1. Pre-defined Lifecycle Segments
How they work: Pre-defined lifecycle segments are created automatically, covering key stages of the customer lifecycle. Visitors defined as shoppers who have not yet bought anything, whereas customers are defined as shoppers who have already bought at least once. In this article we refer to both using a term shopper.
Pre-defined Lifecycle Segments Overview:
Shoppers who visit the site the very first time.
Shoppers who have visited the site at least once.
Segment created by Nosto's intelligence engine for commerce, consisting of shoppers who are very close or likely to buy. Essentially the best group to re-target offsite or offer an incentive to purchase on-site. Based on cart abandonment, visit recency, frequency, traffic sources and similar behavioural signals. As the patterns differ from site to site, parameters are unique.
Shoppers who have bought once. (For specific parameters and definition, use or enhance with custom segments)
Shoppers who have bought more than once, looking also at average repeated purchase rate as this varies between sites. (For specific parameters and definition, use or enhance with custom segments.)
Loyal customers consist of all customers who ordered at least 3 times. As sense of loyalty differs from one store to another, custom targeting options such as Lifetime Spent or Avg. Order Value are available as custom segmentation capabilities.
2. Segment Targeting Options or Custom Segments
How they work: Each store is unarguably different from another. Targeting Options for Custom Segments are a set of rules to create segments in order to cover different use cases, scenarios and business needs for each individual store. A custom segment can be based on multiple inclusions and exclusions.
For example, you could create a highly complex and targeted segment out of:
Mobile shoppers (Contextual, location)
From California (Contextual, location)
Customer lifetime value over $1.000 (Lifecycle, lifetime spend)
Buying intent for Nike (Behavioral affinities, brand affinity)
Who have not yet bought from the brand (Purchase History, Purchased brand exclusion)
Note that using multiple inclusion and exclusion rules can narrow down the segment to a very small subset of customers. Thus, it is likely best to start with a broader segment and narrow down as you go.
3. Custom Customer Lifecycle
How they work: Lifecycle segment rules support creating segments based on lifecycle data that is focused on customer purchase history.
Custom Customer Lifecycle Segments Overview:
Rule to target shoppers who have abandoned their cart with options to limit the group based on product attributes: Categories, Brands, ProductIDs, Custom Product Attributes (tags and custom fields) and Cart Value Range.
Added to Cart
Rule to target shoppers who have added something specific to their cart.
Options include creating segments based on product attributes: Categories, Brands, ProductIDs, Custom product attributes (tags) and cart value range.
'Added to Cart' is designed to trigger an experience or a campaign when something is added to the shopping cart. In order to remove shoppers from the segment, consider excluding users based on their purchase behaviour (number of purchases) or using the 'Abandoned Cart' segment.
Average Order Value
Rule to target shoppers based on an average order value based on all orders by the same user, thus can be average of AOV for returning customers.
Identified by Email
Rule to target shoppers based on customers that Nosto can identify by email and the opposite as true & false rule. Emails are processed from check-outs, log-ins, pop-ups and optionally through custom implementations leveraging for example Nosto JS API
Items Per Order
Rule to target shoppers based on items per order based on all orders by the same user, thus can be an average of items ordered by a returning customer.
Rule to target shoppers based on their overall lifetime spend. Note that the data processing and calculation starts from the time Nosto was implemented. For a holistic approach or when you've just got started, we recommend importing customer data and creating segments based on those imported lists.
Rule to target shoppers based on email marketing consent, which can be true, false or unknown. Unknown refers to customer not clearly consenting for marketing. Unknown is typically due to the detail missing from the implementation.
Number of Purchases
Rule to target shoppers based on how many times a customer has bought from the site. Note that the calculation starts from the time Nosto was implemented. For a holistic approach or when you've just got started, we recommend importing customer data and creating segments based on those imported lists.
'Payment Provider' supports creating segments based on the payment provider or method used, with option to limit the time to previous purchase or given time period. For example, payment provider targeting option enables possibility to create segment out of shoppers who used Applepay or Klarna presuming these would be available payment options.
The payment provider-based targeting option is, by default, enabled only on Nosto extension based platforms such as Magento, Shopify, Shopware, Prestashop and BigCommerce. to add this detail on other platforms, please refer to our tagging guidelines.
Rule to target shoppers who have viewed something specific. This technically refers to loading a product detail page or a similar product view such as an overlay.
Options include limiting the group based on product attributes: Categories, Brands, ProductIDs, Custom product attributes (tags & custom fields) and product value range. Supports single and multi-session configuration.
Rule to target shoppers based on whether the customer has logged in on the website and thus registered. Value can be either true or false. Once a customer has logged in once, they permanently belong to the segment.
Rule to target shoppers based on their number of visits. New would only include first-time landing shoppers and Returning would only include all customers who already visited the site once. Additionally, you can adjust the number of visits to only include shoppers who visit the store for the 3rd time, 4th time & so forth.
Rule to target shoppers based on custom pages that are not product or category pages the shoppers either have or have not visited. Used typically to target shoppers who have visited a specific campaign or content page or area on the website, such as blog or a specific blog post.
4. Behavioral Affinities
How they work: Behavioral affinities are essentially composed of buying intent and interest segments which analyze every signal triggered by a visitor or customer during their visit. This means that they are broader and more flexible than segments based on solely transactional purchase data. For instance, behavioral affinities support changes in the shopping experience during the current visit and even first visit to the store, whereas purchase history-based segments only track order data.
Behavioral Affinity Overview:
Rule to create a segment based on brand affinity
Rule to create a segment based on category affinity.
Custom Affinity - Tags
Tag-rules support creating affinity segments based on product context mapped into the field.
Rule to create a segment based on discount affinity, splitting customers into groups of users who are more likely to buy regardless if discounts are offered and those who are especially likely to shop discounted products.
5. Contextual Segments
How they work: Contextual segments supports segmenting based on context of the visitor or a customer.
Contextual Segment Overview:
Location rule based on the user's IP-address. Depending on the country and available IP database of the selected area, follows hierarchy: Country / state or province / city.
The audience can be either desktop or mobile. Following Google's definition, tablet traffic is desktop e.g. mobile is smartphone screen-size traffic.
Visit Custom Event
Supports selecting single- and multi session option. Single session (default) keeps the shopper in the segment only during the visit when they trigger the event. Multi session keeps the shopper in the segment.
6. Segments and Exclusions
How they work: Rule to support inclusions and exclusions based on an existing segment to support targeting specific sub-segments. This includes customers who bought certain brand within certain category affinity segment.
To create an exclusion, start by creating a segment which includes a group of customers and then narrow it down by excluding the unwanted group of customers.
For example, include First Time Customers as the Pre-defined Segment and exclude Behavioral Affinity Brand: Nike from that segment.
7. Purchase History
How it works: 'Purchase History' options look solely at historical transactional data and focus on products - which means they are different to behavioral affinities and, therefore, current buying intent.
'Purchase History' options have a time parameter which supports narrowing down the time when a purchase was made. This enables the possibility to create "lapsed customer" types of segments based on product purchase dimension.
Purchase History Overview:
Rule to create a segment based on brand or brands purchased in the past.
Rule to create a segment based on product category or categories. purchased in the past.
Tag fields are custom fields where any custom product parameter or property can be stored (which varies depending on the store). Typically, property like color, size or style is stored in tag-fields.
Tag-rules support creating segments based on product context mapped into the field.
Rule to create a segment based on an individual product that was bought previously.
Purchased Custom Field(s)
Custom fields are similar to tag(s), but typically nest more details about the products, SKUs and consequently purchased products, such as bought size, color, campaign and similar attributes.
Custom fields support creating segments based on more granular detail than what's commonly mapped to tags.
8. External Signals
How they work: 'External Signals' consist of data points and signals which are not processed from the site traffic or purchases in general.
External Signals Overview:
Rule to create a segment based on customer attributes, received and processed through imported customer list.
Rule to create a segment based on imported customer data.
Rule to create a segment based on the referring domain.
Rule to create a segment based on Shopify Flow workflows.
Rule to create a segment based on URL parameter(s), for example for traffic landing on the site from emails, paid ads like social or search.
Supports selecting single- and multi session option. Single session (default) keeps the shopper in the segment only when the parameter is present on the page for example when a shopper lands from an email campaign or paid ad campaign. Multi session keeps the shopper in the segment.