Group related values into categories in CRM analytics for easier analysis

Group related values into categories in CRM analytics for easier analysis

Greetings from Enterprise Support!

I am sure you can relate that analyzing large datasets can be tricky when dealing with scattered data points, making it hard to find useful insights. To solve this, we’re introducing options for creating categories within analytics components. Now, you can group related data fields, making it easier to analyze and understand.

For example, if you're analyzing leads by their sources—like Cold Call, Advertisement, Employee Referral, Trade Show, and Chat. You can categorize them by type of engagement:
  • Active engagement: Cold Call, Employee Referral, Chat
  • Passive engagement: Advertisement, Trade Show
This grouping helps you see which sources involved direct interaction (Active) versus indirect (Passive), making data analysis clearer.

What does this mean for you?
  • Easier data analysis: Grouping similar data simplifies spotting trends and patterns.
  • Better visualizations: Categories make your charts and reports clearer and more focused.
  • Custom groupings: You can create categories that fit your specific business needs.
You can create categories for two types of grouping fields:
  • Picklist fields
  • Numeral fields (like Currency, Number, Long Integer, Decimal, Percent, Formula, and Roll-up summary
Let's break down how to create categories for each type:

Category for picklist fields

Imagine you have a picklist field called "Country" in a chart. Analyzing each country separately can be overwhelming. You may also want to group countries based on business goals, like regions or continents.
  • With category creation, you can group countries into larger categories, such as Asia, Europe, Africa, and North America, or create custom groups that fit your business needs.


  • In this case, "Continent" is the Category field, and "Asia," "Europe," "Africa," and "North America" are its Categories.



  • Rather than looking at each country individually, you can easily see how entire continents or regions are performing, making it easier to make informed decisions.
Category for numeric fields

Another useful update to the category feature is the ability to group numeric fields. This lets you organize numerical data into ranges, making analysis clearer and more intuitive.

For example, if you're working with Deal Amounts in an analytics component, analyzing individual deal values can be overwhelming, especially with large datasets. By grouping deal amounts into ranges, you can easily see which deals fall into different revenue brackets, helping you spot trends and take action.

You could create a "Deal Size" category for the Amount field, with ranges like:

  • "Small value deals" for amounts under $15,000
  • "Medium value deals" for amounts between $15,000 and $50,000
  • "High value deals" for amounts between $50,000 and $100,000
  • "Enterprise deals" for amounts over $100,000


When creating categories for a numerical field, the range values are continuous, ensuring no data is left uncategorized in the "Others" section.



Key points to remember:
  • Category fields and the categories you create are specific to each analytical component.
  • You can add up to 10 categories per field, with up to 20 values in each category.
  • The "Others" category includes unassigned values and records with no values for the field.
  • Category creation is supported for all analytical components, except Anomaly Detector and Stage Component.

Release plan:
These enhancements will be rolled out in a phased manner soon!