And a lot more information on that 8-K -
Sounds like a solid business plan - $HLLK
About Jubilee
Executive Summary
Our company’s proprietary SEM platform automates the creation, optimization, and scaling of digital advertising campaigns across key platforms like Facebook, GDN, and Taboola. By leveraging a direct feed from Yahoo’s partner network, we access real-time data that significantly enhances campaign performance. Through the integration of machine learning and AI, our platform optimizes ad spend, scales profitable campaigns, and pauses or restructures underperforming ones in real time.
In addition to our technological advancements, we are pursuing a strategic reverse merger into a fully reporting public shell company. This move will enable us to expand rapidly, gain access to capital markets, and position ourselves for a future acquisition. Our reverse merger, combined with a potential acquisition, provides a pathway for sustained growth and increased market presence.
SEM Automation and Machine Learning
Our platform integrates machine learning and AI to automate the entire SEM process, from keyword research to campaign generation and optimization. This system eliminates the need for manual intervention, drastically reducing the time and resources required for effective SEM management.
? Keyword Research: Our platform analyzes massive datasets of search queries, user behaviors, and historical performance metrics to identify high-intent keywords that are most likely to convert. Unlike traditional methods of keyword selection, our machine learning algorithms continuously refine keyword targeting by learning from the performance of active campaigns, adjusting in real time. This enables us to stay ahead of changing trends and user behaviors, ensuring that our campaigns are always aligned with current search demand.
? Campaign Generation: The platform generates thousands of ad variations tailored to specific audience segments. Using advanced data analytics, our system determines the best combination of ad copy, visuals, and keywords to maximize engagement. We analyze historical performance across similar campaigns and identify which variables contribute most to conversions, allowing us to create highly personalized and optimized ads at scale.
? Optimization Algorithms: Our AI-driven optimization algorithms constantly analyze live data to adjust bids, placements, and budgets in real time. This ensures that ad spend is directed toward the most profitable opportunities while minimizing waste. Through predictive modeling, the platform anticipates shifts in user behavior patterns, adjusting the campaign strategy preemptively to maintain performance.
? Scalability: One of the key strengths of our platform is its ability to automatically scale profitable campaigns. Once the system identifies a campaign that is delivering strong results, it rapidly increases the budget and bid amounts to capitalize on the momentum. Conversely, underperforming campaigns are paused or restructured, ensuring that ad spend is allocated efficiently.
? Yahoo Partner Network Feed – Our access to a direct feed from Yahoo’s partner network gives us a competitive edge by providing access to first-party data, allowing us to target high-quality traffic that many other advertisers cannot reach. The integration enables us to refine our targeting strategies and optimize campaigns in real time, resulting in higher engagement and conversion rates. Additionally, this data allows us to build detailed audience profiles that help improve the precision of our keyword targeting, leading to better overall campaign performance.
Technical Overview
Predictive Keyword Research Using Random Forest Regressor
We utilize a random forest regressor to process and analyze vast amounts of keyword data, often encompassing hundreds of thousands of data points. A random forest regressor is a machine learning algorithm used for predicting continuous values. It builds multiple decision trees during training, each using different subsets of the data, and averages the results to make predictions. This approach improves accuracy and reduces overfitting by leveraging the collective decision-making of several trees, which helps handle the complexity of large datasets.
The random forest model is particularly effective for our use case because it excels at handling large, complex datasets and can account for non-linear relationships between variables. Our model factors in key metrics such as search volume, bid cost, competition level, and historical performance to make accurate keyword predictions.
? Feature Engineering: Our model utilizes a wide array of features, including search intent, seasonality, and location-specific factors, to improve the precision of its predictions. By incorporating dynamic features that adapt to user behavior in real time, our system is able to consistently identify the highest-performing keywords for each campaign. This continuous learning process ensures that our campaigns are always aligned with the most current trends and opportunities.
? Scalability: The random forest model can handle extremely large datasets, making it ideal for processing the vast amounts of data generated by SEM campaigns. This scalability allows us to quickly adapt to new data inputs, ensuring that our keyword targeting remains sharp even as market conditions change.
Timeseries Prediction Using Support Vector Regressor (SVR)
For predicting profitability based on time-series data, we utilize the Support Vector Regressor (SVR). SVR is a powerful machine learning model that excels at capturing non-linear relationships in the data. By using a kernel trick, SVR can map input data into higher-dimensional spaces, allowing it to capture complex patterns and trends in campaign profitability over time.
? Time-of-Day Optimization: By using SVR to identify non-linear trends in profitability, we can predict the optimal time windows for launching campaigns, ensuring that ad spend is allocated during periods with the highest likelihood of conversions. This model helps us capture profitability dynamics more accurately than linear models, which may miss crucial non-linear relationships.
Traffic Quality Improvement Using Support Vector Machine (SVM) Classifier
To improve traffic quality and block underperforming sites, we use a Support Vector Machine (SVM) Classifier. SVM is a supervised learning model that finds the optimal boundary between different classes of data points. It is particularly effective in high dimensional spaces and works well when there’s a clear margin of separation between classes. We use SVM to classify traffic sources as “good” or “bad” based on various engagement and performance metrics.
? Traffic Quality (TQ) Scores: The SVM classifier evaluates traffic sources based on factors such as engagement rates, conversion potential, and historical performance. It assigns a quality score to each site, allowing us to block low-quality traffic while directing ad spend to high-quality sources. This classification process ensures that our advertisers receive the best possible traffic for their campaigns.
Market Data 
Markets 
AI
