Compound Insights

Recipes for chaining Wood Wide AI tasks into clearer, multi-step analyses.

Compound insights combine two Wood Wide AI tasks so each step answers a different part of the question.

A single task can score rows, group rows, flag outliers, or summarize latent factors. A compound workflow uses the first result to choose the right subset or lens for the second task. That keeps the analysis focused: find the rows that matter, then explain them; find the groups, then inspect what is unusual inside them; predict an outcome, then segment or diagnose the records that deserve action.

How to Read These Recipes

The recipes below use the same task names you see in the console:

Recipe term	Wood Wide AI task
predict	Prediction
segment	Clustering
outliers	Anomaly Detection
factors	Factor Analysis

The arrow shows the order. For example, outliers -> factors means: run anomaly detection first, filter to the unusual rows, then run factor analysis on that filtered dataset.

Recipe Matrix

Chain	Good recipe framing
outliers -> factors	Detect unusual records, then diagnose what common factors explain them.
outliers -> segment	Detect unusual records, then cluster the anomalies into subtypes.
segment -> outliers	Find natural groups, then identify atypical rows inside the largest or selected segment.
segment -> factors	Find natural groups, then explain the underlying factors in a segment or across grouped behavior.
predict -> outliers	Predict an outcome, then find unusual records among high-risk, predicted-positive, or confident predictions.
predict -> segment	Predict an outcome, then segment the high-risk or high-opportunity rows.
predict -> factors	Predict an outcome, then diagnose latent factors among a predicted subset.

The strongest general-purpose starting point is outliers -> factors: first find records that do not behave like the baseline, then explain what those records have in common.

Recipe 1: Outliers, Then Factors

Use this when you want to turn an anomaly list into a root-cause hypothesis.

Question it answers: What common pattern explains the records that look unusual?

1. Cluster your dataset

   Upload your dataset and run Clustering. Use the columns that describe behavior or structure, not the outcome you want to predict. Let the model find the natural groupings.

2. Run anomaly detection

   Train an Anomaly Detection model on a baseline dataset, then run inference on the rows you want to inspect.

3. Export each cluster as a separate dataset

   Tag each row with its cluster label, then split into separate files, one per cluster.

4. Train a prediction model per cluster

   Upload each cluster file and train a Prediction model on each one, using the same target column across all of them.

5. Run inference within each segment

   When new data arrives, assign it to a cluster first (using the clustering model), then run inference with the matching prediction model.

6. Filter to outliers

   Keep rows with high anomaly scores or rows marked as anomalous. If the output is compact, use the anomalous row IDs to pull the matching records from your source data.

7. Run factor analysis on the outlier set

   Upload the filtered rows and run Factor Analysis. The factors summarize the shared structure inside the unusual records.

8. Name the factor pattern

   Look at the highest-loading columns for each factor and translate them into a business explanation.

Revenue anomaly diagnosis

Detect unusual account-months using ARR, expansion, contraction, churn, new bookings, and usage. Run factor analysis on the flagged months to see whether the anomaly is mostly contraction-heavy accounts, unusually low new bookings, or a churn spike in a specific motion.

Fraud investigation

Detect unusual transactions, then run factor analysis on the flagged set. A factor dominated by amount, transaction hour, merchant type, and geography can become a concrete fraud pattern to review.

Operations drift

Detect unusual fulfillment rows, then diagnose the flagged set with factors. The factor output can separate supplier delays from SKU-specific demand spikes or route-level logistics issues.

Recipe 2: Outliers, Then Segment

Use this when a single anomaly bucket is too broad.

Question it answers: Are there different kinds of anomalies hiding inside the flagged set?

1. Train an anomaly detection model

   Upload your historical data and run Anomaly Detection. The model learns what normal looks like across your numeric columns.

2. Run anomaly detection

   Use Anomaly Detection to score the dataset and identify unusual rows.

3. Run inference and export flagged records

   Run inference on your current data. Export only the rows with high anomaly scores; these are your outliers.

4. Run factor analysis on the flagged set

   Upload the flagged records and run Factor Analysis. This surfaces the underlying dimensions that the anomalies have in common.

5. Interpret the factors

   The factors point to which combinations of columns are driving the unusual pattern. Use this to form a hypothesis about root cause.

6. Create an anomaly-only dataset

   Filter to the anomalous rows or to the top-scoring rows above your review threshold.

7. Run clustering on the anomaly-only dataset

   Run Clustering on the filtered dataset. Each cluster becomes a subtype of anomaly.

8. Prioritize the anomaly subtypes

   Compare cluster size, severity, value at risk, and operational owner. Review the subtype that combines high frequency with high impact first.

Support escalation subtypes

Detect unusual support tickets by handle time, reopen count, customer tier, and sentiment. Cluster the outliers into subtypes such as billing escalations, product reliability incidents, and implementation blockers.

Claims review queues

Detect unusual insurance claims, then cluster the flagged claims. One cluster may represent high-dollar legitimate exceptions while another represents suspicious submission patterns.

Recipe 3: Segment, Then Outliers

Use this when global outlier detection is too noisy because different groups behave differently.

Question it answers: Which rows are unusual within a specific segment, not just unusual overall?

1. Run clustering

   Run Clustering on the full dataset to find natural groups.

2. Choose a segment

   Select the largest segment, the highest-value segment, or a segment with a clear business owner.

3. Run anomaly detection inside that segment

   Filter to the selected segment and run Anomaly Detection on those rows.

4. Review within-segment anomalies

   Treat the flagged rows as exceptions relative to their peer group. This is often more actionable than comparing them to the entire dataset.

Enterprise account exceptions

Segment customers by contract size, usage depth, and tenure. Inside the enterprise segment, detect accounts whose usage collapse is unusual relative to other enterprise accounts.

Manufacturing line monitoring

Segment machines by operating condition, then detect outliers inside the high-load segment. A temperature value that is normal globally may be abnormal for machines with similar load and runtime.

Recipe 4: Segment, Then Factors

Use this when you need to explain what separates a group or what drives behavior within one group.

Question it answers: What latent dimensions define this segment or explain differences across segments?

1. Run clustering

   Run Clustering and review the generated cluster labels and descriptions.

2. Pick the comparison frame

   Either run factors on one selected segment or run factors separately for each segment to compare them.

3. Run factor analysis

   Use Factor Analysis on the selected segment data or on each segment export.

4. Turn factors into segment language

   Use high-loading columns to describe the underlying behavior: engagement intensity, price sensitivity, operational friction, account maturity, or another domain-specific concept.

Customer segment explanation

Cluster customers by usage, spend, tenure, and support history. Run factor analysis on a high-churn segment to identify whether the shared structure is low adoption, poor support experience, price pressure, or late-stage inactivity.

Portfolio behavior

Cluster loans by borrower profile, then run factor analysis within each cluster. The factor comparison can show whether one group is driven by utilization, another by payment timing, and another by loan structure.

Recipe 5: Predict, Then Outliers

Use this when the predicted-positive or high-risk set contains records that do not fit the usual pattern.

Question it answers: Which high-risk or high-opportunity predictions are unusual enough to deserve manual review?

1. Train a prediction model on your full dataset

   Upload your labeled data and train a Prediction model. This gives you a model that scores any new record.

2. Train and run prediction

   Train a Prediction model on your labeled target, then run inference on the current rows.

3. Cluster your inference data

   Upload your current (unlabeled) data and run Clustering to assign each record to a segment.

4. Run inference with the prediction model

   Run your prediction model on the same dataset. Each record now has both a cluster label and a predicted outcome score.

5. Compare predicted outcomes across clusters

   Group by cluster and look at the distribution of predicted scores. Which segments have the highest predicted churn? The highest predicted revenue? The most at-risk accounts?

6. Filter to a predicted subset

   Keep high-risk rows, predicted-positive rows, high-opportunity rows, or predictions above a confidence threshold.

7. Run anomaly detection on that subset

   Use Anomaly Detection to find unusual records inside the predicted subset.

8. Review exceptions before action

   Send typical high-scoring rows into the standard workflow and route unusual high-scoring rows to investigation.

Churn saves with exceptions

Predict churn, filter to high-risk accounts, then detect outliers inside that group. A high-risk account with unusually high usage or recent expansion may need a different retention motion.

Credit review

Predict default risk, filter to likely defaults, then detect outliers. Unusual high-risk borrowers can be routed to manual underwriting instead of automated decline.

Recipe 6: Predict, Then Segment

Use this when you know who to act on but need different playbooks for different groups.

Question it answers: What subgroups exist inside the high-risk or high-opportunity population?

1. Run prediction

   Train and run a Prediction model for the target you care about.

2. Create an action subset

   Filter to rows above a score threshold or to rows with the desired predicted class.

3. Cluster the action subset

   Run Clustering on the filtered rows to find natural groups inside the population you plan to act on.

4. Assign an action per segment

   Compare each segment’s size, expected value, and defining traits. Use those differences to choose a treatment, owner, or priority.

Expansion campaigns

Predict expansion likelihood, filter to high-opportunity accounts, then cluster them. One segment may need a product-adoption campaign while another is ready for enterprise sales outreach.

Lead routing

Predict conversion, filter to high-probability leads, then segment them by firmographics and behavior. Use the segments to route leads to different sales motions.

Recipe 7: Predict, Then Factors

Use this when you need to explain the structure behind a predicted subset.

Question it answers: What latent factors characterize the rows the model predicts as important?

1. Run prediction

   Train and run a Prediction model on your target column.

2. Filter to the subset worth explaining

   Keep predicted-positive rows, high-risk rows, high-opportunity rows, confident predictions, or surprising prediction errors if labels are available.

3. Run factor analysis

   Run Factor Analysis on the filtered subset.

4. Translate factors into drivers

   Treat the factors as a summary of what the predicted subset has in common. Use them to write a hypothesis, not as proof of causality.

Churn driver diagnosis

Predict churn, filter to high-risk customers, then run factor analysis. The resulting factors can separate product inactivity, support friction, contract maturity, and price sensitivity.

Conversion opportunity diagnosis

Predict conversion, filter to high-probability leads, then run factor analysis. Factors may reveal whether the strongest leads share company size, engagement depth, industry, or buying-stage behavior.

Choosing a Recipe

Start with	Choose this when	Good second step
outliers	You do not know what is wrong yet, but you can define normal behavior.	Use factors to diagnose common causes or segment to split anomalies into subtypes.
segment	Your dataset contains different populations that should not all be compared together.	Use outliers to find exceptions inside a group or factors to explain group behavior.
predict	You have a target column and want to act on scored rows.	Use outliers, segment, or factors on the predicted subset to decide how to act.

Interpretation Prompt

Use this prompt after you run any recipe:

I ran a compound Wood Wide AI workflow:

Step 1: [FIRST TASK: predict / segment / outliers]
Step 2: [SECOND TASK: segment / outliers / factors]

The business goal is:
[DESCRIBE THE DECISION YOU NEED TO MAKE]

Here is the filtered subset or grouped summary from step 1:
[PASTE SCORES, CLUSTERS, OUTLIER IDS, OR FILTER CRITERIA]

Here is the output from step 2:
[PASTE FACTORS, CLUSTER DESCRIPTIONS, OR ANOMALY RESULTS]

Please help me:
1. Explain what the two-step result means in plain business language
2. Identify the most actionable subset, segment, factor, or outlier pattern
3. Suggest 2-3 hypotheses that could explain the result
4. Recommend what I should verify before making an operational decision