A HubSpot deal conversion and win rate dashboard in Power BI helps sales managers understand how effectively opportunities move through the sales process. Instead of looking only at pipeline value, the dashboard shows how many deals reach a closed outcome, how many are won or lost, and where conversion performance differs by owner, pipeline or period.
In this guide, we will build the dashboard using the reporting-ready HubSpot data supplied through Connectorly. We will use fields from the hubspot deals, hubspot deal_pipelines, hubspot owners and hubspot dates tables, together with practical DAX measures and recommended Power BI visuals.
We will also clarify an important reporting decision: win rate should normally compare closed-won deals with all deals that reached a closed outcome. Dividing won deals by every deal—including opportunities that remain open—measures something different and can make recent periods appear weaker than they really are.
If you need a broader introduction to the available data, connection options and reporting architecture, start with our complete guide to HubSpot reporting in Power BI.
What Should a HubSpot Deal Conversion Dashboard Show?
A useful conversion dashboard should explain both the final sales outcome and the context behind it. A single win-rate percentage is rarely enough because the result can change significantly by pipeline, owner, deal type, reporting period and the way the calculation is defined.
The dashboard should include:
- Deals created: the number of new opportunities added during the selected period.
- Closed deals: all opportunities that reached either a won or lost outcome.
- Closed-won deals: opportunities completed successfully.
- Closed-lost deals: opportunities that reached a closed outcome without being won.
- Win rate: closed-won deals divided by all closed deals.
- Average days to close: the typical time taken for successful deals to move from creation to a won outcome.
- Conversion by owner: a comparison of outcomes across sales representatives.
- Conversion by pipeline and stage: a view of how performance differs across sales processes.
- Closed-lost reasons: the most common explanations recorded for unsuccessful deals.
- Monthly trends: whether deal volume and win rate are improving or declining over time.
These measures should be presented together. For example, an owner may have a high win rate but only a small number of closed deals. Another owner may close more revenue while working with a more difficult pipeline or customer segment. Showing the underlying deal counts prevents the headline percentage from becoming misleading.
What You Need Before You Start
To follow this guide, you will need an active Connectorly for HubSpot and Power BI connection, access to Power BI Desktop and permission to use the HubSpot data included in your Connectorly database.
The main tables used in this dashboard are:
hubspot dealsfor deal outcomes, amounts, creation dates, closed-won dates, owners, pipelines and lost reasons.hubspot deal_pipelinesfor pipeline and stage definitions, sort order, closed-stage status and probability.hubspot ownersfor the people responsible for deals.hubspot datesfor monthly, quarterly and yearly reporting.
If you are using a Connectorly Power BI template, some of the required relationships and formatting may already be present. You should still review them before creating measures because conversion calculations depend on the correct deal, pipeline, owner and date context.
It is also worth checking how your organisation uses HubSpot before building the dashboard. Confirm which pipelines are in scope, whether test or archived deals should be excluded, whether closed-lost reasons are recorded consistently, and whether reporting should use the deal currency or the Connectorly reporting-currency fields ending in (RC).
Step 1: Check the Deal Data and Relationships
Before creating any measures, open Model view in Power BI and check that the deal table can be filtered correctly by pipeline, owner and date. Connectorly templates may already include these relationships, so review the existing model before adding anything new.
The principal relationships for this dashboard are:
hubspot deal_pipelines[Pipeline Stage ID]tohubspot deals[Pipeline Stage ID]hubspot owners[Owner ID]tohubspot deals[Owner ID]hubspot dates[Date]tohubspot deals[Created Date]
The relationship using Created Date is normally the active date relationship because it supports analysis of when opportunities entered the pipeline. However, a conversion dashboard also needs to analyse when deals reached a closed outcome.
For that purpose, create or verify an inactive relationship between:
hubspot dates[Date]andhubspot deals[Forecast Close Date]
Despite its Connectorly display name, Forecast Close Date is derived from HubSpot’s deal close-date property. It can therefore provide the date context for both won and lost closed deals. The separate Closed Won Date field applies specifically to successful deals.
Use the Boolean fields Is Closed and Is Closed Won when defining deal outcomes. Do not identify won and lost deals by searching for particular stage names because every HubSpot organisation can use different pipeline labels.
Also decide whether archived records should be excluded. In most operational dashboards, measures should filter Archived to FALSE(). If several HubSpot organisations are grouped in Connectorly, retain Connection Name as a slicer so users can review each organisation separately as well as the combined result.
Step 2: Create the Core Deal Conversion Measures
Create the following measures in Power BI. Each count uses Deal ID rather than counting table rows, while archived records are excluded from the analysis.
Total Deals
Total Deals =
CALCULATE(
DISTINCTCOUNT('hubspot deals'[Deal ID]),
'hubspot deals'[Archived] = FALSE()
)This measure counts every non-archived deal in the current filter context.
Closed Deals
Closed Deals =
CALCULATE(
[Total Deals],
'hubspot deals'[Is Closed] = TRUE()
)Closed deals include both successful and unsuccessful outcomes.
Closed-Won Deals
Closed-Won Deals =
CALCULATE(
[Total Deals],
'hubspot deals'[Is Closed] = TRUE(),
'hubspot deals'[Is Closed Won] = TRUE()
)Closed-Lost Deals
Closed-Lost Deals =
CALCULATE(
[Total Deals],
'hubspot deals'[Is Closed] = TRUE(),
'hubspot deals'[Is Closed Won] = FALSE()
)Win Rate
Win Rate =
DIVIDE(
[Closed-Won Deals],
[Closed Deals]
)Format this measure as a percentage. It compares successful deals with all opportunities that reached a closed outcome.
Average Days to Close
Average Days to Close =
CALCULATE(
AVERAGE('hubspot deals'[Days To Close]),
'hubspot deals'[Archived] = FALSE(),
'hubspot deals'[Is Closed Won] = TRUE()
)Connectorly defines Days To Close as the number of days between the deal’s created date and closed-won date. The measure therefore describes successful deals rather than every closed opportunity.
Closed-Won Value in Reporting Currency
Closed-Won Value (RC) =
CALCULATE(
SUM('hubspot deals'[Closed Amount (RC)]),
'hubspot deals'[Archived] = FALSE(),
'hubspot deals'[Is Closed Won] = TRUE()
)Use the reporting-currency measure when several currencies or HubSpot organisations are included. If you only report in the original deal currency, use Closed Amount instead.
Step 3: Choose the Correct Date for Win-Rate Analysis
A deal conversion dashboard can answer two different questions:
- Created-date analysis: Of the deals created during a period, how many eventually became won or lost?
- Close-date analysis: Of the deals that closed during a period, what proportion were won?
Because hubspot dates[Date] is actively related to hubspot deals[Created Date], the core measures from the previous step perform created-date cohort analysis by default.
To analyse outcomes by the period in which deals closed, use the inactive relationship to Forecast Close Date and temporarily disable the active created-date relationship.
Closed Deals by Close Date
Closed Deals by Close Date =
CALCULATE(
[Closed Deals],
CROSSFILTER(
'hubspot dates'[Date],
'hubspot deals'[Created Date],
NONE
),
USERELATIONSHIP(
'hubspot dates'[Date],
'hubspot deals'[Forecast Close Date]
)
)Closed-Won Deals by Close Date
Closed-Won Deals by Close Date =
CALCULATE(
[Closed-Won Deals],
CROSSFILTER(
'hubspot dates'[Date],
'hubspot deals'[Created Date],
NONE
),
USERELATIONSHIP(
'hubspot dates'[Date],
'hubspot deals'[Forecast Close Date]
)
)Closed-Lost Deals by Close Date
Closed-Lost Deals by Close Date =
CALCULATE(
[Closed-Lost Deals],
CROSSFILTER(
'hubspot dates'[Date],
'hubspot deals'[Created Date],
NONE
),
USERELATIONSHIP(
'hubspot dates'[Date],
'hubspot deals'[Forecast Close Date]
)
)Win Rate by Close Date
Win Rate by Close Date =
DIVIDE(
[Closed-Won Deals by Close Date],
[Closed Deals by Close Date]
)Use Win Rate by Close Date for monthly or quarterly performance reporting. Use the original Win Rate measure when analysing cohorts of deals created during a selected period.
Created-date cohorts need time to mature. A recently created cohort may contain many open deals, so compare it carefully with older periods and always display open or total deal counts alongside the conversion rate.
Step 4: Build the Deal Conversion KPI Cards
Start the dashboard with a row of KPI cards that summarises the selected reporting period. A practical layout includes:
- Total Deals
- Closed Deals by Close Date
- Closed-Won Deals by Close Date
- Closed-Lost Deals by Close Date
- Win Rate by Close Date
- Average Days to Close
- Closed-Won Value (RC)
Keep the cards visually consistent and place the win-rate card near the won and lost counts. This makes the calculation easier to understand and allows readers to see whether a percentage is based on a meaningful number of closed deals.
Label the cards carefully. If the date slicer controls close-date measures, use titles such as Deals Closed, Deals Won and Win Rate for Deals Closed. Avoid generic labels that leave readers unsure whether the period refers to when deals were created or closed.
Use percentage formatting for the win rate, whole numbers for deal counts and the appropriate reporting-currency format for Closed-Won Value (RC). One or two decimal places are normally enough for averages, while win-rate percentages rarely need more than one decimal place.
For a broader explanation of sales-performance measures and how they should be interpreted together, see our guide to HubSpot sales KPIs in Power BI.
Step 5: Show Won, Lost and Win-Rate Trends Over Time
Add a Line and stacked column chart to show how closed outcomes change by month.
Configure the visual as follows:
- Horizontal axis:
hubspot dates[Calendar Month Name] - Column values:
Closed-Won Deals by Close DateandClosed-Lost Deals by Close Date - Line value:
Win Rate by Close Date
Sort Calendar Month Name by Calendar Month Start so the months appear chronologically rather than alphabetically. Display the win-rate line on a secondary axis and format it as a percentage.
The columns show the volume behind the percentage. This is important because a sharp increase in win rate may result from only a small number of closed deals. Conversely, a lower win rate can still accompany an increase in the total number or value of successful deals.
Use a date filter that shows enough history to reveal a meaningful pattern. Twelve completed months is often a practical starting point. If the current month is incomplete, label it clearly or exclude it when comparing periods because partial-month results can distort both deal volume and win rate.
Add slicers for Pipeline Label, Owner Name and Connection Name. These allow users to investigate whether a change comes from a particular sales process, representative or HubSpot organisation.
Step 6: Compare Win Rate by Owner and Pipeline
Use a horizontal bar chart or matrix to compare conversion performance across sales owners.
Configure the visual with:
- Category or rows:
hubspot owners[Owner Name] - Values:
Closed Deals by Close Date,Closed-Won Deals by Close DateandWin Rate by Close Date
Sort the visual by closed-deal volume or closed-won deals rather than win rate alone. A representative with one successful deal would otherwise appear above someone who won twenty deals from thirty closed opportunities.
Next, create a similar visual using hubspot deal_pipelines[Pipeline Label]. Different pipelines may represent distinct products, regions or sales processes, so comparing their outcomes can reveal where further investigation is needed.
Do not assume that a lower win rate automatically indicates weaker performance. Owners and pipelines may handle opportunities with different values, levels of qualification, sales cycles or customer profiles. Display deal volume, won value and average days to close alongside the rate to provide the necessary context.
Can This Dashboard Calculate Conversion Between Every Pipeline Stage?
The measures in this guide calculate outcome conversion: the proportion of closed deals that were won. They do not reconstruct how every deal moved historically from one pipeline stage to another.
A current deal record shows its present pipeline and stage, but reliable stage-to-stage conversion requires historical stage-entry and stage-exit information. Do not calculate funnel conversion from the current stage label alone, because deals that have already progressed no longer remain in their earlier stages.
For current pipeline value, stage distribution and deals requiring attention, use our guide to building a HubSpot sales pipeline dashboard in Power BI.
Step 7: Analyse Why Deals Are Lost
Add a horizontal bar chart using:
- Category:
hubspot deals[Closed Lost Reason] - Value:
Closed-Lost Deals by Close Date
Sort the chart by the number of lost deals in descending order. This helps sales managers identify the reasons recorded most frequently, such as price, timing, competition, missing functionality or loss of contact.
Expect some records to have a blank Closed Lost Reason. Do not silently remove them from every report. A large blank category is itself a useful data-quality finding because it shows that teams are closing opportunities without recording why they were unsuccessful.
Add a supporting detail table containing:
Deal NameOwner NamePipeline LabelForecast Close DateAmount (RC)Closed Lost ReasonInternal Link
Set the data category of Internal Link to Web URL so authorised users can open the underlying deal in HubSpot. This turns the report from a passive summary into a practical review tool.
You can also create a drill-through page for individual owners, pipelines or lost reasons. Our guide to creating a Power BI drill-through page explains how to build the navigation and preserve the selected filter context.
Interpret lost reasons carefully. They depend on consistent user input and may reflect subjective judgement. Review the category values periodically and standardise them in HubSpot where possible before using them for strategic decisions.
Step 8: Arrange the Finished Dashboard
Keep the report on one focused page so users can move from the headline result to the underlying explanation without navigating through several tabs.
Top of the Page
Place the date, pipeline, owner and connection slicers at the top. Directly beneath them, add the KPI cards for closed deals, won deals, lost deals, win rate, average days to close and closed-won value.
Middle of the Page
Use the central area for the monthly won-versus-lost trend and the win-rate line. Place the owner and pipeline comparisons alongside or immediately below it.
Bottom of the Page
Add the closed-lost reason chart and detailed deal table at the bottom. This allows users to investigate the records behind an unexpected result after reviewing the summary visuals.
Recommended slicers include:
hubspot dates[Calendar Year Name]orCalendar Month Namehubspot deal_pipelines[Pipeline Label]hubspot owners[Owner Name]hubspot deals[Deal Type]hubspot deals[Connection Name]
Include Data current as of somewhere unobtrusive on the page so readers can see when the HubSpot data was last extracted. This is particularly useful for shared or scheduled Power BI reports.
Finally, test the visual interactions. Selecting an owner, pipeline, month or lost reason should filter the relevant cards and detail table. Disable interactions that produce confusing or circular results, and make sure every chart title explains whether its measures use created date or close date.
Step 9: Validate the Dashboard Against HubSpot
Before sharing the dashboard, compare it with a small, clearly defined group of deals in HubSpot. Use one pipeline, one closed period and one HubSpot organisation so differences are easier to investigate.
Check the following:
- The same close-date range is applied in HubSpot and Power BI.
- The same pipelines and owners are included.
- Archived or test deals are treated consistently.
- Closed-won and closed-lost stages are mapped correctly.
- Power BI counts distinct
Deal IDvalues rather than rows. - The Power BI measures use
Forecast Close Datefor close-period analysis rather than the activeCreated Daterelationship. - Currency comparisons use either original deal values or reporting-currency values consistently.
- The latest Connectorly extraction has completed and
Data current as ofis recent enough for the comparison.
If the totals differ, inspect the individual deal records rather than adjusting the DAX immediately. Common causes include different date definitions, blank close dates, archived records, recently updated deals, pipeline filters and HubSpot reports that use a different underlying property.
Also check whether HubSpot and Power BI display dates in different time zones. Datetime values close to midnight can occasionally appear on adjacent calendar dates after conversion, although the prepared Connectorly date fields reduce this problem for most reporting scenarios.
Once the small test group matches, repeat the check across a longer period and another pipeline before treating the dashboard as validated.
Common Deal Conversion Dashboard Mistakes
Dividing Won Deals by Every Deal
Open opportunities have not reached an outcome. Including them in the denominator produces a different metric and can make recent periods look artificially weak. Define win rate as closed-won deals divided by all closed deals.
Mixing Created Date with Close Date
A report filtered by Created Date analyses deal cohorts. A report filtered by Forecast Close Date analyses outcomes completed during the period. Both are useful, but they answer different questions and should be labelled clearly.
Counting Rows Instead of Deal IDs
Use a distinct count of Deal ID. This keeps the measure reliable if the model later includes expanded associations or other structures that can repeat deal information.
Inferring Outcomes from Stage Names
Pipeline labels differ between HubSpot organisations. Use Is Closed and Is Closed Won rather than looking for text such as “Closed Won” or “Closed Lost”.
Treating Current Stages as Historical Funnel Movement
The current stage shows where a deal is now. It does not prove which stages the deal previously entered, skipped or returned to. True stage-to-stage conversion requires reliable historical movement data.
Ranking Owners Without Showing Deal Volume
A high win rate based on one or two deals is not comparable with a similar rate based on dozens of outcomes. Always display the closed-deal count and relevant commercial context.
Ignoring Blank Lost Reasons
Blank values often indicate an incomplete sales process rather than irrelevant data. Show or monitor them so managers can improve data quality in HubSpot.
Combining Currencies Without Conversion
When deals use different currencies, summing the original values can produce meaningless totals. Use Connectorly’s (RC) reporting-currency fields for consolidated value reporting.
Final Thoughts
A HubSpot deal conversion and win rate dashboard in Power BI becomes useful when it does more than display a single percentage. The strongest reports show the won and lost deal counts behind the rate, use the correct date context and allow users to compare outcomes by owner, pipeline and period.
Clear definitions matter just as much as the visuals. Closed-won deals divided by all closed deals provides a practical outcome-based win rate, while created-date cohort analysis answers a different question. Keeping those calculations separate prevents misleading comparisons.
Connectorly provides structured HubSpot deal, pipeline, owner and date data that can be used directly in Power BI. This removes the need to build and maintain the complete HubSpot extraction process yourself while leaving the report, calculations and visual design fully customisable.
To learn more about the available data model, templates and connection process, explore Connectorly for HubSpot and Power BI.
Frequently Asked Questions
How do you calculate HubSpot win rate in Power BI?
Divide closed-won deals by all deals that reached a closed outcome. Do not include open deals in the denominator unless you deliberately want to calculate a different metric.
Which Connectorly table contains HubSpot deal outcomes?
The hubspot deals table contains fields including Deal ID, Is Closed, Is Closed Won, Closed Lost Reason, Created Date, Forecast Close Date and Closed Won Date.
Should win rate use the deal created date or close date?
Use close date when measuring the proportion of deals won during a reporting period. Use created date when analysing how a cohort of opportunities created during a particular period eventually performed.
Why does this guide use Forecast Close Date for closed deals?
Connectorly’s Forecast Close Date is derived from HubSpot’s deal close-date property and can provide the closing date for both won and lost outcomes. Closed Won Date applies specifically to successful deals.
Can Power BI compare HubSpot win rate by owner?
Yes. Relate hubspot owners[Owner ID] to hubspot deals[Owner ID], then use Owner Name with the closed-deal, won-deal and win-rate measures.
Can this dashboard report across multiple HubSpot organisations?
Yes. Connectorly supports reporting across grouped HubSpot connections. Use Connection Name as a slicer and use the (RC) fields when monetary values must be presented in a consistent reporting currency.
Can current HubSpot deal stages show historical stage conversion?
Not reliably. A deal’s current stage does not show every stage it previously entered, skipped or returned to. Accurate stage-to-stage conversion requires historical stage-movement data.
Why might Power BI and HubSpot show different win rates?
The reports may use different close dates, pipelines, owners, archived records or calculation definitions. Check the underlying deals for a small period and confirm that both systems use the same filters and denominator.
Does Connectorly provide a ready-to-use HubSpot data model?
Yes. Connectorly provides structured HubSpot tables for Power BI, including deals, pipelines, owners, dates and related CRM information. Customers can use the supplied model and templates as a starting point, then customise their measures and dashboards.
