User Segmentation
Segmentation in Netcore Unbxd enables businesses to group users based on behavioral patterns, preferences, and contextual attributes. By defining segments, teams can customize the search and browse experience for specific user groups and apply targeted merchandising strategies.
Segmentation helps teams:
- Personalise product discovery for different user groups.
- Apply targeted merchandising rules
- Analyse user behaviour across different segments
- Identify high-value and high-engagement customer groups
Segments are created using user groups, which classify users based on attributes such as recency, frequency, spending patterns, device type, geographic location, and product affinity. Once a segment is defined, it can be used in the Console to tailor the experience for those users.
Navigation to Segmentation
Log in to the Netcore Unbxd Console and navigate to Merchandising > Segmentation. You will see the following tabs:
Overview
The Overview page is the starting point for any personalisation strategy. It allows merchandisers to see exactly who is visiting the site and what they are interested in before a single merchandising rule is even created. It displays insights such as:
- Total users on the platform
- Distribution of users across different segments
- Performance of segments used in merchandising
- Overall impact of segmentation on search and browse interactions
This view helps teams understand how segmentation is currently being used and how different user groups contribute to business metrics. The Overview page is divided into two primary sections:
- Traffic Coverage by Segments
The Traffic Coverage by Segments section shows how users are distributed across key behavioural and contextual attributes. These attributes help identify patterns in user engagement and purchasing behaviour.
| Segment Type | Description | Example User Groups | Business Use Cases |
|---|---|---|---|
| Recency | Measures how recently a user has visited the website. It helps identify active users versus users who have not interacted with the platform recently. | High Recency (recent visitors), Medium Recency, Low Recency (inactive users) | Re-engage inactive users with promotions, prioritize trending products for recently active users |
| Frequency | Measures how often a user visits the website within a defined time window. It identifies highly engaged users versus occasional visitors. | Extreme Frequency (very frequent visitors), High Frequency, Medium Frequency, Low Frequency, Very Low Frequency | Target loyal users with exclusive offers, encourage repeat visits through personalized recommendations |
| Monetary | Represents how much a user spends relative to the site's average order value (AOV). This helps identify high-value customers. | High Monetary (high spenders), Medium Monetary, Low Monetary | Promote premium products to high-value users, offer discounts to price-sensitive customers |
| Device | Groups users based on the device or operating system they use to access the site. This provides insights into how users interact with the platform. | Mobile, Desktop, iOS, Other devices | Optimize merchandising for mobile-heavy traffic, tailor experiences for different device types |
- Affinity Insights
The Affinity section visualises how users interact with various catalogue attributes, helping identify preferences across different dimensions. Affinity represents a user's likelihood to interact with specific attribute values based on their browsing and purchase behaviour
For example: A business may prioritise products from high-affinity brands in search results or highlight popular sizes or colours for better conversions.
Segments
The Segments tab allows you to create and manage segments that represent specific groups of users. Each segment consists of rules that combine one or more user group attributes. For example:
- High-value users with brand affinity toward a specific brand.
- Frequent visitors using mobile devices
- Users from a specific geographic region
Segments allow merchandising teams to customise product rankings and promotions for those users.
Segment Metrics
Each segment displays performance metrics to help evaluate its effectiveness. These metrics help identify which segments are driving the most engagement and revenue.
| Metric | Description |
|---|---|
| Users Percentage | Percentage of users belonging to the segment |
| Hits | Total interactions generated by the segment |
| CTR | Click-through rate |
| Conversion Rate | Percentage of users who completed a purchase |
| ADV | Average order value |
| PSV | Product sales value |
| Revenue | Total revenue generated from the segment |
Create a new Segment
- To create a new segment, navigate to Merchandising > Segmentation.
- Click Add Segment. Select the relevant user groups to define the segment.
- Assign a name to the segment and then save the segment.
Once saved, the system begins tracking the segment's performance and populates analytics metrics. Segments can also be:
- Edited
- Duplicated
- Deleted
- Reordered by priority (Priority determines which segment applies if a user belongs to multiple segments).
User Groups
User Groups define the attributes used to classify users. These attributes serve as building blocks for creating segments. User groups include:
| Segment Type | Description |
|---|---|
| RFM Segments | Groups users based on behavioral metrics such as how recently they visited (Recency), how often they visit (Frequency), and how much they spend (Monetary). |
| Device & OS | Classifies users based on the device type and operating system they use to access the website or application. |
| Geographic | Segments users based on their geographic location such as country, region, or city. |
| Affinity | Identifies user preferences based on their interactions with specific product attributes such as brand, color, size, or category. |
| Stores | Groups users based on the store identifier, enabling segmentation for businesses operating multiple stores or locations. |
| Custom Segments | Allows businesses to create their own user groups by uploading custom labels or defining specific user classifications. |
Each user is automatically assigned labels based on these attributes, which can then be combined to create segments.
Example Use Case: Understanding and Using User Segmentation
User segmentation helps businesses understand how different groups of users interact with their website and enables them to tailor product discovery experiences accordingly.
Consider an online fashion retailer using Netcore Unbxd Segmentation to analyse user behaviour.
Step 1: Analyze User Distribution from the Overview Dashboard
The Overview page graphs provide a visual summary of how users are distributed across different behavioral attributes such as Recency, Frequency, Monetary value, and Device type. For example:
Recency Graph: Shows how recently users have visited the site.
-
25% of users visited recently (High Recency)
-
20% visited moderately recently
-
55% have not visited in a long time (Low Recency)
This indicates that a large portion of users may need re-engagement strategies such as promotions or personalized recommendations.
Frequency Graph: Displays how often users visit the website.
-
20% Extreme frequency users (very frequent visitors)
-
15% High frequency users
-
12% Medium frequency users
-
15% Low frequency users
-
38% Very low frequency users
This helps identify loyal customers versus occasional visitors.
Monetary Graph: Shows how users are distributed based on their spending behavior. Example:
-
35% High-value spenders
-
25% Medium-value spenders
-
40% Low-value spenders
This insight helps businesses optimize product discovery for the most commonly used devices.
Step 2: Identify User Preferences Using Affinity Graphs
The Affinity graph shows user preferences based on interactions with product attributes. For example:
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Brand affinity shows strong interest in Nike and Puma
-
Color affinity shows strong interest in Red products
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Size affinity indicates demand for Medium size items
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Category affinity highlights strong engagement in Fashion
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Region affinity identifies key customer markets such as Australia
These insights help businesses understand what customers prefer, enabling more relevant product experiences.
Step 3: Create Targeted Segments Using User Groups
Using the user group insights, the merchandising team can create segments such as:
Segment Example: Loyal Nike Shoppers, Segment rules: Frequency = High or Extreme, Monetary = High, Affinity = Nike
This segment represents frequent high-spending customers who prefer Nike products. Merchandising strategy:
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Prioritize Nike products in search results
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Show new Nike arrivals
-
Promote premium collections
Segmentation ensures that different types of users receive the most relevant shopping experience, ultimately improving engagement, conversions, and revenue.
Updated 9 days ago
