True ROAS Modeling With Cannibalization and Lifetime Value
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Solution Overview
Problem
Existing commerce systems and digital marketplaces face challenges in accurately measuring the effectiveness of advertising due to factors like cannibalization, returns, customer lifetime value, organic ranking, and review impact, which are not adequately considered in traditional ROAS calculations.
Innovation Solution
A system and method that adjusts ROAS calculations by incorporating cannibalization, return, customer lifetime value, organic rank, and review values to provide a more comprehensive analysis of advertising effectiveness for a target product.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ROAS calculation is used, then calculation simplicity is maintained, but measurement precision of advertising effectiveness deteriorates
Solution Approach 1:
The patent segments the advertising effectiveness measurement into multiple distinct components: cannibalization value (short-term negative impact), return value (returns and exchanges), lifetime customer value (long-term positive impact), and organic rank value (search ranking effects). Each component is calculated separately using specific formulas and then aggregated to produce the true ROAS, allowing for precise measurement while maintaining systematic organization
Solution Approach 2:
The patent introduces an intermediary calculation framework that mediates between simple traditional ROAS and complex multi-factor analysis. The true ROAS formula serves as an intermediary equation that incorporates multiple adjustment factors (cannibalization, returns, lifetime value, organic rank) to bridge the gap between simplicity and precision, enabling accurate measurement without requiring completely new calculation systems
2Reliability
If advertising spend is increased to overcome product density, then product recognition is improved, but advertising cost increases
Solution Approach 1:
The patent implements feedback mechanisms by providing sellers with actionable reports that show the true ROAS and its component factors. This feedback loop enables sellers to understand the actual effectiveness of their advertising spend, identify areas of waste (such as cannibalization or returns), and adjust their advertising strategies accordingly to improve recognition while optimizing cost
Solution Approach 2:
The patent enables dynamic parameter adjustment by allowing sellers to modify advertising bids, budgets, and targeting parameters based on the true ROAS analysis. The system calculates optimal advertising parameters that account for cannibalization, returns, lifetime value, and organic rank, enabling sellers to achieve better product recognition at optimized cost levels through parameter optimization
3Productivity
If short-term advertising profits are prioritized, then immediate revenue is improved, but long-term customer value deteriorates
Solution Approach 1:
The patent applies dynamics by making the ROAS calculation adaptive and time-aware. The true ROAS formula dynamically balances short-term advertising sales against long-term lifetime customer value, allowing the measurement to reflect different time horizons. Sellers can adjust their advertising strategies based on whether they prioritize immediate revenue or long-term value, with the system providing flexible optimization for different time-oriented goals
Data Source
AI summary
A method includes: receiving a first product value; determining a cannibalization value based on at least one of experimental data and non-experimental data; generating a second product value by adjusting the first product value using the cannibalization value; determining a return value based on return data; generating a third product value by adjusting the second product value using the return value; determining a lifetime customer value based on at least the cannibalization value; generating a fourth product value by adjusting the third product value using the lifetime customer value; determining an organic rank value based on organic placement data and value of organic placement data; generating a fifth product value by adjusting the fourth product value using the organic rank value; generating, based at least on the fifth product value, an actionable report descriptive of an efficacy rate of a target product in a computer-networked marketplace.