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Advanced Analysis (Iara)

Iara is the advanced analytics workflow for questions that need structured methods such as clustering, forecasting, anomaly detection, basket analysis, or driver analysis.

You can still start directly from this page, but the current product also hands questions here from Ask when a conversational answer is not enough.

Screenshot of the Advanced Analysis page showing the hero prompt area with example question chips

How It Works​

  1. Ask — Describe what you want to analyze in plain language
  2. Plan — The AI creates an analysis plan with multiple techniques
  3. Run — Analyses execute in parallel in the background
  4. Results — View comprehensive findings with charts, tables, and explanations

Starting an Analysis​

There are now two common entry paths:

  1. Direct entry — open Advanced Analysis when you already know the question needs a deeper method
  2. Ask handoff — continue a question from Ask when the assistant detects that it needs structured analysis

Screenshot showing the question input area with example prompts listed below

Example Prompts​

PromptWhat It Runs
"Segment customers by purchasing behavior"Clustering + RFM Segmentation
"Products frequently bought together"Basket Analysis
"Forecast next quarter's revenue"Time Series Forecasting
"What drives my sales the most?"Causality / Driver Analysis
"Find unusual patterns in my data"Anomaly Detection

Analysis Plan​

After submitting your question, the AI generates a plan showing which analysis types it will run:

Screenshot of the analysis plan showing checkboxes for each analysis type with descriptions

You can toggle specific analyses on or off before starting the run.

Analysis Types​

1. Customer Clustering​

Groups your customers into segments based on behavioral patterns.

Screenshot showing clustering results with segment visualization and descriptions

Results include:

  • Number of clusters identified
  • Cluster descriptions (e.g., "High-value frequent buyers", "Occasional bargain hunters")
  • Data points per cluster
  • Clustering method used (K-Means, DBSCAN, etc.)

2. Basket Analysis​

Discovers products frequently purchased together (association rules).

Screenshot showing basket analysis results with top association rules

Results include:

  • Number of rules found
  • Top rules with:
    • Support — How often items appear together (%)
    • Confidence — How likely B is bought when A is bought (%)
    • Lift — How much more likely compared to random chance

3. Time Series Forecasting​

Predicts future values of a metric based on historical trends.

Screenshot showing a line chart with historical data and forecast projection

Results include:

  • Historical trend line
  • Forecast projection with confidence interval
  • Forecasting method (ARIMA, Prophet, etc.)
  • Forecast horizon (configurable)

4. Causality / Driver Analysis​

Identifies which factors most influence a target metric.

Screenshot showing driver analysis with coefficient bars and Granger test results

Results include:

  • Lasso coefficients — Which variables have the strongest impact
  • Granger causality tests — Whether one time series predicts another
  • Rankings of drivers by impact size

5. Anomaly Detection​

Finds unusual patterns and outliers in your data.

Screenshot showing detected anomalies highlighted on a time series chart

Results include:

  • Total anomalies detected
  • Detection method used
  • Table of anomalous data points with dates and values
  • Severity scoring

6. Survival Analysis​

Estimates customer lifetime and retention probabilities.

Screenshot showing a Kaplan-Meier survival curve

Results include:

  • Median survival time
  • Survival probability curve (Kaplan-Meier)
  • Retention rates at key intervals (30, 60, 90 days)

7. RFM Segmentation​

Classifies customers by Recency, Frequency, and Monetary value.

Screenshot showing RFM treemap, heatmap, and actionable segment labels

Results include:

  • Segment breakdown — Treemap showing segment sizes
  • R × F Heatmap — Recency vs Frequency matrix
  • Top/Bottom customers — With anonymized IDs
  • Actionable labels: Reward, Upsell, Engage, Win-Back, At-Risk, etc.

Interpreting Results​

Data Quality Warnings​

The AI may show warnings when data quality affects analysis:

WarningMeaningWhat to Do
Few rowsNot enough data points for statistical significanceUpload more historical data
No varianceAll values are the same — nothing to analyzeCheck data for errors
Short date rangeTime series too short for reliable forecastingWait for more data to accumulate

Suggested Alternatives​

When an analysis type produces low-quality results, the AI suggests alternative approaches:

"Clustering results are low confidence with your current data. Consider running RFM Segmentation instead, which works well with transactional data."

Analysis History​

View past analyses in the History sidebar (right side of the page):

Screenshot of the history sidebar showing past analyses grouped by batch with status chips

  • Analyses grouped by batch (each prompt creates a batch)
  • Status chips (Completed, Running, Failed)
  • Click to view results from any past analysis

Exporting Results​

Click Export PDF to download a formatted report of any completed analysis, including charts and summary text.

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Tip: Deep analyses are most powerful when combined. For example:

  1. Run Clustering to segment customers
  2. Run Basket Analysis per segment to find segment-specific product affinities
  3. Run Forecasting on revenue per segment to predict growth