Business User Reference (Optimization - Price Guidance)

To run the Price Guidance Accelerator, create a new model from the Optimization > Models menu, using the Price Guidance Model Class to instantiate the machine learning model and give it the required parameters. The model is then added to the list of models and is editable.

In this page:

New Model

1. Go to Optimization > Models and add a new model.

   

2. Define the name of the model and choose Price Guidance as Model Class.

   

The new model opens. The interface has three steps - Definition, Feature Preparation, and Results, all explained below.

Model Steps

Definition Step

This is the first step of the model. There are two tabs: Definition and Configuration.

Definition Tab

In the Definition tab, you map the transaction source and its fields, and optionally narrow the model's scope with the Filter input. Both the source and the field mapping are pre-populated with the defaults from the accelerator's Data Mapping installation step and can be changed here without affecting those partition defaults.

There are mandatory and optional mapping fields. Their meaning is explained in Data Requirements (Optimization - Price Guidance).

• The Metric and Metric Type together determine what the model predicts.

• Product and Customer are optional inputs.

• Transaction Date is used to split the data into training and test windows.

• Revenue, Quantity, and Profit are required even when the chosen Metric is not directly derived from them - they support monetary calculations and cost-based business checks. Cost is derived as Revenue − Profit.

• Weight is optional. When set, each transaction is weighted by this field during training, so higher-weight transactions influence the predictions more. Leave it empty to weight all transactions equally. Recommendation is to use the revenue as the weight so a high revenue transaction will be considered with a higher importance.

• Currency is the model's currency. Monetary values from a Datamart source are normalized to this currency. For a Data Source no conversion is applied and values are treated as already being in this currency.

• Feature Candidates is an input matrix listing the attributes that can be evaluated as model features. Select at least one candidate. This candidate set is not the final feature set - the analysis in Feature Preparation step will help you to choose which candidates to actually train on, so it is fine to be generous here.

• Aggregation for Outlier Definition selects the fields that set the granularity of outlier detection. Outlier bounds are then computed separately within each combination of the selected field values, instead of over the whole dataset. The dropdown lists the categorical features you selected as Feature Candidates. Leave it empty to detect outliers over the entire dataset. Product ID is usually a good starting point.

Finally, there are three default filters that exclude transactions with negative or zero Revenue, Quantity, or derived Cost. They are useful to ensure the model runs cleanly. The boxes can be unchecked, but doing so may cause calculation failures if the underlying data contains invalid values. Transactions with an empty Metric are always excluded as well, independently of these filters.

Once you apply the settings, the right panel provides:

• Transactions - Included - Data that will be materialized in the model and used for training.

• Transactions - Excluded - Data that is filtered out by the source-level filter and the default negative-value filters, or because the Metric is empty.

Configuration Tab

In this tab, you set the parameters that govern the scope of the training data.

• Outliers acceptable range - A multiplier of the interquartile range (IQR) used to determine outlier bounds on the chosen Metric. Transactions whose Metric value falls outside [Q1 − k*IQR, Q3 + k*IQR] are excluded from training. When an Aggregation for Outlier Definition is set in the Definition tab, these bounds are computed separately within each group, otherwise they are computed over the whole dataset. Default is 1.5 (classical Tukey rule). Lower values remove more transactions. Set to 0 to skip outlier removal entirely.

• Number of days of transactions kept for the test set - A subset of the source data is reserved as a test set and is not used to train the model. It is used to check the validity of the model and avoid overfitting. The default is 30 days but can be changed. Aim for around 25% of the data in the test set, but also take care of the periodicity of the data. For instance, if your data contains a bit more than one year of transactions, it can be useful to keep exactly one year in the training set and use the start of the following year as the test set.

Once the Definition step is set up, use the Continue button at the top right to proceed to Feature Preparation.

Feature Preparation Step

This step runs a preliminary analysis to recommend which candidate features should be used for training, and collects the parameters that govern the main model's training behavior. There are two tabs: Feature Selection and Model Configuration.

Clicking Continue from the Definition step triggers the Feature Preparation calculation, which:

• Materializes the training data and applies outlier removal based on the Outliers acceptable range set in Definition > Configuration.

• Runs a feature-importance analysis on the candidate features picked in the Definition step.

Feature Selection Tab

This tab displays the outputs of the feature importance analysis and lets you confirm which features to actually train on.

Review and Check Features

In the left panel, you can find a matrix listing all candidate features. The matrix is pre-populated with the analysis's recommended subset, but you can modify this selection before triggering training. The recommended subset typically covers features whose cumulative importance accounts for the majority of the explained variance, the rest are unselected by default, to keep the training focused.

Candidate Feature Importance

A bar chart of relative importance per feature, ordered by descending importance. The subtitle shows the total explained variance - a rough indicator of how much of the target's variation the candidate features collectively explain.

Summary of the features

A table summarizing the analysis output, ordered by descending importance. Columns:

• Rank - order by importance.

• Recommended - Yes/No, whether the analysis suggests including this feature.

• Extended - for a numerical feature, whether its value already includes quantity. Extended means the value scales with quantity (for example a line revenue or an extended price), By Unit means it is a per-unit value. Empty for non-numerical (text) features.

• Relative Importance - the feature's share of total importance, as a percentage.

• Reasons - short notes explaining why a feature is or isn't recommended (e.g., low importance, duplicate of another feature, highly correlated with another feature, or another level of the same hierarchy already covers it).

• Duplicate With - if the feature is effectively a duplicate of another candidate (identical values), the other feature is listed here.

• Highly Correlated With - if the feature is strongly correlated with another candidate, the other feature is listed here.

Duplicates and highly correlated features are typically not recommended, keeping both adds redundancy without improving the model.

Transactions Over Time

A summary of the input transactions grouped by week, showing the periodicity of the data and how the training/test split aligns with it. Use this to check that the test window is representative - if the test set falls during an unusual period (a holiday, a price-list change, an unusually large customer), the model's measured accuracy may not reflect typical performance.

Hierarchies

This portlet visualizes features that form a hierarchical relationship in the data (for example, Product Category containing Product Subcategory containing Product Name). Including multiple levels of a hierarchy in training is usually redundant. From each connected hierarchy, the recommendation keeps only the single highest-importance feature selected.

Feature Interaction Data

A table that lists every pair of features and how related they are, ordered from the most related pair at the top to the least related at the bottom. For each pair of features, Interaction is a value indicating how strongly the two features are related. Higher values mean the two features carry overlapping information and including both of them generally does not improve the model.

Feature Interactions

A circular chart that shows which features are most related to each other. Each feature sits around the edge of the circle, and lines connect pairs that interact - the thicker the line, the stronger the relationship. Each feature has its own color, which makes it easy to spot all the connections a single feature has.

To keep the chart readable, only the strongest interactions are shown. The busiest parts of the circle point to groups of features that say similar things about the data. Use this chart together with the Feature Interaction Data table to decide which feature to keep when several are pointing in the same direction.

Model Configuration Tab

In this tab, you set the parameters that govern the model's training behavior.

Feature Binning

An optional input matrix that groups a numerical feature's values into bins before training, so the model learns on a single representative value per bin instead of the raw values. The matrix is empty by default. Add one row per feature you want to bin, choosing a feature, a method, and a number of bins.

• Only numerical features can be binned. The Feature dropdown lists the numerical features in scope.

• Each binned value is mapped to its bin center, so the feature stays numerical and monotonicity constraints can still be applied to it.

• Values outside the configured range are mapped to the nearest bin: a value below the lowest bin falls into the lowest bin, a value above the highest bin into the highest bin.

• The available methods:

  • Uniform - bins of equal width between the feature's minimum and maximum.

  • Uniform Log - equal-width bins on a log scale, useful for values spanning several orders of magnitude.

  • Percentiles - bins that each carry an equal share of the data.

  • Long Tail - percentile-style bins that grow denser towards the extremes.

  • Custom - bin edges you define yourself, entered in the Number of Bins cell as a semicolon-separated, increasing list (for example 0;2;5;10;20;50).

• Number of Bins must be a whole number of at least 2.

Model Parameters

Advanced training parameters with default values appropriate for most datasets. Adjust only if you understand the effect of the change.

Monotonicity Constraints

An optional input matrix to constrain selected numerical features to be monotonically increasing or decreasing in their effect on the target. Each row pairs a numerical feature with a direction.

• Use when business logic requires the relationship to be monotonic. For example, the impact of Quantity on Unit Price should be decreasing (more units → lower unit price).

• Enforcing monotonicity may reduce accuracy where the actual data pattern is not monotonic.

Once the parameters are set, use the Continue button at the top right to proceed to Results - this triggers training.

Results Step

Clicking Continue from Feature Preparation triggers the model training calculation, then opens the Results step. This step has four tabs: Metrics, Feature Impact, Pairwise Impact, and Evaluation.

Throughout the Results step, chart titles and axis labels include the specific Metric Type chosen in the Definition step - for example, "Impact on predicted Margin %" or "Impact on predicted Unit Price".

Metrics Tab

This tab summarizes the trained model.

Feature Importance

A bar chart showing the importance of each feature in the trained model, ordered from most to least important. This is the importance of features as the trained model actually uses them - distinct from the Candidate Feature Importance shown during Feature Preparation, which was a preliminary estimate.

Metrics

A table comparing model performance metrics across the training set and the test set. Use this to check overfitting: if training and test values diverge significantly, the model may be overfitting and recommendations on new data will be less reliable.

Model Information

A list of model-level facts that surface diagnostics from training:

• Features that were null or constant across the training data. These are removed from the model and listed here.

• The total number of transactions excluded as outliers (according to the Outliers acceptable range set in Definition > Configuration).

Feature Impact Tab

This tab shows how a chosen feature affects the prediction. In the left panel, pick a feature from the list. The dashboard then shows two charts for that feature.

Impact chart

The top chart shows the impact of each value of the selected feature on the predicted target.

• For a numerical feature, the chart is a step line showing the impact at each value seen in training.

• For a categorical feature, the chart is a column chart showing the impact of each category.

Density chart

The bottom chart shows how frequently each value of the selected feature appears in the training data. Its X axis aligns with the Impact chart above. Regions of the X axis with low density are based on few observations, so the impact in those regions is less reliable.

Pairwise Impact Tab

This tab shows the combined impact of two features that the model captured as a pairwise interaction. In the left panel, pick Feature 1 and Feature 2. The dropdown for Feature 2 is filtered to show only valid partners for the chosen Feature 1. Not every pair of features is captured as a pairwise interaction.

Impact heatmap

A heatmap with Feature 1 values on the X axis and Feature 2 values on the Y axis. The color shows the combined impact of the pair on the prediction:

• Blue cells - the pair lowers the predicted target relative to what either feature contributes alone.

• Yellow cells - no joint effect beyond what each feature contributes individually.

• Red cells - the pair raises the predicted target.

The color scale adapts to the actual range of impact values in the heatmap.

Evaluation Tab

This tab predicts the target for a specific set of input values and shows what drove the prediction. In the left panel, set a value for each feature in the model:

• Categorical features - pick a value from the dropdown of values seen during training. Two special options are available:

  • Null value - explicitly sets the feature to null, matching training rows where this feature was empty.

  • No data - the feature has a value not seen during training. The model treats this as an unseen category.

• Numerical features - type the value directly.

If a feature was binned in Feature Preparation, the value you enter is mapped to its bin center before the prediction is computed. Values that fall in the same bin therefore produce the same contribution, and values outside the binned range use the nearest bin.

Overview

Displays the predicted target

Target Drivers

A waterfall chart showing how the prediction was built up. Each bar represents a driver

• The first bar is the model's baseline.

• Each subsequent bar adds the contribution of one feature's value to the prediction. Positive contributions raise the predicted target, negative contributions lower it.

• The final bar is the predicted target.

Drivers are listed in order of magnitude and their tooltip shows the exact contribution.

Model Evaluation

In addition to the Evaluation tab, the model can be queried programmatically from anywhere in the Pricefx platform via the target_prediction model evaluation API. The API uses the same calculation as the Evaluation tab, so the same inputs produce the same outputs in both places.

See Technical User Reference (Optimization - Price Guidance) | Model Evaluation for the API signature, parameters, and result structure.