Adobe Mix Modeler Features

Marketing measurement models

Empower teams to quickly access tailored insights and evaluate marketing ROI and budget plans in a scalable, repeatable way. Mix Modeler offers customisable model configuration and output through a self-service interface so teams can continuously access relevant insights, visualisations and model transparency to drive confidence in the results.

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Flexible model configuration

Teams can quickly customise and refine model configurations based on unique business objectives and characteristics. This helps improve output relevance and accuracy and strengthens forecasting.

Unique business traits. Account for business characteristics, including marketing touchpoints, campaign date ranges, prior beliefs and internal and external factors, such as promotional calendars.
Model variations. Create variants of models based on different sets of variables, dimensions and outcomes — such as revenues, units sold and leads.
Model management. View, manage, configure, train and score models whenever you need with an intuitive, self-serve interface.

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UI screens of AI-recommended channel spend

AI and machine learning algorithms

Get increased accuracy in marketing measurement results and predictions over time as AI and machine learning (ML) algorithms run in the background and automatically train the model against your data, business characteristics and objectives.

Multiplicative regression model. Account for cross-channel synergies and media interactions crucial for accurate marketing measurement and predictive channel planning using a model that has multiplicative ridge regression analysis as the foundation.
Expedited results. Get deep insights faster with AI and ML algorithms that continuously analyse and adapt, leveraging the latest data. Additionally, Adobe AI in Mix Modeler automates typical manual measurement workflows so you can quickly scale, save resources and decrease time-to-value.

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Business factor insights

Enhance measurement, forecasting and planning by ingesting data from internal and external business factors that affect marketing performance.

Internal and external factors. Ingest datasets for internal business factors such as price changes, promotional calendars and brand awareness survey results, as well as external market and economic factors, such as stock price movements, unemployment rates, consumer spend reports and competitive performance.
Predicted impact. Forecast the incremental effects that internal and external factors have on your marketing goals and easily incorporate the projections into your scenario planning to increase the relevance and confidence of your marketing investments.

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A line graph of conversions across touchpoints

Model insights

Guide your strategy and optimisations with rich model insights, such as summarised marketing performance, channel contributions and incrementality scoring, as well as options to dive deeper into historical performance over time or event-level attribution insights.

Incrementality scoring. Use ROI to measure and score the incremental impact of marketing channels, identifying high and low performers to guide optimisation opportunities for channel strategy and budget allocations.
Marginal response curves. Visualise and compare the marginal returns generated by your investment in marketing channels and identify the breakeven point where your spend begins to have diminishing returns.
Historical overviews. Explore historical performance with visualisations that show your spend by channel, your conversion and spend by fiscal quarter and product, your touchpoint spend and your touchpoint volume.
Attribution insights. Take a closer look to understand touchpoint effectiveness in marketing campaigns when event-level data is available.

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Model transparency

Gain confidence in your investment and planning decisions with visibility into the statistical significance of your model results.

Model assessment. Explore metrics and visualisations to assess your model, comparing actual, predicted and residual conversions.
Model quality metrics. Further assess your model fit and quality with industry-standard model evaluation metrics that define model accuracy and fit, including R2, MAPE and RMSE.