Lifecycle segments are created automatically covering key stages of the customer lifecycle. Visitors are shoppers who have not yet bought anything, whereas customers are shoppers who have already bought at least once. In this article we refer to both using a term shopper. Full glossary below:
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 for each site.
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.
Targeting Options for Custom Segments are a set of different types of rules to create segments in order to cover different use cases, scenarios and business needs as each store is unarguably different from another. 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 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, hence it is likely best to start from broader segments and narrow down as you go.
Supports segmenting based on context of the visitor or a customer.
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
Lifecycle segment rules support creating segments based on lifecycle data of the customer.
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.
Number of Purchases
Rule 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.
Average Order Value
Rule to create segments based on an average order value based on all orders by the same user, thus can be average of AOV for returning customers.
Items per Order
Rule to create segments 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.
Overall lifetime spent by the customer. 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.
Purchase history rules look solely at historical transactional data, not behavioral affinities or current buying intent. Supports time parameter rules which can narrow down the time when a purchase was made enabling the possibility to create "lapsed customer" type of segments.
Rule to create a segment based on an individual product bought in the past.
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 colour, size or style is stored in tag-fields.
Tag-rules support creating segments based on product context mapped into the field.
Behavioral affinities are essentially buying intent and interest segments, which look into all signals a visitor or customer makes during their visit, meaning they are more flexible and broader than segments based on solely transactional purchase data. For instance, behavioral affinities support changing the experience during the current and even first visit to the store, whereas purchase history based segments only track order data.
Rule to create a segment based on brand affinity.
Rule to create a segment based on category affinity.
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.
Custom Affinity - Tags
Tag-rules support creating affinity segments based on product context mapped into the field.
External signals consist of data points and signals which are not processed from the site traffic or purchases in general.
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.
Rule to create a segment based on the referring domain.
Rule to create a segment based on customer attributes, received and processed through imported customer list.
Rule to create a segment based on Shopify Flow workflows.
Rule to create a segment based on imported customer data.
Rule to support inclusions and exclusions based on existing segment to support targeting specific sub-segment such as customers who bought certain brand within certain category affinity segment.