Context
The Rev.Up platform allows you to build an unlimited amount of rules based models. This article covers what a rules based model is and some example uses for it.
What is a rules based model?
A rules based model allows you to create your own custom scoring model. In the rules based model wizard you are able to build segments and then apply a rating to that segment. Like other models, once the rules-based model is published the rating can be used elsewhere in the platform to build audiences to include in campaigns.
What is a good use case for a rules based model?
Rules based models are useful for multiple reasons. Some examples of when you want to use a rules based model are below:
- When you have an idea for how you want to segment your audience in a consistent way and do not want to duplicate the segmentation logic again and again. This is most useful when you are experimenting with engagement models.
- Use Case Example 1:
- Bombora intent topics in Rev.Up are given a High, Medium or Low value. A customer wanted to consistently use these values in many segments without creating the logic over and over again in each segment. In order to achieve this, they built a rules based model with the logic of:
- All accounts with an intent topic that is high are given a score of A
- All accounts with an intent topic that is medium are given a score of B
- All accounts with an intent topic that is low are given a score of C
- Result: The models evaluates the criteria every day and accounts that have changing intent will be given a new rating This allows for the ratings to be used in multiple segments and always have the most up to date audience membership.
- Bombora intent topics in Rev.Up are given a High, Medium or Low value. A customer wanted to consistently use these values in many segments without creating the logic over and over again in each segment. In order to achieve this, they built a rules based model with the logic of:
- Use Case Example 2:
- Another example of a rules based model being used to score accounts based on engagement with your company is using opportunity in the model. One customer built a model that segmented accounts based on the number and stage of opportunities. The criteria of the model were:
- All accounts with an no opportunities or an opportunity in a late stage were scored as A; this allows the customer to ensure they can pay special attention to prospects with no opportunities
- All accounts with an opportunity in a later stage were given a rating of B
- All accounts with an opportunity in an early or middle stage were given a rating of C
- Another example of a rules based model being used to score accounts based on engagement with your company is using opportunity in the model. One customer built a model that segmented accounts based on the number and stage of opportunities. The criteria of the model were:
- Use Case Example 3:
- Another example of a rules based model being used to score accounts based on engagement with your company is using web visit data and intent data in the model. One customer built a model that includes visits to specific product web pages and intent for specific topics. This model can be used to create rules based models that are product specific.
- All accounts with intent or visits to the web page that relate to a specific product or solution are given a rating of A
- All other accounts were given a rating of F
- Another example of a rules based model being used to score accounts based on engagement with your company is using web visit data and intent data in the model. One customer built a model that includes visits to specific product web pages and intent for specific topics. This model can be used to create rules based models that are product specific.
- Result: The models evaluates the criteria every day and accounts that have changing intent will be given a new rating This allows for the ratings to be used in multiple segments and always have the most up to date audience membership.
- Use Case Example 1:
- When you do not have enough data to be able to build a AI machine learning model.
-
- Some times there is not enough data to be able to build a AI machine learning model. This happens when the product or service you are trying to sell is new and there are not enough success events to be able to build a model.
Considerations for using rules based models
- The output of a rules based model is a rating of A - F
- Rules based models can only be used to score accounts
- An account record will always be given the highest rating that it qualifies for. An account cannot qualify for two ratings.
- Any attribute that can be used to build a segment can be used to build a rules based model
- Rules based models are not predictive in the sense that they are not using machine learning to score and apply ratings to records
- Once a rules based model is published the ratings for the model can be used as an attribute in any other segment you build
Comments
0 comments
Please sign in to leave a comment.