How we use aggregates for regression models on Hydrolix for user targeting

Context

AdTech companies should be able to explore and test several aggregation strategies on the same dataset for debugging and model learning purposes.

But let’s be honest, this really isn’t happening. DSP’s typically implement decision trees and logistic regression. Or as we like to call it, a spray and pray approach – where the bid request data that is most used is price and the cookie ID for web traffic. 

Objective

Machine learning models are becoming an increased priority for DSPs, especially with the cookie deprecation looming. 

In the ever-evolving landscape of digital advertising, precision targeting has become the Holy Grail for marketers. Gone are the days of casting wide nets and hoping for the best. Today, success lies in understanding your audience on a granular level and delivering tailored messages that resonate with them. But how do advertisers achieve this level of precision? One key tool in their arsenal is the use of aggregates in regression models.

Aggregates, in the context of regression modeling, refer to summarized data points that capture trends and patterns within larger datasets. These aggregates can include averages, sums, counts, or other statistical measures that distill complex information into actionable insights. When applied to digital advertising, aggregates allow marketers to identify meaningful correlations between user attributes and behaviors, enabling more targeted and effective campaigns.

At the heart of this approach is regression analysis, a statistical technique used to model the relationship between a dependent variable (such as user engagement or conversion) and one or more independent variables (such as demographic information, browsing history, or past purchase behavior). By analyzing historical data, regression models can uncover hidden patterns and predict future outcomes with remarkable accuracy.

  1. Data Collection: DSPs gather vast amounts of data from various sources, including user behavior, demographics, device information, browsing history, location data, and more. This data can be obtained from first-party (collected directly from users interacting with the DSP’s own properties), second-party (data shared directly from partners), or third-party sources (data acquired from external providers).
  2. Data Processing and Aggregation: Once the data is collected, DSPs process and aggregate it to create meaningful audience segments. This involves organizing the data into categories such as demographics, interests, behaviors, intent signals, etc. Aggregation helps in simplifying the dataset and making it more manageable for analysis.
  3. Feature Engineering: Feature engineering involves selecting and transforming the relevant features (variables) from the aggregated data that are likely to have predictive power for the regression model. This step is crucial for improving the model’s performance.
  4. Regression Model Building: DSPs use regression models to predict the likelihood of a user taking a specific action (e.g., clicking on an ad, making a purchase) based on the selected features. Common regression techniques used in adtech include logistic regression, ridge regression, and gradient boosting machines (GBM).
  5. Training the Model: The regression model is trained using historical data, where the input features are the aggregated data points, and the output is the target variable (e.g., probability of clicking on an ad). Training involves optimizing the model parameters to minimize prediction errors.
  6. Validation and Testing: Once the model is trained, it is validated using separate validation datasets to ensure that it generalizes well to unseen data. Testing involves evaluating the model’s performance metrics such as accuracy, precision, recall, and area under the curve (AUC).
  7. Deployment and Optimization: After validation, the model is deployed into the DSP’s ad serving infrastructure, where it is used to make real-time bidding decisions. The model’s performance is continuously monitored, and adjustments are made as needed to improve targeting precision and effectiveness.

So how exactly do aggregates come into play? Imagine a scenario where an e-commerce retailer wants to promote a new line of running shoes to its online audience. Rather than targeting all users indiscriminately, the retailer can leverage regression models to identify the characteristics of users who are most likely to be interested in running-related products.

To accomplish this, the retailer might aggregate data on user interactions with previous marketing campaigns, website visits, purchase history, and demographic information. By summarizing this data at a higher level (e.g., average time spent on site, total number of purchases), the retailer can create a more manageable dataset for regression analysis.

Next, the retailer would use regression techniques to model the relationship between these aggregated variables and the likelihood of a user being interested in running shoes. By examining coefficients and statistical significance, the retailer can identify which attributes (such as age, gender, or geographic location) have the strongest influence on user behavior.

Once the regression model is trained and validated, the retailer can apply it to new data in real-time to make targeted advertising decisions. For example, the model might recommend displaying ads for running shoes to users who exhibit similar characteristics to those identified in the analysis.

By leveraging aggregates in regression models, advertisers can optimize their marketing efforts in several ways:

  • Precision Targeting: Aggregates help advertisers pinpoint the most relevant audience segments for their campaigns, leading to higher conversion rates and ROI.
  • Resource Allocation: By focusing resources on users with the highest propensity to engage or convert, advertisers can maximize the impact of their advertising spend.
  • Continuous Improvement: As new data becomes available, regression models can be updated and refined to adapt to changing market conditions and consumer preferences.

In conclusion, aggregates play a vital role in the success of regression models for user targeting in digital advertising. By distilling complex datasets into actionable insights, aggregates enable advertisers to deliver more relevant and personalized experiences to their audience, driving better results and ultimately, business growth. Stay tuned for our upcoming deep dive into advanced techniques for optimizing regression models in digital advertising.