Documentation Mix Modeler Mix Modeler Guide

Models overview

Last update: Tue Feb 25 2025 00:00:00 GMT+0000 (Coordinated Universal Time)

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Models

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The model functionality in Mix Modeler allows you to configure, train, and score models specific to your business objectives. The training and scoring supports AI-driven transfer learning between multitouch attribution and marketing mix modeling.

The models are based on the harmonized data that you create as part of the Mix Modeler application workflow.

A model in Mix Modeler is a machine learning model employed to measure and predict a specified outcome based on a marketer’s investments. Marketing touchpoints and summary-level data can be used as an input. Mix Modeler allows you to create variants of models based on different sets of variables, dimensions, and outcomes, such as revenues, units sold, leads.

A model requires:

One conversion.
One or more marketing touchpoints (channels) comprised of summary-level data, marketing touchpoint data (event data) or both.
A configurable lookback window.
A configurable training window.

A model can optionally include:

External factors.
Internal factors.
Prior knowledge of marketing contributions from other sources such as past stakeholder experience, incrementally testing, other models.
Spend share, which uses relative spend share as a proxy when marketing data is sparse.

When a model is first created, the creation immediately kicks off the training and scoring process. After the completion of the initial training and scoring run, model insights are available for review. A model may subsequently be re-trained. Also, data may be added to the model which requires you to re-score the model manually. Re-trainng and re-scoring are an iterative process as new findings and information emerge and adjustments are needed to obtain a model fit that is most appropriate for your business objectives.

Build models

To build a model, use the Mix Modeler step-by-step guided model configuration flow available when you select Open model canvas. See Build models for more details.

Manage models

To view a table of your current models, in the Mix Modeler interface:

Select Models from the left rail.
You see a table of the current models.

The table columns specify details about the model.

Column name
Details
Name
Name of the model
Description
Description of the model
Conversion event
The conversion you have selected for the model.
Run frequency
The running frequency of training the model.
Last run
The date and time of the last training of the model.
Status
The status of the model.

The reported status of the model is dependent on where a model is within its lifecycle. For example, whether a model is created, (re-)trained successfully or not, or (re-)scored successfully or not.

In the table below:
- - indicates a successful execution of a step in the model lifecycle.
- - indicates a current ongoing execution of a step in the model lifecycle.
- - indicates a failed execution of a step in the model lifecycle.
Status
Create
Train
Score
Re-train
Re-score
In progress

In progress

In progress

In progress

In progress

Training failed

Training failed

Training successful

Training successful

Scoring failed

Scoring failed

Scoring successful

Scoring successful
To change the columns displayed for the list, select and toggle columns on or off.

You can take the following actions on a specific model.

Model insights

The model insights functionality is only available on successfully trained and scored models.

To view the insights of a model:

Select Models from the left rail.
Select the model name.

You are redirected to Model Insights.

View details

To view more details of a model:

Select Models from the left rail.
Select for a model to show a pop-up with details.

Duplicate

You can quickly duplicate a model.

Select Models from the left rail.
Select for a model, and from the context menu select Duplicate.

You are redirected to the steps to create a new model, with a proposed name composed of the original model’s name appended with (Copy) (n).

Edit

You can edit the name, description and the scheduling of training and scoring of a model.

Select Models from the left rail.
Select for a model, and from the context menu select Edit.

In the Edit model dialog:
- Enter a new Name and Description.
- To enable scheduling, enable Status. You can only enable scheduling for models that are trained and scored.
  1. Select a Scoring frequency:
    - Daily: Enter a valid time (for example 05:22 pm) or use .
    - Weekly: Select a day of the week and enter a valid time (for example 05:22 pm) or use .
    - Monthly: Select a day of the month from the Run on every dropdown menu and enter a valid time (for example 05:22 pm) or use .
  2. Select a Training frequency from the dropdown menu: Monthly, Quarterly, Yearly, or None.
Select Save.

Re-train

Re-train a model is only available on successfully trained models.

Consider to re-train a model when you want to:

Include new incremental marketing and factor data. For example, over the last quarter, market dynamics have changed or your marketing data distribution has changed significantly.

To re-train a model:

Select Models from the left rail.
Select for a model, and from the context menu select Train. Alternatively, select Train from the blue action bar.

In the Train model dialog, select the option to:
- Train model with last 2 years of marketing data, or
- Train model using specific date range of data.
  Specify the date range. You can use the to select a date range. You have to select a data range with a minimum of one year.
Select Train to re-train the model.

Score or re-score

You can incrementally score a model based on new marketing data or re-score a model for a specific date range.

Consider to re-score a model when you want to:

Correct incorrect marketing data. For example, the recent paid search data you included in the training and scoring of the model missed a week of data.
Use new incremental marketing data that has become available through updates in the datasets you have configured as part of your harmonized data.

To score or re-score a model:

Select Models from the left rail.
Select for a model, and from the context menu select Score. Alternatively, select Score from the blue action bar.

In the Score marketing data dialog, select the option to:
- Score new marketing data from mm/dd/yyyy, to score your model incrementally using new marketing data, or
- Score specific date range of marketing data to re-score for a specific date range.
  Specify the date range. You can use the to select a date range.
Select Score. When re-scoring a model using a specific data range, you see an Existing model is replaced dialog, prompting you to confirm to replace the model with new scores for the selected date range. Select Replace model to confirm.

Delete models

To delete a model:

Select Models from the left rail.
Select for a model, and from the context menu select Delete. Alternatively, select Delete from the blue action bar.
Select Delete in the Delete model confirmation dialog to delete the model. Select Cancel to cancel.

To delete multiple models:

Select multiple models.
From the blue action bar, select Delete to delete the models.
Select Delete in the Delete x models confirmation dialog to delete the models. Select Cancel to cancel.

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