Segmentation Filters

Appboy’s SDK provides you with a powerful arsenal of filters to segment and target users based off of specific features and attributes.

Custom Data

Custom data filters allow you to segment users based on self-defined events and attributes. With them, you can use features specific to your mobile app to more specifically group the users whom you wish to target.

Custom Attributes

Determines whether or not a user matches a customly recorded attribute.

Custom Event

Determines whether or not a user has performed a specially recorded event.

First Did Custom Event

Determines the earliest time that a user has performed a specially recorded event (above).

Last Did Custom Event

Determines the most recent time that a user has performed a specially recorded event (above).

X Custom Event In Y Days

Determines whether or not a user has performed a specially recorded event between 0 and 50 times in the last 1, 3, 7, 14, 21, and 30 days.

User Activity

User activity filters allow you to segment users based on their in-app actions and purchases. Among other things, these filters can be used to target active users who have had a history of interacting with your app.

First Made Purchase

Segments users by the earliest recorded time that they have made a purchase in your app.

First Purchased Product

Segments users by the earliest recorded time that they have bought a specific item from your app (special membership, gift certificate, etc…)

First Used App

Segments users by the earliest recorded time that they opened your app.

Last Made Purchase

Segments users by the most recent time that they have made a purchase in your app.

Last Purchased Product

Segments users by the most recent time that they have bought a specific item from your app (special membership, gift certificate, etc…)

Last Submitted Feedback

Segments users by the most recent time that they have sent a feedback message or filed an issue about your app.

Last Used App

Segments users by the most recent time that they have opened your app.

Median Session Duration

Segments users by the median length of their sessions in your app.

Money Spent In-App

Segments users by the amount of money that they have spent in your app.

Most Recent App Version

Segments users by the latest version of your app that they have used.

Most Recent Location

Segments users by the last recorded location at which they have used your app.

Number of Feedback Items

Segments users by the number of feedback messages and issues that they have sent through your app.

Purchased Product

Segments users by products purchased in your app.

Session Count

Segments users by the number of sessions they have had in your app.

Total Number of Purchases

Segments users by how many purchases they have made in your app.

Uninstall Date

Segments users by the date they uninstalled your app.

Uninstalled

Segments users by whether they have uninstalled your app.

X Money Spent in Last Y Days

Segments users by the amount of money that they have spent in your app in the last 1, 3, 7, 14, 21, and 30 days. This amount will only include the sum of the last 50 purchases.

X Product Purchased in Last Y Days

Segments users by the number of times (between 0 and 50) they have bought a specific item from your app in the last 1, 3, 7, 14, 21, and 30 days. (special membership, gift certificate, etc…)

X Purchases in Last Y Days

Segments users by the number of times (between 0 and 50) they have made a purchase in the last 1, 3, 7, 14, 21, and 30 days.

X Sessions in Last Y Days

Segments users by the number of sessions (between 0 and 50) they have had in your app in the last 1, 3, 7, 14, 21, and 30 days.

Retargeting

Retargeting filters allow you to segment users whom you have already attempted to target. These filters are effective for reaching out to users who have previously responded positively to your marketing campaigns.

Clicked Card

Segments users based off whether or not they have clicked a specific card or promotion.

Clicked/Opened Campaign

Segments users based off whether or not they have responded to a specific campaign.

Converted From Campaign

Segments users based off whether or not they have converted on a specific campaign.

Converted From Canvas

Segments users based off whether or not they have converted on a specific Canvas.

Entered Canvas Variation

Segments users based off whether or not they have entered a variation path of a specific Canvas.

Clicked/Opened Campaign or Canvas With Tag

Segments users based off whether or not they have responded to a specific campaign or Canvas with a specific tag.

Last Received Campaign or Canvas With Tag

Segments users based off when they received a specific campaign or Canvas with a specific tag.

Received Campaign or Canvas with Tag

Segments users based off whether or not they have received a specific campaign or Canvas with a specific tag.

Received Canvas Step

Segments users based off whether or not they have received a specific Canvas step.

Has Never Received A Campaign

Segments users based off whether or not they have ever received any campaign.

In Campaign Control Group

Segments users based off whether or not they were in the control group for a specific multi-variant campaign.

In Canvas Control Group

Segments users based off whether or not they were in the control group for a specific Canvas.

Last Received Specific Campaign

Segments users based off when they received a specific campaign.

Received Campaign

Segments users based off whether or not they have received a specific campaign.

Received Campaign Variant

Segments users based off which variant of a multivariate campaign they have received.

Received Campaign with Tag

Segments users based off whether or not they have received a campaign that currently has a specific tag.

Marketing Activity

Marketing filters segment users based on their previous interactions with your campaigns. These filters are effective for reaching out to large numbers of users without spamming them.

Has Marked You As Spam

Segments users by whether or not they have marked your messages as spam.

Last Engaged With Message

Segments users by the last time that they have clicked or opened one of your messaging channels (email, in-app, push).

Last Enrolled in Any Control Group

Segments users by the last time that they fell into the control group in a campaign.

Last Received Any Campaign

Segments users by the last time that they have received a campaign on any messaging channel.

Last Received Email Campaign

Segments users by the last time that they have received one of your email messages.

Last Received Push Campaign

Segments users by the last time that they received one of your push notifications.

Last Received Webhook Campaign

Segments users by the last time that Appboy sent a webhook for that user.

Last Viewed News Feed

Segments users by the last time that they have visited your app’s news feed interface.

News Feed View Count

Segments users by the number of times that they have viewed your app’s news feed interface.

User Attributes

User attribute filters segment users by their constant attributes and characteristics. These filters are effective for grouping and targeting large demographical groups (such as specific age groups or foreign language speakers).

Age

Segments users by how old they are.

Background Push Enabled

Segments users on whether they have enabled background push or not.

Birthday

Segments users by their birthday. Users with a birthday on February 29th will be included in segments including March 1.

City

Segments users by their last known city location.

Country

Segments users by their last known country location.

Date of Custom Attribute

Segments users based upon the calendar date of custom attributes.

Device Carrier

Segments users by their device carrier.

Device Count

Segments users by how many devices they have used your app on.

Device Model

Segments users by their mobile phone’s model version.

Device OS

Segments users by their mobile phone’s operating system.

Email Available

Segments users by whether or not they have reported their respective email addresses.

Email Opt In Date

Segments users by the date on which they opted into email.

Email Subscription Status

Segments users by their subscription status for email.

Email Unsubscribed Date

Segments users by the date on which they unsubscribed from future emails.

First Name

Segments users by first name.

Gender

Segments users by gender.

Hard Bounced

Segments users by whether they have had a hard bounce from an e-mail message.

Language

Segments users by their preferred language.

Last Name

Segments users by their last name.

Location Available

Segments users by whether or not they have reported their locations.

Most Recent Watch Model

Segments users by their most recent smartwatch model.

Phone Number

Segments users by their phone number. Only use digits [0-9]. Do not include parenthesis, dashes, etc.

Push Enabled

Segments users who have explicitly activated push notifications for your app.

Push Opt In Date

Segments users by the date on which they opted into push.

Push Subscription Status

Segments users by their subscription status for push.

Push Unsubscribed Date

Segments users by the date on which they unsubscribed from future push notifications.

Random Bucket Number

Segments users by a randomly assigned number (0 to 9999 inclusive). Can enable the creation of uniformly distributed segments of truly random users for A/B and multivariate testing.

Update/Imported from CSV

Segments users based on whether they were a part of a CSV upload or not.

Web Browser

Segments users by the web browser they use to access your website.

Install Attribution

Install Attribution Ad

Segments users by the ad that their install was attributed to.

Install Attribution Adgroup

Segments users by the adgroup that their install was attributed to.

Install Attribution Campaign

Segments users by the ad campaign that their install was attributed to.

Install Attribution Source

Segments users by the source that their install was attributed to.

Social Activity

Social activity filters segment users by their social media activity - namely through Facebook and Twitter.

Connected to Facebook

Segments users who have granted Facebook account access within your app.

Connected to Twitter

Segments users who have granted Twitter account access within your app.

Number of Facebook Friends Using App

Segments users by the number of friends that they have on Facebook that are using your app.

Number of Twitter Followers

Segments users by the number of followers that they have on Twitter.

Testing

Testing filter segments allow you to test your campaigns by sending messages to individually designated users only.

Device IDFA

Allows you to designate your campaign recipients by IDFA for testing.

Device IDFV

Allows you to designate your campaign recipients by IDFV for testing.

Email Address

Allows you to designate your campaign recipients by individual email addresses for testing.

External User ID

Allows you to designate your campaign recipients by individual user IDs for testing.

Segment Funnels

Segment funnels allows you to see how each added filter impacts the your segment statistics. When creating a segment, a row of data will appear under each filter. This data will provide the following information for users that are targeted by all filters up to that point:

  • The total number of users targeted and the percentage of your audience base
  • The LTV and LTV for paying users
  • The number of users emailable
  • The number of users opted in to email
  • The number of users that are push enabled
  • The number of users opted in to push

Segment funnel overview

Best Practices

  • By adding filters that document your user flow, you can see the points where users fall off. For instance, if you’re a social networking app and you want to see where you might be losing users during your onboarding process, you may want to add custom data filters for signing up, adding friends, and sending the first message. If you find that 85% of users are signing up and adding friends, but only 45% sent the first message, then you’ll know to focus on encouraging more message sends during your onboarding and marketing campaigns.

  • Segment funnels let you compare the percentage of users who commit different actions. For instance, do active users, or those with high LTV, tend to interact more with push or email? To find out, create a segment of active users with one or more filters, and then see how statistics change when you add a filter for opting in to push, and when you add a filter for opting in to email.

  • Analyze how LTV changes as you add filters. For active users, do those who connect to Facebook or those who connect to Twitter have a higher LTV? Or is LTV significantly higher to those who have connected to both? If you find, for instance, that connecting to Twitter has very little impact on LTV but connecting to Facebook has a large impact, you may want your marketing campaigns to focus on incentivizing Facebook connections.

Sample Use Cases

Impact of a specific user action on conversions

By analyzing the impact of a certain user action (such as adding items to a wish list) on a conversion (such as making purchases) you can answer the following questions:

  • Does the user action coincide with more purchases?
  • How prevalent is the user action? Should you create marketing campaigns that encourage more of that action?

In the example below, all users who added items to a wish list also made a purchase. Since only a small percentage of users added items to a wish list, this app may want to incentivize this behavior more through marketing campaigns.

Wish list users

Compare messaging channels

Create a segment of active users (or users with desired traits) and compare their interactions with different engagement channels, such as the News Feed, email and push notifications. For instance, if more loyal users are subscribed to push, you may want to spend more time on sending active user campaigns via push. If you find that the LTV is higher, however, for those who are subscribed to email, you might want to prompt more active users to subscribe to email.

Push

Email

Segment Insights

Segment Insights Dash

As Appboy continues to build out its Intelligence Suite, we’re pleased to launch Intelligent Targeting with Segment Insights. Segment Insights allows you to quickly and easily see how a segment is performing compared to another across a set of pre-selected KPIs. From the segment section of your dashboard, clicking on the “Segment Insights” button in the top right of the page brings you to a screen where up to four different segments can be compared against a baseline. The baseline can either be a specific segment you select or the stats for all of your users. Currently, Appboy can compare the following statistics on the Segment Insights page:

Measurement Description Formula
Session Frequency Average number of segment users’ sessions per day (total # of sessions)/(# days since first session)
Time Since First Session Average time between segment users’ first session and now today - date of first session
Time Since Last Session Average time between segment users’ last session and now today - date of last session
Lifetime Revenue Average lifetime revenue for segment users user lifetime spend
Time to First Purchase Average time between segment users’ first session and first purchase date of first purchase - date of first session
Time Since Last Purchase Average time between segment users’ last purchase and now today - date of last purchase

You can easily share specific comparisons with the page’s unique URL, and users can also click beneath each segment to reveal more information about that segment. These comparisons will reset when a user changes app groups.

Segment Insights Expanded

Segment Insights have also been built right into the the segment details view. When looking at a particular segment you’ve previously set up, you can find the same six statistics outlined within the dynamic, grey Segment Statistics box. From here, you can quickly launch the Segment Insights tool to compare this particular segment with any other you’ve previously set up, but note that this will overwrite any segments you’ve previously selected within the Segment Insights tool.

Segment Insights Details

As we continue to build out our Intelligent Suite, we’ll update Appboy Academy and Relate, our blog, with more information.

Sample Use Cases

Comparing demographic usage and purchasing patterns

One of the best usages of Segment Insights is answering questions about the impact of user demographics on app usage and campaign effectiveness, such as:

  • Are certain user demographics performing significantly better or worse than average?
  • Should I rethink the localization of a particular campaign?
  • Is a campaign engaging a certain demographic?
  • What goals should I set for a campaign aimed at a certain demographic?

Segment Insights can help uncover differences between user demographics. The example below shows a comparison of an app’s user base by their language, illustrating how English speakers tend to have a higher LTV and activity levels than speakers of other languages.

Segment Insights by Language

Notice that on average, German and French speakers signed up a longer time ago, which might explain why they’re no longer as active. This could be due to a multitude of factors, for example if the app first launched in Europe but is now more popular in the U.S., where most people speak English or Spanish. For more robust findings, when analyzing KPIs across demographics, it’s sensible to test the findings from a general study of demographics (e.g. if language impacts LTV in all users) by looking at a smaller, more similar population and seeing if the findings persist. In the below example, we restricted the surveyed population to those who have used the app at least ten times and have been active in the past month, and analyzed language within this population to find similar findings to the ones before:

Active User Segments by Language

To improve conversions among speakers of languages other than English, a good first step would be to localize campaigns to the user’s device language and making sure that the copy of those messages is engaging users by using a multivariate campaign to test different versions of the foreign language copy.

Understanding indicators of higher revenue

Getting users to convert to purchasers can be difficult, and trying to push new, inactive or disengaged users directly toward purchasing may lead the user to uninstalling your app. Segment Insights can help you discover actions that lead users further down the purchase funnel without requiring them to purchase just yet, for example adding items to their wish list, sharing on social media or favoriting content. Below is an example charting out the impact on purchases different behaviors within an e-commerce app.

User Actions contributing to purchases

We can see that relatively few users are currently signed up for the newsletter, but these users are generally more active. To keep new users engaged, it would be a good idea to include an invitation to order the newsletter in onboarding campaigns. To re-engage lapsed users, a good plan would be to send out a typical lapsed user campaign and target users who converted with a subsequent campaign to sign up for the newsletter.

Using User Search

Feature Overview

The User Search feature allows you to view user profiles. User profiles are a great way to find information about specific users. The User Search feature is located in the Users section of the Appboy Dashboard.

User_Search

You can search your user base using a user’s e-mail or user ID. Most of the time, the user search will return one result but do note that if you enter a non-unique e-mail (i.e. an e-mail that belongs to more than one user) into the user search, it will return more than one user profile. Once you’ve entered an e-mail or user ID into the User Search, you’ll be able to see the information that you’ve recorded on this user with the Appboy SDK. If you do enter a non-unique e-mail, clicking on next will allow you to view the other profiles that are associated with that e-mail.

Nonunique_User_Search

Overview Tab

In the Overview Tab you can see information about the user’s Profile, App Usage, Custom Attributes, Custom Events, Purchases and the Most Recent Device that the user logged in on. For more information on this data, see User Data Collection.

User_Search_Overview

Engagement Tab

You can click on the Engagement Tab to view information about the user’s Contact Settings, Campaigns Received, Segments, Communication Stats, Install Attribution, News Feed Cards Clicked and Random Bucket #.

User_Search_Engagement

Feedback Tab

The Feedback Tab allows you to view any feedback that a user has submitted through your app.

User_Search_Feedback

Social Tab

The Social Tab allows you to see the social accounts that a user has connected to the app. You’re also able to view a user’s activity on these connected social accounts.

User_Search_Social

Use Cases

The User Search feature is a great resource for trouble shooting and testing because you can easily access information about a user’s engagement history, segment membership, device and operating system.

For example, if a user reports a problem and you are not sure what device and operating system they are using, you can use the Overview Tab to find this information (as long as you have their e-mail or user ID). You can also view a user’s language, which could be helpful if you are troubleshooting a multi-lingual campaign that did not behave as expected.

You can use the Engagement Tab to verify whether a certain user received a campaign. In addition, if this particular user did receive the campaign, you can see when they received it. You can also verify whether a user is in a certain segment and whether a user is opted in to push and/ or e-mail. This information is useful to have for troubleshooting purposes. For example, you’d want to check this information if a user does not receive a campaign that you expected them to receive or receives a campaign that you did not expect them to receive.

Location Targeting

Appboy’s location targeting feature allows you to specifically segment users based off of their most recent location.

Step 1: Create your Segment

Under the Engagement header on the dashboard, click the Segments bar to view all of your current user segments. On this page, you can create and name new segments as shown below.

Navigate to Section

Step 2: Customize your Location

Once you have created your segment, add a “Most Recent Location” filter to target users by the last place that they used your app(s). You have the option of either highlighting users in a standard circular region or a customizable polygonal region.

Create Segment

Circular Regions

For circular regions, you can move the origin and adjust the location radius for your segmentation.

Circular Region

Polygonal Regions

For polygonal regions, you can more specifically designate which areas you wish to be included in your segment.

Polygonal Region 1

Polygonal Region 2

Beacon and Geofence Support

Combining existing beacon or geofence support with Appboy’s targeting and messaging features allows you to learn more about your user’s physical actions and message them accordingly.

Gimbal Places Support

Connecting your Gimbal Account to Appboy lets you track when your users enter or leave your defined places and trigger events off of these entries and exits. In addition, you can track extra information like the Place name or the Dwell visit as an event property so that you can personalize your messaging even further. Please reference Gimbal’s documentation along with our instructions for iOS and Android integration. Note that this will work the same for Gimbal’s beacons as well as their geofence solutions.

PlaceIQ Segmentation

PlaceIQ is a location intelligence service that gives you the ability to track your user’s location and automatically create intelligent segments called Audiences and Dwells.

How it works

The Appboy SDK connects to PlaceIQ’s servers and sends over a user’s IDFA. PlaceIQ takes this and creates a new user ID for the device that it detected. Then, Appboy collects a user’s location through background location checks and sends it to PlaceIQ. PlaceIQ takes this information and uses it to create Audiences and Dwells. Audience and Dwell information can then be used in Appboy to segment target audiences and trigger messages respectively.

Audiences

Audiences are pre-defined personas that PlaceIQ has created to group people by interests and common activities. These Audiences are groups such as movie goers, ski and snowboard enthusiasts, and Starbucks lovers.

How to Create Segments with PlaceIQ Audiences

During the segmentation creation process, add a new filter and search for PlaceIQ Audience. You will then be able to choose the Audience name to segment based off. You can also choose to either include or not include the specific group. The Audience data will be collected as array custom attributes for each user.

To narrow your segment even further to a specific location, add the Most Recent Location filter and add a search within a circle or polygon.

Dwells

PlaceIQ analyzes location data to determine whether a frequent location is someone’s Home Dwell or Work Dwell. Appboy will start by only using Work and Home Dwells, but will continue to develop this feature as time goes by.

How to Trigger Campaigns Based on Dwells

Home and Work Dwells are currently used to send trigger campaigns. To achieve this, select Action-Based Delivery and add “Enter Location” as the New Trigger Action. You can then select Home or Work Dwell as the location at which the user enters to trigger the message.

Note: PlaceIQ location information is only available in the United States