RFM Customer Segmentation (BigQuery + LookerStudio)

  • (1)   6 purchases

€199   €399

Use RFM analysis to segment your customers based on their past purchasing behavior and tailor your marketing strategies to maximize engagement. Designed exclusively for Google BigQuery and Looker Studio.
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  • Minor technical knowledge needed — Run SQL Queries
    Tech used
    /images/products/source/crm.svg /images/products/source/google_bigquery.svg /images/products/source/looker-studio.svg
    ( +JustSQL Builder support)

    By proceeding with your purchase, you acknowledge that you have read and agree to our Template License Agreement.

    • One-Time payment
    • No access to your data


    RFM Segmentation Analysis is a data-driven customer segmentation technique used by businesses to understand and categorize their customers based on their past purchasing behavior.

    The primary objective of RFM analysis is to efficiently identify and target distinct customer segments for marketing campaigns, aiming to retain valuable customers, engage existing ones, and re-engage those who have left.

    This technique relies on three important components: Recency (days since the last transaction), Frequency (total number of transactions), and Monetary Value (total amount spent).

    This Template is designed exclusively for Google BigQuery and Looker Studio.

    What you'll get

    Access to QueryBuilder

    For this professional-grade level of analysis, you will need to preprocess your data. To accomplish this, you will have to create some views or tables in your BigQuery.
    QueryBuilder generates complex queries for you easily by just mapping your database schema.
    This way, you do not need any prior experience, and you ensure that access to your data is not shared with anyone but yourself.

    Own the Dashboard
    1. RFM Overview
    • Get an Overview for RFM Segments characteristics and understand Customer behavior.
    2. RFM Movements
    • Inspect the movements of RFM Segments over time using Historical RFM or RFMt.
    3. RFM Cohorts
    • Inspect RFM Segments per Cohort Month - User Acquisition Month.
    4. Recency Analysis (Days Since Last Transaction)
    • Analyze the Recency Component.
    5. Frequency Analysis (Total Transactions)
    • Analyze the Frequency Component.
    6. Monetary Analysis (Total Net Revenue)
    • Analyze the Monetary Component.
    7. Recommendations
    • Get recommended strategies per Customer.

    🡒 You gain access to our resources such as Template Google Sheet and 1click report generator for 7 days from the date of purchase, subject to fair use as outlined in the Templates License Agreement.


    Edit access to Google BigQuery Console for executing SQL statements.

    Slug rfm-customer-segmentation-crm-bq-ls-template
    Product Type Templates
    Category Customer Segmentation
    Tags RFM, Customer Segmentation
    Audience eCommerce, Agencies
    Business Type Subscription Based, Repeat Retail
    Setup Time 10'
    Setup Difficulty 2 | Minor technical knowledge needed — Run SQL Queries
    Tech Used CRM - Google BigQuery - Looker Studio

    How to Use

    Video Thumbnail

    This guide provides instructions on how to perform professional-grade RFM Customer Segmentation analysis in Looker Studio, for data stored in Google BigQuery.

    Step 1 : Get your data ready

    To generate your report, you will need to prepare your data by creating some views or tables in BigQuery.
    No SQL knowledge is required—just access to BigQuery where your data is stored to run the SQL provided in Query Builder.

    A. Open your Google BigQuery Console here.

    B. Visit the Query Builder and succesfully complete all the steps to prepare your data.

    Note 1 : To generate SQL statements, you will need to provide your Dataset ID. Please make a note of it because you will need it in Step 2.
    If you do not know how to find your Dataset ID, please read here.

    Step 2 : Generate your Report

    To automatically create your own copy of the report in 1-click, follow the steps below.

    A. Enter your BigQuery Dataset ID in the input box and click the Generate report button, located at the end of this page.

    B. After being redirected to the report, click the Edit & Share button to review the data sources that are linked to the Report.
    The button is located on the top navbar, on the right. If you do not see it, you will need to hover over the top navbar.

    C. Finally click the Acknowledge and Save button.
    You have now generated your own copy of the report and is saved to your Looker Studio account. You have full access and control over it.

    Step 3 : Keep your Report up-to-date

    If you choose to create views in your database in Step 1 then then the report will automatically update when the database updates.
    If you choose to create tables in your database in Step 1 then you will need to rerun or schedule the SQL generated in Step 1.

    Note 1 : You only have access to the copy of the report.
    Note 2 : We do not have access to your data.


    Data Analyst & Engineer

    I got this template working in a few clicks and in no time at all (to be accurate, in 15 minutes)! I could not imagine that something as complicated as RFM Historical could be so easy to implement. The design is so clean and straightforward, which helps a lot in figuring out what needs to be done. I definitely recommend it to anyone, and I can't wait to see what will come up next!

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    We have categorized our templates into four groups based on the setup difficulty. Below is the breakdown:

    1 (Green) - Setup Difficulty: No technical knowledge
    Skills : No technical knowledge needed — Use the Browser or Spreadsheets
    Description: 1-click setup for Services that are supported as native connections. For example, connect Google services like Google Ads, Google Search Console, and Google Analytics 4 to Looker Studio with a single click or connect a Google Spreadsheet with your data.

    2 (Blue) - Setup Difficulty: Minor technical knowledge
    Skills : Minor technical knowledge needed — Run SQL Queries
    Description: Utilize JustSQL Builder for more complex query generation for Bigquery or PostgreSQL, then establish a native connection with the database. Suitable for backend data or data from ELT services like Airbyte, Fivetran, or Stitch Data.

    3 (Orange) - Setup Difficulty: Moderate technical knowledge
    Skills: Moderate technical knowledge needed — Run Python Scripts & SQL Queries
    Description: Run Python code to generate data and save them in BigQuery or PostgreSQL, then establish a native connection with the database.

    ***Several cases will support more than 1 method.

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    JustSQL Builder is an advanced SQL Builder designed to simplify the creation of complex queries. For example, to prepare your data for RFM segmentation and cohort analysis, simply adjust the schema (modify fields and tables based on your database setup) and then click 'Generate' to generate the final SQL statement. You can access JustSQL Builder after you purchase a Template.

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