What this feature does and when to use it?
Summarizing data helps answer questions like "How much revenue was made each day last week?" by calculating averages, totals, and other key metrics.
You'll reach for summarization any time you need a single number or a grouped comparison instead of raw rows. For example, total placements this quarter, average deal value by owner, or candidate counts by pipeline stage. It's the step to use when you've already got the right data in front of you and just need to roll it up into something you can read at a glance or turn into a chart.
What is Summarization?
Summarizing means combining data from multiple records together and producing a single number, like the “average of product ratings”. So you need to determine:
The numerical metric that’s going to be used to combine value. For example, count, sum, or average
The column whose values will be combined. For example, quantity, price, or rating
(optionally) Breakouts. They determine the groups that the data is broken out by (or grouped). For example, you can compute order quantity grouped/broken out by months.
For example, in Advanced Analytics, you can find:
Average of Deal Value:
Average of Deal Value by Owner Name:
Average Deal Value by Owner Name for each month:
In all these cases, the metric is Average, and the column is Deal Value, but adding breakouts creates different summaries and charts.
Step-by-step instructions on how to summarize data by a column in Advanced Analytics
You can quickly summarize your data by clicking on a column header in a table. The available summary options depend on the column type (e.g., numerical, categorical, date).
Summarize a chart or a table:
There are multiple ways to add summaries in Advanced Analytics:
1. Using the Table View:
Click on the column header to summarize by that column.
2. Using the "Summarize" Button:
Click “Summarize” button (top-right corner) when viewing a table or chart.
In the “Summarize by” block, select a metric (e.g., Sum, Count, Average).
In the “Group by” block, choose how to categorize your data.
Click “+” to add more breakouts (grouping levels).
3. Using the Query Builder:
Add a Summarize block in the query builder and select metrics, columns, and breakouts.
For more details, refer to Ask a question in the query builder.
How to pick the right chart for your summaries?
When you click “visualize” on a question with summaries, Advanced Analytics will automatically select a chart that it believes is the most appropriate for your result.
For example, if you group by a time series column, Advanced Analytics will create a line chart. In most cases, the chart that Advanced Analytics picks will be the best option.
You can change the chart type. Advanced Analytics charts have requirements on the number and the kind of breakouts you can use in summaries that feed into the chart:
CHART TYPE | BREAKOUT REQUIREMENTS | BREAKOUTS ARE USED FOR |
Table | No breakouts required | Displays raw or summarised query results as rows and columns, no grouping needed |
Number, gauge, and progress charts | Single number, no breakouts | N/A |
Pie charts | 1 breakout | Color of segments |
Waterfall and funnel charts | 1 breakout | X-axis |
Maps | 1 breakout by a column with geographical data | Location |
Trend charts | 1 breakout by a column with time series data | Time period for comparison |
Line, bar, row, area, and combo charts | 1 or 2 breakouts | First breakout for the X-axis (or Y-axis for row charts), second breakout for color |
Pivot tables | at least 2 breakouts | Rows and columns |
Scatter plots | 1 to 3 breakouts | First breakout for the X-axis, second for color, and third for the bubble size |
Sankey charts | 2 breakouts + 1 metric | First breakout for source nodes, second breakout for target nodes; metric column determines the flow size between nodes |
Detail | No breakouts | Displays the full record for a single row, used for drilldown views |
Box plots | 1 required breakout; optional 2nd breakout | First breakout for the X-axis categories; optional second breakout creates colored series per category |
You can switch chart types (e.g., Pie → Bar) but some charts (e.g., Pivot Table) may have restrictions based on the number of breakouts used.
Summarize with custom expressions
If you need more complex summaries, such as calculating average or applying conditional logic, you can use Custom Expressions in Advanced Analytics.
For example, summing up of deal values only when the weighted deal percentage is 33%:
SumIf([Deal Value], [Weighed Deal Percentage] = 33)
Check out our article on custom expressions to learn more.
Summarizing tips
Group by dates and times:
You can group by date and time columns.
Advanced Analytics automatically selects a granularity to group by: for example, for date columns, it will automatically group by months. groups date/time columns (e.g., by month).
To change the granularity, click on the time period and select a different unit (day, week, quarter, etc.).
Group by numeric variables:
In Advanced Analytics, you can also group by numeric variables, like Deal Value.
Advanced Analytics will bin the numerical variable for you, creating range-based “categories” for grouping:
If you group by a numeric variable and select "Count" as the metric, Advanced Analytics will generate a histogram (a distribution chart) of this variable.
Cumulative Summaries
Advanced Analytics has two types of cumulative summaries:
cumulative sum
cumulative count
For every record, they will return the sum or count of all values up to this record in the table.These summaries work a bit differently from other summaries because the data they return depends on the order of the data in your table.
Let’s say you have a table with values by month. Cumulative sum will be computed like this:
Month | Value | Cumulative sum |
July | 5 | 5 |
November | 4 | 5+4 = 9 |
March | 2 | 5 + 4 + 2 = 11 |
But if the months ordering is changed (while values remain the same), then the cumulative sum is changed as well:
Month | Value | Cumulative sum |
March | 2 | 2 |
July | 5 | 2 + 5 = 7 |
November | 4 | 2 + 5 + 4 = 11 |
Distinct values
Advanced Analytics “Distinct values” summary returns the number of unique values in a column.
To see the distinct values themselves, you can create any summary with a breakout by that column instead.
Example: Counting Distinct Deals per Owner
If you want to see how many different deals each Owner has handled, use:
🔹Distinct count of Deal Name grouped by Owner Name.
This will give you a column with all the Owner Names, and a column with row counts for every Owner Name (you can hide the count column from the results):
Note
In SQL, you summarize by adding a function like COUNT into a SELECT statement — similarly to how you’d add a new column computed based on the values of other columns.
In Advanced Analytics, these two operations are distinct: to add a new column, you can use a Custom column block, but to add an aggregation, you use a Summary block.
Troubleshooting
Problem: If the chart is blank or empty, with no error displayed.
Problem: If the chart is blank or empty, with no error displayed.
Likely cause: The query has not been summarized. Most visualizations require at least one aggregation (such as Count, Sum, or Average) and, in many cases, a grouping field.
What to check: Verify that a Summarize step has been added. Ensure that at least one aggregation (for example, Count of, Sum of, or Average of) is selected.
Resolution path: Add the required aggregation and grouping in the Summarize step, then rerun the query and visualize the results.
Conclusion:
Summarization turns raw records into meaningful metrics by combining a numerical measure, a target column, and optional breakouts, giving you totals, averages, counts, and more. With options like custom expressions, cumulative summaries, and distinct values, you can tailor summaries to answer even complex business questions.
If you need help at any stage, feel free to reach out to Recruit CRM support for further assistance
Hope this helps!