Behavioral Segmentation: Types and Examples
Behavioral segmentation divides a market into groups based on how people buy, use, or interact with a product or service, including their usage patterns, purchase occasions, and desired benefits. OpenStax’s Principles of Marketing
Google Analytics documents product viewers, cart abandoners, and purchasers who have stopped buying as groups defined by recorded actions and, where specified, time windows (Google’s suggested audiences).
These groups can support both analysis and marketing: Google Analytics lets audiences be used in reports and shared with linked advertising products, while Mailchimp supports segments based on website, app, campaign, and purchase activity. Google’s audience introduction, Mailchimp’s behavioral targeting
Behavioral segmentation vs. other market segmentation methods
The main consumer segmentation methods use different kinds of information (OpenStax’s segmentation methods).
| Method | Basis for grouping people |
|---|---|
| Behavioral | Patterns of product interaction, purchasing, and usage. OpenStax |
| Demographic | Characteristics such as age, income, education, and occupation. OpenStax |
| Geographic | Location and related factors such as region or climate. OpenStax |
| Psychographic | Lifestyle, personality, interests, and values. OpenStax |
Google Analytics supports combined audiences that join purchase behavior with descriptive conditions such as location. Google’s audience introduction In a combined definition, specify both the behavioral requirement and the descriptive restriction.
Common types of behavioral segmentation
Common types of behavioral segmentation include:
| Type | What it groups people by |
|---|---|
| Purchasing behavior | How customers approach and make purchases. Qualtrics |
| Usage behavior | How much they use a product, including non-users and light, medium, or heavy users. OpenStax |
| Occasion or timing | The occasions when they purchase or plan to buy. OpenStax |
| Benefits sought | The product benefits or features most relevant to them. OpenStax |
| Customer loyalty | Their continuing relationship and interaction with a brand. Qualtrics |
| Customer journey stage | Their position across awareness, consideration, purchase, retention, and advocacy. Qualtrics |
Purchasing behavior also has its own classification: complex, dissonance-reducing, habitual, and variety-seeking buying are four categories described by Qualtrics. Qualtrics’ purchasing behavior guide Use these names for approaches to buying, and event names such as view_item or purchase for operational audience rules.
For usage segments, define what counts as use and how the usage bands are calculated. For benefits-sought segments, record the stated preference or research supporting the classification. For loyalty segments, specify whether the measure is repeat purchasing, interaction, or a separately measured preference for the brand.
Behavioral segmentation examples from published audience definitions
Published Google Analytics audience definitions include:
| Audience | Documented inclusion or exclusion |
|---|---|
| Item viewers | A view_item event with an item_id matching the specified item. Google Analytics |
| Cart abandoners | Include add_to_cart; exclude purchase. Google Analytics |
| Checkout starters | Include begin_checkout; exclude purchase. Google Analytics |
| Disengaged purchasers | Prior purchasing, with no purchase during a specified recent period. Google Analytics |
| Tutorial abandoners | Include tutorial_begin; exclude tutorial_complete. Google Analytics |
Use these as documented starting definitions. Before applying them, specify the membership duration, exclusion behavior, and any item or time restrictions required for the intended use. A customer-level purchase exclusion and an exclusion limited to a particular item should be written as different requirements.
The required events and parameters must actually be collected for suggested audiences to populate (Google’s suggested audience requirements).
Recency, frequency, and RFM segmentation
Recency measures how recently a qualifying action occurred, while frequency measures how often it occurred; Adobe Audience Manager allows both to be used over a defined daily interval (Adobe’s recency and frequency documentation).
Adobe’s documented example combines the two by requiring at least three qualifying occurrences within the last five days. Adobe’s recency example Choose and document thresholds for the activity being segmented.
RFM combines recency, how recently a customer purchased; frequency, how often purchases occur; and monetary, spending associated with purchases (Microsoft Learn’s RFM guide).
In Microsoft Dynamics 365 Commerce, the analysis has a start and end date, configurable scoring divisions and weights, and a choice of gross or net invoice amounts; returns can also be subtracted. Microsoft’s RFM setup documentation Document these choices alongside the scores so that the meaning of a value segment is clear.
Use purchase-based RFM when recency, repeat buying, and spending are the intended segmentation dimensions. For product activity or content interaction, define the relevant action and its frequency directly; add a monetary condition only when spending is part of the decision.
How to build a behavioral segment
1. Choose the use and the behavior
Decide whether the segment will support reporting, advertising, or a specific message. Write down the qualifying behavior and the action or analysis it will enable. Use a measurable event name and relevant product, content, or feature identifier in the definition.
Google’s ecommerce specification separates item viewing, adding to a cart, starting checkout, purchasing, and issuing a refund into distinct events, with item information supported within those events. Google’s ecommerce measurement guide Select the event that represents the required action, and verify its associated parameters.
2. Check collection and identity
Verify that the selected events fire when the actions occur and that the necessary parameters are present. Google recommends enabling debug mode to inspect events and troubleshoot an ecommerce implementation, and setting currency when sending revenue value data (Google’s implementation recommendations).
Specify the identity used to associate actions. Google Analytics can unify journeys using User-ID or device identifiers, while its device-based reporting option uses only the device ID. Google’s reporting identity documentation Persistent User-IDs must be assigned consistently and sent with the collected data. Google’s User-ID requirements Document the identity method before interpreting a segment count as a count of customers.
3. Write the membership rule
Record the following in the segment definition:
- The qualifying event and any required item or feature attributes.
- The counting unit: events, sessions, purchases, or another explicitly defined measure.
- The count threshold and observation period.
- Any required order of actions.
- The exclusion conditions and duration of membership.
Google Analytics supports conditions scoped to one event, one session, or all sessions, along with time-windowed metrics and ordered sequences. Google’s audience builder documentation Choose the scope and order deliberately when translating the written definition into platform settings.
Distinguish the observation window used to qualify from the duration of membership after entry. Google’s builder exposes membership duration separately and supports temporary or permanent exclusions. Google’s membership and exclusion settings Record what should happen after a purchase, completion, or other event that changes eligibility.
4. Check eligibility before using the segment
Compare qualifying event records with the intended inclusion and exclusion rules. Inspect entry, completion, and expiry cases, and record how overlapping groups should be handled. Use the same written rule for analytics and message configuration.
Google Analytics reevaluates audience membership as new data arrive and removes users who no longer meet the criteria. Google’s audience membership documentation Check the actual platform behavior before treating the segment as a current list of eligible recipients.
Using behavioral segments and measuring results
Mailchimp documents action-based automations for welcome messages, product recommendations, and abandoned cart messages when the relevant data and integrations are connected. Mailchimp’s behavioral marketing features Define the message, its trigger, and its exclusions together.
For analysis, Google Analytics’ Audiences report includes distinct users who visited the site or app, sessions, and revenue associated with audience users. Google’s Audiences report Choose the measure that answers the intended question, specify its denominator and observation period, and keep user counts separate from session counts.
Google states that current behavior does not change historical audience membership shown in reports, and warns that exported audiences can differ significantly from the Audiences report. Google’s audience reporting definitions Include the reporting period and destination when comparing counts.
To evaluate whether an advertisement changed an outcome, use an experiment designed to measure that effect. Google Ads’ Conversion Lift compares a treatment group that sees ads with a control group that does not, measuring incremental conversions attributable to ad exposure; the feature is not available to every account. Google’s Conversion Lift documentation Define the outcome before activation, and distinguish reported conversions from evidence of incremental impact.
Limits to account for
Google Analytics’ predictive audiences require modeled metrics that estimate future purchasing, churn, or revenue. Google’s predictive audience explanation Label an observed group by its recorded condition and identify any predictive condition separately.
Google Analytics explains that when users decline Analytics identifiers, behavioral data for those users are unavailable and modeling may be used to fill reporting gaps. Google’s reporting identity explanation Record missing-data and identity limitations when interpreting non-purchase or inactivity groups.
Review segments when event collection, the intended message, or the qualifying behavior changes. Update the written definition, check entries and exits again, and measure the result over a stated period.