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We helped identify personas with customer profiling and segmentation for targeted and effective marketing initiatives
Learn how ADA refined marketing strategies and customer profiling optimized campaigns and enhanced effectiveness for Lotte Rental’s target audience. Read more.
The Results
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The Execution
Lotte, being one of the largest companies in South Korea, establishes itself across diverse industries such as financial services, industrial chemicals, IT, and more. Lotte Rental, a subsidiary, deals with a wide range of equipment for rent.
Traditionally, customer knowledge at Lotte is limited to predefined features in the CRM framework, e.g., gender, age, total purchase amount, and total service years. Recognising the need for a more comprehensive understanding, the challenge lies in leveraging behavioural data obtained from web and mobile app usage — a valuable source for gaining objective insights.
While having detailed information about each customer’s persona and behaviour is beneficial, the focus of most marketing activities revolves around customer segments or groups with similar characteristics. The client faced the challenge of extracting unbiased and accurately categorised segments for targeted and effective marketing initiatives.
ADA helped resolve this challenge by identifying customer behaviour traits and segmenting them. The client can tailor their marketing efforts for more effective results.
The Approach
First, we defined and extracted all aggregated data within the Customer Data Platform, with a focus on web behaviour data. This involved creating new variables to understand customer personas, such as preferred content types (video, image, text) and price sensitivity (inferred from price checks and rental contract simulation behaviour)
Then, we applied auto-clustering to analyse the full spectrum of customer data variables. The final segments that hold meaningful significance from a data perspective were identified.
We conducted Exploratory Data Analysis (EDA) to uncover distinctive characteristics for each segment. Each group with a primary persona category was labelled and categorised.
The derived persona segments will be used in the planning and design of future campaigns and in any customer segmentation-related task.
10
Distinct personas were identified. Some of these personas were previously unknown to the client.
~200 increase
Increase in Revenues
Case Studies
We helped identify personas with customer profiling and segmentation for targeted and effective marketing initiatives
The Challenge
M1 needed to rethink how customers pre-ordered the Samsung Galaxy S25. The traditional experience of long queues, complex forms, and multiple customer service calls wasn't delivering the seamless journey their customers expected from a leading Singapore telco.
What we have achieved
~200 increase
Increase in Revenues
The Results
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The Execution
Lotte, being one of the largest companies in South Korea, establishes itself across diverse industries such as financial services, industrial chemicals, IT, and more. Lotte Rental, a subsidiary, deals with a wide range of equipment for rent.
Traditionally, customer knowledge at Lotte is limited to predefined features in the CRM framework, e.g., gender, age, total purchase amount, and total service years. Recognising the need for a more comprehensive understanding, the challenge lies in leveraging behavioural data obtained from web and mobile app usage — a valuable source for gaining objective insights.
While having detailed information about each customer’s persona and behaviour is beneficial, the focus of most marketing activities revolves around customer segments or groups with similar characteristics. The client faced the challenge of extracting unbiased and accurately categorised segments for targeted and effective marketing initiatives.
ADA helped resolve this challenge by identifying customer behaviour traits and segmenting them. The client can tailor their marketing efforts for more effective results.
The Approach
First, we defined and extracted all aggregated data within the Customer Data Platform, with a focus on web behaviour data. This involved creating new variables to understand customer personas, such as preferred content types (video, image, text) and price sensitivity (inferred from price checks and rental contract simulation behaviour)
Then, we applied auto-clustering to analyse the full spectrum of customer data variables. The final segments that hold meaningful significance from a data perspective were identified.
We conducted Exploratory Data Analysis (EDA) to uncover distinctive characteristics for each segment. Each group with a primary persona category was labelled and categorised.
The derived persona segments will be used in the planning and design of future campaigns and in any customer segmentation-related task.
10
Distinct personas were identified. Some of these personas were previously unknown to the client.
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