The firm achieved a 45% improvement in forecasting accuracy, a 30% boost in pricing optimization, and faster, more transparent decision-making, all of which positioned it as a leader in data-driven advertising.
AI media forecasting and pricing optimization
We used Cognitive Hive AI to optimize media buying for a major advertising firm
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Executive summary
We provided AI-driven sales forecasting and pricing optimization for a large advertising company. Using Cognitive Hive AI (CHAI), we developed a modular system to predict sales, set optimal prices, and adapt to market changes. The project delivered a 45% improvement in forecast accuracy and a 30% boost in pricing efficiency. Transparent, explainable processes helped stakeholders make informed decisions. This approach positioned the company as an industry leader in data-driven advertising strategies.
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PROBLEM
The media advertising company struggled with inaccurate sales forecasting, ineffective pricing strategies, and limited adaptability to market changes, relying on static models that lacked transparency and flexibility.
SOLUTION
CHAI, a modular AI architecture, improved sales forecasts, optimized pricing, and enhanced market adaptability through data aggregation, specialized modules, recursive refinement, and explainable decision-making tools.
RESULT
Background
The advertising industry depends on accurate market forecasts and flexible pricing strategies. Static models often fail to account for market fluctuations or provide actionable insights. A major advertising firm sought an AI solution to address these gaps, aiming to improve predictions, pricing, and transparency.
Project highlights
CHAI delivered predictability and explainability for media buying.
The accuracy and effectiveness of pricing decisions went up by 30%.
Forecasting accuracy increased by 45%.
CHAI's modular architecture enabled cross-checking and recursive refinement.
Objectives
- Build an AI system to accurately forecast ad sales.
- Optimize pricing across formats, demographics, and regions.
- Provide clear and traceable decision-making processes.
- Enable fast responses to changing market conditions.
Methodology
Talbot West used CHAI to meet the project’s goals. Each AI module tackled a specific task while contributing to the system’s overall intelligence. Here are some of the different roles that modules played in the CHAI ensemble:
- Data aggregation: Collect and organize data from past sales, competitor pricing, market trends, and customer demographics.
- Sales forecasting: Model econometric data and perform time-series analysis to predict demand across regions and formats.
- Pricing optimization: Analyze seasonal trends, competitor data, and ad performance to recommend granular ad spend that maximizes revenue.
- Sentiment analysis: Sentiment modules analyzed customer feedback and social media data to detect shifts in preferences.
- Recursive refinement: Critique outputs of other models and ask for revision through iterative cycles, which improved forecasts and pricing recommendations with each pass.
- Explainable AI features: Logged decision paths so users could trace how predictions and recommendations were generated.
Results
- Better sales forecasting: Forecast accuracy rose by 45%, which improved resource allocation and revenue predictions.
- Efficient pricing: The system achieved a 30% increase in pricing optimization, with fewer errors and enhanced profitability.
- Faster market response: The modular design allowed the company to quickly adjust strategies to align with emerging trends.
- Decision transparency: Clear logs and explainable outputs fostered trust among stakeholders.
Challenges and solutions
- Data complexity: Unstructured data from multiple sources created challenges. We used vector databases to organize and standardize the inputs.
- Market volatility: Rapid market changes demanded agility. CHAI’s modular design allowed for fast updates to individual AI modules.
- Building trust: Stakeholders require confidence in AI recommendations. Transparent decision logs and explainable outputs provided visibility and allowed for granular, segmented adjustments to fine-tune the system.
Lessons learned
- Modular AI architectures allow the exact right capabilities set to be assembled to spec for complex and volatile environments.
- Explainable systems build trust and improve adoption.
- Iterative feedback loops ensure continuous improvements and alignment with business needs.
Future implications
CHAI’s success shows how modular AI can transform decision-making in industries with fast-changing markets. The architecture can expand to include advanced models for greater numerical precision, providing even deeper insights in forecasting and pricing.
Conclusion
Talbot West’s modular AI approach delivered precise sales forecasts, adaptive pricing strategies, and transparent decision-making tools. CHAI enabled the company to navigate market shifts and make informed, confident decisions, setting a new standard for data-driven strategies in media advertising.
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