AI Prediction of Mobile Device Network Join Probability
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Solution Overview
Problem
Many users of mobile devices do not join wireless telecommunication networks despite efforts to contact them, resulting in lost time and effort.
Innovation Solution
A system that uses artificial intelligence (AI) to predict whether a mobile device will join a wireless telecommunication network by analyzing multiple attributes such as lead age, DUNS confidence score, website presence, and source of contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual outreach methods are used to contact users about adding a mobile device to the network, then the network can reach out to users, but the conversion rate remains extremely low (0.15%) resulting in lost time and effort
Solution Approach 1:
The system performs preliminary analysis of user attributes (lead age, DUNS confidence score, website presence, source of contact) before the actual conversion process. By pre-evaluating these attributes through AI prediction, the network identifies which users are most likely to join, enabling targeted outreach that improves conversion rate from 0.15% to 14% while reducing wasted time on unlikely candidates
Solution Approach 2:
The system continuously monitors and uses feedback from user attributes and conversion outcomes to refine the AI prediction model. The feedback loop allows the system to learn from actual conversion data and adjust its predictions, progressively improving the accuracy of identifying users who will join the network
2Quantity of substance
If the network contacts all users about adding their mobile device to the network, then the network maximizes outreach efforts, but the majority of users do not join resulting in inefficient use of resources
Solution Approach 1:
Instead of treating all users uniformly, the system applies local quality analysis by evaluating individual user attributes (lead age, DUNS confidence score, website presence, source of contact) to determine each user's specific likelihood of joining. This enables differentiated outreach strategies tailored to each user's profile, improving overall conversion rate while maintaining targeted quantity of active outreach
Data Source
AI summary
The system obtains multiple attributes of the UE, where the multiple attributes include: a lead age, an indication of a DUNS confidence score, an indication of whether a website of the UE is provided, and an indication of a source of the UE. The lead age indicates an amount of time since the UE contacted the wireless telecommunication network. The DUNS confidence score indicates reliability of the UE. The source of the UE indicates whether the UE entered the physical premises of the wireless telecommunication network. The system provides the multiple attributes to an AI and obtains from the AI an indication of whether the UE will join the wireless telecommunication network. Upon determining that the UE will join the wireless telecommunication network, the system initiates a communication with the UE.