AI in Media Planning and Buying: How to Optimize Media Buying

With the rise of AI and digital media, many traditional processes , like spreadsheet-based planning and manual negotiations, have become less central, replaced by automated, data-driven workflows. Today, media buying is powered by technologies like real-time bidding, audience segmentation, and creative personalization, all of which enable greater precision and improved return on ad spend.

In this article, we invite you to take a closer look at how AI is revolutionizing media buying and how you can leverage this technology to power your digital ad efforts and optimize media investments.

The Role of Artificial Intelligence in Media Buying

Here is how marketers can use AI to enhance media planning and buying:

• Data-driven decision making. AI tools can analyze large volumes of user and contextual data. Such insights can be valuable for detecting the best-performing channels and ad formats, as well as understanding audience preferences and purchasing intent.
• Programmatic advertising. AI enhances programmatic advertising by improving targeting, bidding strategies, and campaign efficiency. While programmatic platforms already automate the buying and placement of digital ads in real time, AI adds an extra layer of intelligence, analyzing vast datasets to optimize decisions and deliver better performance.
• Campaign optimization. AI models can continuously track ad performance to test different creatives and formats, optimize return on ad spend, and enhance user experiences by controlling the frequency of showing the same ads to the same users.
• Personalization. AI enables highly personalized ad experiences by matching ad creatives to user profiles and tailoring messages based on real-time data.
• Fraud detection and brand safety. AI in media environment can detect ad fraud, bot traffic, and prevent inappropriate ad placements.
• Predictive analytics. ML models can provide insights into ad campaign outcomes even before launch and predict future customer behavior and media consumption trends.
• Cross-channel and omnichannel strategy. It’s quite challenging to monitor and unify ad performance across platforms manually. But AI can facilitate this task. AI can unify data from mobile, web, and CTV platforms to analyze behavior patterns and optimize cross-channel ad strategies.

How AI Automates Ad Placements

With the help of AI, businesses can efficiently automate ad placements. Here’s how this process is organized.

Real-time bidding (RTB)

• When a user visits a mobile application or website, a real-time auction starts.
• AI analyzes the user, device, context, and historical data.
• AI then makes a bid for the most appropriate ad space.
• The biggest bid wins.

Audience targeting and segmentation

Artificial intelligence in media relies on demographic, behavioral, and psychographic parameters to detect users who are most likely to be interested in the offered products or services. The technology can tailor personalized ads to the needs and interests of different audiences.

It’s vital to highlight that AI-driven target segments are dynamic as they are continuously refined based on changing performance data.

Dynamic creative optimization (DCO)

The use of AI in media allows advertisers to leverage real-time ad creative customization. It means that artificial intelligence can change headlines, CTAs, and images in accordance with the user’s profile, device, location, and browsing history.

As a result, each viewer can see the most relevant AI-generated version of an ad.

Cross-platform placement

AI in media and entertainment can be highly helpful for choosing the best-suited channels for each ad.

Tools powered by artificial intelligence can analyze various parameters of each channel (mobile apps, social media, CTV, etc.), including potential reach, user habits, and performance data. AI can also factor in historical performance by platform. Based on the results of such analysis, AI can detect which ad will work best on each channel at any given time.

Budget allocation

Advertisers can leverage AI-powered tools to automatically allocate budgets across campaigns and platforms for maximum ROI. These algorithms analyze performance data in real time and adjust spending based on dayparting and weekly performance trends, such as higher engagement on weekends or holidays for some verticals.

What AI Tools to Use for Media Campaigns?

AI-powered tools that are typically used in the media space can be categorised into several groups in accordance with their type and purpose. Let’s consider the most common of them.

AI for media strategy

The correct use of data is one of the core factors of the success of your media strategy. AI models can help businesses to avoid complex and time-consuming processing and analysis of data, including:

• Audience demographic parameters;
• User behavior patterns;
• Historical campaign performance;
• Current market conditions.

Apart from this, when you use artificial intelligence in media strategy creation, you can rely on this technology for tracking the results of your campaigns in real time. AI can suggest budget shifts, provide actionable insights, and predict performance.

AI-powered automated media buying and source media placing

AI can be applied to select and optimize where your ads will be shown to achieve the best visibility and ROI.

By analyzing user behavior, content context, and past performance, artificial intelligence can identify platforms and channels where the target audience is most likely to engage.

AI-driven media buying presupposes purchasing ad inventory in real time, without manual input. AI algorithms can evaluate thousands of parameters to make the most cost-effective bidding decisions.

Want to know more about different types of tools for automated media buying? Read our comprehensive guides on ad exchanges, SSPs, and DSPs.

AI for automating Ad Ops

Artificial intelligence is very helpful for streamlining and optimizing operational tasks related to managing digital advertising campaigns, including but not limited to ad traffic tracking, performance monitoring, and reporting.

For instance, AI tools can automatically set up ad campaigns across platforms based on predefined rules and creative assets, as well as dynamically adjust budgets, bids, and targeting parameters.

AI in automating reconciliations

AI is also of great help for verifying and settling advertising data between multiple parties (advertisers, publishers, and ad platforms).

AI systems can compare ad delivery data from various sources to identify discrepancies and mismatches. In case of common reconciliation issues, such solutions can auto-resolve them or escalate complex cases to human teams.

Apart from this, machine learning models are good at defining unusual patterns, like unexpected cost variations or irregular impression counts that can be signs of fraud.

Thanks to this automation, you can significantly improve financial accuracy in campaign billing and reporting.

AI in media production

Generation of content in different formats is one of the potential use cases of AI for media companies and marketing teams.

AI-driven media production can include:

• Scriptwriting;
• AI video generation based on text or audio prompts;
• Editing automation (cutting, adding effects, etc.);
• Voiceovers and dubbing;
• Translation and localization;
• Camera tracking and background removal;
• Image generation;
• Banner creation;
• Image cleanup and enhancement;
• Headline and ad copy optimization;
• Content calendar generation, etc.

Closing Word

AI is rapidly transforming media planning and buying. It helps marketers make smarter, faster, and cost-effective decisions related to ad campaigns.

The use of artificial intelligence in media buying is not just a modern trend. With the tough competition in the media space and all the benefits that AI can provide, the application of this technology is gradually becoming an industry standard. Nevertheless, human oversight remains essential to ensure compliance, quality, and strategic alignment.