Customer Adoption Scoring via ML and Usage Data
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Solution Overview
Problem
Customer success teams in enterprise software face challenges in proactively addressing customer satisfaction due to reactive remedial actions, underutilization of diverse customer intelligence from usage data, and failure to account for customer-specific preferences and behaviors, leading to inefficiencies in monitoring product adoption and satisfaction.
Innovation Solution
A system and method that leverage historical product usage and services data through a machine learning model to generate product and customer adoption scores, incorporating product usage parameters, customer profiles, and service metrics to automatically determine customer adoption and satisfaction levels, enabling proactive measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customer success teams manually monitor usage data to assess customer satisfaction, then they can identify customer needs and provide support, but the massive volume of product-related transactions makes this approach infeasible and inefficient
Solution Approach 1:
The patent introduces an intermediary system comprising usage data collection module, machine learning model, and adoption score generation module that automatically processes usage data and generates customer adoption scores. This intermediary system bridges the gap between raw usage data and actionable customer satisfaction insights, eliminating the need for manual monitoring while maintaining high measurement precision
Solution Approach 2:
The patent replaces the mechanical manual monitoring process with an automated computational system using machine learning models. The system automatically collects usage data, processes it through trained models, and generates adoption scores without human intervention, thereby maintaining measurement precision while dramatically improving productivity
2Reliability
If customer success teams wait for customer feedback or complaints to take remedial actions, then they can respond to critical issues, but many customers will wait until issues reach critical stage or abandon the product
Solution Approach 1:
The patent implements preliminary action by continuously monitoring usage data and detecting early signs of customer difficulties before they become critical issues. The machine learning model analyzes usage patterns in real-time and generates adoption scores that indicate potential problems, enabling customer success teams to intervene proactively before customers abandon the product
Solution Approach 2:
The patent establishes a continuous feedback loop where usage data is constantly collected, analyzed by machine learning models, and converted into adoption scores that feed back to customer success teams. This real-time feedback mechanism enables immediate detection and response to customer issues, improving reliability while reducing the time loss associated with waiting for customer complaints
3Adaptability or versatility
If existing systems use generic support service statistics to assess customer satisfaction, then they can provide basic oversight, but they cannot account for customer-specific preferences and behaviors
Solution Approach 1:
The patent applies local quality by customizing the customer adoption assessment for each individual customer based on their specific usage patterns, preferences, and behaviors. The machine learning model is trained on customer-specific data and generates personalized adoption scores that reflect individual customer needs and expectations, rather than applying generic metrics to all customers
Solution Approach 2:
The patent implements universality through a multi-functional system that simultaneously handles diverse data sources (product usage data, support service data, customer profile data), processes multiple types of customer behaviors, and generates comprehensive adoption scores. The machine learning model serves multiple functions including data integration, pattern recognition, and personalized assessment across different customer segments
Data Source
AI summary
A system, method, and computer-readable medium for determining customer adoption based on monitored data, including receiving product usage parameters from a product data store on the computer network, each product usage parameter being determined based on tracking usage of the product by the customer over a predetermined time period, storing a customer profile for the customer comprising customer parameters, the customer parameters being determined based on customer information stored in a customer database on the computer network, receiving service parameters from a customer support data store on the computer network, each service parameter being determined based on tracking support services provided to the customer for the product over the predetermined time period, and generating a product adoption score by applying a machine learning model to the product usage parameters and the customer profile to generate a usage-based adoption score and adjusting the usage-based adoption score based on the service parameters.