Long-Term Brand Multiplier Calculation System
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Solution Overview
Problem
Current methods for determining long-term effects of marketing campaigns rely on heuristic techniques, which are inadequate in accounting for variations in marketing campaigns, brands, and customer behaviors, leading to inefficient resource allocation and potential marketing waste.
Innovation Solution
The development of a system that calculates long-term brand multipliers by analyzing purchase behaviors, purchase depth, and exposure periods to quantify the long-term effects of advertising and promotional campaigns, allowing for more precise resource allocation and campaign optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If heuristic techniques and rules of thumb are used to determine long-term effects of marketing campaigns, then the analysis process is simple and quick, but the accuracy and reliability of the results are insufficient and cannot account for variations in marketing campaigns, brands, and customer behaviors
Solution Approach 1:
The patent segments the long-term effect measurement into multiple components: short-term effect measurement, multiplier calculation, and long-term effect derivation. The system divides marketing campaign analysis into distinct phases (exposure period, measurement period) and calculates different metrics (sales lift, purchase depth, brand multipliers) for each phase, allowing for accurate yet manageable analysis of complex marketing variations
Solution Approach 2:
The patent introduces multipliers as an intermediary element that bridges short-term measurement data and long-term effect predictions. These multipliers account for variations in marketing campaigns, brands, and customer behaviors by serving as adjustment factors that transform basic short-term metrics into accurate long-term projections without requiring direct long-term observation
2Productivity
If heuristic techniques are used for determining long-term effects, then resource allocation is simple, but resource allocation efficiency is poor leading to marketing waste
Solution Approach 1:
The patent implements feedback mechanisms where measurement data from post-campaign periods is used to refine and recalculate multipliers for future campaigns. The system continuously learns from actual long-term performance data, adjusting the multiplier calculations to better predict future outcomes, thereby improving resource allocation efficiency over time while maintaining a manageable calculation framework
3Measurement precision
If simple multiplication of short-term effects is used to estimate long-term effects, then the calculation is straightforward, but the measurement precision is insufficient to capture actual long-term marketing impact
Solution Approach 1:
The patent performs preliminary measurements during the campaign exposure period itself, collecting data on sales lift, purchase depth, and brand engagement while the marketing campaign is active. This preliminary data is then used with calculated multipliers to project long-term effects without requiring extended waiting periods, reducing the time loss while improving measurement precision
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed to calculate long-term effects of marketing campaigns. An example method includes for respective participants, identifying, with a processor, a trial purchase of a first product associated with a brand of interest following a marketing stimulus for the brand of interest, for the respective participants, incrementing, with the processor, a brand purchase count of purchase occasions when a subsequent product associated with the brand of interest is purchased during an exposure period of the marketing stimulus, for the respective participants, resetting, with the processor, the brand purchase count to zero when a subsequent product unassociated with the brand of interest is purchased during the exposure period of the marketing stimulus, generating, with the processor, purchase groups indicative of consecutive purchase occasion values of the respective participants based on respective highest values of the brand purchase count, and calculating, with the processor, the sales effect for the respective purchase groups based on the respective values of the brand purchase count.