Decision List node

Decision List models identify subgroups or segments that show a higher or lower likelihood of a binary (yes or no) outcome relative to the overall sample.

For example, you might look for customers who are least likely to churn or most likely to say yes to a particular offer or campaign. The Decision List Viewer gives you complete control over the model, enabling you to edit segments, add your own business rules, specify how each segment is scored, and customize the model in a number of other ways to optimize the proportion of hits across all segments. As such, it is particularly well-suited for generating mailing lists or otherwise identifying which records to target for a particular campaign. You can also use multiple mining tasks to combine modeling approaches—for example, by identifying high- and low-performing segments within the same model and including or excluding each in the scoring stage as appropriate.

Segments, rules, and conditions

A model consists of a list of segments, each of which is defined by a rule that selects matching records. A given rule may have multiple conditions; for example:
RFM_SCORE > 10 and

Rules are applied in the order listed, with the first matching rule determining the outcome for a given record. Taken independently, rules or conditions may overlap, but the order of rules resolves ambiguity. If no rule matches, the record is assigned to the remainder rule.

Complete control over scoring

The Decision List Viewer enables you to view, modify, and reorganize segments and to choose which to include or exclude for purposes of scoring. For example, you can choose to exclude one group of customers from future offers and include others and immediately see how this affects your overall hit rate. Decision List models return a score of Yes for included segments and $null$ for everything else, including the remainder. This direct control over scoring makes Decision List models ideal for generating mailing lists, and they are widely used in customer relationship management, including call center or marketing applications.

Mining tasks, measures, and selections

The modeling process is driven by mining tasks. Each mining task effectively initiates a new modeling run and returns a new set of alternative models to choose from. The default task is based on your initial specifications in the Decision List node, but you can define any number of custom tasks. You can also apply tasks iteratively—for example, you can run a high probability search on the entire training set and then run a low probability search on the remainder to weed out low-performing segments.

Data selections

You can define data selections and custom model measures for model building and evaluation. For example, you can specify a data selection in a mining task to tailor the model to a specific region and create a custom measure to evaluate how well that model performs on the whole country. Unlike mining tasks, measures don't change the underlying model but provide another lens to assess how well it performs.

Adding your business knowledge

By fine-tuning or extending the segments identified by the algorithm, the Decision List Viewer enables you to incorporate your business knowledge right into the model. You can edit the segments generated by the model or add additional segments based on rules that you specify. You can then apply the changes and preview the results.

For further insight, a dynamic link with Excel enables you to export your data to Excel, where it can be used to create presentation charts and to calculate custom measures, such as complex profit and ROI, which can be viewed in the Decision List Viewer while you are building the model.

Example. The marketing department of a financial institution wants to achieve more profitable results in future campaigns by matching the right offer to each customer. You can use a Decision List model to identify the characteristics of customers most likely to respond favorably based on previous promotions and to generate a mailing list based on the results.

Requirements. A single categorical target field with a measurement level of type Flag or Nominal that indicates the binary outcome you want to predict (yes/no), and at least one input field. When the target field type is Nominal, you must manually choose a single value to be treated as a hit, or response; all the other values are lumped together as not hit. An optional frequency field may also be specified. Continuous date/time fields are ignored. Continuous numeric range inputs are automatically binned by the algorithm as specified on the Expert tab in the modeling node. For finer control over binning, add an upstream binning node and use the binned field as input with a measurement level of Ordinal.