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AdRoll BidIQ: Machine Learning for Better Marketing

Matthew Wilson

Sr. Staff Data Science Engineer

With the release of ChatGPT, machine learning has burst into the public consciousness. However, for AdRoll, machine learning has long been a key technology for turning internet-scale data into customer value and differentiating AdRoll from our competitors.

What is Machine Learning?

Machine learning is a catch-all for different techniques that decipher patterns in data. Nowhere is there more data than in ad-tech, where billions of data points are generated daily by internet user’s interactions with their favorite websites. Using machine learning is essential to effectively leverage this data to optimize your marketing strategy and achieve your performance goals.

That’s why we’ve developed BidIQ, our machine learning engine, over the course of the last decade. We use it to predict user intent and behavior, understand the context of both publisher and advertiser websites, recommend the most relevant ads and products, prevent ad fraud, and most importantly, to set optimal ad bids and pace budgets. In doing so, BidIQ considers billions of data points, and factors in hundreds of features specific to the advertiser, the page on which the ad will be displayed, and the person seeing the ad. All of this happens at an incredible scale with extremely low latency; BidIQ is able to determine an exact value for every display ad opportunity across the internet in real-time

Thanks to BidIQ, you can benefit from advanced machine learning methodologies without a team of in-house machine learning engineers. BidIQ is what allows AdRoll to buy the most economically efficient ads for you and maximize the ROI of your advertising budget. In other words, BidIQ ensures your advertising spend is going to the right places and driving performance for your business.

To make this more concrete, let’s dive into a typical scenario in which AdRoll you can leverage AdRoll BidIQ to drive performance for your ad campaigns.

BidIQ In Action


An advertiser is running an online ad campaign with a budget and a performance goal.


  1. Valuing an ad opportunity correctly:

    1. Bidding too high will result in overpaying for ad impressions.

    2. Bidding too low will result in missed opportunities for ad impressions.

  2. Pacing budgets optimally:

    1. Overly aggressive budget pacing will result in missed opportunities for ad impressions when the budget is exhausted.

    2. Overly passive budget pacing will result in regret over missed opportunities to have allocated leftover budget.

  3. Selecting the right users (cookies) to target.

  4. Selecting the right ad. In the case of dynamic creative this means selecting the right products to embed in the ad.

  5. Selecting the right website(s) on the internet to show the ads.

  6. Preventing wasted spend on ad fraud.

How BidIQ Helps

  1. We use machine learning to accurately predict the probability that an internet user will take various actions after being served an ad. This, along with your performance goal (eg. optimize for conversions), allows our system to value an ad opportunity correctly.

  2. We use control theory to pace budgets optimally to maximize ROI.

  3. We allow fine-grained control over user-targeting, but also encourage targeting a broad audience so that our automated bidding system can sift through the stream of bid opportunities and find the valuable impressions.

  4. We use recommendation systems to select the best ad to display and the best products to display within dynamic creative ads.

  5. We understand the context of publisher web pages using natural language processing with deep learning. This context factors into our valuation of the ad opportunity and our targeting logic.

  6. We use in-house machine learning in conjunction with other techniques to block invalid domains and traffic.

The AdRoll Machine Learning Difference

Though there are many demand side platforms (DSPs) to choose from in the marketplace, they are not equal. Some DSPs don’t leverage machine learning, and instead bid the same amount on every impression matching some ad hoc criteria. This is wasteful because every ad opportunity is different and is of different value. While other DSPs may leverage machine learning, they may only be doing so at a surface level.

At AdRoll, we’ve been developing our machine learning systems to optimize every level of performance for over a decade. In fact, we have several teams of incredibly talented data scientists and engineers focused on exactly that. To complement the machine learning systems we’ve developed, AdRoll also has one of the richest datasets in the industry to power these algorithms. This includes data from tens of thousands of advertisers across dozens of industries, as well as the actions and interests of billions of internet users. This rich dataset is another way in which AdRoll offers more to our customers. 

Learn more about getting started with AdRoll here.

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