Customer 360 & Behavioral Insights
From fragmented records to holistic insights; building a single view of the customer
At L&T Finance, we harness advanced data engineering and behavioral analytics to construct unified customer profiles and generate actionable insights. By combining structured data (applications, repayments, bureau records) with unstructured sources (call transcripts, feedback, digital footprints), we are able to better understand intent, personalize offerings, and proactively address customer needs.
Advanced Technology Groups
We consolidate information across applications, bureau histories, repayment records, call center logs, and external APIs into a comprehensive 360-degree customer view. This unified profile underpins risk scoring, marketing campaigns, and service personalization.
Benefits:
- Elimination of fragmented or duplicate customer records
- Faster, more accurate underwriting decisions
- Enriched customer data for cross-sell and upsell strategies
Our unsupervised clustering models group customers by product affinity, responsiveness, and interaction style. These insights help personalize credit offers, nudge strategies, and repayment reminders.
Applications:
- Segmenting customers into high-value, at-risk, and dormant clusters
- Personalizing loan terms and repayment structures
- Improving campaign ROI by targeting the right segments
We visualize digital usage and repayment behavior across time to detect friction points and opportunities. Heatmaps highlight repayment punctuality, app logins, and feature engagement, informing both product design and intervention strategies.
Metrics tracked:
- On-time repayment vs overdue ratios by cohort
- Daily/weekly login activity trends
- Drop-offs during digital journeys
By mining call center transcripts, app reviews, and customer complaints with NLP, we surface common pain points and customer sentiment. These insights flow back into process design, enabling faster resolution and more customer-friendly service journeys.
Capabilities:
- NLP-driven sentiment classification
- Complaint clustering and root-cause tagging
- Early-warning indicators for churn risk
Featured Projects
Understanding Customer Emotions
Mining Feedback to Improve Service
Heatmaps for Engagement Insights
Visualizing Patterns of Timely and Late Repayments
Smarter Customer Segmentation
Data-driven Cohorts for Personalization
