', [Max Order Date]))
    THEN [Invoice No]
    END

ELSEIF [Period] = "Last Year" THEN
    IF DATETRUNC('year', DATE([Invoice Date])) =
       DATEADD('year', -1, DATETRUNC('year', [Max Order Date]))
    THEN [Invoice No]
    END
END)

`Current Period Customers`

IF [Period] = "Last Week" THEN
    IF DATETRUNC('week', DATE([Invoice Date])) =
       DATEADD('week', -1, DATETRUNC('week', [Max Order Date]))
    THEN [Customer ID]
    END

ELSEIF [Period] = "Last Month" THEN
    IF DATETRUNC('month', DATE([Invoice Date])) =
       DATEADD('month', -1, DATETRUNC('month', [Max Order Date]))
    THEN [Customer ID]
    END

ELSEIF [Period] = "Last Quarter" THEN
    IF DATETRUNC('quarter', DATE([Invoice Date])) =
       DATEADD('quarter', -1, DATETRUNC('quarter', [Max Order Date]))
    THEN [Customer ID]
    END

ELSEIF [Period] = "Last Year" THEN
    IF DATETRUNC('year', DATE([Invoice Date])) =
       DATEADD('year', -1, DATETRUNC('year', [Max Order Date]))
    THEN [Customer ID]
    END
END

`Current Period AVG Invoice`

IF COUNTD([Current Period Orders]) = 0 THEN NULL
ELSE
    SUM([Current Period Sales]) / COUNTD([Current Period Orders])
END

`Previous Period Sales`

IF [Period] = "Last Week" THEN
    IF DATETRUNC('week', DATE([Invoice Date])) =
       DATEADD('week', -2, DATETRUNC('week', [Max Order Date]))
    THEN [Sales]
    END

ELSEIF [Period] = "Last Month" THEN
    IF DATETRUNC('month', DATE([Invoice Date])) =
       DATEADD('month', -2, DATETRUNC('month', [Max Order Date]))
    THEN [Sales]
    END

ELSEIF [Period] = "Last Quarter" THEN
    IF DATETRUNC('quarter', DATE([Invoice Date])) =
       DATEADD('quarter', -2, DATETRUNC('quarter', [Max Order Date]))
    THEN [Sales]
    END

ELSEIF [Period] = "Last Year" THEN
    IF DATETRUNC('year', DATE([Invoice Date])) =
       DATEADD('year', -2, DATETRUNC('year', [Max Order Date]))
    THEN [Sales]
    END
END

`Previous Period Orders`

FLOAT(IF [Period] = "Last Week" THEN
    IF DATETRUNC('week', DATE([Invoice Date])) =
       DATEADD('week', -2, DATETRUNC('week', [Max Order Date]))
    THEN [Invoice No]
    END

ELSEIF [Period] = "Last Month" THEN
    IF DATETRUNC('month', DATE([Invoice Date])) =
       DATEADD('month', -2, DATETRUNC('month', [Max Order Date]))
    THEN [Invoice No]
    END

ELSEIF [Period] = "Last Quarter" THEN
    IF DATETRUNC('quarter', DATE([Invoice Date])) =
       DATEADD('quarter', -2, DATETRUNC('quarter', [Max Order Date]))
    THEN [Invoice No]
    END

ELSEIF [Period] = "Last Year" THEN
    IF DATETRUNC('year', DATE([Invoice Date])) =
       DATEADD('year', -2, DATETRUNC('year', [Max Order Date]))
    THEN [Invoice No]
    END
END)

`Previous Period Customers`

IF [Period] = "Last Week" THEN
    IF DATETRUNC('week', DATE([Invoice Date])) =
       DATEADD('week', -2, DATETRUNC('week', [Max Order Date]))
    THEN [Customer ID]
    END

ELSEIF [Period] = "Last Month" THEN
    IF DATETRUNC('month', DATE([Invoice Date])) =
       DATEADD('month', -2, DATETRUNC('month', [Max Order Date]))
    THEN [Customer ID]
    END

ELSEIF [Period] = "Last Quarter" THEN
    IF DATETRUNC('quarter', DATE([Invoice Date])) =
       DATEADD('quarter', -2, DATETRUNC('quarter', [Max Order Date]))
    THEN [Customer ID]
    END

ELSEIF [Period] = "Last Year" THEN
    IF DATETRUNC('year', DATE([Invoice Date])) =
       DATEADD('year', -2, DATETRUNC('year', [Max Order Date]))
    THEN [Customer ID]
    END
END

`Previous Period AVG Invoice`

IF COUNTD([Previous Period Orders]) = 0 THEN NULL
ELSE
    SUM([Previous Period Sales]) / COUNTD([Previous Period Orders])
END

`Sales PoP %`

IF SUM([Previous Period Sales]) = 0 THEN NULL
ELSE
    (SUM([Current Period Sales]) - SUM([Previous Period Sales])) 
    / SUM([Previous Period Sales])
END

`Orders PoP %`

IF COUNT([Previous Period Orders]) = 0 THEN NULL
ELSE
    (COUNT([Current Period Orders]) - COUNT([Previous Period Orders]))
    / COUNT([Previous Period Orders])
END

`Customers PoP %`

IF COUNTD([Previous Period Customers]) = 0 THEN NULL
ELSE
    (COUNTD([Current Period Customers]) - COUNTD([Previous Period Customers]))
    / COUNTD([Previous Period Customers])
END

`AVG invoice PoP %`

IF [Previous Period AVG Invoice] = 0 THEN NULL
ELSE
(
    [Current Period AVG Invoice]
    -
    [Previous Period AVG Invoice]
)
/ [Previous Period AVG Invoice]
END

`Sales Icon`

IF ZN([Sales PoP % ]) = 0 THEN ""
ELSEIF [Sales PoP % ] > 0 THEN "▲"
ELSEIF [Sales PoP % ] < 0 THEN "▼"
END

`Orders Icon`

IF ZN([Orders PoP %]) = 0 THEN ""
ELSEIF [Orders PoP %] > 0 THEN "▲"
ELSEIF [Orders PoP %] < 0 THEN "▼"
END

`Customer Icon`

IF ZN([Customers PoP %]) = 0 THEN ""
ELSEIF [Customers PoP %] > 0 THEN "▲"
ELSEIF [Customers PoP %] < 0 THEN "▼"
END

`AVG Invoice Icon`

IF ZN([AVG Invoice PoP %]) = 0 THEN ""
ELSEIF [AVG Invoice PoP %] > 0 THEN "▲"
ELSEIF [AVG Invoice PoP %] < 0 THEN "▼"
END

KPI Card Design Rules

Each KPI card should contain:

Large KPI value
KPI subtitle
Growth percentage
Previous period comparison
Directional indicator

The KPI value should always be visually dominant.

Entire View & Format

Choose the Entire View and navigate to the format sellect borders and change everything into None, as shown in the file.

Currency

Right Click on the numbers choose the respective fields and choose the required currency.

KPI Color Rules

Situation	Color
Positive Growth	Green
Negative Growth	Red
Neutral	Gray

Use directional arrows to reinforce performance visually.

KPI Card Size Recommendations

Element	Recommendation
Width	240–280 px
Height	120–150 px
Inner Padding	10–15 px
Space Between Cards	15–20 px

All KPI cards should maintain identical sizing for visual consistency.

Recommended Typography Hierarchy

Element	Font Size	Weight
Dashboard Title	24–32 px	Bold
Section Titles	16–20 px	Semi-bold
KPI Values	28–40 px	Bold
KPI Labels	10–14 px	Regular
Growth %	12–16 px	Semi-bold

Try within the above mentioned sizing.

Recommended Tableau Fonts

Font	Usage
Tableau Book	Default dashboard text
Tableau Medium	KPI values and titles
Arial	Universal readability
Verdana	Small dashboard labels

Step 2 | Sales Trend Analysis

Purpose of Sales Trend Chart

The Sales Trend chart helps users understand how business performance changes over time.

This is usually the primary analytical chart after the KPI section.

The chart should help users identify:

Growth trends
Declines
Seasonality
Performance spikes
Long-term business direction

Sales Trend Design Rules

Do:

Highlight latest point
Use minimal gridlines
Use one dominant color
Keep labels minimal
Maintain smooth visual flow

Avoid:

Too many colors
Heavy formatting
Excessive labels
Too many trend lines

Recommended Sales Trend Styling

Element	Recommendation
Line Width	Medium
Gridlines	Soft
Labels	Minimal
Colors	Single dominant color

Dual Axis Design

Right click on the axis and choose dual axis

Show Marks

Borders

Step 3 | Geographic Analysis

Purpose of Geographic Analysis

The geographic analysis section helps users identify which countries or regions generate the highest revenue.

This analysis helps answer:

Which markets perform best?
Which regions need attention?
Where are business opportunities concentrated?

Geographic Analysis Design Rules

If using maps:

Use soft color gradients
Highlight key regions
Keep labels minimal

If using bars:

Sort descending
Show regional contribution %

Maps should support analysis rather than decoration.

Calculated Fields

`valid_date`

To validate the filter

IF [Period] = "Last Week" THEN
    DATEDIFF('week', [Invoice Date], { MAX([Invoice Date]) }) = 1

ELSEIF [Period] = "Last Month" THEN
    DATEDIFF('month', [Invoice Date], { MAX([Invoice Date]) }) = 1

ELSEIF [Period] = "Last Quarter" THEN
    DATEDIFF('quarter', [Invoice Date], { MAX([Invoice Date]) }) = 1

ELSEIF [Period] = "Last Year" THEN
    DATEDIFF('year', [Invoice Date], { MAX([Invoice Date]) }) = 1
END

`valid_sale`

To validate the filter

NOT [is canceled]
AND [Quantity] > 0
AND [Unit Price] > 0
AND NOT ISNULL([Customer ID])

`Log Sales`

In order to kind of zoom-in the differences there might be a need to use logarithmic scale

LOG(SUM([Sales]))

AS we can see the highest generated sales belong to the United Kingdom

Visualization Steps

Note

Remember the effect of the logarithmic scale is to zoom in in order to see the difference more explicitly

Step 4 | Product Performance Analysis

Purpose of Product Analysis

The product section identifies:

Best-selling products
Low-performing products
Revenue contribution
Product demand

This section helps stakeholders identify opportunities and risks quickly.

Calculated Field

Index

Here we can also create an index column in order follow properly

INDEX()

Product Analysis Design Rules

Do:

Sort descending
Highlight top products
Keep labels readable
Use consistent colors

Avoid:

Too many products displayed
Tiny unreadable labels
Excessive color variations

Table Construction Steps

Filters

Marks

SUM(Sales)

Format

Step 5 | Sales By Weekday, Hour

Purpose of Time Analysis

This section helps identify purchasing behavior patterns.

Possible analyses:

Sales by Weekday
Sales by Hour
Peak purchasing periods

Recommended Chart Types

Use:

Heatmaps
Line Charts
Bar Charts

Calculated Fields

In order to be able to build the graph first wee need to create the following calculated fields:

Order Hour
Weekday

`Order Hour`

DATEPART('hour',[Invoice Date])

`Order Weekday`

DATENAME('weekday',[Invoice Date])

Step 7 | Clustering

Purpose of Clustering

Automatically splits the data into multiple clusters.

Navigate to Analytic \(\rightarrow\) Cluster \(\rightarrow\) drag into cluster

Calculated Fields

Here wee need to add RFM components in order to visualize, for the comming dashboard we will add a new representation for the calculated field.

Product Orders
Product Revenue
Product Quantity Sold

`Product Orders`

{ FIXED [Stock Code]: COUNTD([Invoice No]) }

Explain the decision for applying COUNTD

`Product Quantity Sold`

{ FIXED [Stock Code] : SUM([Quantity]) }

`Product Revenue`

{ FIXED [Stock Code] : SUM([Sales]) }

Visualzing

Drag Product Orders and Product Quantity Sold part into Columns and Rows

right click on the column → dimansion

Video

Checkout the following video

Step 8 | Return Value by Order Month

Calculated Fields

Revenue
Return Value

Note

We have the same as filed named Sales as well, I did so simply to train yourself :)

Revenue

[Quantity] * [Unit Price]

Return Value

IF [Quantity] < 0 THEN ABS([Quantity] * [Unit Price]) END

Final Results

Step 9 | Retrun Value By Product

Similar to the above case

Step 10 | Dashboard Development

Container Structure

flowchart TB

A[Main Vertical Container]

A --> B[KPI Horizontal Container]
A --> C[Middle Analytics Section]
A --> D[Bottom Supporting Charts]

C --> E[Sales Trend]
C --> F[Geographic Analysis]
C --> G[Product Analysis]

Dashboard Design Principles

A professional dashboard should:

Guide user attention
Prioritize important insights
Reduce clutter
Maintain spacing consistency
Support storytelling
Simplify decision-making

The dashboard should feel analytical and structured rather than visually crowded.

Common Dashboard Mistakes

Avoid:

Too many chart types
Excessive colors
Tiny labels
Misaligned containers
Heavy borders
Decorative visualizations
Overcrowded layouts

Every dashboard element should have a clear analytical purpose.

Final Dashboard Checklist

Before publishing:

KPI section visually dominant
Charts aligned properly
Colors consistent
Font hierarchy clear
Filters functional
Tooltips readable
Interactions working
Dashboard spacing balanced
Storytelling understandable
Dashboard presentation-ready

Resources

GitHub

Tableau Course Code Repository for this session is available in the GitHub repository linked above. It includes:

Tableau workbook with all examples
Sample datasets

Articles

Dataviz Design Checklist