AI-Powered Customer Segmentation for Targeted Campaigns

A B2B software company with 12,000 active accounts ran quarterly outbound campaigns using three broad segments: company size, industry vertical, and recency of last purchase. Conversion rates were declining, and the marketing team suspected that behavioural signals in the product usage data were going unused.

G|AI Works ran a four-week engagement:

Week 1 — Data audit: Mapped all available signals: CRM data (firmographics, deal history), product telemetry (feature usage, login frequency, support tickets), and campaign response history. Identified 23 predictive features after removing correlated and low-coverage columns.

Week 2 — Segmentation model: Applied k-means clustering with silhouette scoring to identify the optimal number of behavioural segments. Results: six distinct clusters, each with a significantly different propensity to expand (increase contract value) versus churn.

Week 3 — Campaign mapping: Worked with the client's marketing team to design segment-specific messaging. High-expansion segments received use-case-led content. High-churn-risk segments received success and support-led content. Messaging was A/B tested across two segments before full rollout.

Week 4 — Operationalisation: The segmentation pipeline was deployed as a weekly scheduled job. Salesforce received segment labels via API, enabling Sales to filter by segment in their outreach workflows.

• 40% reduction in cost-per-qualified-lead compared to previous-quarter baseline
• 2.3× increase in email open rate for high-propensity segments
• Segmentation latency: under 4 minutes per full account refresh (12,000 accounts)

• Feature engineering: pandas + scikit-learn
• Clustering: k-means with automated silhouette optimisation
• Scheduling: Airflow DAG, weekly refresh
• CRM integration: Salesforce REST API