Pre-quote Report: Difference between revisions
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== Summary == | == Summary == | ||
This is a report that shows you a breakdown of the pre quoted, which were lost and which were turned into quotes or bookings. | |||
== Filters == | == Filters == | ||
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! style="width: 75%;" |Description | ! style="width: 75%;" |Description | ||
|- | |- | ||
| | |Database||If you have access to multiple entity databases, you can filter and select specific ones. | ||
| | |- | ||
|Year||Allow you to select the year | |||
|- | |||
|Site||Allows you to filter and focus on specific sites within your entity access. | |||
|- | |||
|Carrier||Enables filtering by carriers associated with the sites you have access to. | |||
|- | |||
|Users||Filters quotes/bookings by specific users. Users will be listed if they have access to the sites you can access. | |||
|- | |||
|Date Filter||Displays only jobs created within the specified date range | |||
|} | |} | ||
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!Description | !Description | ||
|- | |- | ||
| 1.Total Prequotes By Month ||Formula: | |||
<pre>Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 </pre> | |||
| 1. | ||The Clustered bar chart breaks down the prequote by Month. | ||
|- | |- | ||
| | | 2.Table breakdown By Total Pre quotes||Formula: | ||
<pre>Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 </pre> | |||
||The table breaks down Total pre quotes, Deleted Incorrect Pre quotes and Accuracy. | |||
|- | |- | ||
| | | 3.Total Pre quotes By time|| Formula: | ||
<pre>Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 </pre> | |||
||The table breaks down pre quotes created over Time by Mailbox | |||
|- | |- | ||
| | | 4.Total Deleted Prequotes|| | ||
Formula: | |||
<pre>Total Deleted Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[deleted_at])) ) + 0 </pre> | |||
|| The table breaks down Delete Reasons by total deleted pre quotes | |||
|- | |- | ||
| 5. | | 5. | ||
Total Pre quotes | Total Pre quotes | ||
||Formula: | ||Formula: | ||
Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 | <pre>Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0</pre> | ||
|| | || | ||
*Counts the number of prequotes: It counts the rows in the quotations table where the pre_quoted_at column is not blank, indicating a valid prequote. | *Counts the number of prequotes: It counts the rows in the quotations table where the pre_quoted_at column is not blank, indicating a valid prequote. | ||
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This measure helps track the total number of prequotes within a specific date range, providing insights into the total number of prequotes made. | This measure helps track the total number of prequotes within a specific date range, providing insights into the total number of prequotes made. | ||
|- | |- | ||
| 6. Lost Pre quotes||Formula: | | 6. Lost Pre quotes||Formula: | ||
Total Lost Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[lost_at])) ) + 0 | <pre>Total Lost Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[lost_at])) ) + 0 </pre> | ||
|| | || | ||
*Counts the number of lost prequotes: It counts the rows in the quotations table where the pre_quoted_at column is not blank (indicating a valid prequote), and the lost_at column is not blank, indicating that the prequote has been lost. | *Counts the number of lost prequotes: It counts the rows in the quotations table where the pre_quoted_at column is not blank (indicating a valid prequote), and the lost_at column is not blank, indicating that the prequote has been lost. | ||
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This measure helps track the total number of lost prequotes within a specific date range. | This measure helps track the total number of lost prequotes within a specific date range. | ||
|- | |- | ||
| 7.Deleted pre quotes||Formula: | | 7.Deleted pre quotes||Formula: | ||
Total Deleted Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[deleted_at])) ) + 0 | <pre>Total Deleted Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[deleted_at])) ) + 0 </pre> | ||
|| | || | ||
*Counts the number of deleted prequotes: It counts the rows in the quotations table where the pre_quoted_at column is not blank (indicating a valid prequote), and the deleted_at column is not blank, indicating that the prequote has been deleted. | *Counts the number of deleted prequotes: It counts the rows in the quotations table where the pre_quoted_at column is not blank (indicating a valid prequote), and the deleted_at column is not blank, indicating that the prequote has been deleted. | ||
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This measure helps track the total number of deleted prequotes within a specific date range, providing insights into the volume of prequotes that were removed | This measure helps track the total number of deleted prequotes within a specific date range, providing insights into the volume of prequotes that were removed | ||
|- | |- | ||
| 8.Pre quote to bookings|| <pre>Total Pre-quote to Converted = | |||
| 8.Pre quote to bookings|| Total Pre-quote to Converted = | |||
CALCULATE( | CALCULATE( | ||
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quotations[Booking_categories] = "Pre-quote to Booking" | quotations[Booking_categories] = "Pre-quote to Booking" | ||
) + 0 | ) + 0 </pre> | ||
|| | || | ||
*Counts the number of prequotes converted to bookings: It counts the rows in the quotations table where the Booking_categories column equals "Pre-quote to Booking," indicating that the prequote has been successfully converted into a booking. | *Counts the number of prequotes converted to bookings: It counts the rows in the quotations table where the Booking_categories column equals "Pre-quote to Booking," indicating that the prequote has been successfully converted into a booking. | ||
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This measure helps track the total number of prequotes that have successfully been converted into bookings, providing insights into the conversion rate of prequotes to actual bookings. | This measure helps track the total number of prequotes that have successfully been converted into bookings, providing insights into the conversion rate of prequotes to actual bookings. | ||
|- | |- | ||
| 9. Incorrect prequote||Formula: | | 9. Incorrect prequote||Formula: | ||
Total Prequotes with IQ = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), quotations[delete_reasons] = "IQ" ) | <pre>Total Prequotes with IQ = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), quotations[delete_reasons] = "IQ" ) </pre> | ||
|| | || | ||
*counts prequotes with "IQ" delete reason: It counts the rows in the quotations table where the pre_quoted_at column is not blank (indicating a valid prequote), and the delete_reasons column equals "IQ," indicating that the prequote was deleted due to an incorrect quotation. | *counts prequotes with "IQ" delete reason: It counts the rows in the quotations table where the pre_quoted_at column is not blank (indicating a valid prequote), and the delete_reasons column equals "IQ," indicating that the prequote was deleted due to an incorrect quotation. | ||
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This measure helps track the total number of prequotes with the "IQ" delete reason, providing insights into the volume of prequotes that were deleted due to IQ-related issues. | This measure helps track the total number of prequotes with the "IQ" delete reason, providing insights into the volume of prequotes that were deleted due to IQ-related issues. | ||
|- | |- | ||
| 10.Prequote conversion | | 10.Prequote conversion | ||
|| Formula: | || Formula: | ||
Prequote Conversion = | <pre>Prequote Conversion = | ||
DIVIDE([Total Pre-quote to Converted], [Total Prequotes]) | DIVIDE([Total Pre-quote to Converted], [Total Prequotes]) </pre> | ||
|| | || | ||
Latest revision as of 14:07, 17 June 2025
Summary
This is a report that shows you a breakdown of the pre quoted, which were lost and which were turned into quotes or bookings.
Filters
| Filter | Description |
|---|---|
| Database | If you have access to multiple entity databases, you can filter and select specific ones. |
| Year | Allow you to select the year |
| Site | Allows you to filter and focus on specific sites within your entity access. |
| Carrier | Enables filtering by carriers associated with the sites you have access to. |
| Users | Filters quotes/bookings by specific users. Users will be listed if they have access to the sites you can access. |
| Date Filter | Displays only jobs created within the specified date range |
Calculations

| Reference | Calculation | Description |
|---|---|---|
| 1.Total Prequotes By Month | Formula:
Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 |
The Clustered bar chart breaks down the prequote by Month. |
| 2.Table breakdown By Total Pre quotes | Formula:
Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 |
The table breaks down Total pre quotes, Deleted Incorrect Pre quotes and Accuracy. |
| 3.Total Pre quotes By time | Formula:
Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 |
The table breaks down pre quotes created over Time by Mailbox |
| 4.Total Deleted Prequotes |
Formula: Total Deleted Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[deleted_at])) ) + 0 |
The table breaks down Delete Reasons by total deleted pre quotes |
| 5.
Total Pre quotes |
Formula:
Total Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])) ) + 0 |
Purpose This measure helps track the total number of prequotes within a specific date range, providing insights into the total number of prequotes made. |
| 6. Lost Pre quotes | Formula:
Total Lost Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[lost_at])) ) + 0 |
Purpose This measure helps track the total number of lost prequotes within a specific date range. |
| 7.Deleted pre quotes | Formula:
Total Deleted Prequotes = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), NOT(ISBLANK(quotations[deleted_at])) ) + 0 |
Purpose This measure helps track the total number of deleted prequotes within a specific date range, providing insights into the volume of prequotes that were removed |
| 8.Pre quote to bookings | Total Pre-quote to Converted =
CALCULATE(
COUNTROWS(quotations),
quotations[Booking_categories] = "Pre-quote to Booking"
) + 0
|
This measure helps track the total number of prequotes that have successfully been converted into bookings, providing insights into the conversion rate of prequotes to actual bookings. |
| 9. Incorrect prequote | Formula:
Total Prequotes with IQ = CALCULATE( COUNTROWS(quotations), USERELATIONSHIP(DimDate[Date], quotations[pre_quoted_at]), NOT(ISBLANK(quotations[pre_quoted_at])), quotations[delete_reasons] = "IQ" ) |
Purpose
|
| 10.Prequote conversion | Formula:
Prequote Conversion = DIVIDE([Total Pre-quote to Converted], [Total Prequotes]) |
Purpose This measure helps track the conversion rate of prequotes to bookings, providing insights into how many prequotes are successfully turned into actual bookings.
|