DeepChat BI: A Smarter Way to Understand and Manage Advertising Performance

A Clear View of Performance

What you are seeing in the demonstration is the operator view. While it is not yet a fully polished agency interface, it illustrates the core mechanics behind the system and how it fits into an agency’s workflow.

The dashboards display the performance metrics you would typically expect to see. They can be configured depending on how an agency structures its accounts, but the example shown represents the view from an individual account perspective.

While the dashboards themselves are familiar, the real value lies in how the system interprets the data across them.

Turning Data Into Explanations

At a high level, the system analyzes performance data and generates structured explanations of what happened and why.

For example, the output may begin with charts that summarize key metrics over a given time period, such as weekly performance. This is followed by a written analysis that highlights:

• What changed
• What factors drove those changes
• Why performance shifted

The most important aspect is not the charts themselves, but the consistent interpretation layer.

Instead of analysts rewriting similar explanations every week or manually combining insights from multiple dashboards, the system helps consolidate and interpret the information automatically.

Teams can quickly answer questions such as:

• What happened this week?
• Why did performance change?
• Which factors influenced the results?

Without needing to start the analysis from scratch each time.

For agencies, this often serves as an internal intelligence layer across accounts. The system maintains contextual understanding of each account, ensuring explanations remain consistent as similar questions arise over time.

Automated Weekly Reporting

Because the interpretation logic is already in place, generating reports becomes much simpler.

For example, the same analysis shown in the dashboard can be automatically scheduled and delivered as weekly reports for each account.

This reduces the amount of time analysts spend assembling reports and allows them to focus more on strategic thinking and decision-making.

Ads Decision Center

Beyond reporting, some agencies use the platform as a decision support layer for advertising strategy.

The Ads Decision Center allows analysts to simulate different budget scenarios and evaluate how these changes may impact performance.

Scenarios can be analyzed across different attribution models, including:

• First-touch attribution
• Last-touch attribution
• Linear attribution
• Time-decay attribution

The system can also surface alerts when campaigns are not performing as expected, helping analysts quickly identify where attention is needed instead of manually reviewing every campaign.

From Insight to Action

DeepChat BI also includes an Ads Management layer, allowing analysts to create and manage ads within the same environment.

The goal is not automation for its own sake. Instead, the focus is on reducing friction in the workflow so analysts and account managers can move from insight to action more efficiently.

Scaling Agency Operations

Together, these capabilities help agencies manage a larger number of accounts without losing context.

By reducing repetitive work and streamlining analysis, teams can spend less time on operational tasks and more time on strategy, optimization, and decision-making.

That is the core idea behind DeepChat BI.