Nosto offers three distinct types of segments, each serving a unique purpose in customer categorization:
Lifecycle Segments: These are unique for their built-in exclusion and automatic progression, ensuring a shopper is classified into only one lifecycle segment at any given time. This segmentation is auto-generated, streamlining the process of tracking a customer's journey through different stages of engagement.
Default Segments: Designed as standard segment types, these are universally applicable across various e-commerce businesses. Default segments are also auto-generated, providing a fundamental framework for customer categorization that is suitable for all e-commerce stores.
Custom Segments: These segments are tailored and created by users to meet specific needs. They are based on custom criteria set by the user, allowing for a high degree of personalization and specificity in segmenting customers.
Overview: Lifecycle Segments in Nosto are predefined and automatically created, capturing the essential stages of a customer's journey. These segments help in categorizing shoppers based on their interaction history and purchase behavior. Note that a shopper is classified into only one lifecycle segment at any given time.
Segment Definitions and Progression:
First-Time Visitors: This segment includes shoppers visiting the site for the first time. They are automatically added to this segment upon their initial page load.
Returning Visitors: Comprising individuals who have previously visited the site, shoppers are added to this segment during the first page load of a new visit.
Prospects: This segment is an intelligent grouping of shoppers deemed close to making a purchase, identified through behaviors like creating a cart, number of products viewed, visit recency and frequency. The criteria for inclusion vary by site, adapting to different browsing and shopping patterns.
First-Time Customers: These are shoppers who have made their first purchase. They join this segment post-purchase and after their browsing session ends.
Repeat Customers: Shoppers with multiple purchases fall into this category.
Loyal Customers: This segment encompasses customers who have made at least three purchases. The criteria for loyalty can vary by store, and options like Lifetime Spend or Average Order Value are available for more tailored segmentation as custom segment option.
The initial purchase on the site is attributed to the two Visitors and Prospects segments.
Subsequent purchases are attributed to the various customer segments.
There is a linear progression from First-Time Visitors to Loyal Customers, although shoppers can skip stages based on their purchasing activities. For example, a First-Time Visitor could directly become a First-Time Customer or even advance to Returning or Loyal Customer status if they make multiple purchases in quick succession.
This segmentation approach allows for nuanced marketing and customer engagement strategies, tailored to the specific stage of each shopper in the customer lifecycle.
Overview: Nosto's Default Segments are pre-defined and automatically generated. They focus on common segmentation opportunities, such as differentiating between mobile and desktop traffic, as well as identifying traffic sources like Google and Facebook. Note that a shopper can belong to multiple default segments if they meet the criteria.
Mobile Traffic: This segment captures users accessing the site via mobile devices. The segmentation is based on the user agent. Shoppers are added to this segment upon their first page load if the criteria for mobile traffic are met.
Desktop Traffic: Similar to the mobile segment, but for desktop users. It segments visitors based on the desktop user agent, with shoppers added to this segment on their first page load if they meet the desktop criteria.
Google Traffic: This segment identifies visitors coming from Google. It uses known and default referrers, including parameters like
utm_mediumset to Google or AdWords, or the presence of a
gclidquery parameter. Custom parameters can also be used but require the creation of a custom segment. Shoppers are added to this segment on their first page load if the Google traffic criteria are satisfied.
Facebook Traffic: This segment is for traffic originating from Facebook. It uses standard referrers like
utm_mediumset to Facebook, or the presence of parameters such as
ad_id. As with Google traffic, custom parameters are possible but necessitate a custom segment. Shoppers are added to this segment on their first page load if they meet the Facebook traffic criteria.
Default Segments provide a foundational approach to understanding and categorizing traffic sources and the type of device used, offering valuable insights for targeted marketing strategies
Overview: Recognizing that each e-commerce store is unique, Nosto's Custom Segments offer a flexible way to cater to diverse business needs and scenarios. These segments allow for the creation of highly specific customer groups based on a combination of inclusion and exclusion criteria.
Creating Custom Segments: Custom Segments are designed to be versatile, enabling stores to tailor their targeting to precise specifications. These segments can be constructed using a range of criteria, including but not limited to:
Contextual Factors: Such as the shopper's location or device type.
Lifecycle Attributes: Like customer lifetime value.
Behavioral Affinities: For example, a preference for certain brands.
Purchase History: To include or exclude shoppers based on their past buying behavior.
Example of a Custom Segment: A complex and targeted segment could be created with the following characteristics:
Mobile shoppers in California (Contextual, location).
With a customer lifetime value over $1,000 (Lifecycle, lifetime spend).
Showing a buying intent for Nike products (Behavioral affinities, brand affinity).
Who have not yet purchased from the Nike brand (Purchase History, Purchased brand exclusion).
Consideration in Segment Creation: While it's possible to create highly specific segments, using multiple inclusion and exclusion rules can result in a very narrow customer group. It's often advisable to start with broader segments and then progressively refine them to achieve the desired focus.
Custom Segments provide a powerful tool for stores to align their marketing efforts with specific customer behaviors and preferences, enabling highly personalized and effective strategies.
Overview: Group of Customer Lifecycle segments are designed to target shoppers based on their interactions and purchase history. These segments are categorized by specific rule options, and therefore differ from automatically created lifecycle segments.
Abandoned Cart: Targets shoppers who have abandoned their cart. Segmentation can be refined based on product attributes like categories, brands, and cart value range. Shoppers are added post-session expiry, indicating cart abandonment.
Added to Cart: Focuses on shoppers who have added specific items to their cart. Segmentation options include product attributes and cart value range. This segment is ideal for triggering specific experiences or campaigns when an item is added to the cart. Shoppers are added when the condition is met, typically upon adding the defined product to the cart.
Average Order Value (AOV): Targets shoppers based on the average value of their orders. This includes repeat customers and is updated post-purchase and session expiry.
Identified by Email: Segments shoppers recognized by their email, sourced from check-outs, log-ins, or other integrations. Shoppers are added upon email identification.
Items Per Order: Focuses on the average number of items per order. Shoppers are added post-purchase and session expiry.
Lifetime Spend: Targets shoppers based on their total spend with the store. This segment is updated post-purchase and session expiry, with data calculation starting from Nosto's implementation.
Marketing Permission: Segments shoppers based on their email marketing consent status (true, false, or unknown). Shoppers are added when their consent status is processed.
Number of Purchases: Focuses on the frequency of a shopper's purchases from the site. The count begins from Nosto's implementation, and shoppers are added post-purchase and session expiry.
Pages Viewed: Segments shoppers based on the number of pages viewed within a specified period. Shoppers are added when they meet the set criteria during their visit.
Payment Provider: Segments shoppers based on the payment method used. This feature is primarily available on platforms integrated with Nosto, such as Shopify, Shopware, and others. Shoppers are added post-purchase and session expiry.
Products Viewed: Targets shoppers who have viewed specific products, based on product attributes and viewing history. Shoppers are added when they view the defined product or product range.
Registered Customers: Segments shoppers based on their registration status on the website. Shoppers are permanently added to this segment upon their first login.
Visit History: Focuses on shoppers based on their visit frequency, distinguishing between new and returning visitors. Shoppers are added upon meeting the visit frequency criteria on their first page load.
Visit URLs: Targets shoppers based on their visit to specific non-product or non-category pages, such as campaign or content pages. Shoppers are added upon visiting the specified URL.
These segments provide a granular approach to targeting, allowing businesses to tailor their strategies based on specific customer behaviors and preferences.
Overview: Behavioral affinities in Nosto are based on AI models focused on discerning buying intent and interest based on the various signals triggered by visitors or customers during their site visit. These segments offer broader and more flexible insights than those based solely on transactional data, capturing shoppers who demonstrate a higher buying intent towards specific products or categories.
Brand Affinity: This rule allows the creation of segments based on shoppers' affinity towards specific brands. It categorizes customers according to their demonstrated preference for certain brands.
Category Affinity: Similar to brand affinity, this rule creates segments based on category preference. It identifies shoppers who show a higher interest in particular product categories.
Custom Affinity - Tags: This feature enables the creation of affinity segments based on product context, mapped into custom tags. It allows for more nuanced segmentation based on detailed product attributes.
Discount Affinity: This rule segments customers based on their response to discounts. It distinguishes between those likely to purchase regardless of discounts and those particularly attracted to discounted products.
Variant Custom Field Affinity: This rule is used for creating segments based on affinity towards specific product variants, such as size, color, or other attributes.
Behavioral Affinity segments are essential for understanding the nuanced preferences and behaviors of customers, predicting next purchase, allowing for more targeted and effective marketing strategies. These segments leverage a comprehensive AI analysis of visitor interactions, providing a deeper insight into customer interests and purchasing tendencies.
Overview: Contextual segments in Nosto are designed to segment shoppers based on the context of their visit, whether they are browsing as a visitor or as an established customer.
Device: Segments the audience based on the device used, distinguishing between desktop and mobile users. In line with Google's definition, tablet traffic is classified as desktop, while mobile refers to traffic from smartphone-sized screens. Shoppers are added to the relevant segment upon their first page load if the device criteria are met.
Geo-location: This segment is based on the user's IP address, identifying their location with varying levels of specificity: country, state or province, and city. Shoppers are added to the segment upon their first page load if they meet the location criteria.
OS and Browser: Categorizes shoppers based on the operating system or browser of their device, or a combination of both. This segment supports most common OS and browser types.
Weather: Segments shoppers based on the weather conditions or forecast in their current location, including parameters like conditions, temperature, and UV index. The data is derived from IP resolution and the nearest weather station, typically within a 30-mile / 50-kilometre radius in populated areas.
Contextual segments provide a dynamic way to understand and interact with shoppers, offering insights based on real-time conditions and user-specific contexts. This allows for more personalized and relevant marketing strategies.
Segments and Exclusions
Overview: Nosto's Segments and Exclusions feature enables the creation of highly specific sub-segments by including or excluding shoppers based on existing segments. This method allows for refined targeting and more precise marketing strategies.
How It Works:
Creating a Base Segment: Start by selecting an existing segment that includes a broad group of customers. This segment serves as the foundation for further refinement.
Applying Exclusions: Narrow down the base segment by excluding specific groups of customers. This exclusion is based on various criteria such as brand affinity or purchase history.
Example of Segment Creation with Exclusions:
Include: Begin with a pre-defined segment, like First Time Customers.
Exclude: Apply an exclusion criterion, such as customers who have shown a Behavioral Affinity for a specific brand (e.g., Nike).
Result: Target all first-time customers, except those who are likely to buy Nike.
By using this approach, you can effectively target a segment of First Time Customers who have not exhibited an affinity for Nike products. This technique is valuable for tailoring marketing efforts to more accurately address the needs and preferences of distinct customer groups. Segments and Exclusions provide a versatile tool in segmenting your audience, allowing for the creation of highly targeted groups for specific marketing campaigns or customer engagement strategies.
Overview: The Purchase History segment in Nosto focuses exclusively on historical transactional data related to products. This differentiates it from behavioral affinities, as it does not consider current buying intent. The segment includes a time parameter to specify when a purchase was made, enabling the creation of segments like "lapsed customers" based on product purchase history.
Purchased Brands: Targets shoppers based on brands they have purchased in the past. Shoppers are added to this segment post-purchase and after the browsing session expires.
Purchased Categories: Focuses on shoppers who have bought products from specific categories. The segment is updated after the purchase event and session expiry.
Purchased Tag(s): Utilizes custom tag fields, which can include any custom product parameter (like color, size, or style). Segments are created based on these product context tags. Shoppers are added post-purchase and after session expiry.
Purchased ProductIDs: Creates segments based on individual products previously purchased. Shoppers are added after they buy the specified product and their session expires.
Purchased Custom Field (tags): Similar to Purchased Tag(s), but with more detailed attributes like size, color, or campaign specifics. This option allows for granular segmentation based on detailed product information. Shoppers are added post-purchase and after session expiry.
Purchase History segments provide valuable insights into customer preferences and past buying behavior, enabling targeted marketing strategies based on previous purchases. They are particularly useful for reconnecting with lapsed customers or encouraging repeat purchases.
Overview: 'External Signals' in Nosto encompass data points and signals that originate outside of the site traffic or general purchase activities. These signals offer additional dimensions for segmenting customers based on diverse external factors.
Customer Attribute: Creates segments based on customer attributes, which are derived from imported customer lists. This allows for segmentation based on detailed customer data not directly available from site interactions.
Imported List: Focuses on segments created from imported customer data. This rule is particularly useful for integrating external customer databases into Nosto's segmentation.
Referring Domain: Targets shoppers based on the domain that referred them to the site. This segment is useful for tracking and optimizing referral traffic.
Shopify Flow: Enables the creation of segments based on workflows defined in Shopify Flow. This is particularly relevant for stores utilizing Shopify as their e-commerce platform.
Traffic Sources: Simplifies segment creation based on URL parameters. This includes grouping common sources like Google into a ready-made rule, facilitating easier segmentation based on traffic origins.
URL Parameters: Allows segmentation based on standard URL parameters, commonly used in email or paid advertising campaigns. It includes options for single-session (default) and multi-session segments. Single-session segments apply only when the parameter is present during a page visit, while multi-session segments persist beyond the initial visit.
URL Parameters - Custom: Similar to standard URL parameters, but focuses on custom parameters that might be used by unique traffic sources or services not utilizing standard UTM tags.
External Signals provide a comprehensive view of customer interactions and preferences, extending beyond on-site behavior. These segments enable businesses to leverage a wider range of data for more effective targeting and personalized customer experiences.
Single Session and Multi-Session Segments
Overview: In Nosto, Single Session and Multi-Session options determine how long a user remains in a given segment. These options are crucial for tailoring the personalization experience to either a specific visit or across multiple visits.
Single Session: As the name implies, Single Session segments are temporary and only valid for the duration of the user's current browsing session. Once the session ends, the user exits the segment.
Usage: Ideal for personalization experiences influenced by immediate context, such as geo-location, current weather conditions, or specific UTM parameters from a referral source.
Multi-Session: These segments are persistent and span across multiple user sessions. A user remains in a Multi-Session segment until the criteria for that segment change.
Usage: Suited for scenarios where the personalization experience should maintain consistency over time. An example includes targeting based on past purchase history, such as a shopper who has bought products from the Nike brand within the past six months.
Application in Personalization:
Single Session Segments provide immediate and contextually relevant experiences, adapting dynamically to the user's current interaction with the site.
Multi-Session Segments ensure continuity in the personalization experience, recognizing and responding to the user's longer-term behavior and preferences.
The choice between Single Session and Multi-Session segmentation allows for strategic personalization, catering to both immediate and sustained customer engagement objectives.
Time Parameters in Segmentation
Overview: Time parameters in Nosto segmentation play a crucial role in defining the temporal dimensions of customer interactions. They offer various options to specify the timeframe within which certain actions or behaviors occur.
Key Time Parameters:
All Time: This parameter covers the entire duration since Nosto's implementation or since the customer's first interaction with the site, providing a comprehensive view of customer activity.
Is Within Last: Allows segmentation based on activities occurring within a recent, specific time period, such as the last 30 days.
In Period: Focuses on customer interactions within a predefined and fixed time frame, for instance, during a specific month or quarter.
In Last Purchase: Targets customers based on their most recent purchase, enabling segmentation based on the recency of buying behavior.
Use Cases: These time parameters are designed to support specific scenarios, such as:
Segmenting customers who made a purchase within the past 30 days.
Identifying users who have not visited or viewed any products in the past 20 days.
Application in Segmentation Strategy: By utilizing these time parameters, marketers and e-commerce managers can create segments that are dynamically tailored to the timing of customer interactions. This enables more precise targeting for marketing campaigns, retargeting efforts, and personalized customer experiences based on recent or historical behavior. Time parameters add a critical dimension to segmentation, allowing for a nuanced understanding of customer activity patterns and enabling timely and relevant engagement strategies.
Overview: Behavioral affinities in Nosto focus on the subtleties of buying intent and preferences, discerned from users' browsing behavior, not just their transactional data. This approach captures invaluable insights into shopping preferences that can often be missed by platforms relying solely on purchase history.
Importance of Non-Transactional Data:
The majority of buying intent signals are generated by users who browse without making immediate purchases or adding items to the cart.
Traditional customer data platforms may overlook these signals, missing crucial opportunities to enhance the shopping experience, particularly for new visitors or existing customers whose interests are evolving.
Nosto's Solution: Nosto's affinity-based segments address these gaps by:
Linking products and shoppers with high relevancy, updating in real-time.
Adapting to each store's unique browsing and shopping patterns.
Offering segment sensitivity settings to control personalization levels and segment population.
Real-Time Responsiveness: Behavioral affinities reflect changes in shopping behavior during the current visit, even on a customer's first visit. For example, a shopper known for buying pants but currently browsing jackets will be added to the "jackets" segment likely during their current visit.
Dynamic Segmentation: Users can enter affinity segments during their session, allowing for immediate segmentation as their interests shift. This is a distinct advantage over segments based solely on purchase history.
Efficiency and Ease: Nosto's dynamic segments save time and effort compared to manually creating rules for each category. The platform's real-time updating scoring system automatically adjusts to changing shopping and browsing patterns.
Affinity Sensitivity Controls: Nosto provides segment controls called "Affinity Sensitivity" to support various merchandising and personalization scenarios, catering to diverse e-commerce needs.
Behavioral affinities offer a more nuanced and responsive approach to segmentation, essential for improving shopping experiences and effectively targeting marketing efforts based on evolving customer interests.
Sensitivity Options in Behavioral Affinities
Overview: The sensitivity options in Nosto's Behavioral Affinities allow for adjusting the reach and quality of a segment based on dynamic criteria. These settings are influenced by both the site's overall and the individual shopper's browsing and shopping patterns, making them uniquely responsive.
Criteria: Includes only shoppers with the highest preference in a given range.
Characteristics: Offers the highest quality [of audience] but the lowest reach.
Example: A shopper predominantly engaged with Adidas products, more than any other brand, qualifies for the Adidas Brand Affinity segment with Top sensitivity. This level requires consistent interaction with a specific product range, including multiple views, cart additions, or purchases.
Uniqueness: A shopper can belong to only one Top brand affinity at a time but may have Top affinities in other segment types, like categories or product attributes.
Criteria: Includes shoppers with a strong, but not exclusive, preference in a given range.
Characteristics: Balances quality and reach effectively.
Example: A shopper frequently interacts with several brands (Nike, Reebok, Adidas) and will have a High affinity for each. Infrequent interactions with another brand (like Puma) might result in a Low affinity for that brand, but not High or Top.
Criteria: Includes shoppers relatively quickly upon showing preference in a given range.
Characteristics: Provides the highest reach but lower quality compared to Top and High levels.
Example: In a scenario where an average shopper views four to seven products per visit, viewing just two or three products within a specific range may be enough to be added to a Low sensitivity affinity segment.
Application in Segmentation: These sensitivity options enable fine-tuning of Behavioral Affinity segments, ensuring that they accurately reflect the varying degrees of shopper engagement and preference. By selecting the appropriate sensitivity level, businesses can create segments that are precisely aligned with their marketing goals, balancing the quality and reach of their target audience effectively. The sensitivity levels in Behavioral Affinities offer a nuanced approach to understanding customer interests, allowing for more tailored and effective marketing strategies.
Scoring Mechanism in the Affinity System
Overview: Nosto's affinity system employs a scoring mechanism to gauge the strength of the relationship (affinity) between a shopper and a range of products. This relationship is based on various product properties such as category, brand, or custom attributes like color or specific features. Additionally, variant affinity tracks preferences for attributes like size, color, or other specific features, which is particularly relevant for products like cosmetics or niche sports equipment.
The scoring is calculated individually for each shopper.
It is based on all interactions made during site visits and is recalculated in real-time, considering multiple sessions and time factors such as.
Repeated views, repeated visits and purchases, recency and frequency
Examples of Interaction Types for Scoring
The specific signals utilized in our scoring and affinity system are somewhat confidential. This is primarily because we are constantly refining our model to enhance its effectiveness. As we explore and adapt new methods or signals, the details of our current model can rapidly evolve. To ensure accuracy and relevance, we maintain a level of ambiguity around these specifics, as documenting them in our public product documentation could lead to outdated information. However, following signals are always included, and work as a great example to understand the basics of scoring mechanism.
Product Listing View: Indicates the weakest intent.
Product Page View: A relatively weak signal of affinity.
Adding a Product to the Shopping Cart: Represents a strong indication of interest.
Buying a Product: The strongest signal of affinity.
Repeated Purchases: Strengthens the affinity further.
Self-Learning and Adaptive Scoring:
The scoring system is self-learning and adapts to the specific context of the website, including factors like website design, product types, and buying cycles.
It accounts for a variety of interactions, not limited to the examples listed, ensuring a comprehensive understanding of shopper behavior.
Global Safety Rules and Interaction Weighting:
To maintain accuracy and account for anomalies, global safety rules are applied. For example, adding a product to a cart is always considered a stronger signal than merely viewing a product page.
Repeated interactions with a product across multiple visits contribute to a stronger affinity score compared to multiple views in a single session.
Recent interactions have a greater impact on the score than those that occurred months ago.
Application in Personalization: A high affinity score towards a product range implies a strong likelihood of purchase conversion if the on-site experience is personalized to highlight preferred product ranges.
General Segment Regression:
In most segment types, custom time parameters play a key role in determining when a shopper is added or removed from a segment.
For example, in a purchase conversion-based segment with the criteria of "purchase made within 30 days," shoppers are automatically removed from the segment once they no longer meet this criterion.
Affinity and Affinity Segment Regression:
Affinity in this context covers both affinity segments and features using affinities e.g. merchandising features.
Affinities have a dynamic regression mechanism where shoppers' affinities are reassessed over time. No interaction on subsequent visits will diminish the strength of affinity.
As a final protective measure, if a shopper does not engage with products related to their previous affinities within their past five website visits, their affinity and interest is deemed to have diminished.
Consequently, they are automatically and fully removed from the affinity segment.
Example of Affinity Regression:
Consider a shopper who has previously purchased three pairs of Nike sneakers, establishing a top affinity for both Nike and sneaker categories.
If a shopper does not interact with Nike products or the sneaker category in their subsequent visit, their affinity is slightly diminished.
If this shopper does not interact with either Nike products or the sneaker category in their subsequent five visits, their affinity to these segments is considered to have regressed.
As a result, they are automatically excluded from these affinity segments.
This regression mechanism ensures that affinity segments remain dynamic and relevant, reflecting the current interests and behaviors of shoppers. It allows for the segments to be continuously updated, maintaining their effectiveness in targeting and personalization strategies.
Challenges with Gifter Segmentation:
Gifters represent a unique buyer group in e-commerce segmentation. Their purchasing behavior is distinct because it reflects the preferences or needs of someone other than themselves.
Identifying gifters implicitly is challenging as their buying intent differs from their personal preferences, but without en explicit signal they can't be determined as gift buyers
Mechanisms to Identify And Process Gifters:
Many e-commerce stores implement features like 'Buying as a Gift' or options for delivery to a different address. These features serve as indicators that a purchase may not align with the buyer's personal affinities.
This shopper group, identified by signals indicating gift purchases, can be segmented into a separate custom group by creating a custom segment.
Once identified, gifters can be strategically excluded from standard affinity segments (and others) to ensure more accurate personalization and targeting for regular shoppers.
The separate gifter segment also allows for targeted campaigns during key gifting seasons, like holidays or special occasions, enhancing the effectiveness of marketing efforts for this specific group.
Overview of Segment Insights and Backdating:
Segment Insights in Nosto do not support backdating due to the possibility of product data changing over time.
Key segments like lifecycle segments and some custom segments are pre-created when Nosto is integrated into a website.
These segments have data processed and aggregated from the time of Nosto's integration, providing ongoing insights from that point forward.
Creation and Eligibility of New Custom Segments:
When a new custom segment is created, only customers who visit the online store after the creation of the segment and meet the eligibility criteria will be aggregated into the segment's Insights.
These customers will be added to the segment only after its creation.
Example of Segment Backdating:
If Nosto was integrated in January 2023, a shopper who made a purchase in January 2023 and revisits the site in August 2023 will be instantly recognized as a First Time Customer upon landing. This is because the segment criteria consider a returning shopper who has made one purchase.
In contrast, if a custom segment with the same criteria was created in February 2023, this shopper would not be included in it, as their purchase predates the creation of the custom segment.
Exceptions apply with extensions. Each extension installation generates a group of buyers up to 30d before installation, which partially reduces the misinterpretation
For advanced needs, consider importing 1st party data to Nosto e.g. existing shoppers and loyal customers, and use as inclusion / exclusion rules together with auto-created segments.
This approach ensures that segment data remains accurate and relevant, reflecting customer interactions from the point of segment creation or Nosto integration. It allows for precise targeting and personalization based on up-to-date customer behavior and preferences.
Segment Priority and Multi-Segment Shoppers
Overview of Multi-Segment Membership:
It is common for shoppers to belong to multiple segments simultaneously. This reflects the complexity and diversity of their shopping behaviors and preferences.
Exception with Lifecycle Segments:
Lifecycle segments are linear in nature, meaning a shopper can only belong to one lifecycle stage and its corresponding segment at any given time.
Behavioral Affinities and Top Sensitivity:
The top sensitivity setting in behavioral affinities also follows a similar linear logic. However, a shopper can have top affinities for different types of affinities, such as categories or brands, simultaneously.
Experience Exposure and Segment Prioritization:
Shoppers are exposed to the experience tailored for the segment that is prioritized.
The Segment Prioritization tool is used to determine which segment should take precedence when a shopper falls into multiple segments.
This prioritization is crucial when a site has multiple campaigns targeting different segments. The most important segment, as defined by the site, is placed at the top and given priority in terms of the shopper's experience.
Implications for Marketing and Personalization:
Segment prioritization allows for more effective targeting and personalization by ensuring that the most relevant and impactful segment criteria are applied to each shopper.
This approach helps in managing the complexity of multi-segment shoppers, ensuring that marketing efforts are aligned with the most pertinent segment for each individual shopper.
By carefully managing segment priority, businesses can ensure that their marketing and personalization strategies are as effective and relevant as possible, catering to the diverse and overlapping characteristics of their customer base.