RFM Customer Segmentation

Automatically segment your customer base by purchasing behavior — so every campaign reaches the right audience at the right time.

RFM AI Signals from Treasure AI's machine learning platform (AI Signal) automatically segments your customer base by scoring each customer on three dimensions from their transaction history — Recency (days since last purchase), Frequency (number of purchases), and Monetary value (total spend) — then maps those scores to ten industry-standard segments such as Champions, Loyal Customers, and Lost Customers. The segments feed directly into Master Segments for use in personalization, suppression, win-back campaigns, and loyalty programs, giving marketing and CRM teams a fast, actionable view of who is drifting away and who is primed to buy again — without writing SQL or building a custom model.

Common use cases:

• Identify and reward your top Champions with VIP offers before they are tempted by a competitor
• Trigger automated win-back campaigns for "Cannot Lose Them" customers who have gone quiet
• Suppress low-priority "Lost Customers" from expensive paid media to reduce wasted spend
• Calibrate email frequency — weekly for Champions, major campaigns only for Hibernating segments
• Feed RFM scores as input features into downstream CLTV and churn propensity models

Who benefits most: Marketing analysts, CRM managers, and lifecycle marketing teams who need fast, actionable customer segments without a data science team on standby.

RFM AI Signals accepts a pre-aggregated customer-level table. Each row represents one customer, already summarized across all transactions.

The model writes the following columns to the output table for each customer record.

A sample of the RFM prediction table after a model run:

Reading the output, rfm_score is the arithmetic mean of the three quartile values. A score of 4.0 is a Champion; 1.0 is a Lost customer. The rfm_quartile string encodes each dimension explicitly — R2F1M1 means moderate recency, low frequency, and low spend. When audience_name is configured, rfm_quartile is also written as an attribute column on the Master Segment, enabling direct filtering in the CDP segment builder.

Tuning notes:

• use_sql: true — Enable for any dataset exceeding 10 million behavior rows. It eliminates Python memory overhead and cuts runtime substantially — see the Scalability section for benchmarks.
• mv_threshold — Set when your transaction data includes free-tier signups or negligible purchases that would otherwise distort quartile boundaries.
• output_mode: append — Safe for scheduled runs since each run adds a new scored snapshot. Use replace only when you want a single current-state table.

RFM AI Signals runs via the ML Batch API, called from a Treasure Workflow. The workflow below is a complete, copy-pasteable example.

# rfm_signal_workflow.dig
# Treasure Workflow: RFM AI Signals (PrecisionML)
# Submits an RFM scoring job to the ML Batch API, then polls
# for completion before proceeding to downstream steps.

# Step 1: Submit the RFM job to the ML Batch API
+run_rfm:
  http>: https://ml-batch-api.treasuredata.com/v1/runs/
  method: POST
  headers:
    - authorization: ${secret:td.apikey}
    - X-TD-ML-SESSION-ID: ${session_id}
    - X-TD-ML-ATTEMPT-ID: ${attempt_id}
  store_content: true
  content:
    input_table: your_database.aggregated_customers
    output_table: your_database.rfm_output
    solution_name: rfm
    solution_arguments:
      user_column: user
      use_sql: true
      # mv_threshold: 0.05   # Uncomment to suppress near-zero monetary values

# Step 2: Log the API response (job ID used for polling)
+print_response:
  echo>: "Job submitted. Response: ${http.last_content}"

# Step 3: Poll until the job is complete
# The API returns HTTP 408 while the job is running — Treasure Workflow
# automatically retries this step until it receives HTTP 200.
+poll_status:
  http>: https://ml-batch-api.treasuredata.com/v1/runs/${JSON.parse(http.last_content)['id']}/status
  method: GET
  headers:
    - authorization: ${secret:td.apikey}

# Step 4: Confirm completion
+finished:
  echo>: "RFM scoring complete. Results written to your_database.rfm_output"

What this workflow does: The workflow submits an RFM configuration to the ML Batch API and receives a job ID in the response. It then polls the status endpoint in a retry loop — the API returns HTTP 408 while the job is running, and Treasure Workflow retries automatically until it receives HTTP 200. Once complete, the scored output table is ready for downstream activation or audience creation.

RFM AI Signals maps quartile combinations to ten industry-standard segments. Higher quartile values indicate better performance on that dimension.

RFM AI Signals is designed to handle enterprise-scale datasets. The table below shows measured runtimes across varying dataset sizes. Enable use_sql: true for any dataset above 10 million behavior rows.

For very large datasets, parallelizing across 8 tasks (the maximum concurrency per account) can reduce wall-clock time dramatically — the 800 million row case dropped from ~445 minutes to ~96 minutes using parallel execution. Contact your Treasure AI account team if your dataset regularly exceeds 500 million behavior rows to discuss infrastructure options.

• Works best with at least 12 months of transaction history. Shorter windows compress the recency signal and make new customers hard to distinguish from returning ones.
• Quartile ranking is relative, not absolute. A "Champion" in a low-activity period may spend less than a "Loyal Customer" in a high-activity period. Interpret segment labels in the context of when the model was run.
• Not designed for subscription or SaaS products. When monetary value is flat across customers (e.g., fixed-price subscriptions), the M dimension loses predictive power. Consider substituting engagement metrics (logins, feature usage) in a pre-aggregated input instead.
• Does not predict future behavior. RFM describes past patterns. For forward-looking CLTV forecasting, combine RFM scores with probabilistic models (BG/NBD, Gamma-Gamma) as downstream features.
• Sensitive to data freshness. If the input table is not kept current, recency scores drift and segments become stale. Schedule workflow runs to match your campaign cadence — weekly for fast-moving retail, monthly for lower-frequency categories.
• Pre-aggregated input requires clean data. Missing value handling is not performed automatically. Null or negative values in recency, frequency, or monetary_value will produce incorrect scores and must be handled upstream.