Dynamic Marketing Asset Generation via ML Component Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional online marketing asset generation is labor-intensive and limited by the use of rigid templates, which restricts the ability to tailor marketing content effectively to target users.
Innovation Solution
The system collects asset delivery event data using machine-learning techniques to generate an asset feature selection model, allowing for the dynamic selection and combination of marketing asset components based on user attributes, enabling the creation of tailored marketing assets without template constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If template-based automatic generation is used, then productivity is improved, but adaptability deteriorates due to fixed layout constraints
Solution Approach 1:
The marketing asset is divided into multiple independent components (e.g., header, body, footer, images, calls-to-action) that can be individually selected and arranged. Each component can be independently customized based on user attributes, allowing flexible recombination without being constrained by fixed templates.
Solution Approach 2:
The system transitions from static templates to dynamic component assembly. Asset components are selected and positioned dynamically based on real-time user attributes and machine-learning predictions, enabling the marketing asset to adapt its structure and content to match user preferences and behaviors.
2Adaptability or versatility
If manual creative team generation is used, then adaptability is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The system enables automatic generation of customized marketing assets through machine-learning models that predict optimal asset components based on user attributes. This self-service approach eliminates the need for manual creative team intervention while maintaining high customization capability, as the system autonomously selects and assembles appropriate components.
Solution Approach 2:
The system uses machine-learning to dynamically determine asset component parameters (selection, positioning, styling) based on user attributes. By changing these parameters automatically according to user data, the system achieves manual-level customization at automated speeds.
3Device complexity
If template constraints are applied, then device complexity is reduced, but manufacturing precision deteriorates in terms of asset-user matching accuracy
Solution Approach 1:
The system replaces rigid mechanical template structures with a flexible component-based assembly system guided by machine-learning algorithms. This substitution allows precise matching of asset features to user attributes through data-driven component selection and positioning, while maintaining relative system simplicity through automated processes.
Data Source
AI summary
Marketing assets are automatically generated from asset components having asset features relevant to target users. Asset delivery event data regarding the delivery of marketing assets is initially collected to identify asset features of delivered marketing assets and user attributes of users receiving the marketing assets. The asset delivery event data is processed using machine-learning techniques to generate a model capable of selecting asset features given a set of user attributes. When a request for a new marketing asset is received for a particular user, user attributes of that user are identified and provided to the model to select a set of asset features. The asset features are used to select asset components, which are combined to form the new marketing asset.