Customer behaviour analytics
Find meaningful segments across POS, web sessions, loyalty activity, and campaign response. See which patterns predict a second purchase.
Typical inputsPOS, CRM, web and loyalty events
Customer analytics Singapore
Stop guessing. Start knowing what your customer wants next.
Your customers expect Amazon-level relevance. If every shopper, guest, member, or subscriber still receives the same message, attention is already slipping.
Lorong AI designs and implements customer analytics engines for Singapore retail, hospitality, media, fitness, telco, and marketplace teams. See the next action clearly.
One customer. Clearer next steps.
Built for customer-facing teams
AI that understands your customer better than your best store manager.
We connect the signals already held in your systems, then turn them into decisions your marketing, product, and commercial teams can act on.
Recommendation engines
Collaborative filtering and session signals place the right product, article, room, or class in front of each visitor.
Marketplace exampleAverage order value lifted by 23%
Dynamic pricing AI
Balance demand, availability, seasonality, and customer sensitivity with pricing rules your commercial team can review.
Hospitality pilotRevPAR increased by 7%
Churn prediction
Score customer risk before cancellation or inactivity. Give retention teams a reason, a timing signal, and a sensible next action.
Telco exampleInvoluntary churn reduced by 18%
Member engagement analytics
Help gyms, clubs, and subscription brands spot fading routines and shape messages around real attendance and usage behaviour.
Useful forMembership, app and visit data
Personalised marketing AI
Choose the audience, offer, channel, and send time from observed behaviour. Keep the final approval with your team.
Designed forRetail, media and digital campaigns
Singapore retail case
How a luxury retailer turned browsers into high-value repeat buyers.
The retailer had rich POS data and a busy online catalogue, yet product suggestions stopped at broad customer segments. Lorong AI connected collaborative filtering and session-based recommendations to the existing POS and commerce stack.
The team could now respond to current intent while keeping product availability and store context in view.
"The recommendations gave our teams a sharper reason to speak with returning customers. We saw more thoughtful cross-category purchases within the first quarter."Digital Director, Singapore luxury retail group
Cross-category purchase rate
Before recommendations41%
After recommendations60%
+19%increase in cross-category purchases
Measured after integrating product affinity signals with store and online purchase history.
Retention intelligence
Spot the 5% of customers who generate 30% of your churn before they leave.
Our churn prediction framework turns transaction history and app interactions into a real-time score. Gradient boosting identifies the behaviours behind risk, then a scoring endpoint places the signal where your retention team already works.
Feature engineering from purchases, visits, cancellations, support contacts, and app activity. Useful for telcos, gyms, subscription services, and loyalty programmes in Singapore. Scores arrive with reasons, so a retention offer does not feel random.
68% churn probability
Hover to see the score move into the high-risk zone.
Trust by design
Personalisation that respects privacy.
Every customer analytics project starts with clear data boundaries, consent choices, and accountable model ownership.
Read about AI governancePDPA-aware foundations
Consent management hooks and purpose-based access are included from the first data map.
Human review points
Your teams can inspect model reasons, adjust rules, and pause an automated action.
Processing options
Choose cloud, private network, or on-prem processing to match your governance needs.
A practical first conversation
See a personalisation engine built for your brand in 45 minutes.
Bring one customer problem and the systems behind it. We will map the signals, show where a model fits, and outline a sensible first release.
No generic presentation. A working discussion about your data and commercial goal.