Case Studies
We excel at solving complex challenges for our clients. With our expertise and experience, we deliver exceptional solutions tailored to your needs.
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ADA’s XACT CoPilot is a robust data repository housing over 400 million profiles and 1 million points of interest and unique apps. It identifies consumer personas, interests, affluence, mobility patterns, and more to fuel business growth.
XACT CoPilot extracts both online and offline data to paint a clear picture of consumer insights, helping your business achieve desired results. Make the most of data generated daily to gain a deeper understanding of your customers and engage with them on a personal level.
Get a taste of what XACT data can do for your business and download your FREE ADA XACT datasets now to find solutions to your business challenges.
Explore practical, real-life use cases for enterprises aiming to expand their business, amplify footfall traffic, and improve customer retention strategies.
With over 400 million profiles and data from 1 million apps and locations, ADA XACT delivers actionable insights for hyper-targeted ads that drive engagement.
We turn data into strategy with advanced analytics, AI, and cutting-edge tech.
Our AI-driven analytics unveil insights to help you acquire customers, discover new markets, and maximise ROI, all while reducing costs.
Turn uncertainty into clarity with meaningful insights.
With insights into consumer personas, affluence levels, interests, device brand preferences, brand affinities, places of interest, and mobility patterns, you can now craft impactful strategies.
Our extensive range of solutions makes it all possible.
Penetrate new markets or products with expansion to ideal new locations
Decision Makers, Sales Managers
Businesses with physical store locations
Businesses can estimate the potential market size and opportunity within a new geographical area by evaluating ADA’s XACT attributes for psychographic behaviour.
A sample of psychographic attributes in ADA XACT data sets:
Wealth Managers, Social Butterflies, Health Fanatics, Gamers, Entertainment Junkies, Budget Managers, Workout Warriors, Bookworm, Creative Crowd, Phone Enthusiast
Healthcare, Sports, Mall, Education, Travel
To evaluate potential store locations for new business expansion, a business may use the attribute filter to identify suitable geographical regions.
Online and offline interest in Malaysia, Indonesia, the Philippines, and Thailand markets:
Distribution of online and offline interest by geographical areas:
Attract higher footfall traffic by gaining a better understanding of the target audience—comparing clients with competitors’ customers
Sales Managers, Data & Insights Leads
Transportation, delivery, oil & gas, etc.
Businesses can identify targeted audience groups through polygonal or radial geofencing of selected locations or Places of Interest. For example, a business can create a 300-metre radial geofence around their branch location to analyse patterns of store visitors and compare them with non-visitors within a polygonal geofence of surrounding areas.
Identify top areas and POIs with higher footfall traffic to target both visitors and non-visitors.
Determine peak traffic hours by analysing visiting patterns acrossweekdays, weekends, and various times of the day.
Track the number of visits to a POI within a week.
Assess the visitor’s willingness to travel to the client’s or competitors’ outlets.
Differentiate between residents and workers to target distinct audience groups effectively
Evaluate the presence of competitors in areas or POIs of interest. Additional insights can be developed by utilising ADA’s XACT attributes
An example of a footfall traffic comparison based on petrol preference between two separate locations:
In the example dataset, we can compare footfall density among major petrol station brands in different geographical areas. Additionally, we can analyse the specific demographics and behaviour of visitors and non-visitors for a particular brand in the two areas.
The insights of footfall density being associated with attributes of demographics, preferences, interest, personas, etc. at different location area could be leveraged to :
Geo-fencing requires GPS coordinate input for branch locations and Places of Interest from the customer.
Granularity of consumer insights captured is dependent on sufficiently tracked device ID’s.
Maintain the loyalty of at-risk customers by precisely identifying dual users of the client’s and competitors’ products. Regain lost customers by gaining insights into direct competitors.
Strategy & Planning Managers, Data & Insights Leads
Telco, Infrastructure services.
Businesses can segregate customers into different groups to develop targeted retention strategies:
Device IDs detected only this month but not in the past few months.
Device IDs that have been present for the past few months up to the recent month.
Device IDs that were present in the past few months but not in the recent month.
Device IDs detected using both the client’s and competitor’s brands within the same month.
Affluence level, age group, gender, etc.
Actual consumer behaviour (rather than recalled behaviour) including personas, online interests, offline interests, etc.
Brand, model, platform, or price tier of the device.
Telco brand, level of data usage, etc.
Differentiation between home fibre internet and mobile internet usage.
For example, a Telco business can:
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Drive new customer acquisition through refined audience targeting in digital campaigns
Marketing Managers
Communication services, property, banking, automotive, manufacturing, insurance, etc.
Digital campaign performance can be optimised through a refined targeting approach that creates ideal audience clusters based on ADA’s XACT attributes, including:
High, Mid, and Low affluence groups
Wealth Managers, Social Butterflies, Health Fanatics, Gamers, Entertainment Junkies, Budget Managers, Workout Warriors, Bookworm, Creative Crowd, Phone Enthusiast, etc.
Businesses can optimise their digital campaign costs via A/B testing with a minimal budget spend (e.g., 5% of the total digital campaign budget) to identify the ideal target clusters. After identifying the best-performing target group, the remaining 95% of the digital advertising budget can be allocated to maximise campaign performance.
Below are potential improvements for digital campaigns using refined audience targeting:
We excel at solving complex challenges for our clients. With our expertise and experience, we deliver exceptional solutions tailored to your needs.
Explore our complimentary ADA XACT datasets and discover how they can address your business needs.
These datasets offer insights into Personas, Places of Interest, Demographics, Telco Profiles, Mobile Carriers, Affluence, and Brand Preferences—empowering you to make data-driven decisions with confidence.
This section provides key technical details, including the Data Schema Overview, Sample Queries, and Geographical Coverage, to help you seamlessly integrate our data into your solutions.
IFA can be considered as the identification number for each smart device that is unique to each for the purposes of advertising. IFA stands for Identification for Advertisers (which is also known as IDFA) which is used for Apple/ iOS devices whereas Android Advertising IDs (AAID) is used for Android devices.
Datasets are updated monthly. Data is primarily used observing historical trends rather than real-time insights.
We will geofence/ map the desired location and capture any IFAs that were seen within the geofenced area during the specified period. The geofence can either be done radially or polygonal. Radial geofence is for when we are assessing a wide area whereas polygonal geofence is for when we want to assess the audience within a specified outlet.
Our location data leverages on GPS data. As such, our capabilities are to pinpoint the mall, but we are unable to assess the individual stores within the mall. As our data leverages on GPS data, it will not be 100% accurate. For Google Maps, the GPS data tracks users’ location up to around twenty meters.
The demographics details are initially obtained from those apps that require the users to declare their information. We then build a prediction model to categorise those without self-declaration information based on similar traits exhibited by those with self-declared information. An example of the logic would be users with period tracker app are likely to be a female.
The affluence model is derived from three main indicators – home location property price, frequented locations and the retail price of the device used. We split our affluence into low, medium, and high affluence by splitting them into percentiles (30:40:30). Our affluence is to be used to understand the spending power of the audience and not to be taken as their income level.
Locations that audiences are seen during night-time will be their home location while locations that the audiences are seen at during working hours on weekdays are considered their work location.
Our standard ten personas are derived based on the apps that are used (e.g., Gamers are those that avidly spend their time on gaming apps).
