Top tips to maximize value with Adobe Experience Platform Data Distiller - OS656
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- Queries
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This page contains the sample dataset for you to apply what you learned in the Adobe Summit session “OS656 - Top Tips to Maximize Value with Adobe Experience Platform Data Distiller”. You’ll learn how to accelerate implementations of Adobe Real-Time Customer Data Platform and Journey Optimizer by enriching Real-Time Customer Profile data. This enrichment leverages deep insights into customer behavior patterns to build audiences for experience delivery and optimization.
Through the Luma case study, you’ll analyze user behavioral data and create a Recency, Frequency, Monetary (RFM) model—a marketing analysis technique for customer segmentation based on purchasing patterns.
Prerequisite
To execute this use case, your Adobe Experience Platform instance must be licensed for Data Distiller. Contact your Adobe representative for more information.
You also need to know your organization’s tenant ID, which is required for executing queries. Your tenant ID is the first part of the URL when you log into Experience Platform, appearing immediately after the @ symbol.
For example, in the following URL:
https://experience.adobe.com/#/@pfreportingonprod/sname:prod/platform/home
The tenant ID is pfreportingonprod.
Overview of the RFM model
RFM, short for Recency ®, Frequency (F), and Monetary (M), is a data-driven approach to customer segmentation and analysis. This methodology evaluates three key aspects of customer behavior: how recently a customer made a purchase, how often they engage, and how much they spend. By quantifying these factors, businesses can gain actionable insights into customer segments and develop targeted marketing strategies that better meet individual customer needs.
Understand customer behavior with the RFM model
The RFM model segments customers based on transactional behavior using three key parameters.
- Recency measures the time since a customer’s last purchase, indicating engagement levels and future buying potential.
- Frequency tracks how often a customer interacts, serving as a clear indicator of loyalty and sustained engagement.
- Monetary value assesses the total spending by customers, highlighting their overall value to the business.
By combining these factors, businesses assign numerical scores (typically on a scale from 1 to 4) to each customer. Lower scores indicate more favorable outcomes. For example, a customer scoring 1 in all categories is considered among the best, demonstrating recent activity, high engagement, and significant spending.
Benefits and limitations of the RFM model
Every marketing modeling technique involves trade-offs, offering both benefits and limitations. RFM modeling is a valuable tool for understanding customer behavior and refining marketing strategies. Its advantages include segmenting customers to personalize messaging, optimize revenue, and improve response rates, retention, satisfaction, and Customer Lifetime Value (CLTV).
However, RFM modeling has limitations. It assumes uniformity within segments based on recency, frequency, and monetary value, which may oversimplify customer behavior. The model also assigns equal weight to these factors, potentially misrepresenting customer value. Additionally, it does not account for context, such as product-specific traits or customer preferences, which can lead to misinterpretations of purchasing behavior.
Build a dynamic RFM score-based SQL audience
Before starting the Luma case study, you need to ingest a sample dataset. First, select the link to download the luma_web_data.zip dataset locally. The sample dataset is a csv file in a compressed .zip format to align with the use case. Decompress this ZIP file using Adobe Acrobat or a trusted file extraction tool, such as your operating system’s built-in utility. In practice, you would typically source data from Adobe Analytics, Adobe Commerce, or Adobe Web/Mobile SDK.
Throughout this tutorial, you will use Data Distiller to extract relevant events and fields into a standardized CSV format. The goal is to include only essential fields while maintaining a flat data structure for efficiency and ease of use.
Step 1: Upload the CSV data into Experience Platform
Follow these steps to upload a CSV file to Adobe Experience Platform.
Create a dataset from a CSV file
In the Experience Platform UI, select Datasets in the left navigation rail, followed by Create dataset. Then select Create dataset from CSV file from the available options.
The Configure Dataset panel appears. In the Name field, input the dataset name as “luma_web_data” and select Next.
The Add data panel appears. Drag and drop the CSV file into the Add data box, or select Choose File to browse and upload the file.
To learn more about this process, refer to the batch ingestion tutorial and the dataset creation workflow in the Dataset UI guide.
Review and complete the upload
Once the file is uploaded, a data preview appears at the bottom of the UI. Select Finish to complete the upload.
The dataset activities view for the “luma_web_data” dataset appears. The manual upload of the CSV file
is ingested as a batch, and identified by a Batch ID. A panel on the right-side displays the table name as luma_web_data.
TIP
Once the data has finished processing, select Preview dataset in the top right corner to preview the dataset. This is how the dataset preview appears:
Schema considerations
A structured XDM schema (for example, record, event, or B2B schemas) is not required because the data is imported as a raw CSV file. Instead, the dataset uses an ad hoc schema.
TIPWhile Data Distiller supports all schema types, the final dataset for ingestion into the Real-Time Customer Profile will use a Record XDM schema.
Step 2: Connect to the data lake and explore available datasets
The next step is to explore data in the Adobe Experience Platform data lake to ensure accuracy and integrity. Data must be accurate and complete to generate meaningful insights, but errors, inconsistencies, or missing values can occur during data transfers. This makes data verification and exploration essential.
TIPUse Data Distiller to verify dataset quality and completeness through various operations. To confirm that data was accurately translated during ingestion, execute SELECT queries to inspect, validate, and analyze it. This process helps identify and resolve discrepancies, inconsistencies, or missing information.
Perform a basic exploration query
In the Adobe Experience Platform UI, select Queries in the left navigation rail then select Create Query. The Query Editor appears.
Paste the following query into the editor and execute it:
SELECT * FROM luma_web_data;
The query results are displayed below the Query Editor in the Results tab. To expand the results in a new dialog, select View results. The results look similar to the image below.
See the general guidance for query execution document for more information.
Focus on orders and exclude cancelled transactions
The RFM model evaluates recency, frequency, and monetary value based on completed purchases. Non-transactional events, such as page views and checkout interactions, are excluded from analysis. Additionally, cancelled orders must be removed, as they do not contribute to valid RFM calculations and require a different processing approach.
To ensure accuracy:
- Identify purchase IDs associated with cancellations and group them using
GROUP BY. - Exclude these purchase IDs from the dataset.
- Filter the data to retain only completed orders.
The following queries demonstrate how to identify and exclude cancelled orders from the dataset.
This first query selects all non-null purchase IDs associated with a cancellation and aggregates them using GROUP BY. The resulting purchase IDs must be excluded from the dataset.
CREATE VIEW orders_cancelled
AS
SELECT purchase_id
FROM luma_web_data
WHERE event_type IN ( 'order', 'cancellation' )
AND purchase_id IS NOT NULL
GROUP BY purchase_id
HAVING Count(DISTINCT event_type) = 2;
The second query retrieves only the purchase IDs that are not in this excluded set.
SELECT *
FROM luma_web_data
WHERE purchase_id NOT IN (SELECT purchase_id
FROM orders_cancelled)
OR purchase_id IS NULL;
The third query removes all non-order events from the dataset.
SELECT *
FROM luma_web_data
WHERE event_type = 'order'
AND purchase_id NOT IN (SELECT purchase_id
FROM orders_cancelled);
Step 3: Enrich the data using Data Distiller functions
Next, use Data Distiller to extract and transform customer data, generate RFM scores, aggregate transactions, and segment customers by purchasing behavior. Follow these steps to calculate Recency, Frequency, and Monetary (RFM) values, build an audience model, and prepare insights for activation.
Calculate the RFM score for each unique user ID
To compute the RFM scores, extract key fields from the raw data using field filtering.
The next query builds on the previous section’s logic by selecting email as the userid, since every order requires an email login. Data Distiller applies the TO_DATE function to convert the timestamp into a date format. The total_revenue field represents the price of each transaction and is later aggregated by summing it for each userid.
SELECT email AS userid,
purchase_id AS purchaseid,
price_total AS total_revenue, -- reflects the price for each individual transaction
TO_DATE(timestamp) AS purchase_date -- converts timestamp to date format
FROM luma_web_data
WHERE event_type = 'order'
AND purchase_id NOT IN (SELECT purchase_id FROM orders_cancelled)
AND email IS NOT NULL;
The results look like the image below.
Next, create a TABLE to store the results of the previous query in a derived dataset. Copy and paste the following command into the Query Editor to create a TABLE.
CREATE TABLE IF NOT EXISTS order_data AS
SELECT email AS userid,
purchase_id AS purchaseid,
price_total AS total_revenue,
To_date(timestamp) AS purchase_date
FROM luma_web_data
WHERE event_type = 'order'
AND purchase_id NOT IN (SELECT purchase_id FROM orders_cancelled)
AND email IS NOT NULL;
The result looks similar to the following image but with a different dataset ID.
As best practice, run a simple explore query to inspect the data in the dataset. Use the following statement to view your data.
SELECT * FROM order_data;
Aggregate the transactions to generate the RFM values
To calculate the RFM values, this query aggregates transactions for each user.
The DATEDIFF(CURRENT_DATE, MAX(purchase_date)) AS days_since_last_purchase function calculates the number of days since the most recent purchase for each user.
Use the following SQL query:
SELECT
userid,
DATEDIFF(CURRENT_DATE, MAX(purchase_date)) AS days_since_last_purchase,
COUNT(purchaseid) AS orders,
SUM(total_revenue) AS total_revenue
FROM order_data
GROUP BY userid;
The results look like the image below.
To enhance query efficiency and reusability, create a VIEW to store the aggregated RFM values.
CREATE VIEW rfm_values
AS
SELECT userid,
DATEDIFF(current_date, MAX(purchase_date)) AS days_since_last_purchase,
COUNT(purchaseid) AS orders,
SUM(total_revenue) AS total_revenue
FROM order_data
GROUP BY userid;
The result resembles the following image but with a different ID.
Again as best practice, run a simple explore query to inspect the data in the view. Use the following statement.
SELECT * FROM rfm_values;
The following screenshot shows a sample result of the query, displaying the calculated RFM values for each user. The result corresponds to the view ID from the CREATE VIEW query.
Generate the RFM multi-dimensional cube
To segment customers based on their RFM scores, use an RFM multi-dimensional cube. The NTILE window function sorts values into ranked buckets and divides each dimension into four equal groups (quartiles), allowing for structured segmentation.
- Recency: Customers are ranked by how recently they made a purchase (
days_since_last_purchase). Those who purchased most recently are in group 1, while those who haven’t purchased for the longest time are in group 4. - Frequency: Customers are ranked by how often they make purchases (
ORDER BY orders DESC). The most frequent buyers are in group 1, while the least frequent are in group 4. - Monetary: Customers are ranked by total spending (
total_revenue). The highest spenders are in group 1, while the lowest spenders are in group 4.
Run the following SQL query to generate the RFM multi-dimensional cube:
SELECT userid,
days_since_last_purchase,
orders,
total_revenue,
5 - NTILE(4)
OVER (
ORDER BY days_since_last_purchase DESC) AS recency,
NTILE(4)
OVER (
ORDER BY orders DESC) AS frequency,
NTILE(4)
OVER (
ORDER BY total_revenue DESC) AS monetization
FROM rfm_values;
The results look like the images below.
Next, use the following statement to create a VIEW for this data.
Creating a VIEW for the RFM multi-dimensional cube improves efficiency by storing pre-segmented data, eliminating the need to recalculate RFM scores in future queries. It simplifies SQL statements, ensures data consistency, and enhances reusability for further analysis.
CREATE OR replace VIEW rfm_scores
AS
SELECT userid,
days_since_last_purchase,
orders,
total_revenue,
5 - NTILE(4)
over (
ORDER BY days_since_last_purchase DESC) AS recency,
NTILE(4)
over (
ORDER BY orders DESC) AS frequency,
NTILE(4)
over (
ORDER BY total_revenue DESC) AS monetization
FROM rfm_values;
The result looks similar to the following image but with a different view ID.
Model RFM segments
With the RFM scores calculated, customers can be categorized into the following six priority segments:
Core: Best customers with high Recency, Frequency, and Monetary value (Recency = 1, Frequency = 1, Monetary = 1).Loyal: Frequent customers who are consistent but not top spenders (Frequency = 1).Whales: Highest spenders, regardless of Recency and Frequency (Monetary = 1).Promising: Frequent but lower spenders (Frequency = 1, 2, 3; Monetary = 2, 3, 4).Rookies: New customers with low frequency (Recency = 1, Frequency = 4).Slipping: Formerly loyal customers with decreased activity (Recency = 2, 3, 4; Frequency = 4).
To streamline access and reuse, create a VIEW that stores the RFM segments, scores, and values.
The CASE statements in the following SQL categorize customers into segments based on their RFM scores and assign the results to the RFM_Model variable.
CREATE OR replace VIEW rfm_model_segment
AS
SELECT userid,
days_since_last_purchase,
orders,
total_revenue,
recency,
frequency,
monetization,
CASE
WHEN recency = 1
AND frequency = 1
AND monetization = 1 THEN '1. Core - Your Best Customers'
WHEN recency IN( 1, 2, 3, 4 )
AND frequency = 1
AND monetization IN ( 1, 2, 3, 4 ) THEN
'2. Loyal - Your Most Loyal Customers'
WHEN recency IN( 1, 2, 3, 4 )
AND frequency IN ( 1, 2, 3, 4 )
AND monetization = 1 THEN
'3. Whales - Your Highest Paying Customers'
WHEN recency IN( 1, 2, 3, 4 )
AND frequency IN ( 1, 2, 3 )
AND monetization IN( 2, 3, 4 ) THEN
'4. Promising - Faithful customers'
WHEN recency = 1
AND frequency = 4
AND monetization IN ( 1, 2, 3, 4 ) THEN
'5. Rookies - Your Newest Customers'
WHEN recency IN ( 2, 3, 4 )
AND frequency = 4
AND monetization IN ( 1, 2, 3, 4 ) THEN
'6. Slipping - Once Loyal, Now Gone'
END RFM_Model
FROM rfm_scores;
The generated VIEW follows the same structure as previous creations, but with a different ID.
As best practice, run a simple explore query to inspect the data in the view. Use the following statement.
SELECT * FROM rfm_model_segment;
The following screenshots display a sample result of the SELECT * FROM rfm_model_segment; query, showing the segmented RFM model data. The output reflects the structure of the generated VIEW, including assigned customer segments based on RFM scores.
Step 4: Use SQL to batch ingest RFM data into Real-Time Customer Profile
Next, batch ingest RFM-enriched customer data into Real-Time Customer Profile. Begin by creating a Profile-enabled dataset and inserting the transformed data using SQL.
Create a derived dataset to store RFM attributes
As this dataset will be ingested into the Profile Store, it requires a partition key.
TIPCreate an empty dataset to store RFM attributes and assign a primary identity.
In this SQL statement:
userId TEXT PRIMARY IDENTITY NAMESPACE 'Email': Defines the userId column as the primary identity using the ‘Email’ namespace.days_since_last_purchase INTEGER: Stores the number of days since the user’s last purchase.orders INTEGER: Represents the total number of orders placed by the user.total_revenue DECIMAL(18, 2): Captures the total revenue generated by the user, with precision up to 18 digits and two decimal places.recency INTEGER, frequency INTEGER, monetization INTEGER: Store the respective RFM scores for the user.rfm_model TEXT: Holds the RFM segment classification assigned to the user.WITH (LABEL = 'PROFILE'): Marks the table as Profile-enabled in Experience Platform, ensuring that the ingested data contributes to building Real-Time Customer Profiles.
NOTESince the Query Editor supports sequential execution, you can include the table creation and data insertion queries in a single session. The following SQL first creates a Profile-enabled table to store RFM attributes. Then, it inserts RFM-enriched customer data from rfm_model_segment into the adls_rfm_profile table, structuring each record under your tenant-specific namespace which is required for Real-Time Customer Profile ingestion.
Since the Query Editor supports sequential execution, you can run the table creation and data insertion queries in a single session. The following SQL first creates a Profile-enabled table to store RFM attributes. Then, it inserts RFM-enriched customer data from rfm_model_segment into the adls_rfm_profile table, ensuring that each record is properly structured under your tenant-specific namespace (_{TENANT_ID}). This namespace is essential for Real-Time Customer Profile ingestion and accurate identity resolution.
IMPORTANT_{TENANT_ID} with your organization’s tenant namespace. This namespace is unique to your organization and ensures that all ingested data is correctly assigned in Adobe Experience Platform.CREATE TABLE IF NOT EXISTS adls_rfm_profile (
userId TEXT PRIMARY IDENTITY NAMESPACE 'Email',
days_since_last_purchase INTEGER,
orders INTEGER,
total_revenue DECIMAL(18, 2),
recency INTEGER,
frequency INTEGER,
monetization INTEGER,
rfm_model TEXT
) WITH (LABEL = 'PROFILE');
INSERT INTO adls_rfm_profile
SELECT STRUCT(userId, days_since_last_purchase, orders, total_revenue, recency,
frequency, monetization, rfm_model) _{TENANT_ID}
FROM rfm_model_segment;
The result of this query resembles previous dataset creations in this playbook but with a different ID.
After creating the dataset, navigate to Datasets > Browse > adls_rfm_profile to verify that the dataset is empty.
You can also navigate to Schemas > Browse > adls_rfm_profile to view the XDM Individual Profile Schema diagram of your newly created dataset, and its custom field groups.
Insert data into the newly created derived dataset
Next, insert the data from the rfm_model_segment VIEW into adls_rfm_profile, which is enabled for Real-Time Customer Profile.
Ensure that the field order in the SELECT query of the INSERT statement matches the structure of rfm_model_segment exactly. This alignment ensures that values from rfm_model_segment are inserted correctly into the corresponding fields in the target table. Misalignment between source and target fields can cause data mismatches.
NOTEINSERT INTO adls_rfm_profile
SELECT Struct(userid, days_since_last_purchase, orders, total_revenue, recency,
frequency, monetization, rfm_model) _{TENANT_ID}
FROM rfm_model_segment;
Once complete, the query output displays “Query complete” in the console.
Step 5: Schedule the query for batch processing
Now that your SQL code generates a derived dataset and enables it for Real-Time Customer Profile, the next step is to automate updates by scheduling the query to run at specific intervals. Through automatic dataset updates, you eliminate the need for manual execution.
Schedule the query execution
After saving your SQL, navigate to the Templates tab to view the saved query and initiate the scheduling process. There are two ways to schedule a query:
Select Add Schedule from the right sidebar.
Alternatively, select the Schedules tab below the template name, then select Add Schedule.
For more details on scheduling queries, refer to the Query Schedules documentation.
The Schedule details view appears. From here, input the following details to configure the schedule:
- Execution Frequency: Weekly
- Day of Execution: Monday & Tuesday
- Schedule Execution Time: 10:10 AM UTC
- Schedule Period: March 17 – April 30, 2025
Select Save to confirm the schedule.
After you have saved the schedule, you can navigate to the Scheduled Queries tab at any point to monitor scheduled Data Distiller jobs. For more details on viewing query execution status, error messages, and alerts, see the monitor scheduled queries document.
Once configured, the SQL query runs automatically at the defined intervals, ensuring data remains up to date without requiring manual intervention.
Step 6: Create and activate an RFM-based audience
There are two methods to create and activate an RFM-based audience in this tutorial.
- Solution 1: Create and activate an audience directly using Data Distiller and SQL queries.
- Solution 2: Define and manage an audience in the Experience Platform UI using precomputed RFM attributes, without SQL.
Choose the approach that best suits your workflow.
Solution 1: SQL audience via Data Distiller
Use the CREATE AUDIENCE AS SELECT command to define a new audience. The created audience is saved in a dataset and registered in the Audiences workspace under Data Distiller.
Audiences created using the SQL extension are automatically registered under the Data Distiller origin in the Audiences workspace. From Audience Portal, you can view, manage, and activate your audiences as needed.
For more details on SQL audiences, refer to the Data Distiller Audiences documentation. To learn how to manage audiences in the UI, see the Audiences Portal overview.
Create an audience
To create an audience, use the following SQL commands:
-- Define an audience for best customers based on RFM scores
CREATE AUDIENCE rfm_best_customer
WITH (
primary_identity = _{TENANT_ID}.userId,
identity_namespace = queryService
) AS (
SELECT * FROM adls_rfm_profile
WHERE _{TENANT_ID}.recency = 1
AND _{TENANT_ID}.frequency = 1
AND _{TENANT_ID}.monetization = 1
);
-- Define an audience that includes all customers
CREATE AUDIENCE rfm_all_customer
WITH (
primary_identity = _{TENANT_ID}.userId,
identity_namespace = queryService
) AS (
SELECT * FROM adls_rfm_profile
);
-- Define an audience for core customers based on email identity
CREATE AUDIENCE rfm_core_customer
WITH (
primary_identity = _{TENANT_ID}.userId,
identity_namespace = Email
) AS (
SELECT * FROM adls_rfm_profile
WHERE _{TENANT_ID}.recency = 1
AND _{TENANT_ID}.frequency = 1
AND _{TENANT_ID}.monetization = 1
);
Create an empty audience dataset
Before adding profiles, create an empty dataset to store audience records.
-- Create an empty audience dataset
CREATE AUDIENCE adls_rfm_audience
WITH (
primary_identity = userId,
identity_namespace = Email
) AS
SELECT
CAST(NULL AS STRING) userId,
CAST(NULL AS INTEGER) days_since_last_purchase,
CAST(NULL AS INTEGER) orders,
CAST(NULL AS DECIMAL(18,2)) total_revenue,
CAST(NULL AS INTEGER) recency,
CAST(NULL AS INTEGER) frequency,
CAST(NULL AS INTEGER) monetization,
CAST(NULL AS STRING) rfm_model
WHERE FALSE;
Insert profiles into an existing audience
To add profiles to an existing audience, use the INSERT INTO command. This allows you to add individual profiles or entire audience segments to an existing audience dataset.
-- Insert profiles into the audience dataset
INSERT INTO AUDIENCE adls_rfm_audience
SELECT
_{TENANT_ID}.userId,
_{TENANT_ID}.days_since_last_purchase,
_{TENANT_ID}.orders,
_{TENANT_ID}.total_revenue,
_{TENANT_ID}.recency,
_{TENANT_ID}.frequency,
_{TENANT_ID}.monetization
FROM adls_rfm_profile
WHERE _{TENANT_ID}.rfm_model = '6. Slipping - Once Loyal, Now Gone';
Delete an audience
To delete an existing audience, use the DROP AUDIENCE command. If the audience does not exist, an exception occurs unless IF EXISTS is specified.
DROP AUDIENCE IF EXISTS adls_rfm_audience;
Solution 2: Create an audience with RFM attributes
Use RFM attributes to segment users based on their behavior and characteristics. This section guides you through the Adobe Experience Platform UI to define an audience using RFM scores.
To verify that the data has been loaded into Real-Time Customer Profile, navigate to Customers > Profiles > Browse. Select Identity Namespace as Email and enter user0076@example.com. Check the profile details to confirm that it contains the expected RFM attributes.
To browse existing audiences, select Audiences from the left navigation panel and ensure the Browse tab is selected. The list of available audiences in the sandbox appears. Selecting an audience displays its description, qualifying rules, and the number of profiles included.
To create a new audience, select Create Audience in the top-right corner. A dialog box appears with two options. Select Build Rule followed by Create.
The Audience Composition UI provides access to profile attributes. Navigate to Attributes > XDM Individual Profile to view available attributes.
For more details on using Audience Composition, see the Audience Composition UI guide. For more details on using Segment Builder, see the Segment Builder UI guide.
Custom attributes created in Data Distiller are stored in the folder matching the tenant namespace name, which appears next to the sandbox name. These attributes can be used to define audience segmentation criteria.
To build an audience using RFM attributes, drag and drop the Rfm_Model attribute into the Audience Composer. These attributes can be used for Edge, Streaming, and Batch Audiences.
To finalize the audience, select Save and Publish in the top-right corner. After saving, the newly created audience appears in the Audiences workspace, where you can review its summary and qualifying criteria.
Use the Segment Builder to access the derived RFM attributes and design additional audiences. Activate the newly created SQL audience based on RFM scores and send it to any preferred destination, including Adobe Journey Optimizer.