How Predictive Intelligence is Transforming AdTech
AdTech is currently characterized by rapid innovation and a plethora of new technologies that are transforming the way that companies engage with consumers. Driven by both technological advancement and the tightening of user privacy laws, businesses of all sizes are looking toward new solutions that deliver results while remaining compliant, cost effective, and scalable.
There is a critical need to address issues related to ad fraud, viewability and brand safety within the AdTech ecosystem. As the industry becomes more complex, it becomes harder for companies to ensure that their ads are viewed by real humans and are not affected by bots or other forms of fraud. Couple this with the growth of data privacy compliance concerns, and you see why companies are investing in cookie-less technologies to identify and better understand their audiences.
Predictive intelligence is an emerging solution within AdTech that tackles many of these challenges. Driven by AI and machine learning algorithms, predictive tech is enabling more sophisticated and personalized ad experiences for consumers, as well as optimizing and automating various aspects of the ad buying and selling process. These technologies are helping companies to make data-driven decisions, and to respond to changes in consumer behavior and market conditions in real-time.
Let’s explore…
Defining Predictive Intelligence
Predictive intelligence is a form of artificial intelligence that specializes in prognostication, or the act of predicting future events or outcomes. One of the primary benefits of predictive intelligence is its ability to facilitate more enlightened decision-making. By providing insights into future trends and outcomes, organizations are able to more efficiently allocate resources, plan for the future, and optimize their operations.
Predictive Intelligence’s Role in AdTech
Predictive intelligence has emerged as a critical component in the realm of AdTech. This form of artificial intelligence has proven to be a valuable tool for advertisers seeking to target the most appropriate audience and maximize the effectiveness of their campaigns.
To perform its role in AdTech, predictive intelligence relies on data analysis and machine learning algorithms to discern patterns and trends, allowing it to make informed predictions about what is likely to occur in the future. These predictions are based on statistical models that have been trained on extensive amounts of data, with the aim of continually improving their accuracy over time.
Some examples of how predictive intelligence is used in AdTech include targeting the most relevant audience for ads, determining the most effective ad placements, forecasting ad performance, and personalizing ad experiences for specific users. While predictive intelligence has the potential to revolutionize the advertising industry, it is important to consider the ethical implications of its use. Ensuring data privacy and security, as well as mitigating biases present in data and algorithms, are crucial to the responsible implementation of predictive intelligence in AdTech.
How Predictive Intelligence Works in AdTech
Gathering and Analyzing Data
One of the most crucial steps in the process of predictive intelligence is gathering and analyzing data. This involves collecting large amounts of information from a variety of sources, including web page content, keywords, online engagement, and previous campaign performance. This data is then analyzed using machine learning algorithms and statistical models to identify patterns and trends. The goal of this analysis is to understand how different variables may be related and how they may influence future outcomes.
By gathering and analyzing data in this way, businesses are able to make more informed predictions about the success of their advertising campaigns. For example, they may be able to identify which audience segments are most likely to respond to a particular ad at the bottom of the funnel, or which ad placements are likely to yield the best results in the awareness stage. It is important to note that the quality of the data and the accuracy of the analysis are critical to the success of predictive intelligence. Poor quality data or flawed analysis can lead to inaccurate predictions, which can have serious consequences for an organization’s advertising efforts.
Using Algorithms to Make Predictions
Once data has been gathered and analyzed, the next step in the process of predictive intelligence is using algorithms to make predictions about future outcomes. There are various types of algorithms that can be used for this purpose, including decision trees, random forests, and neural networks. These algorithms are trained on large amounts of data and are designed to continuously improve their accuracy over time.
To make a prediction, the algorithm takes in new data as input and uses the patterns and trends identified during the training process to make a prediction about what is likely to happen next. For example, a company might use a predictive intelligence algorithm to predict which audience segments are most likely to respond to a particular ad, or which ad placements are likely to yield the best results at specific stops on the path to purchase.
A.I. In Predictive Intelligence
Artificial intelligence (AI) plays a key role in the field of predictive intelligence. AI refers to the ability of computers and machines to perform tasks that normally require human-like intelligence, such as decision-making, problem-solving, and learning.
One of the key benefits of using AI in predictive intelligence is that it can process vast amounts of data much faster than a human could, allowing for more accurate and timely predictions. AI is also able to identify subtle patterns and trends that may not be immediately apparent to a human, leading to more accurate predictions.
However, it is important to note that the use of AI in predictive intelligence is not without its challenges. Ensuring that data is of high quality and free from bias, and that machine learning algorithms are designed and trained properly, are crucial to the success of AI in predictive intelligence.
Applications of Predictive Intelligence in AdTech
Audience Targeting
One of the key applications of predictive intelligence in AdTech is audience targeting, or the process of identifying and reaching the most relevant audience for a particular campaign.
By analyzing data on factors such as demographics, online behavior, and previous ad performance, AdTech companies are able to identify which audience segments are most likely to respond to a particular ad. This allows them to target their ads more effectively, leading to higher engagement and better return on investment.
Predictive intelligence is particularly useful in audience targeting because it allows AdTech companies to make more informed decisions about where to place their ads and who to target. By analyzing data and making predictions about which audience segments are most likely to respond to an ad, AdTech companies can optimize their ad campaigns for maximum effectiveness.
Optimizing Ad Placements
Predictive intelligence is becoming a crucial component within AdTech for a myriad of reasons, and the optimization of ad placements is one of the most impactful. By leveraging machine learning algorithms, ad placements can be dynamically adjusted to improve performance metrics such as click-through rate (CTR), cost-per-click (CPC), and conversion rate.
The goal is to accurately predict the likelihood of an ad being clicked on, based on a data set that includes the on-page content they engage with and their unique location on the path to purchase. This provides a context for the website or app where the ad is being displayed. By stack ranking websites based on their content and how users engage, the most promising ad placement can be selected, resulting in a drastically improved performance.
As algorithms run overtime, we can use reinforcement learning to enable improvement of future decisions. Feedback is received based on the performance of past ad placements and adjusts the strategy moving forward. Additionally, predictive intelligence can be utilized in programmatic advertising, where the ads are bought and sold in real-time through a bidding process. In this case, predictive models can be used to predict the value of ad inventory, allowing for more effective bidding strategies. This results in higher overall performance metrics and increased revenue for advertisers.
Forecasting Ad Performance
Predictive intelligence enables advertisers to better predict future performance metrics such as CTR, conversion rate, and revenue generated by an ad campaign. This is achieved by utilizing historical data to model patterns over time and forecast future values. Similarly, causal inference methods help determine the cause-and-effect relationship between different features and the performance of an ad campaign. This allows for a more detailed understanding of the factors that drive ad performance and can be used to optimize future campaigns.
Additionally, predictive intelligence can also be used for uplift modeling, which aims to predict the incremental effect of a campaign on specific target groups. By identifying the users that are most likely to be influenced by an ad, advertisers can optimize their campaigns and allocate resources more efficiently.
Personalizing Ad Experiences
Predictive intelligence is revolutionizing the way AdTech personalizes ad experiences, making them practically invisible to the user. By using machine learning algorithms, advertisers can tailor ad content and timing to the specific needs and preferences of each individual user, resulting in a more seamless and unobtrusive advertising experience.
Content on a webpage, and the ways in which users engage allows for the construction of personalized recommendations. Advertisers can use this data to predict which ad a unique user is most likely to be interested in. Natural language processing techniques help match the tone and style of the ad content with the context of the website or app where the ad is being displayed. This allows the advertiser to ensure that the ad is presented in a way that is consistent with the overall look and feel of the website or app, making it less intrusive and more likely to be well-received by the user. By only displaying ads that are relevant to the user, the ads become less disruptive and more likely to be engaged with.
Challenges of Predictive Intelligence in AdTech
Ensuring Data Privacy & Security
As the industry shifts towards a cookie-less advertising ecosystem, one of the main challenges of predictive intelligence is ensuring data privacy and security. In a cookie-less environment, advertisers rely on alternative methods of identifying and tracking users, such as device fingerprints and browser fingerprints. However, these methods can raise concerns about data privacy and security, as they involve collecting and storing sensitive information about users.
To mitigate concerns, advertisers must adopt robust data privacy and security measures, such as encrypting sensitive information, implementing strict access controls, and regularly monitoring for and mitigating data breaches. Advertisers should comply with relevant data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).
One approach to addressing these challenges is to use federated learning to protect user privacy while still allowing for personalized ad experiences. Federated learning allows advertisers to train machine learning models on user data without actually storing the data.
It’s important for all companies to consider user’s data protection as a top priority and maintain transparency and accountability when collecting and utilizing data.
Overcoming Bias in Data and Algorithms
One of the main challenges of predictive intelligence is overcoming bias in data and algorithms. Bias can occur when the data used to train a machine learning model is not representative of the population it is intended to make predictions about, leading to errors and inaccuracies in the predictions. Algorithmic bias can also occur when the model itself is designed in a way that reinforces existing societal biases.
One example of bias in AdTech is the impact of data collection and modeling on underrepresented groups. For example, if an advertiser only collects data on a certain demographic, the model may not be able to accurately predict the behavior of other demographic groups, resulting in poor performance and lower ROI for the advertiser. To overcome these challenges, it’s important to use diverse data sets to train models and perform regular audits to detect any bias in the data and algorithms.
Dealing with Changing User Behavior and Trends
As user preferences and behaviors constantly change, the models used to predict ad performance must be regularly updated to reflect these changes. One approach is to use online learning techniques that enable the model to continuously update itself as new data becomes available, allowing it to adapt to changes in user behavior and trends.
Another problem that arises is the concept of drift. This is the gradual change in the underlying probability distribution of the data and it could cause the model to become less accurate over time. To overcome this challenge, techniques such as change detection algorithms or concept drift detection could be used to monitor the model’s performance and detect when drift occurs and re-train the model or change the feature set.
Examples of Successful Use of Predictive Intelligence in AdTech
At Vatic, we’ve been busy building predictive intelligence for a better internet. Sitting on over 2000 successful campaigns with some of the planet’s biggest advertisers, we’ve demonstrated the market-leading effectiveness at all stages of the funnel, across all types of ad-formats. When comparing the performance of Vatic’s enhancement of DSP data to traditional methods, the capabilities of predictive intelligence in AdTech become abundantly clear.
- 54% – Saved on Average Costs (CPC, CPE, CPV)
- 12x – Longer Visit Duration
- +35% – Increased Engagement Rate
- +43% – Increased Average Time on Site
- +18% – Increased Number of Page Visits Per Visitor
These stats combined with the ability to generate last click conversions within the first week of testing demonstrates how our technology out performs normal campaigns reliant on retargeting. This is how we delivered an 87% cheaper cost per engagement (CPE) for one of our financial clients, 60% cheaper cost per click for a B2B tech client, and 82% cheaper cost per visit for a recruitment client.
Conclusion
Predictive intelligence is a monumental advancement that is revolutionizing the AdTech industry by enabling advertisers to harness the power of machine learning algorithms to optimize ad placements, forecast ad performance, and personalize ad experiences for individual users. This results in higher overall performance metrics and increased revenue for advertisers.
While there are some challenges to overcome with this burgeoning technology, predictive intelligence will transform the advertising industry by providing advertisers with powerful tools to reduce advertising friction and drastically improve results. The successful implementation of predictive intelligence in AdTech requires addressing data privacy, security, and bias, as well as adapting to changing user behavior and trends.
Vatic is building a better internet using predictive intelligence. Our proprietary technology makes advertising seemingly invisible by predicting with unprecedented accuracy where a user will be, what they want to see and how they want to see it. Whether you’re on the buy side and looking to tap into high-intent audiences without cookies while reducing ad-fraud and wastage, or on the sell side looking to make your inventory work smarter with real-time predictive intelligence insights. Vatic is delivering predictive intelligence for a better internet.
Book a demo with us today to see exactly how predictive intelligence can take your campaigns to a new level.