Automated Marketing Budget Allocation Using Econometric Data
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Solution Overview
Problem
Current automated decision support tools for marketing budgeting are inefficient, often requiring historical data that may not be available, and are typically manual, leading to subjective and disadvantageous results, especially for new offerings.
Innovation Solution
A software facility that uses qualitative descriptions of a subject offering to automatically prescribe a total marketing budget and its allocation across multiple spending categories, optimizing business outcomes based on experimentally-obtained econometric data, without the need for historical performance data, by leveraging historical marketing efforts from similar offerings and external data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated decision support tools are used for marketing budgeting, then productivity is improved, but device complexity increases
Solution Approach 1:
The system automatically retrieves historical marketing data from multiple sources without requiring manual data collection. The automated decision support tool independently performs data analysis, model selection, and budget optimization without needing extensive user input or manual configuration, thereby improving productivity while managing complexity through self-service operations.
Solution Approach 2:
The patent introduces an intermediary layer of automated data retrieval and processing mechanisms that bridge the gap between raw data sources and the decision-making model. This intermediary system automatically aggregates, cleans, and prepares data from various sources, reducing the complexity burden on the user while maintaining high productivity through automated intermediate processing steps.
2Measurement precision
If historical performance data is required for automated tools, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system creates virtual copies of historical marketing data by automatically retrieving and replicating data from multiple sources. These copied datasets are then used for model training and budget optimization without requiring direct access to original data sources, thereby maintaining measurement precision while reducing information loss due to data unavailability or access restrictions.
Solution Approach 2:
The automated tool performs preliminary data retrieval and preparation actions before the actual budget optimization occurs. By pre-fetching and pre-processing historical data from various sources, the system ensures that all necessary information is available when needed, preventing information loss and maintaining data completeness for accurate measurement and decision-making.
3Device complexity
If manual budgeting methods are used, then device complexity is reduced, but productivity deteriorates
Solution Approach 1:
The patent replaces manual mechanical processes of data collection, analysis, and budget formulation with automated electronic systems. The automated decision support tool uses computational algorithms and data processing mechanisms to perform tasks that would otherwise require manual intervention, thereby dramatically improving productivity while the user interface maintains simplicity by abstracting away the underlying computational complexity.
Solution Approach 2:
The automated tool is designed as a universal system that can handle multiple functions including data retrieval from various sources, data cleaning, model selection, budget optimization, and result interpretation. This multi-functionality consolidates what would otherwise require multiple separate manual processes into a single integrated system, improving productivity without proportionally increasing the complexity perceived by the user.
4Measurement precision
If user-provided historical data is required, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The automated decision support tool performs self-service by automatically retrieving and collecting historical marketing data from multiple sources without requiring user intervention. The system independently accesses databases, downloads files, and aggregates data from various formats, thereby eliminating the tedious manual data entry process while maintaining high measurement precision through automated data validation and verification mechanisms.
Solution Approach 2:
The patent introduces intermediary data collection mechanisms that act as buffers between the user and the data sources. These intermediaries automatically handle data retrieval, formatting, and validation tasks, allowing the user to simply initiate the process without dealing with the complexity of data collection. This intermediary layer maintains measurement precision by ensuring accurate data capture while dramatically improving ease of operation by eliminating manual data entry requirements.
Data Source
AI summary
In some embodiments, a software facility performs a method of automated specification of models, estimation of elasticities, and discovery of drivers using the framework(s) discussed elsewhere herein is provided. The facility first obtains the client, business, and/or brand goals in terms of profit optimization, volume or revenue goals, acquisition of new customers, retention of customers, share of wallet and upsell. In conjunction with these goals, the facility obtains cross-section meta-data related to the planning time horizon, markets, geographies, channels of trade and customer segments. In combination, the goals and meta-data define the structure of the data stack and the number of demand generation equations that are needed.