Universal Identification Graph for Cross-Device Customer Journey Mapping
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Solution Overview
Problem
Marketers face challenges in translating vast amounts of data from the connected economy into actionable intelligence due to information overload, leading to 'data paralysis,' where the benefits of data are not fully realized, as they struggle to keep pace with the volume, velocity, and variety of data and lack the right mix of algorithms and technology for personalized experiences.
Innovation Solution
A Machine Intelligence Platform that uses machine learning algorithms to analyze and compare structured, semi-structured, and unstructured data, providing statistical evidence and predicting customer interactions, enabling real-time personalized experiences through a Micro-Moments Value Algorithm and High-Frequency Intelligence Hub, which processes millions of signals to deliver targeted communications and customize shopping experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If marketers collect and structure more data to meet customer expectations, then the quantity and variety of data increases, but the ability to process and act on the data decreases due to information overload
Solution Approach 1:
The patent extracts only the most relevant data points and insights from the vast amount of available data through machine learning algorithms. The system identifies and isolates key patterns, customer segments, and actionable intelligence while filtering out redundant or less valuable information, thereby enabling marketers to focus on processing and acting on only the critical data that drives business decisions.
Solution Approach 2:
The patent introduces machine learning algorithms and automated analytics platforms as intermediary systems between data collection and marketing decision-making. These intermediaries process raw data, identify patterns, generate insights, and recommend actions, thereby bridging the gap between data volume and productive utilization without requiring marketers to manually process all data themselves.
2Power
If marketers increase processing power to handle more data, then the computational capability increases, but the time delay in processing and responding to customer needs increases
Solution Approach 1:
The patent implements preliminary processing of data in advance by continuously pre-processing data streams, pre-segmenting customer bases, and pre-identifying potential opportunities. This allows the system to have ready-to-action insights and prepared marketing campaigns that can be deployed immediately when triggers occur, eliminating time delays associated with processing data in real-time from scratch.
Solution Approach 2:
The patent establishes continuous data processing pipelines that operate constantly to maintain updated customer profiles, real-time analytics, and ongoing pattern recognition. This continuous operation ensures that when marketing decisions are needed, the system already has processed and ready the necessary information, eliminating interruptions and delays in the decision-making process.
3Measurement precision
If marketers use more algorithms and technology to analyze data, then the analytical capability improves, but the complexity of the system increases leading to data paralysis
Solution Approach 1:
The patent segments the complex data analysis system into distinct functional modules, each handling specific tasks such as data collection, data cleaning, pattern recognition, insight generation, and recommendation delivery. This modular segmentation allows each component to be optimized independently and simplifies the overall system architecture by dividing complex analytical functions into manageable, specialized units that work together seamlessly.
Solution Approach 2:
The patent implements self-service capabilities where the machine learning system automatically performs data processing, pattern identification, insight generation, and even campaign optimization without requiring constant human intervention. This automation reduces the operational complexity burden on marketers while maintaining high analytical capability, as the system manages its own complexity through automated workflows and self-tuning algorithms.
Data Source
AI summary
A universal identification graph algorithm connects identities across computing devices and digital channels to one customer. The universal identification allow marketers to engage customers with relevant brand experience as they move between devices and across all digital channels. The universal identification graph algorithm enables mapping of a customer's journey across multiple, different identifications, allows deep personalization based on behaviors, habits, and preferences across the entire customer journey, helps create a more comprehensive customer profile to enable marketers to target the customer with relevant content at the right time and through the right channels, and also provides the customer with the ability to quickly opt out.