Segmenting

Overview

Sending relevant messages to customer segments is key to increasing engagement and sales. Filters let you create customer segments in a powerful, flexible and human-readable way. Here are some basic use cases to get you started:

Use Case	Filter
Get at-risk customers	tag_active = 'at_risk'
Get customers with predicted customer lifetime value of more than $2000	clv > 2000
Get customers who made only one purchase	no_of_purchases = 1
Get customers who have not bought since 31 December 2015	last_purchase < '2015-12-31'
Get at-risk customers with customer lifetime value of $500 or more	tag_active = 'at_risk' and clv >= 500
Get customers in cluster 1 or 2	cluster_id = 1 or cluster_id = 2
Get customers who signed up yesterday	first_signup = {{date:1d}}
Get customers who bought less than 4 weeks ago	last_purchase > {{date:4w}}

Creating a basic filter

Use our filtering interface (Insight - Customers - Create Filter) to get a sense of how to create customer segments. You can get a good idea of how filters work by playing around.

If you decide to venture into the advanced realm, read more below.

Creating an advanced filter

Expressions

Expressions are the basic building block of a filter. They take the following form:

{{field}} {{operator}} {{value}}

For instance, in the expression below, last_purchase is the field, < is the operator and '2015-12-31' is the operator. This filters customers whose last purchase date is before 31 December 2015.

last_purchase < '2015-12-31'

Expressions can be chained using 'and' and 'or' operators and can be excluded using the 'not' operator.

Read more about fields, operators and values.

Fields

Fields form part of an expression. You can filter on the following fields. Note that fields are only available if data is collected in your store.

Label	Field Name	Description
Age	age	Age of customer, if available.
Churn	churn_rate	Probability (from 0 to 1) that a customer is at risk.
CLV	clv	Predictive customer lifetime value, the expected present value of future purchases of a customer.
First name	first_name	First name of a customer.
First signup date	first_signup	Date that customer registered for store account.
Gender	gender	M (male) or F (female), if available.
Language	language	Language choice of a customer.
Last name	last_name	Last name of a customer.
Last purchase	last_purchase	Date of most recent purchase.
Last signin date	last_signin	Date of most recent sign-in.
Lifecycle	tag_active	Classifies customers into one-time (one_time), active (active), at-risk (at_risk) or lost (lost). A customer is considered active if his churn rate is less than 0.5, at-risk when between 0.5 and 0.9 and lost when more than 0.9.
Number of purchases	no_of_purchases	Number of purchases a customer to date.
Purchase status	tag_purchase	Classifies customers into newly registered (new_register) or existing customers (new_customer).
Segment	tag_segment	Classifies customers into VIP (vip), core (core), customers with potential (potential) and non-core (non_core) customers according to our predictive segmentation algorithm.
Total purchase amount	total_purchase	Total dollar value of purchases from a customer to date.
Categories (Only available for particular stores)	tag_categories	The set of categories for all the products the customer has traded.

Operators

Overview

You can use the arithmetic operators such as equality (=), less than or equal to (<=), more than or equal to (>=), less than (<) and more than (>) to filter customers that meet certain criteria.

Here are a list of operators:

Operator	Symbol	Description
Equals	=	Equality.
Equals (case insensitive)	~=	Case sensitive equality.
Regex	~~	Checks if field matches regular expression.
Greater than	>	Strictly greater than.
Greater than or equal	>=	Greater than or equal to.
Less than	<	Strictly less than.
Less than or equal	<=	Less than or equal to.
Contains	contains	Field contains a certain string.
Contains (case insensitive)	~contains	Field contains a certain case-insensitive string.
Starts with	startswith	Field starts with a certain string.
Starts with (case insensitive)	~startswith	Field starts with a certain case-insensitive string.
Ends with	endswith	Field ends with a certain string.
Ends with (case insensitive)	~endswith	Field ends with a certain case-insensitive string.
Is null	isnull	Field is null.

Chaining Operators

Often, you need to run complex queries that are the result of many expressions. You can chain two or more expressions using logical operators 'and' and 'or'. They work like logical operators in mathematics and you can use parenthesis () to indicate which expressions should be evaluated first.

Not Operator

You can use the 'not' operator to exclude the results of expressions. The 'not' operator appears in front of an expression, like so:

not tag_active = 'active'

The expression above filters customers who are not active.

Values

Values can be strings, numbers or dates.

Strings

Strings should be wrapped with apostrophes (') or double quotes (").

Numbers

Numbers are integers and decimals. They do not have to be wrapped in quotes.

Dates

Metisa supports four types of dates. Dates should be provided in the format below and text should be wrapped with apostrophes (') or double quotes (").

Type	Description	Format
Date	Dates with year (YYYY), month (MM) and day (DD) and no time component.	"YYYY-MM-DD"
Naive datetime	Date with a time component in hours (HH), minutes (MM) and seconds (SS) but without a timezone component.	"YYYY-MM-DDTHH:MM:SS"
Timezone aware datetime	Datetime with a specified timezone offset (+hh:mm).	"YYYY-MM-DDTHH:MM:SS+hh:mm"
Relative dates	Specified number of days (d), weeks (w), months (m) or years (y) prior to today. e.g. {{date:4w}} returns the day 4 weeks prior to today.	{{date:2y}}