Orchestration Engine ROI Modeling for Multi-Lever Campaign Allocation
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Solution Overview
Problem
Retailers face challenges in optimizing investment allocation across various value levers such as pricing, promotions, and personalization due to fragmented and siloed spending decisions, leading to reduced return on investment (ROI) and confusing pricing propositions for consumers.
Innovation Solution
An orchestration engine that converts input data into a common currency, calculates historical ROI, simulates investment using machine learning, and optimizes across multiple levers to determine an optimized campaign plan, providing a standardized metric for comparing different investment strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If retailers make multiple investments across different value levers (pricing, promotions, personalization), then consumer attraction and retention improve, but investment allocation becomes fragmented and siloed between departments, reducing ROI
Solution Approach 1:
The patent combines multiple siloed investment levers (pricing, promotions, personalization, loyalty) into a single unified orchestration engine that manages all investments centrally. This merging eliminates fragmentation and enables holistic optimization across all value levers while maintaining the versatility to invest in multiple channels simultaneously.
Solution Approach 2:
The orchestration engine serves as a universal platform that handles diverse investment types (pricing strategies, promotion campaigns, personalization initiatives, loyalty programs) through a single system. This multi-functional approach allows the system to manage various investment levers consistently while optimizing their combined impact on ROI.
2Productivity
If retailers implement stacked promotions and markdowns for the same products, then short-term sales may increase, but consumer confusion increases and ROI decreases
Solution Approach 1:
The orchestration engine performs preliminary simulation and optimization of promotion stacks before implementation. By pre-evaluating the combined impact of multiple promotions on the same products, the system identifies and prevents confusing stacked promotions, ensuring that only optimized, non-conflicting promotion combinations are executed.
Solution Approach 2:
The system implements feedback loops that track the performance and consumer response to promotion stacks in real-time. This feedback mechanism allows the orchestration engine to adjust promotion combinations dynamically, eliminating stacks that cause consumer confusion while maintaining those that drive sales effectively.
3Reliability
If retailers use traditional siloed tracking methods for investment spending, then departmental autonomy is maintained, but consistent tracking and ROI assessment across categories and SKUs becomes impossible
Solution Approach 1:
The patent merges siloed tracking systems into a unified data collection and reporting framework within the orchestration engine. This consolidation enables consistent tracking of all investments across departments, categories, and SKUs, while centralizing knowledge that was previously fragmented and lost between departments.
4Adaptability or versatility
If retailers increase investment in digital marketing and personalization, then competitiveness improves, but the complexity of optimizing spending across multiple levers increases
Solution Approach 1:
The orchestration engine transforms the complex multi-lever optimization problem into a manageable form by changing the parameters to a common currency (ROI). This parameter transformation allows the system to evaluate and optimize spending across diverse investment levers (including digital marketing and personalization) using unified metrics, reducing optimization complexity while maintaining competitive responsiveness.
Data Source
AI summary
A computer system comprising of an orchestration engine that allows for optimizing of investment allocation across plural levers, the orchestration engine configured to: receive input data that includes historic data regarding pricing and sales according to plural levers; prepare the received input data by converting the plural levers in the received input data into a common currency; calculate historical return on investment based on the common currency; simulate return on investment using the calculated historical return on investment and a machine learning model of a given campaign based on historical sales, campaign definitions, and investment metrics; optimize the simulated return on investment from the campaign with respect to one or more optimization goals and constraints; and output from the optimizer an optimized, campaign plan.