Digital Display Ad Allocation System
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Solution Overview
Problem
Existing Out Of Home (OOH) advertisement systems struggle to optimize the allocation of digital displays in response to quickly changing advertisement demands, leading to inefficiencies in both planned and unplanned bookings.
Innovation Solution
A computer-implemented method and system that allocates planned bookings for advertisement campaigns on digital displays within an OOH inventory, while also utilizing real-time bidding to select unplanned bookings. This system takes into account forecasts of unplanned bookings demand to maximize the objective function, which includes objectives such as occupancy balancing, geographical spread, and audience objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the system allocates planned bookings for advertisement campaigns on digital displays, then revenue from planned bookings is improved, but the available share of time for unplanned bookings decreases
Solution Approach 1:
The system dynamically adjusts the allocation of time between planned and unplanned bookings based on real-time demand forecasts and actual performance. The computer system continuously monitors occupancy rates, revenue generation, and demand patterns, then reoptimizes the allocation strategy to maximize overall revenue while maintaining high occupancy rates throughout the period.
Solution Approach 2:
The system changes key parameters such as the share of time allocated to planned versus unplanned bookings, pricing strategies, and allocation thresholds based on forecasted demand and actual performance metrics. By adjusting these parameters dynamically, the system resolves the contradiction between securing planned booking revenue and maximizing overall occupancy utilization.
2Productivity
If the system uses real-time bidding to select unplanned bookings, then revenue from unplanned bookings is improved, but the complexity of the allocation process increases
Solution Approach 1:
The system segments the allocation process into distinct modules: a forecasting module that predicts demand, a real-time bidding module that handles unplanned bookings, and an optimization module that reconciles both sources. This segmentation allows each module to specialize in its function, improving revenue optimization while managing overall system complexity through modular design.
Solution Approach 2:
The computer system acts as an intermediary that coordinates between the real-time bidding process and the planned booking allocation. It mediates conflicts between the two processes by using demand forecasts and performance metrics to make balanced decisions, thereby simplifying the overall complexity while maintaining revenue optimization benefits.
3Productivity
If the system takes into account forecasts of unplanned bookings demand in allocating planned bookings, then allocation efficiency is improved, but the accuracy of forecasts is difficult to achieve
Solution Approach 1:
The system implements feedback loops where actual unplanned booking outcomes are continuously fed back into the forecasting model. This allows the system to learn from past performance, adjust forecast parameters, and improve forecast accuracy over time. The feedback mechanism enables the system to progressively enhance allocation efficiency while managing forecast uncertainty.
Solution Approach 2:
The system performs preliminary actions by generating demand forecasts before the actual allocation process. These forecasts serve as preliminary guidance for allocating planned bookings, improving allocation efficiency in advance. The system then refines these preliminary forecasts based on actual performance data, continuously improving accuracy through iterative learning.
4Productivity
If the system maximizes the objective function considering multiple objectives, then overall performance is improved, but the difficulty of optimization increases
Solution Approach 1:
The system applies local quality by prioritizing different objectives for different time periods, display types, and market conditions. Instead of applying a uniform optimization approach across all scenarios, it tailors the objective function weights and priorities to local conditions, improving overall performance while managing optimization complexity through contextual adaptation.
Data Source
AI summary
A computer implemented method includes allocating to an advertisement campaign, planned bookings for certain time periods and for certain digital displays from an Out Of Home inventory. The method also allocates, by a real-time bidding process, unplanned bookings to the time periods. Forecasts of unplanned bookings demand are taken into account in allocating planned bookings.