AI for Ad Ops
Problem Statement
The quality of user acquisition heavily relies on the accuracy of the data used in the decision-making process for ad buying. Data is sourced from numerous systems, and its precise collection hinges on the effectiveness of manual labor performed by dozens of individuals. For instance, consider events from client applications that require the inclusion of the correct IDFA. Mistakes occur frequently, and avoiding them entirely is virtually impossible. Our actual objective is to expedite the detection and resolution of these errors.
In practical terms, even if an organization has a robust set of business rules, humans often introduce issues - people make errors, they overlook necessary checks, resulting in financial losses for the organization. A potential solution involves the development of a program, essentially a script, which periodically checks data flows and issues alerts. However, this approach has its own set of challenges, mainly the requirement to maintain a team of engineers to ensure the script's functionality. Without a dedicated engineering team, the script is prone to continuous breakdowns.
Our Approach
Within the organization, ad buying experts articulate the intricacies of their company's operations and the business rules governing advertising purchases, all in plain English. An AI agent uses this knowledge base and generates the necessary code and executes the essential checks and alerts. When the generated program becomes obsolete, the AI has the capability to regenerate the program to adapt to a new environment, such as a new ad channel or different URL format, along with updated business rules. Remarkably, this regeneration occurs without the need for developer intervention.
Business Value
The AI agent serves as a replacement for employees with lower to mid-level skills, who are typically responsible for managing daily advertising operations. With this technology in place, there is no necessity to hire additional personnel as the volume of operations grows. Even in the event of a trained employee's departure, there is no need to invest time in training a replacement. Furthermore, the time required to identify issues is significantly reduced, eliminating concerns related to holidays or illnesses affecting monitoring quality.
