Helping BTL focus where it counts —
across every dealer segment.
Our AI splits BTL’s dealer network into four high-impact segments—driving precise strategies, smarter resource use, and exponential partner value
Machine Learning
Data
Strategy
Organizational Change & Talent
Planning and Delivery
Systems Implementation- Python, Pandas, Scikit-Learn
- K-Means & cluster validation (silhouette, DB index)
- SHAP for interpretability
- PostgreSQL feature store
- React dashboard & Docker microservices
THE VISION
BTL needed a data-driven way to engage a diverse dealer network with tailored offers and support.
BTL’s dealers and distributors span from low-frequency volume orders to high-touch strategic partners. A one-size-fits-all approach led to wasted marketing spend and missed growth. To maximize partner lifetime value, BTL required clear, actionable segments based on real behavior—with insights that guide promotions, support allocation, and channel strategies.
THE SOLUTION
A balanced blend of business insight and technical rigor to create actionable partner segments for BTL.
Embedding AI into Partner Strategy
BTL’s leadership recognized that their nationwide dealer network—from small-volume outlets to flagship distributors—required more than static reports.
They needed a repeatable segmentation engine embedded directly into CRM and marketing tools, so each partner interaction could be tailored, timely, and data-driven.
Swabhav ran discovery workshops with channel managers, sales heads, and marketing teams to define success metrics for engagement uplift, order value growth, and support efficiency—ensuring the AI roadmap aligned with BTL’s business goals.
Co-Design & Rapid Prototyping
Rather than hand off a finished model, Swabhav and BTL co-created through iterative sprints.
First, we mapped key dimensions—order frequency, revenue share, product mix, and support touchpoints—into a working prototype. BTL stakeholders validated early clusters, suggesting refinements like regional nuances and promotional responsiveness.
This collaborative process accelerated buy-in, surfaced hidden data quality issues, and produced a segmentation blueprint that the entire organization trusted from day one.
Robust Technical Pipeline & Explainability
Our engineering team ingested BTL’s historical transactions, CRM logs, and service tickets into a centralized feature store.
Over twenty derived features—such as spend-per-order, growth trends, and loyalty indices—were standardized. We ran K-Means (k=4), selected via elbow, silhouette (0.63), and Davies-Bouldin (0.46) validation, to reveal four distinct partner profiles.
To demystify the clusters, we integrated SHAP explainability: global plots show which features drive each segment, while individual force plots reveal why any given dealer sits in its cluster.
Real-Time Activation & Measurable ROI
The final solution deploys as Dockerized microservices with REST endpoints feeding a React dashboard.
Marketing and sales users see up-to-the-minute segment assignments and feature-importance insights, then trigger customized campaigns or support workflows with a single click.
In the first quarter post-launch, BTL reported a 20% uplift in partner engagement, a 15% increase in average order value, and a 30% reduction in overhead by focusing resources only where they matter most.
Priya Sharma,
Head of Channels, BTL
BUSSINESS IMPACT
Priority Support: Assign dedicated account leads to 15% Frequent High-Spenders.
Customized Communications: Roll out tiered loyalty programs and personalized training for 35% Moderate Spenders.