Dynamic Advertisement Pricing Using Consumer Traffic Data
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Solution Overview
Problem
Conventional advertisement pricing methods charge merchants a fixed rate regardless of consumer traffic activity, leading to inefficient spending as high traffic occurs only during limited hours, making the fixed rate unbeneficial for businesses.
Innovation Solution
An electronic system and method that generates an electronic map of a geographical area, demarcates it into regions, retrieves transaction data, and uses consumer traffic data from telecommunications providers to dynamically determine advertisement prices based on transaction and traffic activity in each region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed price rate is charged for advertisements regardless of location or time, then the pricing system is simple and easy to operate, but merchants spend money inefficiently when consumer traffic activity is low
Solution Approach 1:
The patent applies dynamics by transitioning from a static fixed price rate to a dynamic pricing model where advertisement prices vary based on real-time consumer traffic activity, time of day, and location. The system continuously updates prices according to measured traffic conditions, allowing merchants to pay more when traffic is high and less when traffic is low, thereby eliminating wasteful spending while maintaining operational simplicity through automated pricing adjustments
2Measurement precision
If a fixed price rate is charged for advertisements at locations with high consumer traffic activity during peak hours, then the pricing reflects higher potential visibility, but merchants must pay the full rate for the entire day including low traffic periods
Solution Approach 1:
The system dynamically adjusts advertisement prices based on measured consumer traffic activity at different times and locations. Instead of charging a flat rate, the pricing algorithm real-time traffic data to determine optimal prices, ensuring merchants pay proportionally to the actual value delivered. This resolves the contradiction by enabling precise measurement of traffic patterns while improving ad placement efficiency through targeted pricing during high-value periods
Solution Approach 2:
The patent changes the pricing parameter from a fixed rate to a variable rate that responds to traffic conditions. The system monitors consumer traffic activity as a key parameter and adjusts prices accordingly, allowing the advertisement pricing to flex with actual demand. This parameter change enables the system to capture the value of high-traffic peak hours while avoiding penalties for low-traffic periods, thereby improving overall ad placement efficiency
3Productivity
If advertisement prices are increased to reflect higher consumer traffic activity in business districts during peak hours, then merchants can optimize their spending, but the pricing system becomes more complex requiring multiple data sources and calculations
Solution Approach 1:
The patent introduces an intermediary pricing algorithm that sits between the simple fixed rate model and the complex data-driven model. This intermediary layer processes multiple data sources (transaction data, traffic data, time, location) through a standardized calculation framework to generate optimized prices. The intermediary absorbs the complexity of data processing while presenting a unified, easy-to-implement pricing interface, thereby enabling efficient merchant spending without exposing them to system complexity
Solution Approach 2:
The system implements feedback mechanisms where real-time traffic data and transaction information continuously inform pricing adjustments. The pricing algorithm receives feedback from measured consumer behavior patterns and automatically adjusts advertisement rates accordingly. This feedback loop enables the system to learn from actual traffic conditions and optimize prices dynamically, improving merchant spending efficiency while managing complexity through automated adaptive pricing rather than manual adjustment
Data Source
AI summary
The present disclosure generally relates to an electronic system, a computerized method, and a non-transitory computer-readable storage medium for advertisement pricing. The system comprises a host server configured for performing steps of the method comprising: generating an electronic map representation of a geographical area; demarcating the electronic map representation into a plurality of demarcated regions; retrieving, from a transaction database, transaction data associated with merchant transactions in the plurality of demarcated regions; receiving, from a telecommunications service provider, mobile traffic data for generating consumer traffic data indicative of consumer traffic activity in the plurality of demarcated regions; and performing an advertisement pricing process to determine advertisement prices for each demarcated region based on at least the transaction data and consumer traffic data.