Digital Display Inventory Allocation via Real-Time Bidding
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Solution Overview
Problem
Existing Out Of Home (OOH) advertisement systems face challenges in optimizing the allocation of digital display inventory due to quickly changing advertising needs, particularly in utilizing available time slots and efficiently integrating unplanned bookings with planned campaigns.
Innovation Solution
A computer-implemented method and system that allocates planned bookings for advertisement campaigns on digital displays within an OOH inventory, utilizing real-time bidding to fill available time slots, and forecasts unplanned bookings demand to maximize revenue through an objective function that balances occupancy, geographical spread, and audience objectives, while determining the feasibility of additional unplanned bookings based on on-schedule indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time bidding is used to fill unplanned bookings, then revenue generation is improved, but system complexity increases
Solution Approach 1:
The patent introduces a computer system as an intermediary that mediates between demand side platforms and supply side platforms. This intermediary manages the real-time bidding process, handles forecasts of unplanned bookings demand, and coordinates the allocation of digital displays, thereby enabling revenue optimization while containing system complexity through centralized management.
Solution Approach 2:
The patent implements preliminary action by generating forecasts of unplanned bookings demand before the actual bidding process. The computer system uses these forecasts to pre-allocate digital displays and time periods, preparing the system in advance for incoming bids. This preliminary preparation enables faster response to real-time bidding while reducing the computational burden during the actual bidding process.
2Productivity
If forecasts of unplanned bookings demand are integrated into allocation, then allocation optimization is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by computing forecasts of unplanned bookings demand in advance, before the actual content allocation process. These forecasts are used to pre-determine the availability of digital displays for planned bookings, allowing the system to optimize allocations based on predicted demand patterns without performing complex real-time calculations during the bidding process.
Solution Approach 2:
The patent segments the allocation process into distinct phases: a forecast generation phase that computes expected unplanned bookings demand, and an allocation phase that uses these forecasts to optimize planned bookings. This segmentation allows complex forecast computations to be performed separately from time-critical allocation decisions, reducing overall computational complexity while maintaining optimization quality.
3Productivity
If available share of time is allocated to planned bookings, then campaign reach is improved, but flexibility for unplanned bookings decreases
Solution Approach 1:
The patent implements dynamics by making the allocation of available share of time adaptive rather than static. The computer system dynamically adjusts the portion of available time allocated to planned bookings versus unplanned bookings based on real-time demand forecasts and actual bidding activity. This dynamic allocation allows the system to optimize campaign reach while maintaining flexibility to capture high-value unplanned opportunities.
Solution Approach 2:
The patent applies parameter changes by adjusting the allocation ratio between planned and unplanned bookings based on forecasted demand parameters. When forecasts indicate high unplanned booking demand, the system increases the portion of available time reserved for unplanned bookings, and vice versa. This parameter-based adjustment enables the system to balance campaign reach with flexibility according to actual market conditions.
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.