Introduction
Model Clients contains comprehensive data about customers and their purchasing behaviors, used for conducting RFM (Recency, Frequency, Monetary) analysis and customer segmentation.
Requirements:
Online store engine: IdoSell. Q2 and Q3 2025 will see integrations with more engines.
Structure and description of data:
Identification data
Client Email - Email address
Client Phone - Phone number
Client First Name - First name
Client Last Name - Last name
Anonymized data in Data Octopus app
Hashed Client Email - Anonymized email address
Hashed Client Phone - Anonymized phone number
Hashed Client First Name - Anonymized first name
Hashed Client Last Name - Anonymized last name
Address data
Client Delivery Address Zip Code - Postal code
Client Delivery Address Country Code - Country code
Order indicators
Days Since First Order - Number of days since first order
Days Since Last Order - Number of days since last order
Order Count - Number of unique orders
Unique Item Count - Number of unique purchased products
Financial indicators
Min Product Net Price - Net price of the cheapest purchased product
Max Product Net Price - Net price of the most expensive purchased product
Total Net Revenue - Total net value of all purchases
Has Invoice - Flag indicating if an invoice was issued (true/false)
RFM indicators and segmentation
Recency Score - Segment determining time since last purchase
Frequency Score - Purchase frequency rating on the RFM scale
Monetary Score - Purchase value segment
Customer Segment - Customer segment determined based on RFM analysis by Data Octopus
Purchase preference indicators
Quantity Of Purchased Brands - Number of unique purchased brands
Purchased Brands - List of names of all purchased brands
Quantity Of Purchased Categories - Number of unique purchased categories
Purchased Categories - List of all purchased categories
Customer Segments
Model Clients uses RFM analysis to automatically categorize customers according to their value and purchasing behaviors based on recency, frequency and monetary value. Below is the characterization of individual customer segments defined in the model:
Champions (Recency 5, Frequency 5, Monetary 5)
The most valuable customers who have made purchases recently, buy frequently, and spend significant amounts. Active brand enthusiasts who regularly return and generate high revenue.
Loyal Customers (Recency 4-5, Frequency 4-5, Monetary 3-5)
Regular customers with high purchase frequency and good order values. They don't always spend the most, but regularly return and form a solid customer base.
Potential Loyalists (Recency 4-5, Frequency 3-4, Monetary 2-4)
Recently active customers with increasing purchase frequency. They show potential to become loyal customers with proper relationship nurturing.
New Customers (Recency 5, Frequency 1-2, Monetary 1-2)
New customers who have just begun their journey with the brand. They made a purchase very recently but have low frequency and purchase value due to their short history.
Promising Customers (Recency 4, Frequency 2-3, Monetary 1-2)
Customers who show interest by purchasing relatively recently with moderate frequency. They spend less but have development potential.
At Risk (Recency 2-3, Frequency 3-5, Monetary 3-5)
Previously loyal customers who haven't purchased for some time. Historically, they showed high value and purchase frequency, but their activity is beginning to decline.
Slipping Champions (Recency 1-2, Frequency 4-5, Monetary 4-5)
Former Champions who have stopped buying. Historically very valuable customers with high frequency and value of purchases, whose activity has significantly decreased.
Hibernating (Recency 1-2, Frequency 1-2, Monetary 1-2)
Inactive customers who previously bought rarely and spent little. They haven't made a purchase for an extended period and have low overall value to the business.
Lost (Recency 1, Frequency 1, Monetary 1)
Customers who have likely permanently left. Lowest scores in all RFM categories – it's been a very long time since their last purchase, they bought rarely, and spent little.
Others
Customers who don't fit into any of the above segments and may require individual analysis.
Use Cases
The data contained in the Clients model primarily serves to:
Conduct RFM analysis and customer segmentation based on recency, frequency, and monetary value of purchases
Analyze purchasing preferences regarding brands and product categories
Build audience groups who purchased specific brands or product categories for use in advertising activities (e.g., overselling)
Design retention strategies for high-value customers
Segments prepared based on the Clients model can be used as recipient lists or similar audience lists in advertising systems such as Google Ads, Meta Ads, or Programmatic. This allows for precise targeting of advertising campaigns to:
High-value and high-frequency customers (e.g., Champions)
Customers making regular, frequent purchases
Customers who recently made a purchase (New Customers)
Customers showing interest in specific categories or brands
Activation of customer segments such as Slipping Champions - valuable customers who haven't made a purchase for an extended period
The Clients model enables effective targeting of advertising messages directly to selected customer groups or creating similar audience groups in advertising systems based on them, which translates to better effectiveness of marketing activities.