How Machine Learning Improves Ad Targeting
How Machine Learning Improves Ad Targeting
Blog Article
How Machine Learning Improves Ad Targeting
Amid a brisk and dynamic digital advertising frontier, the harmonisation of intuitive marketing intellect with transformative machine learning capabilities lays bare new vistas of personalisation. Capable of parsing vast echelons of user data, these algorithms reveal latent preferences, enabling marketers to deliver ads that resonate with nuanced audiences.
Consider predictive modelling for example, which hones audience targeting precision by anticipating the likelihood of specific actions. This capability also enables marketers to optimise creative for contextual relevance and resonance.
Real-time Bidding
Machine learning is changing the world of advertising by automating ad bidding and providing predictive analysis. It enables marketers to better target audiences, forecast trends and optimize their advertising strategies.
For example, StackAdapt recently helped a car manufacturer to reduce ad waste by delivering more relevant ads that align with users' expectations. As a result, they experienced higher engagement and conversion metrics.
In addition, machine learning enables precise timing for ad delivery. This maximizes the impact of each impression and ensures that it reaches users in the moment when they are most receptive.
As a result, we've seen clients achieve double the return on their ad spend using our contextual advertising solution. Additionally, ML tools help to manage brand safety and alignment by ensuring that your ad isn't shown next to biased or negative content. This ensures your brand is perceived in a positive light and maintains the trust of your customers. It also helps protect your reputation and keeps you compliant with industry regulations.
Predictive Modeling
Using machine learning, marketers can anticipate customer needs and wants. This allows them to create and deliver targeted ads at the right time, which in turn drives higher engagement rates.
Predictive modeling ingests vast amounts of data and uses algorithms like decision trees, logistic regression, support vector machines and random forests to identify patterns and predict future trends. These models can also be trained to make predictions based on specific variables such as gender, age or income.
One example of predictive modeling in advertising is click-based optimization or uplift modelling, which determines the likelihood that each individual will click on an ad or make a purchase. This is particularly useful for brands with limited ad budgets, as it maximizes the return on every ad dollar. For example, a car manufacturer using this approach saw a 40% increase in test drive bookings. It also decreased ad spend wastage by only showing ads to people who were likely interested in purchasing a car.
Audience Segmentation
In addition to enabling more precise ad targeting, machine learning also provides the ability to better understand audiences and identify their unique attributes and behaviors. This enables marketers to create more personalized ads that resonate with them, leading to higher click through rates and conversions.
In programmatic advertising, machine learning identifies optimal ad placements in real-time based on user behavior and context. It takes into account factors such as the time of day, the user’s location, their browsing history, and other relevant information app retention tracking tools to deliver hyper-relevant ads. It’s not uncommon for this approach to double an advertiser’s ROI on ad spend.
In this way, AI and machine learning ensure that your ad is seen in places that align with your brand and are safe for your audience. This is a critical factor in maintaining brand loyalty, building trust, and increasing favorability. It also helps prevent your ads from appearing next to negative, biased, or non-factual content — which can damage the credibility of your company and decrease brand affinity.
Automated Bidding
Automated bidding optimizes your budget allocation by analyzing search query performance signals and predefined goals to dynamically adjust bids and target high-value opportunities. This ensures your advertising budget is allocated effectively and maximizes your return on ad spend (ROAS).
By analyzing data in real time, automated bidding algorithms can predict user actions such as clicking or conversions and adjust bids accordingly. This reduces the risk of making costly mistakes and optimizing for incorrect objectives.
Google Ads offers a variety of automated bidding strategies that are optimized for different marketing goals. For example, the Maximize Conversions strategy optimizes bids to increase conversions within your budget. It also raises bids during peak conversion periods to improve the likelihood of achieving your desired results. By utilizing machine learning, these bidding strategies improve campaign performance and optimize for a better return on ad spend. This eliminates manual bid management and frees up your time to focus on other marketing initiatives.