Understanding Territory Optimization with AI


After completing this lesson, you will be able to:

  • Understand the territory optimization with AI process

Understanding Territory Optimization with AI

The AI Optimization feature can help you optimize your territory program by providing data-driven alignment recommendations based on parameters that you specify. This feature works to ensure balance while considering the given constraints and the specified weight, or importance, of each constraint. It then provides recommendations for the account or geography alignments, depending on the optimization process you run. You can save the recommendations as a scenario to check the alignments before adopting them.

The following two constraints are considered by the AI Optimization:

  • Churn - the Churn value reflects the age of existing alignments. For example, if an account has been aligned with the source territory for 10 months and then gets realigned to another territory as part of the optimization recommendations, then the churn will be 10 for that account. The AI Optimization attempts to keep churn to the lowest value possible. You can either choose to exclude Churn as a constraint or you can consider it with Low importance.
  • Workload - Workload is the number of assigned accounts for a specific territory. The AI Optimization attempts to keep the number of accounts balanced between the target territories. There are 3 options for the Workload: Excluded, Low or High. You can choose to exclude the constraint or consider it with Low importance or High importance.

You can also customize the AI recommendations by adding up to 5 standard or custom attributes as constraints for the AI Optimization to consider.

Account optimization is triggered from the Account alignments workspace, while Geography optimization is triggered from the Geography alignments workspace. For both accounts and geographies, you have access to attributes from corresponding objects, however, you cannot mix their attributes together when using the AI Optimization feature.

The AI Optimization detects the pattern based on predefined inputs or adjusted versions of the parameters, and then predicts the outcome. The result of the model is a scenario that provides an optimized solution for existing territory alignments. Sales managers can compare the differences between current and proposed scenarios and accept or reject individual recommendations from the scenario.

In order for the Territory Alignments AI Optimization to reach an acceptable level of confidence for recommendations, the following data must be available:

  • Minimum of two territories
  • Minimum of one account for territory account alignment optimizations OR minimum one geography for territory geography alignment optimizations

The Territory Alignment AI Optimization Process

The Territory Alignment AI Optimization helps reduce manual work and increases efficiency in territory and quota planning. Sales Admins and Sales Managers can review the recommendations made for alignment and make territory alignment changes based on these recommendations.

The following describes the general process for the Territory Alignment AI Optimization:

  1. The user selects territories for which they want to get alignment recommendations. Optionally, the user can choose to also get recommendations for how to assign unassigned accounts. Note that unassigned accounts in this context refers to accounts that belong to the manager and need to be assigned to lower-level territories.
  2. The user sets constraint weights for a set of custom values. The custom values can be attributes such as size, revenue, or potential sales revenue. The user chooses the importance of the given criteria in relation to territory alignment. Note that for account alignment, the user can add account attributes, and for geography alignments, the user can add geography attributes. Geography and account alignments can't be combined.
  3. When the user runs the AI Optimization feature, it returns recommendations for territory alignments. Along with the recommendations, this AI use case provides the user with helpful statistical information, including the churn value; the number of accounts assigned to each selected territory; and the sum of the custom values, before and after the recommendations, that were used as constraints in step #2.

The user can choose to accept the recommendations as is or modify the suggested alignment. They can also save the current alignment scenario and then run the use case again with adjusted constraint weights. This enables the end user to compare multiple scenarios and choose which alignment scenario to apply.

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