Sales Dashboard Examples: How to Build One in Power BI

A sales dashboard is a screen that tells you, at a glance, whether the business is on track. It usually answers three things: how much has been sold against target, what is in the pipeline for the coming months, and where deals are being lost.

Power BI has a naming quirk. What the tool calls a dashboard is a single page in the service where you pin visuals taken from one or more reports. What almost everyone calls a "Power BI sales dashboard" is really a report: one or more pages with filters, built on a data model. This guide is about that, because it is what is almost always needed.

Whether a sales dashboard works is decided underneath the screen: a tidy data model and well-defined measures. So this guide starts there and leaves the charts for the end.

A star schema separates what happens (facts) from what describes it (dimensions). In a typical sales dashboard it looks like this:

• Orders: one row per order line, with date, account, rep, product and amount.
• Opportunities: one row per CRM opportunity, with created date, expected close date, stage, status, probability and amount.
• Targets: one row per rep and month, with the amount they have to sell.
• Accounts, Reps, Products and Calendar: the dimensions, one row per item.

Relationships run from dimension to fact, one-to-many, filtering in a single direction. When someone filters by region on the reps table, the filter flows to orders, opportunities and targets at once, and the three figures agree with each other.

The most common mistake is the opposite: one huge table exported from the CRM with everything mixed in. It works for a few months, and then every new measure is a patch.

DAX time intelligence functions —previous year, year to date, same period— need a continuous date table: every day, none skipped, from the first date in your data to the last. A simple way to create one is a calculated table:

Calendar = CALENDAR(DATE(2024, 1, 1), DATE(2026, 12, 31))

Then you add year, month and quarter columns, mark it as a date table (in Power BI Desktop, "Mark as date table") and relate it to the date in each fact table. If targets are monthly, relate them on the first day of each month.

Dashboard figures belong in measures, not calculated columns: a measure is evaluated against whatever filters are active (rep, month, region), while a calculated column is fixed row by row. These five cover almost any sales dashboard. They use the tables from the example above.

Revenue

Revenue = SUM(Orders[Amount])

Revenue last year

Revenue LY = CALCULATE([Revenue], SAMEPERIODLASTYEAR(Calendar[Date]))

It works with any date filter: if you are looking at March this year, it returns March last year.

Attainment

% of target = DIVIDE([Revenue], SUM(Targets[Amount]))

DIVIDE avoids an error when a rep has no target that month: it returns blank instead of dividing by zero.

Weighted pipeline

Weighted pipeline = CALCULATE(SUMX(Opportunities, Opportunities[Amount] * Opportunities[Probability]), Opportunities[Status] = "Open")

It adds up the amount of each open opportunity multiplied by its probability. Probability has to be stored as a number between 0 and 1.

Win rate

Win rate = DIVIDE(CALCULATE(COUNTROWS(Opportunities), Opportunities[Status] = "Won"), CALCULATE(COUNTROWS(Opportunities), Opportunities[Status] IN {"Won", "Lost"}))

It counts only decided opportunities. If open ones go into the denominator, the rate drops every time the team opens new deals.

For the full list of sales KPIs with their formulas, see sales dashboard KPIs.

These are typical examples: what each dashboard usually contains depending on who uses it. They are not client case studies or screenshots of real projects.

1. Sales leadership dashboard

For the sales director's weekly meeting. At the top, four figures: revenue this month against target, revenue year to date against last year, weighted pipeline for the quarter and win rate. Below, the monthly trend of revenue against target and a table by team or region with the same figures. No activity metrics: those go in another report.

2. Pipeline and forecast

To know whether the quarter will land before it ends. Pipeline by stage, pipeline by expected close month, pipeline coverage against what is left of the target, and each rep's forecast compared with the weighted one. A separate page lists the deals that have been stuck in the same stage the longest.

3. Rep dashboard

What each rep opens on Monday. Their revenue against their target, their open opportunities sorted by expected close date, the ones nobody has touched for weeks and their accounts that are buying less. With row-level security, one report serves the whole team and each person sees only their own.

4. CRM sales dashboard: from marketing to sales

So marketing and sales look at the same funnel. Leads by channel, lead-to-opportunity conversion, won deals by source channel, spend by channel and cost per customer. It requires the CRM to store where each lead came from; otherwise the funnel stops halfway. For the cost side, see how to calculate true CAC.

5. Balanced scorecard

The balanced scorecard was proposed by Robert Kaplan and David Norton in 1992, in the Harvard Business Review. It groups indicators into four perspectives, which for a B2B sales team could look like this:

• Financial: revenue, gross margin and customer acquisition cost.
• Customer: retention, revenue from key accounts and satisfaction.
• Internal processes: sales cycle, stage conversion and lead response time.
• Learning and growth: months a new rep takes to reach target, training and real CRM usage.

In Power BI it works well as one page per perspective plus a summary page with one indicator and its target for each.