Chapter 9 Interview Prep
9.1 Look alike Model walk thru
9.1.1 Situation
I worked on a look-alike modeling project where the goal was to predict new high-value customers for a marketing campaign. The challenge was to build a model that could identify potential customers who are likely to be similar to the existing high-value customers, using available demographic and behavioral data.
9.1.2 Task
The task was to train a machine learning model that scores potential customers based on their likelihood of being high-value customers, defined by our client. The output would be used to optimize user acquisition strategies.
9.1.3 Action
Data Preparation:
We started with two datasets: one for the high-value customers (labeled dataset) and another for the potential customers (scoring dataset).
The labeled dataset included demographic data, browsing behavior, engagement data, and other personal financial and interest attributes.
The scoring dataset contained the same types of features but did not include the target variable.
Feature Engineering:
Conducted exploratory data analysis (EDA) to identify significant features.
Generated new features using domain knowledge and interacting age and gender with other features.
Standardized and normalized continuous variables to ensure they had the same scale, which helps with model convergence.
Model Selection and Training:
Tried a range of machine learning algorithms: Logistic Regression, Random Forest, XGBoost, and CatBoost. Logistic Regression served as a baseline due to its interpretability.
Emphasized tree-based algorithms (Random Forest, XGBoost, CatBoost) because they handle high-dimensional, sparse data well, and can capture complex interactions between features.
Used a grid search with cross-validation to fine-tune hyperparameters such as the number of trees, learning rate, max depth, and minimum child weight for tree-based models.
Handling Class Imbalance:
Since the proportion of high-value customers was small, I applied techniques to handle class imbalance:
Used SMOTE (Synthetic Minority Over-sampling Technique) to create synthetic samples for the minority class.
Experimented with class weighting in algorithms to penalize incorrect predictions on the minority class more than the majority class.
Model Evaluation:
- The models were evaluated using metrics such as Precision, Recall, F1-Score, and ROC-AUC to balance between identifying true high-value customers and minimizing false positives.
- Conducted feature importance analysis, particularly for tree-based models, to identify which features contributed most to the prediction, helping in feature selection and further model refinement.
Model Scoring:
Once the model was finalized, we applied it to the scoring dataset. Since the scoring universe had no transactional or purchase behavior data, we relied purely on the engineered features based on available non-transactional attributes.
The model output provided a probability score for each potential customer indicating their likelihood of being a high-value customer.
9.1.4 Result
The final model, which was a tuned XGBoost, achieved a high ROC-AUC and F1-score, indicating strong performance in distinguishing high-value potential customers. This model was then used to rank and score potential customers for targeted marketing efforts, significantly improving customer acquisition efficiency.
This approach ensured a robust and scalable solution, adaptable to different datasets without relying on specific purchase or transactional data.
9.5 Do you prefer R or python?
I prefer Python because it has a wide range of libraries for data analysis, machine learning, and visualization, which makes it very versatile for different tasks. It’s also easy to integrate with other tools and platforms.
However, I do use R when needed, especially for specific statistical analysis and visualization tasks, as it has strong packages for these areas. I believe both languages have their strengths, and I choose based on the specific project requirements.
9.6 What is your main domain?
My main domain is data science with a strong focus on marketing analytics. I have experience across various areas, including predictive modeling, customer segmentation, and campaign evaluation. I enjoy working on projects that involve data-driven decision-making, whether optimizing marketing strategies, understanding consumer behavior, or any other area where data can provide valuable insights.
9.7 Is this work culture fast-paced? Do you deliver value quickly or what?
Yes, I do thrive in fast-paced environments and am comfortable delivering value quickly.
I believe in balancing speed with quality to ensure that the work is both timely and impactful.
In my current role, I often work under tight deadlines, and I’ve developed efficient methods to analyze data and provide actionable insights promptly.
9.8 Are you involved in any efforts convincing business stakeholders to adept models or analysis that you do
Yes, I am often involved in convincing business stakeholders to adopt models or analyses that I develop.
For example, in a recent project, I created a predictive model for customized user bids, which initially met some skepticism.
I presented clear A/B test results that showed a 15% increase in conversion rates and a 10% reduction in costs. By explaining the value in simple terms and showing how it directly impacts their goals, I was able to get support for the model.
9.9 Have you been in a situation where you feel like the model is the right way to go but either client or manager that you need to convince?
Yes, I’ve faced situations where I strongly believed a model was the right approach, but I needed to convince either a client or a manager.
For instance, I once advocated for a customized bidding model based on predictive analytics. Despite initial skepticism, I presented data-driven insights and A/B test results that demonstrated significant improvements in conversion rates and cost efficiency.
By clearly explaining the model’s benefits and providing evidence of its effectiveness, I successfully gained their support and implemented the model.