Recommendation & Next-Best-Action
The right offer, content or action for each customer, in real time.
A recommendation and decisioning engine that chooses what to show, offer or do next for every customer. It learns from each interaction and stays within the business rules you set.
01 · Overview
Everyone gets the same offer
Static campaign rules and "customers also bought" widgets treat everyone the same. Relevance stays low, fatigue goes up, and revenue is left on the table.
Built with
- Neural recommenders
- Bandits
- A/B testing
Deployment
On-premise, private cloud or in-region.
02 · How it works
Decide, don’t just rank
- 1
Learn preferences
User and item embeddings from collaborative filtering, neural recommenders and sequence models.
- 2
Decide
Next-best-action combines propensity, value, eligibility and contact-fatigue rules into one recommended action, including "do nothing".
- 3
Explore safely
Multi-armed bandits test new options on a small share of traffic and shift towards what works.
- 4
Prove it
Built-in A/B testing and holdout groups measure true incremental lift.
- Customer contextBehaviour, holdings, channel
- Candidate generationProducts, content, actions
- Ranking modelPropensity × value
- Rules & eligibilitySuitability, fatigue caps
- ChannelsApp · web · branch ↺ feedback
03 · Capabilities
Where it’s used
- Cross-sell and upsell in banking apps
- Product recommendations across web and mobile
- Content recommendations for media platforms
- In-app guidance and onboarding flows
- Real-time promotion targeting
04 · Outcomes
What we measure
- Higher conversion
- Incremental conversion vs. holdout
- Bigger baskets
- Average order / products held per customer
- Less fatigue
- Opt-out and complaint rates
- Faster learning
- Time for a new offer to reach its best audience
Example scenarios
Illustrative- Digital bankPersonalised product offers in the mobile app, within suitability rules.
- Online retailerReal-time recommendations across web and app.
- Media platformContent recommendations that increase engaged time.
- Contact centreNext-best-action prompts for agents during customer calls.
05 · Governance
Explainability & guardrails
Controls built in
- A "because you…" reason on every recommendation
- Eligibility, suitability and fair-treatment rules applied first
- Contact frequency caps across all channels
- Holdout groups and A/B tests on every change
Technology
- Python
- TensorFlow
- PyTorch
- implicit
- Kafka
- Redis
- ClickHouse
- PostgreSQL
06 · FAQ
Common questions
Can it respect regulatory suitability rules?
Yes. Eligibility and suitability rules are applied before any recommendation is shown.
What if we have little customer data?
We start with content and context signals and popular items, and the bandit layer learns quickly from live interactions.
Does it work across channels?
Yes. One decision service feeds app, web, email, branch and contact centre, so customers get a consistent next step.
Make every interaction count.
Start with one channel and measure the lift against a holdout.
Start a project
