Dynamic Job-Post Budget Recommendation System
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Solution Overview
Problem
Corporate recruiters face challenges in determining a suitable budget for job postings due to various influencing factors, leading to either excessive spending or inadequate visibility, which affects recruitment outcomes and revenue for job websites.
Innovation Solution
A method and system that analyze data to recommend an optimal daily budget for job posts by using a multiplier-based approach, combining models for conversion rate and budget behavior to maximize committed bookings, and presenting ROI information to recruiters, allowing them to set budgets effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the jobs website provides a high budget recommendation to recruiters, then the job post visibility and performance are improved, but the recruiter may skip posting the job due to excessive cost
Solution Approach 1:
The system dynamically adjusts the budget recommendation by applying a multiplier to the initial budget value based on observed recruiter responses. The multiplier is modified within a range (e.g., 0.5 to 2.0) to optimize both job post performance and recruiter acceptance, transforming the static recommendation into an adaptive parameter that balances effectiveness and affordability.
Solution Approach 2:
The system implements a feedback loop where recruiter responses to budget recommendations are collected and analyzed. This feedback is used to update the multiplier value for future recommendations, allowing the system to learn from actual recruiter behavior and improve its budget suggestions over time, thereby increasing both performance accuracy and recruiter satisfaction.
2Ease of operation
If the jobs website provides a low budget recommendation to recruiters, then the recruiter is more likely to post the job, but the job post performance and revenue opportunity are reduced
Solution Approach 1:
The system uses a multiplier parameter that can be adjusted above or below 1.0 to scale the initial budget recommendation. When recruiter acceptance is high but performance is low, the multiplier is increased to raise recommendations. When acceptance is low, the multiplier is decreased. This dynamic parameter adjustment allows the system to optimize the balance between recruiter affordability and job post effectiveness.
Solution Approach 2:
The budget recommendation system transitions from a static fixed-value approach to a dynamic adaptive system. The multiplier is continuously updated based on real-time recruiter response data, making the recommendation system flexible and responsive to changing conditions, thereby optimizing both recruiter engagement and job post performance across different scenarios.
3Ease of operation
If recruiters set budgets based on personal judgment without data, then the process is simple and quick, but the budget effectiveness and ROI are uncertain
Solution Approach 1:
The system enables recruiters to set budgets autonomously by providing them with data-driven recommendations and performance metrics. Instead of requiring manual analysis or external consultation, the system self-serves the recruiter by presenting optimized budget values and expected ROI calculations, making the process both simple and scientifically grounded.
Solution Approach 2:
The system replaces the mechanical/manual process of budget estimation with an automated computational system. Machine learning models and algorithms automatically analyze historical data, calculate optimal multipliers, and generate budget recommendations, substituting human judgment with a more precise and scalable computational approach while maintaining ease of use through automated presentations.
Data Source
AI summary
Methods, systems, and computer programs are presented for presenting return-on-investment (ROI) information, for budgeted services that resulted in a successful service delivery, on a user interface for setting the budget for a service request. One method includes an operation for identifying daily budgets for budgeted services that resulted in a successful service delivery (BSSSD). Each daily budget indicates an amount for spending in promotion of the BSSSD in an online service. The method further includes receiving a request, in a graphical user interface (GUI) of the online service, for posting a daily budget for a first budgeted service. Further, a performance value, associated with the daily budgets of the BSSSD that are similar to the first budgeted service, is selected. Further, the method includes causing presentation, by the one or more processors, of the performance value in the GUI.