A team is analyzing the success rate of a campaign. For the team, the old problem still exists. How can every impression be valuable when the decision needs to be taken quickly? It has always been about speed and efficiency in programmatic ads, but now, with AI, things have moved up a notch. Be it the targeting or the bidding on an impression, AI facilitates it all.
The article will help you understand how AI helps with programmatic advertising.
How Do AI Algorithms Work in Programmatic Ads?
AI algorithms continuously learn from data and become better with each interaction. Unlike traditional methods, that assume the same value for each impression, AI calculates each chance and predicts what could happen as a result, whether click or conversion.
Insights can be gained without manually analyzing data. The biggest feature of AI is making decisions promptly without spending much time.
How does AI Cut Down Ad Spend Waste in Programmatic Campaigns?
Here’s how AI works in Programmatic Advertising to avoid ad spend waste.
1. Eliminating Poor Performing Inventory
The AI algorithm knows which platforms generate good results for your campaign and which do not. It moves funds away from underperforming inventory.
For instance, if a particular platform generates traffic but doesn’t convert, AI will minimize or even stop placing bids.
2. Avoiding Ad Fatigue by Adjusting Frequency
Excessive frequency of the same ads can result in ad spend waste. AI detects user engagement and adjusts frequency.
For instance, if the user sees your ad several times but doesn’t click on it, AI will discontinue sending it.
3. Fraud & Invalid Traffic Detection
The technology identifies anomalies which suggest bot traffic or false impressions, allowing advertisers to prevent any waste due to non-human traffic.
Example: When there is a high level of traffic without actual user interaction from a particular source, AI immediately identifies it and blocks it.
AI in DSP vs SSP: How Decisioning Differs Across Platforms
Here’s how decisioning differs across both sides of programmatic advertising.
1. AI-powered Ad Bidding vs Price Optimization
DSPs rely on AI-powered ad Bidding to determine how much to bid for each impression based on performance. SSPs, on the other hand, use AI to optimize and maximize yield.
Example: A DSP may bid higher for a user likely to convert, while an SSP may increase the minimum price for premium inventory when demand is high.
2. Data Usage and Perspective
DSP AI models use data such as audience behavior, campaign goals, and conversion history. AI algorithms used in SSP use inputs such as quality of inventory, consumer engagement level, and demand patterns.
Example: In DSP, AI finds potential customers who may be interested in SaaS platforms, whereas in SSP, AI finds bids in a platform where there is high bidding.
3. Optimization Goals
DSPs optimize toward performance metrics like clicks, leads, or conversions. SSPs optimize toward fill rate and revenue.
Example: A DSP reallocates budget to high-performing audience segments, while an SSP prioritizes demand sources that consistently deliver higher bids.
4. Inventory Selection vs Inventory Packaging
DSP AI selects the most relevant impressions to bid on. SSP AI packages inventory in ways that make it more attractive to buyers.
Example: A DSP targets decision-makers in finance, while an SSP groups premium finance content to attract higher bids from advertisers.
How AI Improves Programmatic Ad Performance and ROI
Here’s how AI makes Programmatic Advertising effective.
1. Audience Segmentation Using AI
AI can prove helpful in segmenting audiences according to their behavior, interests, and interaction history.
Example: In place of casting the net widely, it targets individuals who are looking for solutions.
2. Ad Bidding Using AI
AI in ad Bidding entails analyzing each impression to gauge the likelihood of generating a certain value. It helps in curbing overspending.
For example, when the impression is associated with intent signals, AI raises the bid amount. When no such signals are detected, the bid is lowered or skipped altogether.
3. Constant Improvement and Learning
All campaigns benefit from constant learning from each other. With more information flowing through, the decision-making process gets better.
Example: Over time, even campaigns that perform averagely can benefit from AI to target and bid.
The Next Phase for AI in Programmatic Advertising
What comes next is a deeper level of autonomy. AI will contribute in the decision-making process throughout the whole journey of creating an effective campaign. It is already helping optimize budgets, reach audiences, and measure results. The next phase is not about technology. It is about building an approach to programmatic advertising.
