Experience Optimization System for Multi-Campaign Scheduling
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Solution Overview
Problem
Existing solutions are inadequate for managing multiple electronic communication campaigns scheduled within a given period, as they require clients to specify non-overlapping time windows for each campaign, leading to operational overhead and inefficiencies in determining optimal send times for maximizing customer engagement.
Innovation Solution
The proposed method employs a global customer value function to determine the optimal send times for multiple campaigns, considering customer information, historical activity, and time period properties, allowing for dynamic decision-making on sending communications to maximize engagement over a time period, thereby reducing operational overhead and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If clients specify non-overlapping time windows for each campaign, then campaign management becomes simpler, but operational overhead increases and flexibility decreases
Solution Approach 1:
The system automatically determines optimal send times for multiple campaigns without requiring client intervention to specify non-overlapping time windows. The engine self-manages the scheduling by evaluating customer eligibility, historical activity, and campaign properties to autonomously select send times that maximize engagement while preventing communication overload.
Solution Approach 2:
The system dynamically adjusts send time optimization across multiple campaigns rather than using static non-overlapping time windows. The optimization engine adapts send times based on real-time factors including customer state, historical activity, and campaign properties, allowing flexible scheduling that responds to changing conditions while managing operational complexity.
2Adaptability or versatility
If multiple campaigns are scheduled in a given period, then campaign diversity and customer engagement opportunities increase, but determining optimal send times becomes more complex
Solution Approach 1:
The system merges the send time optimization process for multiple campaigns into a unified evaluation framework. Instead of optimizing each campaign separately, the engine simultaneously considers all campaigns a customer is eligible for, evaluating them together to determine the optimal send time that maximizes overall engagement while preventing communication overload.
Solution Approach 2:
The optimization engine serves multiple functions simultaneously: it evaluates customer eligibility across campaigns, analyzes historical activity patterns, assesses current customer state, determines optimal send times, and prevents communication overload. This multi-functional approach handles the complexity of multiple campaigns through a single unified system rather than separate optimization processes.
3Productivity
If send times are optimized for each campaign independently, then individual campaign performance improves, but overall customer engagement value decreases due to communication overload
Solution Approach 1:
The system deliberately limits the number of communications sent to customers by evaluating all eligible campaigns together and selecting only the optimal send times. Rather than sending communications for every eligible campaign (excessive action), the engine applies frequency capping and send time optimization to ensure the right number of communications are sent at the right times, balancing individual campaign performance with overall customer engagement value.
4Measurement precision
If the system evaluates all customer information, historical activity, and campaign properties, then send time optimization accuracy improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary evaluation of customer eligibility for multiple campaigns before determining send times. By pre-assessing which campaigns a customer is eligible for and gathering relevant customer information and historical activity data in advance, the system reduces the complexity of the final send time determination while maintaining high optimization accuracy through comprehensive data consideration.
Data Source
AI summary
Methods and corresponding systems are provided for configuring communications to customers when there are multiple campaigns scheduled in a given period. An optimal number of communications are sent prioritized according to business or performance measures where send times are spaced out in a manner that strives to attain the highest value. An example method includes determining a number of electronic communications, for a plurality of campaigns to send to the particular customer during a particular time period; determining the optimal send time during the particular time period; determining for which campaigns the particular customer is eligible; for each determined optimal time: determining a strategy including selecting an electronic communication for one of the campaigns to send to the eligible particular customer so as to maximize the value over the particular time period; and causing the selected electronic communication to be sent to the particular customer at the determined optimal time.