Reference engagement
AI-Powered Customer Segmentation for Targeted Campaigns
A B2B software company cut campaign cost-per-qualified-lead by 40% after replacing manual segmentation with an ML-based clustering pipeline.
// Delivery pattern
This page describes a representative engagement of this shape — how the system is scoped, built, and handed over. Specific figures reflect typical outcomes of the pattern when delivered with the operational discipline described on the About page. Named customer engagements are shared under NDA on request.
Engagement shape
Typical outcomes
- ✓ 40% lower cost-per-qualified-lead vs prior-quarter baseline
- ✓ 2.3× email open rate on high-propensity segments
- ✓ Segmentation latency under 4 minutes per full account refresh
Stack
- — pandas + scikit-learn (feature engineering)
- — k-means with silhouette optimisation
- — Airflow DAG (weekly scheduled refresh)
- — Salesforce REST API (CRM integration)
Typical timeline
4 weeks
kick-off to handover
Risks & guardrails
- Cluster instability — validate segment count with silhouette scoring pre-deployment
- CRM write latency at volume — test full account throughput before launch
Challenge
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.
Approach
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.
Results
- 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)
Technical stack
- Feature engineering: pandas + scikit-learn
- Clustering: k-means with automated silhouette optimisation
- Scheduling: Airflow DAG, weekly refresh
- CRM integration: Salesforce REST API
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