Predictive Appointment Model for Proactive Customer Outreach
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Solution Overview
Problem
The scheduling of appointments in professional services is inefficiently reactive and customer-driven, leading to perishable appointment slots that are difficult and costly to fill, with little guarantee of offsetting additional costs with new appointments.
Innovation Solution
An automated system and method that uses a predictive appointment model to identify likely customers to fill open appointment times by training on labeled data, extracting features, and applying weightings and coefficients to generate a contact list for proactive customer outreach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If support staff manually contact customers to fill cancelled appointment slots, then appointment filling attempts are made, but labor costs increase and success rate remains low
Solution Approach 1:
The system enables automated self-service by using machine learning models to automatically identify and contact likely customers for cancelled slots, eliminating the need for support staff manual intervention. The predictive model autonomously processes customer data, ranks prospects, and triggers outreach campaigns without human labor.
Solution Approach 2:
The patent replaces the mechanical manual process of support staff calling customers with an automated electronic system. Machine learning algorithms process customer data, predict acceptance likelihood, and automatically initiate contact through digital channels, substituting human mechanical action with computational automation.
2Quantity of substance
If support staff spend time contacting customers to fill slots, then some appointments may be filled, but the process is time-consuming and costly
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing customer data in advance to build predictive models. When a slot is cancelled, the system has already prepared customer profiles and acceptance probability ratings, enabling immediate automated outreach without time-consuming manual analysis.
Solution Approach 2:
The patent maintains continuous useful action by implementing real-time monitoring of appointment status and continuous operation of predictive models. The system continuously updates customer profiles and immediately activates outreach processes when slots become available, eliminating idle time between cancellation and refilling attempts.
3Productivity
If customers are contacted about open slots, then some may accept, but most customers do not respond or accept by chance
Solution Approach 1:
The system implements feedback by using historical customer response data to continuously train and improve predictive models. Customer acceptance patterns, response times, and preferences are fed back into the system to refine probability calculations, making increasingly accurate predictions about which customers will accept offered slots.
Solution Approach 2:
The patent replaces subjective human judgment about customer likelihood to accept with objective machine learning-based probability calculations. The system automatically processes customer behavioral data, applies trained models, and generates quantified acceptance probabilities, substituting human intuition with computational analysis.
Data Source
AI summary
A non-transitory storage medium having stored thereon instructions, the instructions being executable by one or more processors to perform operations comprising responsive to a trigger, generating a model based on extracted features from a first selection of data from a dataset, testing the predictive appointment model based on a second selection of data from the dataset. Applying the predictive appointment model to the dataset to generate customer preference probabilities. Aggregating a list of available appointments. Predicting and proactively contacting customers most likely to accept one or more available appointments based on the customer preference probabilities.