Reliance Retail Dashboard in Excel: 12 KPIs, Charts, 4 Types of Analytics and a Forecast (6,000 Rows of Example Data)

By Ravindra Bagale · Last updated: 27 October 2026

About this data. All numbers in this workbook are simulated example data for practice. They are not real Reliance Retail figures and do not come from any Reliance Retail report. Only public facts are used: Reliance Retail is headquartered in Mumbai, Maharashtra, India and works in retail. Money is shown in Indian rupees (₹). Every data sheet in the file starts with the same note.

Download Reliance_Retail_Dashboard.xlsx · See the Power BI version · All 50 company dashboards

1. The finished dashboard

This is what you will build. Yellow cells at the top are drop-down filters. Change one and every tile, chart and analysis sheet updates.

2. The 12 KPIs: formula, meaning and why

A KPI (key performance indicator) is one number that tells you if the business is on track. The value column shows the result with no filter (all 6,000 rows), checked in LibreOffice and pandas. Data[Revenue] means the Revenue column of the Data sheet; in the file it is written as a full range such as Data!$O$5:$O$6004.

3. Every tile and chart explained

The tiles

• Filter band (top, yellow): drop-downs for Region, Format, Channel. Choose "All" to see everything.
• Rows in view: counts how many of the 6,000 rows match the filters, so you always know how much data a number is based on.
• KPI tiles: each one has an icon and a label, one big number, and one small line under it (ΔPY, a target or a meaning). Tiles turn green or red with conditional formatting when a target is met or missed.
• Insight strip: three live sentences built with INDEX, MATCH and TEXT, such as the top region and whether revenue is up or down. The title states the finding, not just the topic.
• Top 5 table: the five biggest formats, ranked with LARGE + INDEX + MATCH, so it re-sorts when you filter.

The charts

All charts read from small helper tables on the Calc sheet, not from the raw data. That keeps the charts fast and lets them follow the filters.

4. The four types of analytics

4.1 Descriptive: what happened?

Why: before you explain anything, you need the facts. What: a pivot-style grid of revenue by format and region, revenue by year and channel, and a top-5 state list. How: one SUMIFS per cell, with the row and column headers as criteria, so it also follows the filters.

4.2 Diagnostic: why did it happen?

Why: total growth was +6.8% on the example data, but a total hides who caused it. What: PY 2025, AC 2026, ΔPY, ΔPY% and "share of change" for each region, format and channel, plus CORREL checks. How: ΔPY = AC − PY; share of change = item ΔPY ÷ total ΔPY. Green = good for the business, red = bad. CORREL near 0 means a weak link; above 0.7 means a strong link.

4.3 Predictive: what will happen next?

Why: plans need a number for next year. What: 24 months of actual revenue, a 3-month moving average, and a 12-month forecast for 2027. How: =FORECAST.LINEAR(t, actuals, t_values), checked with =TREND(); SLOPE gives the change per month and RSQ shows how well the straight line fits. On the example data the trend gives ₹208.33M for Dec 2027. A low R² means: use the forecast as a rough guide only.

4.4 Prescriptive: what should we do?

Why: a dashboard is only useful if it leads to a decision. What: yellow what-if inputs (price, discount, cost, volume), the new profit and margin, a Goal Seek setup, and an action table. How: change a yellow cell and the result updates. Goal Seek (Data › What-If Analysis › Goal Seek) finds the price change for the target margin; on the example data that is +0.9%. The action table uses IF rules: margin below target → fix price or discount; ΔPY% below 0 → find the cause; ΔPY% 10% or more with a good margin → invest to grow; otherwise keep steady.

5. Rebuild it from a blank file

Follow these 15 steps in order. When you finish, your file will match the download.

Step 1. Import the data

Open a blank workbook. Data › Get Data › From Text/CSV and pick the Sales file (or copy the Data sheet from the download). Put a title in row 1 and the About note in row 2, so nobody mistakes the example data for real Reliance Retail numbers. Headers go in row 4.

Step 2. Make it a Table

Click inside the data and press Ctrl+T, tick 'My table has headers', and name it Data (Table Design › Table Name). A Table grows by itself when you add rows, and formulas fill down.

Step 3. Add the drop-down lists

Make a Lists sheet with one column per filter (Region, format, Channel), each starting with 'All'. On the Dashboard select the filter cell › Data › Data Validation › List › point to the list.

Step 4. Add the Inc helper column

Last column of Data: Inc = 1 when the row matches every filter. Example: =--AND(OR(RegionFilter="All",[@Region]=RegionFilter), OR(ProductFilter="All",[@Product]=ProductFilter), OR(ChannelFilter="All",[@Channel]=ChannelFilter)). Every formula then adds ,Data[Inc],1 so it follows the filters.

Step 5. Write the Rules sheet

One line per column: what it means and what values are allowed. This keeps new rows clean and tells a new user how to add data.

Step 6. Build the Calc helper tables

Make small tables for month, region, format and channel with SUMIFS. Example: =SUMIFS(Data[Revenue],Data[Inc],1,Data[Month],A5,Data[Year],2026). Charts read from these.

Step 7. Write the KPI formulas

Type each KPI formula from the table in section 2 into its tile. Use IFERROR(...,0) around every division. The Formulas used sheet lists every formula in the file.

Step 8. Cross-check with a PivotTable

Insert › PivotTable from the Data table, format in Rows, Region in Columns, Revenue in Values. It must match the Descriptive grid. If it does not, a SUMIFS criterion is wrong.

Screenshot: see section 4 above (Descriptive grid; a PivotTable on the Data table must give the same totals.).

Step 9. Insert the charts

Select a Calc table › Insert › Recommended Charts. Line for months (AC dark, PY light grey), clustered column + line on the secondary axis for region and margin, one donut for the mix, sorted bars for channels. Turn off 3D, gridlines and chart borders. Write the finding in the chart title.

Screenshot: see Figure 1 above (Dashboard: chart cards on a dark canvas.).

Step 10. Make the tiles and sparklines

Merge a block of cells for each tile: label in small text, number in 22–26 pt bold, the ΔPY line in 9 pt. To add a sparkline: select the cell under the number › Insert › Sparklines › Line › Data Range = the 12 month values on Calc. Use Home › Conditional Formatting for green and red.

Step 11. Add real slicers (optional)

Formulas do not listen to slicers, so this file uses drop-downs. If you prefer slicers: build the charts from PivotTables, then PivotTable Analyze › Insert Slicer › Region, and Report Connections to link one slicer to all pivots.

Step 12. Forecast

On the Predictive sheet list t = 1..36, the 24 actual months, then =FORECAST.LINEAR(C30,$D$6:$D$29,$C$6:$C$29) for months 25–36. Or select the month dates and values › Data › Forecast Sheet for a quick chart with a confidence band.

Screenshot: see section 4 above (Predictive sheet: actuals, moving average and the 2027 forecast.).

Step 13. What-if and Goal Seek

Put the inputs in yellow cells. New revenue = (Gross × (1+price) − Discount × (1+discount)) × (1+volume). Then Data › What-If Analysis › Goal Seek: Set cell = new margin, To value = target, By changing = price change.

Screenshot: see section 4 above (Prescriptive sheet: inputs, Goal Seek and actions.).

Step 14. Layout and theme

View › untick Gridlines. Fill the canvas with the background colour, tiles with the tile colour, and keep to one font (Segoe UI or Calibri) in three sizes. Use the hex codes in the table below (Home › Fill Color › More Colors › Custom › Hex).

Step 15. Protect and share

Unlock the filter and yellow input cells (Format Cells › Protection › untick Locked), then Review › Protect Sheet. Set Dashboard as the active sheet before saving. Share the .xlsx, or File › Export › PDF for people who only need to read it.

Theme colours (hex codes)

6. How the numbers were checked

Every KPI was recalculated in LibreOffice and compared with the same sum done in Python pandas on the raw rows: 23 of 23 checks matched, with 0 formula errors. The Power BI DAX measures were checked the same way.

Quick revision cards

Save or share these cards. Each card stands on its own with a small example, and the QR code opens this lesson.

7. FAQ

Is this real Reliance Retail data?

No. It is simulated example data for practice. Only public facts such as the headquarters (Mumbai, Maharashtra, India) and the industry are real.

Why SUMIFS and not a PivotTable?

SUMIFS updates instantly, follows the drop-downs through the Inc column, and lets you place each number exactly where you want on the dashboard. A PivotTable is a good cross-check.

Can I add my own rows?

Yes. Type under the last row of the Data table. The table grows, Inc fills down, and every KPI updates.

Does it work in Google Sheets or LibreOffice?

Yes for the formulas and charts. FORECAST.LINEAR is FORECAST in some older versions, and the chart styling may look a little different.

8. Quick quiz

Six questions on this page. Sign in to save your score.

Next: build the same Reliance Retail dashboard in Power BI with DAX or go back to all 50 company dashboards.