iAudinece - Comprehensive big data tag system to generate user portraits

Problems and Challenges

Problems and Challenges
Product Function
Product Value
Application Scenario
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High traffic cost

As Internet traffic dividends have disappeared, user acquisition costs are getting higher while companies are seeking a higher ROI.

Unclear User Portraits

The imprecise advertising towards target groups leads to unstable marketing results. High costs and low conversion rate of marketing are common in operations, and it is hard to monitor marketing outcome in real time.

Difficulties in Precise Marketing

Lack of Data for Target Group Modeling

Insufficient data on target groups by tags or inadequate multi-dimensional user portraits make modeling difficult.

Product Functions

Our abundant data about user portraits and the capability to recognize retrospective/real-time scenarios enable a full understanding of users from multiple dimensions, helping companies gain deep insights into their users' online and offline behaviors to build a comprehensive, precise, and multi-dimensional user portrait system.

Our multi-dimensional, accurate and reliable data enable companies to improve user portraits, develop individualized operation strategies, and build recommendation systems, search engines, and advertising systems, to enhance service accuracy and decision-making and business growth.

Our user screening by user portraits enables companies to precisely locate target groups. With JUnion's light push notification services, companies can customize ad content for precise marketing targeting different user groups to improve advertising efficiency and achieve precise user growth. With iAudience, companies can monitor data and view advertising results in real time.

We can develop models and strategies based on specific products or target groups, and tailor solutions and risk control services that suit different business scenarios, maximizing the value of data.

Why Should You Choose iAudience？

Wide Coverage

Our number of accumulated SDK installations has reached 46.7 billion, and our SDK has been installed in 1.7 million+ apps in China. Our data have covered over 90% of mobile users in China in the past nine years.

Accurate Data

With stable internal data and cross-validation by supplementary external data from mutation sources, the accuracy and reliability of data trends can be secured.

Fast Data Update

Our automated data analysis platform can ensure daily data updates with ultra-fast interface response.

Diverse Tags

We have a total of 2,000+ user tags under 8 categories, including basic attributes, social attributes, device attributes, consumption value, geolocation, online and offline behaviors, and preference types, to fully analyze user interests and characteristics.

Diverse Cooperation Ways

In addition to standardized data access interfaces, we provide customized development, on-site modeling, and other services as per customer needs.

Application Scenarios

With the help of massive mobile device data, iAudience can analyze users' online and offline behaviors to construct a multi-dimensional, accurate, and timely holographic portrait system, providing open interface services to the entire industry

Holographic Portraits Online Behavior Offline Behavior

Population Attributes

Description of users' objective information, e.g., gender, age, marriage, and children
Device Analysis

Basic parameters of users' devices, including but not limited to type, brand, price, operator, and size
General Behavior

Covering users' most basic information and preferences in dozens of dimensions, e.g., automobile, income, expense and preference
Segmented Behavior

Description of users' online behavior in hundreds of dimensions, including finance, travel, gaming, lifestyle, social aspects, etc.

Offline Visit Insight

Detecting user's regional visit preferences(visit time, frequency, etc.) in automotive industry, education and training industry, real estate industry and tourism industry.
Offline Brand Preference

Predicting users'offline brand preferences, such as catering brand, 4S store brand preference, etc.
Travel preferences

Detecting user's travel preferences such as occupational distance, travel habits, etc.

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