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power-bi-salespulse-360-dashboard

by reason-machinesData & Databases

Master the SalesPulse 360 Power BI retail analytics dashboard for profit analysis, regional insights, and predictive sales forecasting

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Works with every agent the skills CLI supports.

npx skills add https://github.com/reason-machines/data-skills --skill power-bi-salespulse-360-dashboard
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads power-bi-salespulse-360-dashboard automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Power BI SalesPulse 360 Dashboard Skill

Skill by ara.so — Data Skills collection.

Overview

SalesPulse 360 is a comprehensive Power BI dashboard for retail analytics, designed to transform the Global Superstore dataset into actionable business intelligence. This dashboard provides:

  • Multi-dimensional sales analysis across regions, categories, and time periods
  • Profit architecture mapping with heatmaps and margin analysis
  • Predictive forecasting for 12-month sales and profit trends
  • Real-time alerts for profit margin drops and anomaly detection
  • Multilingual support with automatic localization
  • Responsive design for cross-device viewing

The project is built entirely in Power BI Desktop using DAX (Data Analysis Expressions) and Power Query M for data transformation.

Installation & Setup

Prerequisites

  • Power BI Desktop version 2.120 or higher (download here)
  • Global Superstore dataset (included in repository as CSV)
  • Minimum 8GB RAM for smooth performance

Initial Setup

  1. Clone the repository:
git clone https://github.com/MahbubNibir/power-bi-retail-analytics-viz.git
cd power-bi-retail-analytics-viz
  1. Extract the dataset:
# Extract the Global Superstore CSV from the data archive
unzip data/GlobalSuperstore.zip -d data/

  1. Open the dashboard:

    • Launch Power BI Desktop
    • Open SalesPulse360.pbix
    • Wait for initial data transformation (2-3 minutes)

  2. Verify data connection:

    • Navigate to Home > Transform Data
    • Check that all queries are loading successfully
    • Close the Power Query Editor

Project Structure

power-bi-retail-analytics-viz/
├── SalesPulse360.pbix          # Main Power BI dashboard file
├── data/
│   ├── GlobalSuperstore.csv    # Source dataset (2011-2015)
│   └── ConfigThresholds.csv    # Alert threshold configuration
├── scripts/
│   ├── DataCleaningSteps.txt   # Power Query M transformation log
│   └── DAXMeasures.txt         # All DAX formulas used
├── preview.svg                 # Dashboard preview image
└── README.md

Core Components

1. Data Model Architecture

The dashboard uses a star schema with the following structure:

Fact Table:

  • Sales (Orders, Sales Amount, Profit, Discount, Quantity, Shipping Cost)

Dimension Tables:

  • Calendar (Date, Year, Quarter, Month, Week)
  • Geography (Region, Country, State, City, Postal Code)
  • Product (Category, Sub-Category, Product Name)
  • Customer (Customer ID, Segment)

Relationships:

  • Sales[Order Date] → Calendar[Date] (Many-to-One)
  • Sales[Postal Code] → Geography[Postal Code] (Many-to-One)
  • Sales[Product ID] → Product[Product ID] (Many-to-One)
  • Sales[Customer ID] → Customer[Customer ID] (Many-to-One)

2. Key DAX Measures

Total Sales

Total Sales = SUM(Sales[Sales Amount])

Total Profit

Total Profit = SUM(Sales[Profit])

Profit Margin %

Profit Margin % = 
DIVIDE(
    [Total Profit],
    [Total Sales],
    0
) * 100

Sales vs Previous Period

Sales vs Previous Period = 
VAR CurrentSales = [Total Sales]
VAR PreviousSales = 
    CALCULATE(
        [Total Sales],
        DATEADD(Calendar[Date], -1, YEAR)
    )
RETURN
    DIVIDE(
        CurrentSales - PreviousSales,
        PreviousSales,
        0
    ) * 100

Moving Annual Total (MAT)

MAT Sales = 
CALCULATE(
    [Total Sales],
    DATESINPERIOD(
        Calendar[Date],
        LASTDATE(Calendar[Date]),
        -12,
        MONTH
    )
)

Profit Alert Flag

Profit Alert = 
VAR ProfitMarginThreshold = 15
VAR CurrentMargin = [Profit Margin %]
RETURN
    IF(
        CurrentMargin < ProfitMarginThreshold,
        "🔴 Alert",
        IF(
            CurrentMargin < ProfitMarginThreshold + 5,
            "🟡 Caution",
            "🟢 Healthy"
        )
    )

12-Month Sales Forecast

Sales Forecast = 
VAR HistoricalData = 
    CALCULATETABLE(
        ADDCOLUMNS(
            Calendar,
            "Sales", [Total Sales]
        ),
        Calendar[Date] <= TODAY()
    )
RETURN
    // Power BI automatically applies forecasting algorithm
    // when this measure is used in a forecast visualization
    [Total Sales]

3. Power Query M Transformations

Clean Column Names

let
    Source = Csv.Document(File.Contents("data/GlobalSuperstore.csv")),
    PromotedHeaders = Table.PromoteHeaders(Source),
    CleanedNames = Table.TransformColumnNames(
        PromotedHeaders,
        each Text.Trim(_),
        each Text.Replace(_, " ", "")
    )
in
    CleanedNames

Parse Dates

let
    Source = #"Previous Step",
    ParsedDates = Table.TransformColumnTypes(
        Source,
        {
            {"OrderDate", type date},
            {"ShipDate", type date}
        }
    ),
    AddedCalendarColumns = Table.AddColumn(
        ParsedDates,
        "Year",
        each Date.Year([OrderDate]),
        Int64.Type
    )
in
    AddedCalendarColumns

Handle Missing Values

let
    Source = #"Previous Step",
    ReplacedNulls = Table.ReplaceValue(
        Source,
        null,
        0,
        Replacer.ReplaceValue,
        {"Discount", "Profit"}
    )
in
    ReplacedNulls

Create Geography Hierarchy

let
    Source = #"Previous Step",
    GroupedGeo = Table.Group(
        Source,
        {"Region", "Country", "State", "City", "PostalCode"},
        {{"Count", each Table.RowCount(_), Int64.Type}}
    ),
    SortedHierarchy = Table.Sort(
        GroupedGeo,
        {{"Region", Order.Ascending}, {"Country", Order.Ascending}}
    )
in
    SortedHierarchy

Configuration

Alert Thresholds

Modify alert thresholds by editing the ConfigThresholds.csv file or creating a DAX measure:

Profit Margin Threshold = 15  // %
Return Rate Threshold = 8     // %
Rolling Average Period = 90   // days

Refresh Schedule

Configure data refresh in Power BI Desktop:

  1. File > Options and Settings > Options
  2. Navigate to Data Load
  3. Set refresh interval (recommended: daily at 02:00 UTC)

For Power BI Service deployment:

  1. Publish dashboard to workspace
  2. Go to Settings > Scheduled refresh
  3. Set refresh frequency and time
  4. Configure credentials for data source

Localization

Add language support by creating a translation table:

Translation Table = 
DATATABLE(
    "Key", STRING,
    "English", STRING,
    "Spanish", STRING,
    "French", STRING,
    {
        {"Total Sales", "Total Sales", "Ventas Totales", "Ventes Totales"},
        {"Profit", "Profit", "Beneficio", "Profit"},
        {"Region", "Region", "Región", "Région"}
    }
)

Then use in measures:

Localized Label = 
VAR UserLanguage = SELECTEDVALUE(Settings[Language])
RETURN
    SWITCH(
        UserLanguage,
        "Spanish", LOOKUPVALUE(Translation[Spanish], Translation[Key], "Total Sales"),
        "French", LOOKUPVALUE(Translation[French], Translation[Key], "Total Sales"),
        LOOKUPVALUE(Translation[English], Translation[Key], "Total Sales")
    )

Common Patterns

Pattern 1: Regional Drill-Down Analysis

Create a hierarchy for seamless drill-down:

// Create hierarchy in Geography table
Geography Hierarchy = 
    PATH(
        Geography[Region],
        Geography[Country],
        Geography[State],
        Geography[City]
    )

Add a matrix visual:

  • Rows: Geography Hierarchy
  • Values: [Total Sales], [Total Profit], [Profit Margin %]
  • Enable drill-down in visual settings

Pattern 2: Time Intelligence Comparisons

// Year-over-Year Growth
YoY Sales Growth = 
VAR CurrentYearSales = [Total Sales]
VAR PreviousYearSales = 
    CALCULATE(
        [Total Sales],
        SAMEPERIODLASTYEAR(Calendar[Date])
    )
RETURN
    DIVIDE(
        CurrentYearSales - PreviousYearSales,
        PreviousYearSales,
        0
    )

// Quarter-over-Quarter Growth
QoQ Sales Growth = 
VAR CurrentQuarterSales = [Total Sales]
VAR PreviousQuarterSales = 
    CALCULATE(
        [Total Sales],
        DATEADD(Calendar[Date], -1, QUARTER)
    )
RETURN
    DIVIDE(
        CurrentQuarterSales - PreviousQuarterSales,
        PreviousQuarterSales,
        0
    )

Pattern 3: Dynamic Top N Analysis

// Top 10 Products by Sales
Top 10 Products = 
VAR CurrentProductSales = [Total Sales]
VAR ProductRank = 
    RANKX(
        ALL(Product[ProductName]),
        [Total Sales],
        ,
        DESC,
        Dense
    )
RETURN
    IF(ProductRank <= 10, CurrentProductSales, BLANK())

Use in a bar chart with Product Name on axis and this measure as value.

Pattern 4: Exception Highlighting

// Flag underperforming regions
Region Performance Flag = 
VAR RegionMargin = [Profit Margin %]
VAR AvgMargin = 
    CALCULATE(
        [Profit Margin %],
        ALL(Geography[Region])
    )
VAR StdDev = 
    STDEVX.P(
        ALL(Geography[Region]),
        [Profit Margin %]
    )
RETURN
    IF(
        RegionMargin < AvgMargin - StdDev,
        "Underperforming",
        IF(
            RegionMargin > AvgMargin + StdDev,
            "Outperforming",
            "Normal"
        )
    )

Apply conditional formatting in table visuals based on this flag.

Pattern 5: Customer Segmentation

// RFM Score (Recency, Frequency, Monetary)
Customer RFM Score = 
VAR Recency = 
    DATEDIFF(
        MAX(Sales[OrderDate]),
        TODAY(),
        DAY
    )
VAR Frequency = COUNTROWS(Sales)
VAR Monetary = [Total Sales]
VAR RecencyScore = 
    SWITCH(
        TRUE(),
        Recency <= 30, 5,
        Recency <= 90, 4,
        Recency <= 180, 3,
        Recency <= 365, 2,
        1
    )
VAR FrequencyScore = 
    SWITCH(
        TRUE(),
        Frequency >= 50, 5,
        Frequency >= 25, 4,
        Frequency >= 10, 3,
        Frequency >= 5, 2,
        1
    )
VAR MonetaryScore = 
    SWITCH(
        TRUE(),
        Monetary >= 10000, 5,
        Monetary >= 5000, 4,
        Monetary >= 2000, 3,
        Monetary >= 1000, 2,
        1
    )
RETURN
    RecencyScore + FrequencyScore + MonetaryScore

Publishing & Sharing

Publish to Power BI Service

  1. In Power BI Desktop:

    • Click Home > Publish
    • Select target workspace
    • Wait for upload completion

  2. Configure access:

    • Open workspace in Power BI Service
    • Click Access button
    • Add users/groups with appropriate roles:
      • Viewer: Read-only access
      • Contributor: Can edit and publish
      • Admin: Full control

  3. Embed in website (optional):

<iframe 
    width="800" 
    height="600" 
    src="https://app.powerbi.com/view?r=YOUR_REPORT_ID" 
    frameborder="0" 
    allowFullScreen="true">
</iframe>

Row-Level Security (RLS)

Implement region-based data filtering:

  1. In Power BI Desktop, go to Modeling > Manage Roles

  2. Create role:

// Role: RegionalManager
[Region] = USERNAME()

For email-based filtering:

// Role: EmailBasedAccess
VAR UserEmail = USERPRINCIPALNAME()
VAR UserRegion = 
    LOOKUPVALUE(
        UserMapping[Region],
        UserMapping[Email],
        UserEmail
    )
RETURN
    [Region] = UserRegion

  1. Test role in Desktop:

    • Modeling > View as Roles
    • Select role and test

  2. Assign roles in Service:

    • Open dataset settings
    • Navigate to Security
    • Add users to roles

Troubleshooting

Issue: Data Refresh Fails

Symptoms: Scheduled refresh shows error in Power BI Service

Solutions:

  1. Check data source credentials:

    • Dataset settings > Data source credentials
    • Re-enter credentials if expired

  2. Verify file paths:

    • In Power Query Editor, check File.Contents() paths
    • Use relative paths or data gateway for cloud refresh

  3. Enable data gateway (for on-premises data):

// Update source to use gateway
let
    Source = Sql.Database(
        "SERVER_NAME",
        "DATABASE_NAME",
        [Query="SELECT * FROM Sales"]
    )
in
    Source

Issue: Slow Dashboard Performance

Symptoms: Visuals take >5 seconds to load

Solutions:

  1. Reduce data model size:
// Remove unnecessary columns in Power Query
let
    Source = #"Previous Step",
    RemovedColumns = Table.RemoveColumns(
        Source,
        {"Column1", "Column2"}
    )
in
    RemovedColumns
  1. Optimize DAX measures:
// BAD: Uses ALL() unnecessarily
Slow Measure = 
CALCULATE(
    [Total Sales],
    ALL(Geography)
)

// GOOD: Uses ALLSELECTED() for context
Fast Measure = 
CALCULATE(
    [Total Sales],
    ALLSELECTED(Geography)
)

  1. Use aggregations:

    • Create pre-aggregated tables in Power Query
    • Enable query folding when possible

  2. Limit visual elements:

    • Reduce number of visuals per page to <15
    • Use bookmarks for alternative views

Issue: Forecast Not Displaying

Symptoms: Forecast line missing from visualization

Solutions:

  1. Enable analytics pane:

    • Select line chart visual
    • Click Analytics pane (graph icon)
    • Add Forecast
    • Set forecast length (e.g., 12 months)

  2. Ensure continuous date axis:

// Create full date range in Power Query
let
    StartDate = #date(2011, 1, 1),
    EndDate = Date.AddYears(Date.From(DateTime.LocalNow()), 1),
    DateRange = List.Dates(
        StartDate,
        Duration.Days(EndDate - StartDate),
        #duration(1, 0, 0, 0)
    ),
    ConvertedToTable = Table.FromList(
        DateRange,
        Splitter.SplitByNothing(),
        {"Date"}
    )
in
    ConvertedToTable

Issue: Incorrect Geographic Mapping

Symptoms: Map visual shows wrong locations

Solutions:

  1. Set data category:

    • Select geography column
    • Column tools > Data category
    • Choose appropriate type (City, State, Country, etc.)

  2. Standardize location names:

// Clean and standardize state names
let
    Source = #"Previous Step",
    StandardizedStates = Table.ReplaceValue(
        Source,
        "CA",
        "California",
        Replacer.ReplaceText,
        {"State"}
    )
in
    StandardizedStates

Issue: Alert Flags Not Updating

Symptoms: Profit Alert measure shows outdated status

Solutions:

  1. Force refresh of calculated columns:

    • Modify measure slightly (add comment)
    • Save and refresh data

  2. Check threshold configuration:

// Debug: Show threshold value
Debug Threshold = 
VAR Threshold = 15
VAR CurrentMargin = [Profit Margin %]
RETURN
    "Threshold: " & Threshold & " | Current: " & CurrentMargin
  1. Verify filter context:
// Add context awareness
Profit Alert Fixed = 
VAR ProfitMarginThreshold = 15
VAR CurrentMargin = [Profit Margin %]
VAR HasData = NOT(ISBLANK(CurrentMargin))
RETURN
    IF(
        HasData,
        IF(
            CurrentMargin < ProfitMarginThreshold,
            "🔴 Alert",
            "🟢 Healthy"
        ),
        BLANK()
    )

Advanced Techniques

Dynamic Measure Selection

Create a slicer that switches between different metrics:

  1. Create parameter table:
Measure Selector = 
DATATABLE(
    "Metric", STRING,
    "MeasureID", INTEGER,
    {
        {"Total Sales", 1},
        {"Total Profit", 2},
        {"Profit Margin %", 3}
    }
)
  1. Create dynamic measure:
Selected Metric = 
VAR SelectedID = SELECTEDVALUE('Measure Selector'[MeasureID])
RETURN
    SWITCH(
        SelectedID,
        1, [Total Sales],
        2, [Total Profit],
        3, [Profit Margin %],
        BLANK()
    )
  1. Add slicer with Metric column and use [Selected Metric] in visuals

Custom Tooltips

Create a detailed tooltip page:

  1. Create new page named "Tooltip - Product Detail"
  2. Set page as tooltip: Page settings > Tooltip > ON
  3. Add visuals showing product-specific metrics
  4. Apply to main visual: Format > Tooltip > Report page > Tooltip - Product Detail

Best Practices

  1. Use variables in DAX: Improves readability and performance
  2. Avoid calculated columns when possible: Use measures instead
  3. Create a date table: Essential for time intelligence
  4. Document complex measures: Add comments explaining logic
  5. Test with different date ranges: Ensure measures work across all periods
  6. Use SELECTEDVALUE() for parameters: Handles multi-selection gracefully
  7. Apply visual level filters: Reduces data volume in memory
  8. Regular model optimization: Use Performance Analyzer to identify slow visuals

Resources

This skill covers the complete workflow for implementing, customizing, and deploying the SalesPulse 360 dashboard for retail analytics and business intelligence.

FAQ

What does the power-bi-salespulse-360-dashboard skill do?

Master the SalesPulse 360 Power BI retail analytics dashboard for profit analysis, regional insights, and predictive sales forecasting

How do I install power-bi-salespulse-360-dashboard?

Run npx skills add https://github.com/reason-machines/data-skills --skill power-bi-salespulse-360-dashboard in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/power-bi-salespulse-360-dashboard/ (personal) or .claude/skills/power-bi-salespulse-360-dashboard/ (project).

Which AI agents can use power-bi-salespulse-360-dashboard?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is power-bi-salespulse-360-dashboard safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains power-bi-salespulse-360-dashboard?

It is published by reason-machines in reason-machines/data-skills. It has 656 recorded installs.

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