ML Case-study Interview Question: Predicting Customer Support Dissatisfaction Risk with Machine Learning Classification.
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Case-Study question
A large-scale platform receives thousands of customer support requests daily. There is a backlog of unresolved cases, each with attributes such as severity, type of issue, and customer information. They want to prioritize these cases with a Machine Learning model that predicts which ones are at high risk of poor customer satisfaction. There is a post-resolution survey with scores from 1 to 5, but the data is skewed, often incomplete, and sometimes reflects frustration with the product itself. How would you build a data-driven model to score customer support cases by likelihood of dissatisfaction, design the model’s architecture, integrate it into their workflow, and measure improvements in customer satisfaction?
Detailed solution
Defining the problem and gathering data
Data includes support case logs with fields like time to resolution, case severity, ticket creation date, customer region, product type, and support agent details. Each ticket’s final survey score (if present) is the indicator of satisfaction. Missing data is addressed by techniques like mean or median imputation or more advanced methods. When the backlog arrives, the objective is to predict which tickets need faster intervention.
Labeling approach
Survey responses supply the ground truth. Tickets with scores below a certain threshold (for example, 3 out of 5) can be labeled as “dissatisfied.” To handle skew, the model trains on historical data where responses exist. Class imbalance methods can be applied if too many tickets have high scores and only a small fraction are labeled poor.
Building the model
Features can include customer segment, severity level, time since ticket creation, product type, and agent workload. A supervised classification model fits well. Logistic Regression and Random Forest models are common. Logistic Regression provides interpretability with coefficients. Random Forest may capture nonlinear interactions. Gradient Boosted Decision Trees might also be considered for better accuracy.
Core logistic regression probability function
p is the probability of ticket dissatisfaction given feature vector x. w represents learned weights, and b is the bias term. Training optimizes w and b to minimize the classification error on historical labeled tickets.
Training and validation
Train-test splits and cross-validation are essential. Hyperparameter tuning applies grid search or Bayesian optimization over parameters like regularization strength in Logistic Regression or max depth in Random Forest. The key metric for optimization is area under the curve (AUC) or a custom cost-sensitive metric if false positives are expensive (unnecessary escalation) but false negatives risk unhappy customers.
Monitoring performance and updating the model
After deployment, ongoing ticket outcomes feed back into the model. Tickets flagged as high risk receive extra attention from senior agents. The model’s predictions and final survey results are compared to refine the feature set. Periodic retraining incorporates seasonal changes, new product lines, and shifting customer profiles.
Measuring success
Survey scores measure improvements. Compare post-intervention scores of flagged tickets versus similarly complex tickets not flagged. Track the percentage of tickets incorrectly flagged (false positives). A reduced backlog of dissatisfied cases and higher average survey scores for flagged tickets indicates success. If a 10 percent improvement in survey results is observed among flagged cases, that suggests the model is achieving its purpose.
What if the survey is biased or incomplete?
Many customers do not respond to surveys, and some only respond when extremely dissatisfied or extremely satisfied. Recalibrating the model with weighted sampling can mitigate bias. Agent feedback on high-risk tickets also provides alternative labels. Even partial signals like repeated call-backs or escalation status can serve as additional ground truth.
How do we handle region-based rating differences?
Certain regions might rate an average experience as a 3, while others might routinely give a 5. Normalizing or standardizing survey scores at a regional level addresses these cultural differences. Confirm that region-based re-scaling aligns with the distribution of other contextual features so that the model does not get confused.
Why not rely on manual prioritization by experts?
Human insights are valuable, but experts cannot feasibly process dozens of attributes in real time for each ticket. A model that automatically ingests severity, ticket age, customer spending level, and agent workload can reduce guesswork. Experts can refine the model’s features, respond to edge cases, and monitor anomalies.
How would you implement a pilot rollout?
Release the model for a subset of tickets and compare outcomes against a control group. Evaluate agent feedback and measure average satisfaction scores. If pilot data confirms improved results, scale gradually and track performance across multiple customer segments. Log every prediction and final resolution outcome for periodic audits and retraining.
What is your approach to production deployment?
Containerize the final model with an API endpoint. When a new ticket arrives, the system extracts relevant features and calls the model to get a probability of dissatisfaction. A scheduling system triggers any escalations for high-risk tickets. Store predictions and final resolutions for audits. Configure alerts if model metrics drift below a threshold, prompting retraining or feature engineering.
How do you ensure the model remains cost-effective?
Monitor the cost of false positives. Each unneeded escalation takes agent time. Confirm that the model’s threshold is set to minimize waste while preserving enough coverage of risky tickets. If the cost of a missed unhappy customer is high, adjust the classification threshold to capture more potential dissatisfaction cases.