Business Introduction
Our client is a leading out-of-home (OOH) media intelligence provider, helping brands measure their visibility and engagement across commercials, especially on television. By analyzing audience and advert data, they enable marketing teams to assess media value and fine-tune their omnichannel campaigns. Their work helps global brands understand when, where, and how to invest in TV ads for maximum reach.
However, despite their extensive brand partnerships, one recurring roadblock was delaying campaign decisions—audience data from TV channels arrived 14 days after a commercial aired. These delays made timely audience analysis nearly impossible, leaving marketing strategies reactive instead of proactive.
The client approached Logesys to address this data lag and build a system that could accurately forecast audience volume in near real-time, enabling brands to adjust their TV media budgets ahead of time rather than after the fact.
Business Objectives
The objective was clear: develop a predictive model that could forecast audience volume by time slot, allowing brands to make quick, confident decisions on when and where to invest in commercials.
To achieve this, the Logesys team needed to design a machine learning solution that could handle high-frequency data, generate timely predictions, and work at scale—offering insights that were as close to life as possible.
Scope of Work
Logesys was tasked with analyzing one year’s worth of historical audience data, predicting audience engagement for upcoming time slots across TV channels, and delivering this through a user-friendly dashboard for brand teams to act on.
Challenges & Solutions
Challenge 1: Massive Data Volume
Audience data was recorded at 15-minute intervals for every channel. This meant over 35,000 data points per channel per year—and there were dozens of channels. Processing this volume quickly and accurately was essential for real-time forecasting.
Solution:
We implemented an efficient data ingestion and preprocessing workflow. Raw data was stored in SQL Server and processed in manageable batches. We used intelligent flags to tag weekends, special events, and holidays, allowing the model to better understand viewer behavior patterns over time.
Challenge 2: Time-Sensitive Analysis
Audience patterns shift rapidly—especially around weekends, public holidays, or special events. The model had to adapt quickly and respond to these fluctuations, as even minor lags could invalidate forecast usefulness.
Solution:
We designed a fast, adaptive pipeline to process incoming data continuously and incorporate it into the model’s predictions. Predictions were refreshed frequently, ensuring that stakeholders always had the most relevant forecast available when planning media buys.
Challenge 3: Time Series Models Lacked Accuracy
Conventional time series forecasting models like ARIMA and LSTM failed to deliver the accuracy required. The predictions fluctuated too widely and couldn’t reliably support strategic ad placements.
Solution:
Logesys pivoted to a Random Forest Regression approach. While not a traditional time series method, Random Forests are excellent at handling large, complex datasets. We extracted granular time-based features—like day, hour, weekend flags, and more—from a single timestamp column, essentially converting the data into a structured time-series-friendly format. This dramatically improved prediction accuracy and stability.
Solution
Our final solution combined robust data processing with a custom machine learning pipeline powered by Random Forest Regression. By deriving time-based variables and integrating behavioral patterns (e.g., increased weekend viewership), the model was able to predict audience volumes with 90% accuracy compared to the actuals that arrived two weeks later.
A comprehensive Power BI dashboard was built to visualize the predictions. Users can view historical audience data alongside forecasts for specific time slots, days, and channels—making it easier to fine-tune ad strategies.
Results
The project was delivered in 3 months, and the results have been nothing short of transformational:
- 90% forecast accuracy for audience volumes, eliminating the 14-day decision lag
- Faster, data-backed ad placement decisions across brands and marketing teams
- Optimized media budgets, with brands reallocating spend based on forecasted reach
- Full visibility into high-impact time slots, such as holidays and weekends
- Dashboards accessible across devices, supporting real-time collaboration
- A scalable machine learning system that improves continuously as more data flows in
Over the last year, the solution has continued to support smarter media planning—driving better ROI for brands and giving the client a competitive edge in audience analytics.
Conclusion
With this predictive forecasting system, Logesys helped the client close the gap between audience behavior and advertising action. Brands no longer have to wait two weeks to know if their ad was seen—they can now plan in advance with data-driven confidence.
This case highlights how modern machine learning, when paired with practical business needs, can turn delayed insights into proactive strategies—saving time, money, and missed opportunities.
The client’s marketing team has praised the solution not only for its accuracy but for the way it has reshaped their entire decision-making process. And as the system continues to evolve, the precision—and the value it delivers—will only grow stronger.