Ad Campaign Optimization via Machine Learning Bid Adjustment
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Solution Overview
Problem
E-commerce retailers face challenges in achieving a balanced advertising expenditure to sales ratio for online advertising campaigns, as manual optimization is time-consuming, costly, and requires skilled manpower, and existing methods lack efficiency in selecting effective keywords and managing inventory and budget constraints.
Innovation Solution
An automated system that uses machine learning and statistical analysis to optimize advertising campaigns by selecting the most promising keywords, adjusting bids to maintain a defined Advertising Cost of Sales (ACoS) within constraints, and forecasting sales to ensure inventory management and profit margins.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manual optimization of advertising campaigns is performed by analyzing word lists and adjusting bids, then the Advertising Cost of Sales (ACoS) can be reduced and sales can be maintained, but the process is expensive, time-consuming, and demands a heavy amount of skilled manpower
Solution Approach 1:
The system enables self-service by automatically analyzing word lists, selecting promising keywords, and adjusting bids without human intervention. The autonomous optimization process continuously monitors campaign performance and makes real-time bid adjustments based on predicted sales and ACoS targets, eliminating the need for manual analysis and bid management while maintaining optimal advertising efficiency
Solution Approach 2:
The patent replaces the manual mechanical process of analyzing word lists and adjusting bids with an automated computational system. Machine learning models predict sales outcomes and optimize bid values automatically, substituting human expertise and manual operations with algorithmic decision-making that processes data faster and more efficiently
2Productivity
If a broad set of words is used in automatic campaigns to maximize sale generation opportunities, then more sales can be achieved, but the advertising cost increases significantly
Solution Approach 1:
The system applies local quality by differentiating between high-value and low-value keywords within the overall campaign. Instead of treating all words equally, the system identifies and concentrates bidding resources on specific promising keywords that have higher conversion potential, while reducing or eliminating bids on less effective words, thereby optimizing the distribution of advertising expenditure across different keyword segments
Solution Approach 2:
The system employs partial action by selecting only the most promising subset of keywords from the broad list rather than bidding on all available words. The automated analysis identifies a focused set of high-value keywords that will generate the majority of effective sales, allowing the system to achieve strong productivity with reduced overall advertising expenditure by concentrating resources on the most effective channels
3Productivity
If higher bid values are placed on keywords to increase the chance of appearing in first place, then sales revenue can increase, but the Advertising Cost of Sales (ACoS) increases and profit margins decrease
Solution Approach 1:
The system applies dynamics by continuously adjusting bid values based on real-time campaign performance data and predicted outcomes. Rather than using static bid amounts, the system dynamically optimizes bids for each keyword based on factors such as conversion probability, expected sales volume, and current ACoS performance, allowing bid values to adapt and change as campaign conditions evolve
Solution Approach 2:
The system changes the parameter of bid values based on calculated optimization metrics. The automated system determines optimal bid amounts by analyzing the relationship between bid level, expected clicks, conversion rate, and resulting ACoS, adjusting bid parameters to achieve the target ACoS while maximizing sales revenue, thereby finding the optimal balance between spending and returns
4Ease of manufacture
If manual refinement of campaigns by selecting keywords from word lists is performed, then advertising efficiency can be improved, but the process requires skilled manpower and is costly
Solution Approach 1:
The system performs self-service by automatically analyzing word lists, evaluating keyword performance potential, and selecting promising keywords without human intervention. The automated process handles the entire refinement workflow including data analysis, keyword selection, and bid configuration, eliminating the need for skilled manual analysis while maintaining or improving campaign quality
Solution Approach 2:
The patent replaces the manual mechanical process of keyword selection and campaign refinement with automated computational analysis. Machine learning models evaluate numerous keywords simultaneously based on historical data and performance metrics, substituting human expertise with algorithmic decision-making that is both faster and more scalable
Data Source
AI summary
A method and associated system of producing an advertising campaign of a product for an online marketplace seller including, under control of one or more processors configured with executable instructions, generating a single keyword advertising campaign of the product; collecting data on the single keyword; executing a machine learning component of an adaptive machine learning platform to generate a machine learning component output based at least in part on the data on the single keyword; generating a behavioral curve or table relating to an advertisement bid value and a cost per click value based at least in part on the machine learning component output; generating sales goal(s) of the product for the online marketplace seller on the online marketplace; generating an optimized advertisement bid value based at least in part on the sales goal(s) and the behavioral curve or table; and generating an optimized advertising campaign therefrom.