AI Is Reshaping Media Buying This Summer, Learn How?

AI is not just a trend, it’s the future. Despite becoming a buzzword, artificial intelligence is not an option anymore; it is a use-it-or-lose-it tool. Using AI in advertising has improved the speed and quality of media buying. Now the question is how to choose a platform that provides the best AI-powered solutions.

Transformation of Scatter and Programmatic AI Buying

The media buying process is now undergoing a radical transformation because of AI. A lot of processes that demanded hours of manual work and analysis can now be processed in less than a second.

We already discussed AI media planning, but now, let’s take a closer look at how AI is enabling more agile planning and real-time optimizations in scatter and programmatic buying.

AI in Agile Planning

1. Automated Data Integration and Analysis

AI aggregates and rapidly analyzes data from diverse sources (the number of sources depends on the software) - historical ad performance, real-time viewership, sales cycles, market trends, and behavioral signals. This rapid synthesis eliminates manual guesswork, giving planners instant access to actionable insights. It helps advertisers identify the best channels, time slots, and audience segments in minutes or even in seconds.

2. Scenario Modeling and Forecasting

AI-driven scenario modeling helps advertisers test multiple strategies before committing budget. For example, it can simulate how different budget allocations, creative messages, or targeting tactics might perform based on historical outcomes and real-time trends. In the scatter market, this helps optimize buys in a condensed timeline.

3. Cross-Channel Planning and Coordination

AI unifies data across linear and connected TV. This cross-platform intelligence ensures budgets are distributed strategically across screens, minimizing overlaps, maximizing reach, and creating a halo effect. It helps scatter buyers to benefit from more precise audience forecasts.

4. Inventory Forecasting and Allocation

AI models trained on ad server data and viewership patterns help forecast inventory availability weeks in advance, even in scatter, where inventory is typically volatile. These predictive insights prevent over-buying or under-selling.

AI in Real-Time Optimizations

1. Continuous Data Analysis at Scale

AI systems ingest and process real-time data streams, from audience behavior, engagement patterns, and inventory availability to campaign performance metrics and market fluctuations. This gives advertisers a live, granular view of what’s working and what’s not across all channels, so advertisers can make mid-flight adjustments.

• In scatter, where buys are often locked in close to airtime, AI analyzes performance signals like spot-level engagement and reaches to recommend reallocations or substitutions before underperformance compounds.
• In programmatic, this same analysis occurs at the impression level, with AI optimizing each bid in real-time based on how likely it is to deliver against the campaign’s specific goals.

2. Instantaneous Bidding and Dynamic Pricing

In the programmatic space, AI drives real-time bidding by evaluating each impression opportunity in less than a second. It considers contextual relevance, predicted user behavior, current inventory pricing, and budget pacing, then places an optimized bid instantly. Bids aren’t static. Dynamic pricing algorithms adjust spend on the fly, ensuring buyers don’t overpay and still win high-value impressions when they matter most.

3. Automated Budget Reallocation

AI automatically reallocates the budget between platforms, audiences, or time slots as performance data changes. For example, if certain CTV placements outperform linear, or if a scatter spot underdelivers, AI can pause that spend and redirect the budget toward channels or creatives that are driving results.

4. Predictive Optimization and Proactive Corrections

AI doesn’t just react to ongoing processes but also forecasts.

• In scatter, AI can forecast the performance of upcoming airings based on similar past buys and recommend strategic shifts.
• In programmatic, it can expand targeting or narrow focus based on predictive engagement scores, constantly refining performance.

5. Real-Time Creative and Audience Adjustments

AI also enables dynamic creative optimization (DCO) - automatically adjusting which creative version is served to which audience segment in real-time, based on engagement trends and viewer profiles.

Forecast of Inventory Demand and Campaign Outcomes with AI

Let’s look closer at predictive models. Powered by machine learning, they use historical and real-time data to provide actionable insights that optimize both ad inventory management and campaign effectiveness.

Forecasting Inventory Demand

Predictive models forecast TV ad inventory by analyzing ad server data and viewer patterns across shows and platforms. It is possible due to time series and machine learning. These models provide granular insights that help avoid overbooking or underutilization, protecting revenue. The forecasts can be updated in real-time, even in terms of constantly changing viewer behavior and market conditions.

Optimizing Campaign Outcomes

Campaign outcomes are optimized by analyzing data on engagement, location, and past performance to enhance targeting and personalize ads. These models forecast metrics such as reach and conversions to guide budget allocation and improve bidding decisions in real-time. Advertisers can also simulate multiple scenarios and use early feedback to refine strategies continuously for better ROI.

Model Performance and Impact

Some machine learning models have demonstrated high accuracy in predicting TV program success and ad demand, with some of them achieving mean absolute percent errors below 8% and R² scores above 0.96. This level of precision reduces manual planning errors and financial risks.

Use Cases In Audience Segmentation And Creative Personalization

With AI advertising, now it's possible to divide audiences into highly specific segments based on demographics, behaviors, psychographics (values, lifestyles, interests), and even real-time intent due to processing vast amounts of data from multiple sources (web activity, social media, purchase history).

What about personalization? Today, this is much more than the customer's name in the email's subject line. Today's creative personalization uses behavioral data, contextual signals (like location, weather, device), and predictive analytics to tailor ad experiences in real-time. Viewers see a TV ad with creative elements, such as images, offers, and messaging, adapted to their needs and interests. For example, a travel brand might show beach scenes to users in snowy regions, or a retailer sees that in Florida, a rainy day is forecasted and might start to run umbrella ads.

AI becomes a business advantage in media buying. The main challenge today is how to choose the right AI-powered tool. Simulmedia's TV+ platform is powered by AI, which can help to improve the media buying process.

Simulmedia's TV+ platform uses AI to:

• Identify and target high-value audiences across linear and streaming TV
• Optimize spend between channels for maximum reach and efficiency
• Continuously adjust flighting and allocation based on real-time performance
• Measure true campaign impact with advanced attribution models

With AI at its core, Simulmedia helps advertisers plan smarter, act faster, and prove results across every TV screen.