End-to-End Mini Case Study: From Raw Data to a Deployed Model
This blog is a high, level end, to, end walkthrough: starting from raw business data, building a model, and getting it into production in a way that actually helps teams make better decisions.
Step 1: Define the problem and metric
Start with a clear, actionable question, not “do some ML”.
Example: “Can we predict which customers are going to churn in next 30 days so success and marketing teams can intervene?”
Decide on:
Target: churn in 30 days (yes/no). Population: active customers with at least N weeks of history. Primary metric: recall at a fixed precision or uplift vs a simple baseline (e.g., “contact customers who haven’t logged in for 30 days”).
Step 2: Build the dataset and features.
Bring data from your warehouse or lake together:
Events: logins, feature usage, sessions. Commercials: plan, tenure, MRR/ARR. Support: tickets, NPS/CSAT, escalations. Engineer features such as:
Activity: sessions in last 7/30 days, change vs previous period. Engagement: number of core feature actions, breadth of feature use. Risk signals: recent critical tickets, negative feedback. Ensure that each row corresponds to a customer at a specific “observation date, ” with features only using data available up to that point (to avoid leakage).
Step 3: Train and evaluate the model
Split by time (train on older cohorts, validate on more recent ones) and set up a simple baseline (e.g., “top X% by inactivity”). Then:
Start with a simple model (logistic regression or tree, based model) using cross, validation on training data. Track metrics aligned to business: precision, recall, F1, ROC AUC; compare vs baseline.
Apply explainability tools (feature importance, SHAP) to ensure that the model is discovering sensible patterns. Only proceed if the model significantly outperforms the baseline and behaves reasonably across segments.
Step 4: Design how the model will be used
A model without a clear consumption pattern is a science experiment.
Decide:
Output: a daily churn score per customer plus a simple risk band (low/medium/high).
Consumers: CSMs, marketing automation, or product surfaces.
Workflow: CSMs get a weekly list of “high risk, high value” customers with reasons (top SHAP features). Marketing triggers a retention campaign for medium risk customers. Keep the first version as simple as possible and the human in the loop; you can automate more once you have confirmed the value.
Step 5: Deploy and monitor
Productionise the pipeline fully:
Data pipeline: scheduled job builds the feature table (e.g., once per day). Inference: batch job scores all eligible customers and writes results back to warehouse/CRM.
Exposure: dashboards and views for CSMs; integration with email/CRM tools. Monitoring to be in place from day one:
Technical: pipeline success, data freshness, row counts, schema changes.
Model: score distributions, churn rates per risk band, performance on recent cohorts.
Regularly compare results for contacted vs non, contacted groups to estimate uplift and decide whether to iterate or extend the use case.
