ML Outgoing Communication Planner for Retailer Message Timing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Uncontrolled messaging in B2B marketplace platforms can damage retailer-wholesaler relationships and lead to the retailer ignoring further messages from wholesalers, and cause retailers to block or ignore messages, necessitating better control over message timing and content.
Innovation Solution
An outgoing communication planner utilizing machine learning to train a communications timing model and retailer interest model, scheduling messages based on preferred timing and interest levels, and applying decision engine controls to manage message delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If messages are sent frequently to retailers, then message transmission volume increases, but retailer interest and engagement decrease
Solution Approach 1:
The system dynamically changes message transmission parameters (timing, frequency, content) based on learned retailer preferences and behaviors. The machine learning models analyze historical data to determine optimal message parameters for each retailer, transforming static messaging into adaptive, personalized communication that maintains retailer interest while maximizing transmission effectiveness.
Solution Approach 2:
The system implements feedback loops where retailer responses to messages (opens, clicks, purchases) are continuously monitored and fed back into the machine learning models. This feedback mechanism allows the system to learn from past interactions and adjust future message timing and content, creating a closed-loop system that optimizes both transmission volume and retailer engagement over time.
2Ease of operation
If message timing is not controlled, then message delivery is simple, but retailer relationships deteriorate due to message overload
Solution Approach 1:
The system enables retailers to effectively control their own message reception experience through the automated timing system. By learning from retailer behaviors (when they open messages, their purchase patterns), the system self-adjusts message timing to match retailer preferences, allowing retailers to self-regulate their message load without manual intervention or complex scheduling by the wholesaler.
Solution Approach 2:
The machine learning models perform preliminary analysis of retailer preferences and behaviors before messages are sent. By pre-processing historical data and predicting optimal timing windows in advance, the system prepares personalized message schedules for each retailer before execution, eliminating the need for real-time decision-making while preventing message overload.
3Reliability
If personalized message scheduling is implemented, then retailer engagement improves, but system complexity increases
Solution Approach 1:
The system replaces complex manual message scheduling mechanisms with machine learning-based automated decision-making. Instead of requiring human analysts to manually analyze retailer preferences and create personalized schedules, the system uses algorithms to automatically process historical data, learn patterns, and generate optimized message timing schedules, reducing operational complexity while improving personalization.
Solution Approach 2:
The system segments the messaging problem into distinct functional components: data collection, model training, preference analysis, timing optimization, and message delivery. By dividing the complex task of personalized scheduling into separate modular modules (retailer interest model, communications timing model, decision engine), the system manages complexity through functional decomposition while maintaining high levels of personalization.
Data Source
AI summary
Computer implemented methods and systems for controlling sending of messages to retailers utilizing a B2B marketplace platform are described herein. Machine learning is used to train a communications timing model and a retailer interest model based on past messages sent to and received by each retailer of a plurality of retailers. The trained communications timing model is used to determine, for each retailer, a preferred timing for the retailer receiving messages from the B2B marketplace. The trained retailer interest model is used to determine, for each retailer, a respective level of interest for each of a plurality of different message types that may be sent to the retailer by the B2B marketplace. A decision engine schedules and controls sending of the messages to each retailer based on outputs of the communications timing model and the retailer interest model.