Every customer in your database is telling you exactly what they want — through their purchase patterns, browsing behavior, and engagement signals. Most businesses aren't listening. Our AI-Powered Customer Segmentation engagement builds the ML infrastructure to turn that behavioral data into automated, personalized revenue.
We build RFM segmentation models, churn prediction algorithms, and LTV forecasting models trained on your actual customer data. Every model feeds directly into your CRM and marketing tools, so the segmentation drives action — not just insights. The result is personalization at scale: the right message, to the right customer, at exactly the right moment.
Key Deliverables
Unified customer data model | RFM scores for every customer | Churn prediction with automated alerts | LTV prediction and tier scoring | CRM integration and personalization rules | Monthly segmentation report
Our Process
Weeks 1–2: Customer data audit and unification. Weeks 3–5: Model build and validation. Weeks 6–7: CRM integration and personalization rules. Week 8: Go-live and team enablement.
✓Prioritized 90-day implementation roadmap
✓Tool selection & architecture design
✓Full build, testing & deployment
✓Team training & documentation
✓90-day post-launch support
Our Process
1. Customer Data Audit
We audit your customer data across every system: transactions, engagement, support, and marketing. We identify gaps, enrich what's missing, and build the foundation for modeling.
Weeks 1–2
2. Model Design & RFM Analysis
We build your RFM scoring framework and design the segmentation model architecture. You see the initial customer clusters before we go deeper.
Weeks 2–4
3. Predictive Scoring & Integration
We deploy predictive LTV and churn scoring models and wire them directly into your marketing automation. Every segment triggers personalized flows.
Weeks 4–6
4. Campaign Launch & Optimization
We launch your first segmented campaigns, monitor performance for 2 weeks, and tune the models based on real conversion data. Full documentation and training delivered.
Weeks 6–8
Discovery & Workflow Audit
We spend 1–2 weeks mapping your current processes, identifying automation opportunities, and calculating ROI potential for each. You get a prioritized list of workflows by business impact, not technical complexity.
Architecture Design & Sign-off
We design the full workflow architecture — tool selection, data model, error handling logic, and security approach. You review and approve before we write a single node.
Build, Test & Staging
Full workflow build in a staging environment. We run every edge case, test failure modes, and validate outputs against your real data before touching production.
Production Deployment & Monitoring
Coordinated production cutover, monitoring setup, and live observation for the first 72 hours. Full handoff documentation delivered within 5 days.
FAQ
How long does an automation engagement typically take?
Most automation engagements are complete in 6–12 weeks from signed contract to production deployment. The timeline depends on complexity — a single workflow can ship in 3 weeks, while multi-system enterprise builds take 10–14 weeks. We’ll give you a specific timeline after the discovery audit.
Do we need technical staff to maintain the automations after delivery?
No. We build with maintainability in mind. Simple parameter changes (like updating an email template or changing a threshold value) can be done by any non-technical team member. For structural changes, our ongoing retainer clients get direct access to our team. Otherwise, you can return to us for a scoped change request.
What’s the typical investment for an automation engagement?
Scoped automation projects start at $8,500 for a single workflow build. Multi-workflow packages range from $18,000–$45,000 depending on complexity and integrations. Enterprise implementations are priced on discovery. All engagements include a full ROI analysis before we ask you to commit — if we can’t justify the investment on paper, we’ll tell you.