Channel Recommender models
The Channel Recommender model is a ready-to-use data science model that helps marketers identify the best marketing channel for customers based on past interactions.
The Channel Recommender model explained
The ready-to-use Channel Recommender data science model ranks engagement channels for every customer in any instance based on the likelihood of conversions.
The following channels are assessed:
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Email
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SMS
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Push
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Web
Parameters of the model
When creating the model, you will need to define the following parameters for the model:
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Algorithm: The algorithm is the piece of code that runs the model.
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Queries: The queries selected for the model generate a dataset for model training and scoring.
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Inputs: The inputs are query attributes from the Unity data model that are used for model training and scoring. You can't make changes to model inputs.
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Outputs: The outputs are data objects and attributes from the Unity data model that are used to store the output values of the model. You can make updates to the default mapping of model outputs.
Model inputs
To generate values, the Channel Recommender model uses the following data.
For the model to successfully run, data needs to be ingested into all the input attributes below.
| Data object | Attribute | Data type | Description |
|---|---|---|---|
| Customer and Campaign | SourceID | String |
The unique identifier for the source. |
| Campaign and Event | SourceCampaignID | String |
The unique identifier for the campaign that originates from the source. |
| Campaign | SourceID_Campaign | String | The unique identifier for the source. |
| Event and Customer | SourceCustomerID | String | The unique identifier for the customer that originates from the source. |
| Event and Customer | EventTS | Timestamp | The date and time when the event occurred. |
| Event and Customer | Type | String | The type of event, such as View, Purchase, Buy, and Click. |
| Event and Customer | Medium | String | The total number of purchase records for the customer. |
| Event | ID | String | The unique identifier for the data object. |
Model outputs
You can review the ranked values generated for each channel in the CampaignChannelRecommender data object. The following attributes will generate output values.
| Attribute | Description | Data Type | Is Key attribute? |
|---|---|---|---|
| SourceCampaignChannelRecommenderID | The unique identifier that is a concatenation of SourceMasterCustomerID, SourceCampaignID, and Medium. | String | Yes |
| SourceCampaignID | The unique identifier for the campaign. | String | Yes |
| SourceCustomerID | The unique identifier for the customer. | String | Yes |
| SourceID | The unique identifier for the source. | String | Yes |
| SourceID_Campaign | The unique identifier for the source and campaign. | String | Yes |
| Campaign_Rank | The rank of the campaign. | Integer | No |
| Channel_Name | The channel for the interaction: Email, SMS, Push, or Web. | String | Yes |
| Channel_Rank | The rank of the channel as a value from one to four, based on the number of channels used. | Integer | No |
Create and use a Channel recommender model
To create and use a Channel Recommender model, you will need to do the following:
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Follow the steps for Creating Channel Recommender models.
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After creating the model, follow the steps for Running training and scoring jobs.
After the model runs and creates output values, you can do the following:
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Access Channel Recommender data to review the values in the output attributes.
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Learn more about Using data science data for the specific needs of your organization.
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If needed, you can review the data science model details.