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7 Real-life Behavioral Segmentation Examples & Case Studies
data analysis Tue, Apr 14, 2020
Repro APAC Written by Repro APAC

7 Real-life Behavioral Segmentation Examples & Case Studies

Why is Behavioral Segmentation important?

Behavioral segmentation is becoming increasingly important when it comes to doing marketing. Not only that, it helps customers to feel more relatable due to more personalized marketing messages, but it also is shown to bring actual results. Forbes has well-documented the importance of personalization, showing more than half of the customers desire personalized content.

In this article, I am going to list out eight different business segmentation, how our clients utilized them to achieve better results in their marketing.

Seven types of segmentation used by our clients

User Journey

User Journey

The best way to start behavioral segmentation is by creating a user journey map to understand how your users move across your websites or apps. With the user journey mapped out, you can create specific marketing messages for users who are at different stages of their journey, making your messages more personalized and relatable. 

Case Studies 1: Osaka Shoin Women's University

User Journey - Osaka Shoin Woman University

Osaka Shoin Women's University wanted to increase the number of submission for their University Open Days. Despite having a good amount of users visiting their website, they noticed their visitors are not aware of the Open Day application. To solve this issue, they analyze how users browsed their website and created a pop-up and placed embedded messages at pages that guide the visitors to the application submission page. 

Case Studies 2: Goo-net

goo-net User Journey

Goo-net, a secondhand car listing website, analyzed the journey user go through before making a transaction. From the analysis, they noticed a high drop-out rate for users who downloaded the App but did not conduct any search. This insight allowed them to take specific actions to deal with the issue. 

Usage Behavior

Usage Behavior

Segmenting your users based on their usage behavior will allow you to gain insights on how to plan for your marketing initiatives. 

Case Studies 1: HaruHaru

HaruHaru - Usage Behavior

HaruHaru is a media company targetted at high school girls. When they first launched their App, they were having difficulty in increasing their userbase. However, after running a series of analyses that looked into how users were consuming their content on their App, they discovered users who read Korean-related content were more likely to continue using their App. Because of that, they started creating more Korean-related content on their App.

Case Studies 2: Mobile Factory

Ekimemo-2

Mobile Factory is a game development company that created Ekimemo, a location-based gaming app that blends reality and virtual together. They created a marketing campaign using the character users chose in-game. This campaign resulted in a significantly higher App Launch rate when compared to non-personalized characters. 

User Activity

Usage Activity

The user activity behavioral segmentation is based on how active users are on your website and App. Usually, based on the level of activeness, companies craft specially crafted messages to low activity users with the intention to reactive them or high activity users to reward them. 

Case Studies 1: Goo-net

goo-net user activity

As mentioned above, after creating a user journey, Goo-net discovered a high drop-out rate for users who downloaded the App but did not conduct any search. This showed that users were highly unfamiliar with the App. To help users familiarize themselves with the App, Goo-net launched a series of marketing campaigns that educate the users on how to use the App. As a result, they saw a 30% increased in their car detail page. 

Case Studies 2: HaruHaru

hruharu - user activity

HaruHaru, on the other hand, created two different campaigns targeted at inactive users to increase the retention and user engagement rate. The first campaign they launched a was consecutive 3-days login present, which resulted in a 10% increase in retention rate.

Haruharu - user activity 2

The other was a special Push Notification series, where they deliver messages pretending to be their mom to capture users' attention.

Read more about HaruHaru’s marketing campaign over here.

Case Studies 3: Mobile Factory

Ekimemo main

Mobile Factory devised an ingenious way to segment user activities. Users were segmented by those who opened push notifications and users who do not open push notifications. With this, they discovered the reason behind the low open rate was not due to their lack of creativity in wording or right creatives but due to the users’ inherent behaviors. 

User Attributes

User Attributes

Users’ attributes are a form of behavioral segmentation that segments users based on their attributes. These attributes can be demographically or based on the status of their companies website (registered users, paid users, Silver Card member)

Case Studies 1: Goo-net

goo-net User Attributes

Goo-net approach to segmenting users by attributes is done by creating a different type of personas based on car users have viewed. Using these personas, they then tailored their marketing messages accordingly. For example, for users who have viewed family K-cars, they will send out marketing messages that are family-related.

Case Studies 2: Mamari

mamari-main

Mamari is a media company targeted at moms and mom-to-be. Because of the nature of their business and users-base, they have devised a marketing campaign targeting users based on their pregnancy weeks. 

User Time

User Time

User time is a behavioral segmentation based on time-related factors. This can be the time of the day, time of the week, or users’ availability in general. 

Case studies 1: and Factory

and factory-1

and Factory is a company that develops and manages mobile Apps. They are famous for co-operating with Square Enix to promote the manga App, Manga UP. For and Factory, they are constantly mindful of users’ available time in mind. As reading a manga takes up a lot of time, they deliver their marketing messages according to users’ possible free time. 

Case studies 2: Mamari

Mamari - user time

As for Mamari, they change the content of the marketing messages based on the time of the day. Based on their finding, their users are more responsive to “Quote of the day” in the morning, while in the afternoon and evening, they are more responsive to content such as “Question of the Day."

Browsing Behavior

Browsing Behavior

Browsing behavior looking at how your users use your website and App. Knowing your users' browsing habits allow you to tackle the issue directly.

Case Studies 1: COEN

COEN

COEN is an online and offline fashion retailer. They analyzed the browsing behavior of its users and noticed a sharp drop-off at certain landing pages. To tackle this issue, they implemented a floating “Return to homepage” button at the bottom of the screen to redirect users to the homepage. After this implementation, they notice a 10% reduction in the bounce rate for those pages.

Case Studies 2: Osaka Shoin Women's University

Osaka Shoin Womens University

Osaka Shoin Women's University had a slightly different problem, instead of having a high bounce rate, visitors who viewed their Press Release were not aware of the University Open Day they wish to promote. To solve this issue, they embedded a website message into their Press Release, letting visitors to learn more about their event. This resulted in a 200% increase in applications when compared to last year. 

Search Behavior

Search Behavior

When you have a large website or App, it’s most likely that you have a search bar built within. Businesses can utilize the data obtained from search behavior to send cleverly crafted messages to their customers. Other than that, marketers also make use of search behaviors to implement other marketing strategies such as SEO and ASO.

Case Study: atHome

at home

athome is a property listing service provider in Japan. They managed to increase their App downloads by analyzing the search behaviors of customers. In their App Search Optimization (ASO), they usually trying to rank high search volume, difficulty keywords. However, after consulting with our ASO experts, they propose plans which target medium traffic volume, medium difficulty keywords. After implementing the strategy, they received a 150% increase in downloads. 

Read more about their case study here.

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