IoT Attribution System Merging Digital and Physical Customer Data
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Solution Overview
Problem
Current systems fail to effectively combine customer's digital and physical interactions across multiple platforms, leading to inaccurate sales projections and forecasting, as they do not account for physical activities at premises, limiting the analysis and forecasting of customer journeys.
Innovation Solution
An online/offline attribution system integrating a script module, pixel URL, consent database, probabilistic device matching module, telecom server unit, sensor unit, and backend server unit to identify and map digital and physical interactions, generating customized marketing data by correlating mobile station international subscriber directory numbers (MSISDN) and physical presence data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If organizations use only digital platform tracking to analyze customer behavior, then digital interaction data can be captured, but physical interaction data at premises is lost leading to inaccurate sales projections
Solution Approach 1:
The patent merges digital platform tracking with physical premises monitoring by integrating sensor units in stores with digital platform data collection. This combination captures both digital interactions (online browsing, clicks) and physical interactions (in-store presence, product interactions) to create a unified customer journey view, eliminating the information loss that occurs when tracking only one channel separately.
Solution Approach 2:
The patent introduces an intermediary attribution system that bridges digital and physical tracking channels. This system uses probabilistic device matching to link online devices with offline sensor data, acting as a mediator that connects previously siloed data sources and enables accurate cross-channel attribution without requiring direct integration between disparate systems.
2Quantity of substance
If sensor units are deployed to capture physical interactions, then physical activity data is collected, but the data remains isolated and cannot determine overall customer intentions
Solution Approach 1:
The patent combines sensor unit data from physical premises with digital platform data to create a comprehensive view of customer behavior. By merging these data sources through the attribution system, the quantity of collected data is maintained while the quality and completeness of customer intention information is enhanced through cross-channel correlation.
Solution Approach 2:
The attribution system serves multiple functions: it tracks digital interactions, processes sensor data, performs device matching, and generates unified customer profiles. This multi-functional approach allows the system to handle both physical and digital data streams simultaneously, extracting customer intentions from the combined dataset rather than losing information from either source.
3Productivity
If separate systems track digital and physical activities independently, then each channel's data can be captured, but integrated customer journey analysis is impossible
Solution Approach 1:
The attribution system is designed as a universal platform that handles multiple data types (digital and physical), performs various matching algorithms, and generates comprehensive customer profiles. This multi-functional design enables the system to maintain high data capture efficiency from independent channels while simultaneously providing integrated cross-platform analysis capabilities.
Solution Approach 2:
The patent introduces an intermediary attribution layer that sits between independent digital and physical tracking systems. This mediator receives data from both channels, performs probabilistic matching to link them to the same customer, and enables integrated journey analysis without requiring the original systems to be directly connected or modified.
4Measurement precision
If probabilistic device matching is used to link digital and physical data, then customer identification across channels is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex manual or rule-based device matching mechanisms with probabilistic algorithms and automated processing. This substitution reduces operational complexity while maintaining or improving identification accuracy, as the automated systems can handle large volumes of data with consistent precision without requiring complex human intervention or system configuration.
Data Source
AI summary
An online/offline attribution system for an internet-of-things platform. The online/offline attribution system includes a script module, pixel URL, consent database, sensor unit, a mapping module, and probabilistic device matching module. The script module is integrated with a digital platform to identify information about customers. The pixel URL is integrated with the script module to identify the MSISDN. The consent database stores consented MSISDN. The probabilistic device matching module determines the digital interactions of the customers across computing units. The sensor unit retrieves a unique identification number associated with the mobile device and transmits to the telecom server unit. The mapping module maps the retrieved unique identification number with stored consented MSISDN to generate and transmit a mapped consented MSISDN to backend server unit.