The right offer for each customer, in real time

Intelligent recommendation engine that identifies the best offer, product or action for each customer at the right moment.

Overview

The NBO accelerator uses machine learning models that analyze each customer's behavior, history and context to recommend the offer with the highest conversion probability.

Services

Recommendation engine

Personalized recommendation engine combining collaborative filtering, content-based, and hybrid models for maximum relevance.

Propensity scoring

Models that calculate each customer's purchase propensity for each product or service in real time.

Offer personalization

Algorithms that define personalized pricing, discounts, and conditions to maximize conversion and margin simultaneously.

Next Best Action

Recommendation of the next best action for each customer — purchase, upsell, retention, or engagement.

Real-time Decisioning

Millisecond decisions integrated into touchpoints — website, app, email, call center — for immediate action.

Offer A/B Testing

Experimentation framework to test and validate offers with control groups and lift metrics.

Where we operate

Where we operate with NBO (Next Best Offer)

E-commerce

Product recommendations on the website, cart cross-sell, and personalized emails with highly relevant offers.

Banking & Insurance

Personalized offers for financial products, insurance, and investments based on the customer's profile and life stage.

Telecommunications

Plan upselling, complementary service offers, and retention actions based on churn propensity.

Physical retail

Personalized offers via app and POS based on purchase history and geolocation.

Marketplace

Personalized product ranking, seller recommendations, and segmented coupon offers.

Media & Entertainment

Content recommendations, personalized subscription offers, and preference-based engagement.

How we work

Customer Data Analysis

Mapping available data: transactional, behavioral, demographic, and brand interaction data.

Feature Engineering

Building predictive variables: recency, frequency, value, preferences, and browsing context.

Engine Training

Training recommendation and propensity models with cross-fold validation and hyperparameter optimization.

Real-Time Integration

Engine deployment with low-latency APIs integrated into contact channels and marketing platforms.

Continuous Optimization

Monitoring conversion metrics, A/B testing strategies, and retraining with new data.