Supercharging TV Ad Revenue With AI-Powered Predictions

How we boosted forecasting accuracy by 10% and saved 86+ hours monthly for a major Czech TV group

Who's the Client?

A leading Czech TV group with massive reach - 1.6 million daily viewers, 50 million monthly, and a staggering 600 million annually. When you're operating at this scale, even small improvements translate to big money.

What Was the Problem?

Their ad revenue forecasting was stuck in the Stone Age. An expert was manually predicting viewership for 25,000 ad blocks across multiple channels - burning hours of valuable time each week. The process relied heavily on gut feeling, making it hard to explain or improve systematically.

What Did We Want to Do?

Our mission was clear:

• •Create a prediction system that beats human accuracy

• •Free up the expert's time for higher-value work

• •Make the forecasting process data-driven and explainable

• •Boost ad revenue through better predictions

How Did We Fix It?

The Game Plan

1. .We dug deep into their data to understand TV watching patterns

1. .Analyzed the psychology behind viewer habits

1. .Tested roughly 40 different ML approaches

1. .Built a system that handles daily habits, show premieres, re-runs, and holidays

1. .Added a crucial "human-in-the-loop" component for oversight

What We Built

We created a machine learning system that:

• •Automatically predicts viewership across all channels

• •Flags only the ~100 most uncertain predictions for human review (instead of all 25,000)

• •Learns and improves with each cycle

• •Provides data-driven explanations for its forecasts

The Results? Game-Changing

• •10% more accurate predictions across all channels

• •86+ hours saved monthly (that's over 2 full work weeks!)

• •Higher ad revenue (we can't share the exact figure, but it impacts a business with 4.2 billion CZK annual revenue)

• •The system now influences advertising decisions that reach 600 million viewers annually

What Did We Learn?

1. The challenges:

• •TV viewership has complex seasonal patterns

• •Balancing automation with expert oversight is critical

• •Building trust in the system took time

2. What Made It Work:

• •Deep understanding of both the data AND the business

• •Rapid testing of multiple approaches

• •The human-in-the-loop design that kept experts engaged

• •Focusing on measurable business outcomes

The Bottom Line

When you're dealing with a TV audience of 600 million annual viewers, even small improvements have massive ripple effects. This project delivered both hard numbers (10% accuracy boost, 86 hours saved monthly) and strategic advantages (better resource allocation, data-driven decisions).

The client can now predict ad block performance with greater confidence and less effort - turning what was once guesswork into a competitive advantage.