Creating a model is a crucial component in the segmentation of your audiences within Rev.Up. If we think about it in terms of the core pillars of Rev.Up, modeling is a key part of the segment pillar.
A quick review of the 3 pillars:
Connect: Import your 1st party data into Rev.Up, match and merge multiple connections
together to create a unified view and append attributes from the D&B data cloud
to your own data.
Segment: Segment your data into audiences that will be used in campaigns.
Activate: Send your audiences to an end platform such as Marketo, Eloqua, Salesforce,
Linked and more.
How are models used within segmentation?
The purpose of any model in Rev.Up is to score a set of accounts for their propensity to convert or take a specific action. A very common example is scoring your prospect accounts for their likelihood to become a customer. The way you do this is to build a segment of your prospects, build a model that is suitable to score your prospects against and then publish your model.
When you publish your model, your prospects will have a rating (A - B) that indicates which ones are the most likely to convert to becoming a customer. These ratings will be available for you to use as an attribute when building a segment. This means that you can include or exclude account records from a segment using their rating from the model and means that you now have the ability to target your prospects differently based on how likely the model thinks they are to convert to a customer.
Here is a segment that includes my prospects that are scored as A and B for their likelihood to convert to a Gizmo customer.
Please note, some models use transaction data and will rank your customers based on their likely amount of spend.
Why should you use model ratings within your segments?
Using a model rating in a segment allows you to narrow down the accounts that you reach out to using a digital channel. This is important when you are running a campaign in a high cost channel e.g. having your sales reps make outbound calls or paying for impressions or clicks on an ads platform. For some channels such as email, using a model rating is less impactful on the overall campaign cost.
What types of models can be built in Rev.Up?
There a several different types of models that can be built within Rev.Up. Here is a short description of each. For a detailed article on how to build these models, please review the links at the bottom of this article.
Rules-based models: 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. This model does not use a machine learning engine behind the scenes. It is similar to how you build segments with the difference being that you can apply a rating to a segment.
Account Fit Models: An account fit model using machine learning to score your prospect or customer accounts against a model and determines how likely they are to 'convert'. Convert is a loose word here as an account fit model can be used to see how likely a prospect is to become a customer or it can be used to determine how likely a customer is to purchase another product.
Cross-sell Models: Cross-sell models are the most advanced model in the platform and use machine learning and product purchase data to determine when an existing customer account is likely to purchase another one of your products. These models require that you have import your product purchase data and are used for prioritizing within your existing customer accounts. There are two types of cross-sell models:
Cross-Sell First Purchase: A model that scores a customer account for its likelihood to purchase a product that they have never bought from you before.
Cross-Sell Repeat Purchase: A model that scores a customer account for its likelihood to purchase a product from you again in a defined timeframe.
Where can I find more resources on how to build models?
Rules Based Models:
Account Fit Models:
- How to use and build an account fit model
- How to review an account fit model
- FAQ: Account fit models
- How to create a cross-sell first purchase model
- How to create a up-sell repeat purchase model
- FAQ: Cross-sell models