A live segment is a dynamic group of WhatsApp users that automatically updates based on real-time conditions or criteria. Some notable points of the live segment are:
The segment continuously updates contacts based on the changing criteria, characteristics or attributes.
Use live segments when you want to target users based on dynamic or changing conditions, ensuring that your messages reach the most relevant audience at any given time.
Example
Segment: "Recent Shooppers"
Criteria:Users who have made a purchase within the last 7 days.
How to Create:Set up an automated rule that adds users to the "Recent Shoppers" segment if they make a purchase in the last 7 days.
Use Case: You can use this segment to send follow-up messages, gather feedback on their recent purchase, or offer complementary products or discounts to encourage repeat purchases.
Before we dive into the steps for creating a live segment, let's look out at filter options. Chatman provides a list of filter options along with numerous conditional logic to exactly select the audience you desire.
Different Filter Options
User attributes
These are characteristics of the user, such as their name, email, language, etc. Filtering based on user attributes helps to personalize communication and target specific user demographics.
First name, Last name, Customer Id, External Id, Contact, Email, Country, City, State, Zip code
Conditions: Is, Is not, Contains, Not contains, Starts with, Ends with, Has any value.
Is: Includes users whose first name exactly matches the specified value.
Is not: Excludes users whose first name exactly matches the specified value.
Contains: Includes users whose first name contains the specified value.
Not contains: Excludes users whose first name contains the specified value.
Starts with: Includes users whose first name starts with the specified value.
Ends with: Includes users whose first name ends with the specified value.
Has any value: Includes users whose first name has any value.
Explanation: These filters help you narrow down users based on their personal information, allowing you to target specific individuals or exclude others.
Last active
Conditions: More than x days ago, Less than x days ago, After, Before, Exact, Not exact, Has any value.
More than x days ago: Includes users who were last active more than x days ago.
Less than x days ago: Includes users who were last active less than x days ago.
After: Includes users who were last active after the specified date.
Before: Includes users who were last active before the specified date.
Exact: Includes users who were last active exactly on the specified date.
Not exact: Excludes users who were last active exactly on the specified date.
Has any value: Includes users who have any last active value.
Explanation: Useful for targeting users based on their activity within a specified time frame. For instance, you can target those who have been active in the last 30 days.
Created on
Conditions: More than x days ago, Less than x days ago, After, Before, Exact, Not exact, Has any value.
More than x days ago: Includes users whose account was created more than x days ago.
Less than x days ago: Includes users whose account was created less than x days ago.
After: Includes users whose account was created after the specified date.
Before: Includes users whose account was created before the specified date.
Exact: Includes users whose account was created exactly on the specified date.
Not exact: Excludes users whose account was created exactly on the specified date.
Has any value: Includes users whose account creation date has any value.
Explanation: Allows segmentation based on when the user account was created, helping you target new or long-time users.
Date of birth
Conditions: After, Before, Exact, Not exact, Has any value.
After: Includes users whose date of birth is after the specified date.
Before: Includes users whose date of birth is before the specified date.
Exact: Includes users whose date of birth is exactly on the specified date.
Not exact: Excludes users whose date of birth is exactly on the specified date.
Has any value: Includes users whose date of birth has any value.
Explanation: Useful for creating segments based on users' birthdays or age groups.
WhatsApp opted
Conditions: Is (True, False), Is not (True, False).
Is (True): Includes users who have opted in for WhatsApp communication.
Is (False): Includes users who have not opted in for WhatsApp communication.
Is not (True): Excludes users who have opted in for WhatsApp communication.
Is not (False): Excludes users who have not opted in for WhatsApp communication.
Explanation: Enables segmentation based on whether users have opted in or out for WhatsApp communication.
Referred
Conditions: In (Select Values - Manual, Contact API, Imported, Message link, Direct, Ad Click, Shopify, Pabbly, Odoo, Google sheet, Salesforce, Zapier, Woocommerce, Zoho, Hubspot),
Is not (Select Values - Manual, Contact API, Imported, Message link, Direct, Ad Click, Shopify, Pabbly, Odoo, Google sheet, Salesforce, Zapier, Woocommerce, Zoho, Hubspot),
Has any value, Has not any value.
In (Select Values): Includes users who have been referred through the specified sources.
Is not (In the same list): Excludes users who have been referred through the specified sources.
Has any value: Includes users who have any referral source.
Has not any value: Excludes users who do not have any referral source.
Explanation: Allows segmentation based on various referral sources, helping you target users from specific channels.
Conversation attributes:
Information related to the conversation, like conversation ID, channel, and status. This is useful when you want to target users based on their interaction history or the channel they used to communicate.
Conversation Status
in (Select Values - open, closed, spam): This condition could be used to filter conversations based on their status. For example, you might want to segment conversations that are currently open, closed, or marked as spam.
Is not (Select Values - open, closed, spam): This would be the opposite of the previous condition, selecting conversations that are not in the specified statuses.
Is24hr_active
Is (True, False): This condition could be applied to filter conversations based on whether they are within the 24-hour window for free-form communication. The business can initiate conversations within the 24-hour window without additional restrictions.
Is not (True, False): This would select conversations that are not within the 24-hour window.
Custom attributes
These are additional user attributes that you can define and set according to your application's needs. It provides flexibility in segmenting users based on specific custom parameters relevant to your business.
Shiprocket attributes
If your application is integrated with Shiprocket, you can filter users based on shipping-related attributes. This is useful for targeting users who have specific shipping preferences or patterns.
Shopify attributes
Similar to Shiprocket attributes, if integrated with Shopify, you can filter users based on their shopping behavior, order history, or any other attributes related to the Shopify platform.
Survey attributes
If you have conducted surveys, this allows you to target users based on their responses. For example, you can create segments of users who have given positive feedback or participated in a specific survey.
User tags
Tags are labels applied to users, allowing for easy categorization. You can create segments of users with specific tags to target or monitor users with similar characteristics or behavior.
Conversation labels
Similar to user tags, labels are applied to conversations. You can create segments based on labeled conversations, helping you target or analyze specific types of interactions.
Campaigns
This allows you to filter users based on their participation in specific marketing campaigns. You can target users who have engaged with particular campaigns or have not participated yet.
Campaign replied
Filters users based on their response to previous campaigns. It helps in targeting users who have or have not responded to specific marketing initiatives.
System events
These are events at the system level, providing insights into user behavior or interactions not directly related to user attributes or conversation data. This might include system upgrades, downtimes, or other global events.
Create Live Segment
Suppose you have an online bookstore, and a segment of customers who have bought at least one book in the last month have shown interest in the mystery genre. Here is how you'll use live segmentation:
Head over to the segment page and tap 'Add segment'.
Type a relevant segment name and opt for 'Live Segmentation'. Say we want to increase sales for 'Mystery' genre books, so an aligning name could be 'genre_mystery'.
Click on 'create' to make a live segment with the name added.
Tap "add filter' and a drop-down appears with a list of filter options. For the sake of example, select attribute, conversation label.
Different Filter Options
User attributes: These are the basic information about the user, such as name, email, phone number, location, language, etc. You can use this filter to find users who match certain criteria, such as those from a specific country or who speak a particular language.
Conversation attributes: These are the information about the user’s conversations with the agents, such as status, rating, etc. You can use this filter to find users with different conversation attributes, such as users who gave a positive rating or who had a long conversation.
Custom attributes: These are the information you can define and assign to the users, such as age, gender, interests, preferences, etc. You can use this filter to find users with specific custom attributes, such as users who are interested in a specific product or who have a particular preference.
Shiprocket attributes: These are the information about the user’s orders and shipments, such as order ID, order status, tracking ID, courier name, etc. You can use this filter to find users with specific order or shipment details, such as users who have pending orders or who have shipped orders.
Shopify attributes: These are the information about the user’s purchases and products, such as product ID, product name, product price, purchase date, etc. You can use this filter to find users who have purchased certain products or have specific purchase details, such as those who have bought a specific product or who have spent a certain amount.
Survey attributes: These are the information about the user’s responses to the surveys, such as survey ID, survey name, survey question, survey answer, etc. You can use this filter to find users who have answered specific surveys or have given certain responses, such as those who have completed a specific survey or who have given positive feedback.
User tags: These are the labels that you can assign to the users, such as VIP, loyal, new, etc. You can use this filter to find users who have certain tags, such as users who are VIPs or users who are new.
Conversation labels: These are the labels you can assign to the conversations, such as resolved, escalated, follow-up, etc. You can use this filter to find users who have conversations with specific labels, such as those who have resolved or escalated conversations.
Segment: These are the groups of users you can create based on specific criteria, such as users who have purchased more than 10 times, who have given a negative rating, etc. You can use this filter to find users belonging to specific segments, such as loyal or dissatisfied customers.
For all those audiences who made a purchase last month and are interested in the mystery genre, you provided them with a 'conversation label' as 'purchase made'. So, select 'In' and add value as 'purchase made'.
Different Conditions In Filters
Is: This filter is used when you want to include only records that exactly match a specific value. For example, if you want to display audiences where the age is exactly 25, you would use "Is" and enter 25 as the value.
Is not: This filter is the opposite of "Is." It is used to exclude records that match a specific value. If you want to display audiences with an age other than 25, you would use "Is not" and enter 25 as the value.
Contains: This filter is useful when you want to include records that contain a particular substring or set of characters. For example, if you want to display audiences whose names contain the word "Marketing," you would use "Contains" and enter "Marketing" as the value.
Not contains: Similar to "Contains," this filter excludes records containing a specific substring or set of characters. If you want to display audiences whose names do not contain "Sales," you would use "Not contains" and enter "Sales" as the value.
Starts with: This filter is employed when you want to include records that start with a particular set of characters. For instance, if you want to display audiences whose email addresses start with "info," you would use "Starts with" and enter "info" as the value.
Ends with: Similar to "Starts with," this filter includes records that end with a specific set of characters. If you want to display audiences whose phone numbers end with "555," you would use "Ends with" and enter "555" as the value.
Has any value: This filter is useful when you want to include records with any value (not null) in a particular field. For example, if you want to display audiences with an email address entered, you would use "Has any value" for the email field.
Has not any value: This filter is the opposite of "Has any value." It includes records where a specific field is empty or null. If you want to display audiences that do not have a phone number entered, you would use "Has not any value" for the phone number field.
Let's say you want to ensure that all customers being added are from 'India'. Tap Add filter>user attributes>Country.
Now select 'Is' and enter the value as 'India.' Remember to change 'OR' to 'AND'.
'AND' means a customer will be added if both the filters are 'true' for him, and 'OR' means he would be added to the segment even if a single filter satisfies.
On the right-hand side, you can see the total number of contacts being added to the segment. Tap 'create segment' to confirm.
The segment will now be visible in the segment table.
Now, if any customer from 'India' (added as a user attribute) begins a conversation and the agent assigns a conversation label 'purchase made', he/she will be automatically added to the segment.
