What problem did Azati solve?

The system initially had incomplete viewer data, preventing accurate targeting. Azati applied data approximation and predictive analytics to reconstruct missing information and optimize broadcast plans.

Which techniques were used for data approximation?

Linear regression with L2-regularization was used to approximate missing data and ensure continuous and accurate predictions of audience characteristics.

How did the system improve advertising decisions?

The optimized system delivered real-time, data-driven recommendations for advertisers, enabling better-targeted campaigns and improved ROI even when complete data was not available.

What technologies were used?

Python, DB2, Netezza, and predictive analytics techniques were applied to optimize data processing and model performance.

Decision-Support System Optimization For Advertising Industry

Improving performance of a system that is designed to compile best offerings for advertisers to broadcast their commercials. Tackling the problem of missing data through applying a data approximation technique.

Discuss an idea

60k+

TV viewers analyzed for targeting

30k

distinct viewer characteristics reconstructed

60-80%

faster plan generation

All Technologies Used

Python

DB2

Netezza

Motivation

The objective was to create a decision-support system that could enable advertisers to effectively target specific audience segments with their commercials at the right time, ensuring the best ROI for advertising campaigns on television. The key task was to reconstruct the system's process to create continuous dependencies using known characteristic values at discrete points in time, enabling accurate and optimal recommendations for advertisers.

Main Challenges

The client wanted to target specific audiences with particular characteristics for optimal advertisement placement. However, the system only contained partial data, making it difficult to calculate the most suitable broadcast plans and target the correct audience at the right time. Azati proposed using a data approximation technique to estimate missing values and fill in the gaps, allowing for continuous and accurate predictions of viewer characteristics at any point in time.

The system required continuous data on viewer characteristics at every point of time. However, the data was only uploaded periodically, which created gaps in the information and hindered the system's ability to make accurate predictions for the future and suggest the best possible plans for advertisers. Azati suggested applying linear regression with L2-regularization to approximate missing data, thus ensuring the system could predict viewer characteristics for future time periods and provide optimal plans for advertising placement.

Our Approach

Data Analysis and Pattern Identification

We analyzed the available data over multiple periods to understand the patterns and gaps in the viewer characteristics.

Regression Model Implementation

We used linear regression with L2-regularization to approximate the missing data and provide more accurate insights about viewer characteristics at discrete time intervals.

Continuous Prediction and Optimization

The optimized system was able to continuously restore the relationships between characteristics, allowing it to make predictions and offer optimal broadcast plans for advertisers at any given moment in time.

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Solution

Data Approximation Module

Uses advanced linear regression with L2-regularization to estimate missing viewer data, reconstructing continuous dependencies from discrete points. This ensures the system can generate accurate predictions even when partial or incomplete data is provided, maintaining reliability for downstream analysis.

Key capabilities:

Approximate missing viewer characteristics
Ensure continuous dataset for predictive analysis
Support accurate audience segmentation
Enable real-time recommendation generation

Optimal Broadcast Planning Module

Automatically generates the most effective advertising plans by integrating audience predictions, budget, program timing, and ad duration. It ensures that every commercial reaches the most relevant viewers at the right time, maximizing the ROI for advertisers while respecting their constraints.

Key capabilities:

Integrate viewer characteristics with ad constraints
Provide actionable broadcast schedules
Maximize advertising ROI
Adapt plans in real-time based on updated data

Targeted Audience Prediction Module

Forecasts viewer characteristics for specific programs at precise times, enabling highly targeted ad placements. This module supports multi-factor targeting and ensures that advertisers reach the right audience segments, improving engagement and campaign effectiveness.

Key capabilities:

Forecast audience composition by time and program
Identify high-value viewer segments
Support multi-factor targeting strategies
Update predictions dynamically with new data

Real-Time Decision Support Module

Delivers instant recommendations for ad placement decisions based on the latest available and approximated data. It allows advertisers to adjust campaigns on-the-fly, respond to emerging trends, and make data-driven decisions in real time.

Key capabilities:

Generate instant ad placement suggestions
React to changing audience patterns in real-time
Enable adaptive campaign planning
Integrate seamlessly with analytics dashboards

Analytics & Reporting Module

Provides comprehensive insights into audience predictions, broadcast plan effectiveness, and optimization opportunities. The module supports strategic decision-making, allowing advertisers to track ROI, identify trends, and continuously refine targeting strategies.

Key capabilities:

Visualize audience predictions and trends
Track campaign effectiveness
Identify areas for optimization
Provide reports for strategic planning

Business Value

Improved Targeting: Advertisers can reach the most relevant audience segments, increasing campaign effectiveness and engagement.

Data Completeness: Missing viewer data is approximated accurately, allowing continuous predictions and more reliable decision-making.

Optimized Campaign ROI: The system produces optimal broadcast plans, balancing audience reach, budget, and timing to maximize advertising returns.

Real-Time Decision Support: Advertisers receive immediate recommendations based on up-to-date and predicted audience characteristics.

Operational Efficiency: Automated data approximation and planning reduce manual analysis, saving time for both the client and advertisers.

Scalable Solution: The system can handle large-scale viewer datasets and extend predictions for future campaigns without manual intervention.

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