POS Data Normalization via Seasonal Adjustment
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Solution Overview
Problem
Current market tracking and reporting methods rely on indirect and often inaccurate data, providing limited insights due to reliance on interviews and surveys, which are time-consuming and prone to inaccuracies, and fail to account for seasonal and macroeconomic factors effectively.
Innovation Solution
A system and method for normalizing point-of-sale (POS) data by aggregating transactions from multiple POS terminals, applying time-based fluctuation factors to account for seasonality, and using historical data to project sales, enabling accurate sales comparisons and predictions across different timeframes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If indirect market data collection methods (interviews, surveys) are used, then data gathering is simpler, but data accuracy and reliability deteriorate
Solution Approach 1:
The patent replaces manual data collection methods (interviews, surveys) with an automated electronic system that directly accesses POS terminal databases. The system electronically retrieves transaction data, applies normalization algorithms, and generates market reports automatically, eliminating the need for manual data gathering while significantly improving data accuracy and reliability.
Solution Approach 2:
The patent introduces an intermediary normalization system that acts as a mediator between raw POS data and market analysis. This intermediary layer processes and normalizes the data by adjusting for seasonal variations, macroeconomic factors, and other confounding variables, thereby transforming raw data into reliable market intelligence without requiring direct manual collection.
2Reliability
If direct POS data aggregation is used, then market data accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modular components: data aggregation module, normalization module (with seasonal adjustment), macroeconomic adjustment module, and report generation module. Each component handles a specific aspect of processing, making the overall complex system more manageable and maintainable while preserving high data accuracy.
Solution Approach 2:
The patent applies parameter changes through normalization algorithms that adjust data based on seasonal factors, macroeconomic indicators, and other variables. By transforming raw data into normalized values that account for these parameters, the system maintains accuracy while providing meaningful insights that would otherwise be obscured by raw data complexity.
3Measurement precision
If seasonal adjustments are applied to sales data, then sales comparison accuracy improves, but calculation time increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing seasonal adjustment factors and macroeconomic normalization parameters before actual data processing. These pre-computed factors are then applied directly to the incoming POS data, avoiding the need for time-consuming real-time calculations while maintaining high measurement precision in sales comparisons.
Data Source
AI summary
Systems and methods are described for generating indexed sales data by aggregating point of sale (POS) datasets are aggregated from transactions at a plurality of POS terminals. The POS datasets for each transaction include a transaction amount, a merchant classifier, and a transaction time. An industry subset of the aggregated POS datasets is obtained for a given timeframe based on the merchant classifier. This industry subset comprises transactions for a given industry. A sales value is calculated for the industry subset, and a monthly fluctuation factor is applied to the sales value. Also, a normalization factor is applied to the sales value based on a percentage of the sales value relative to an overall market size to obtain the indexed sales value.