Common-Currency Orchestration Engine for Cross-Lever ROI Planning
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Solution Overview
Problem
Retailers face challenges in optimizing investment allocation across various 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 utilizes a common currency to standardize the measurement of ROI across different investment levers, employing machine learning to simulate and optimize investment strategies, ensuring holistic and consistent tracking of investments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If retailers make multiple investments across different departments and levers (pricing, promotions, personalization), then the ability to attract and retain consumers is improved, but the investments become fragmented and siloed, reducing ROI
Solution Approach 1:
The patent merges previously siloed investment levers (pricing, promotions, personalization, loyalty, markdowns) into a unified orchestration engine that optimizes all investments simultaneously. This combines multiple departmental functions into a single system that prevents stacked promotions and optimizes ROI across all levers.
Solution Approach 2:
The orchestration engine serves as a universal platform that handles multiple investment levers and optimization goals simultaneously. It provides a common currency framework that can measure and optimize the impact of different investment types (price, promotion, personalization) using a standardized metric system.
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 implements feedback mechanisms that track the impact of each investment lever and provide visibility into how different promotions interact. This feedback loop prevents stacked promotions by showing the cumulative effect of multiple investments on the same products, allowing retailers to adjust before consumer confusion occurs.
Solution Approach 2:
The system performs preliminary optimization before promotions are executed, simulating the impact of different investment combinations and selecting the optimal mix that maximizes ROI without creating stacked promotions. This preliminary action prevents consumer confusion before it occurs.
3Ease of operation
If retailers use traditional siloed departmental structures for investment decisions, then departmental autonomy is maintained, but best practices cannot be shared and ROI assessment is sub-optimal
Solution Approach 1:
The orchestration engine acts as an intermediary layer that connects previously siloed departments. It receives input from various investment levers, processes them through a common optimization framework, and outputs coordinated decisions. This intermediary structure enables information sharing and best practice propagation while maintaining operational flexibility.
4Adaptability or versatility
If retailers lack a common currency for measuring investment impact, then flexibility in measuring different levers is maintained, but consistent tracking and ROI assessment become impossible
Solution Approach 1:
The patent introduces a common currency framework that transforms different investment metrics (price impact, promotion lift, personalization response) into a standardized measurable unit. This parameter transformation enables consistent tracking and comparison across all investment levers while maintaining the ability to measure each lever's unique characteristics.
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.


