CHALLENGES
The volume of customers, students, and patients exceeds what any team can attend to individually. The default becomes segmentation, batch outreach, and manual judgment calls, while the signal that would tell each team what to do next, for each person, gets lost in the volume.
The same message goes to everyone, regardless of where each person sits in their journey.
By the time the signal is visible, the window to act has already closed.
Reps, advisors, and care teams make judgment calls manually across hundreds or thousands of individuals every week.
High-value prospects, at-risk students, and disengaging patients slip through because nobody had the right signal at the right time.
Approach
We build Next Best Action systems, combining behavioral, transactional, and contextual data into models that recommend the highest-impact next step for each individual.
01
Use case scoping and data readiness assessment
02
Model development, training, and validation against historical outcomes
03
Integration into the CRM, SIS, advancement, or clinical systems
04
MLOps infrastructure that keeps recommendations accurate as patterns shift
The output is a recommendation that surfaces inside the tools your teams already use, at the moment they need it, along with the data infrastructure and MLOps practices that keep accuracy steady.
Applications
Variant
Built for
Variant
Built for
Marketing and revenue teams driving customer engagement and conversion
Variant
Built for
Higher Education enrollment and advancement teams prioritizing outreach across recruitment, retention, and giving
Variant
Built for
Healthcare and payer teams recommending timely clinical interventions, care pathways, and member engagement
Accelerator
For organizations ready to move beyond static rules and manual judgment calls, we deploy the Next Best Action Accelerator, a production-ready recommendation system built on OneSix's reinforcement learning framework for action recommendation at scale.
What's included
OneSix's reinforcement learning framework for action recommendation at scale. Spark-based pipeline components that combine into a configurable, transparent decision system, deployable in a single Python application or as the foundation for a larger solution.
Reward functions, action spaces, and exploration parameters tuned to your business objective, so the model optimizes for what your business actually cares about: conversion, retention, intervention timeliness, or any defined outcome.
Recommendations surface at the moment of decision inside the systems your teams already use. Spark-based distributed compute handles millions of decisions per day with sub-second response times.
Deployed models, inference infrastructure, monitoring, and the feedback loop that lets the system learn from each interaction. The model gets sharper with every response.
Process
01
Use case scoping, reward function definition, and data readiness assessment
02
RL framework setup, action space definition, and exploration parameter tuning
03
Offline evaluation against historical decisions and comparison to current baseline
04
Production rollout, real-time inference, and feedback loop activation
FAQ
It depends on the use case but typically includes behavioral data, interaction history, demographic or profile data, and outcome data from past engagements. We assess your data readiness as part of engagement scoping and flag any gaps early.
Recommendations surface inside the systems your teams already use: CRM platforms like Salesforce or HubSpot, student information and advancement systems, EHRs, or marketing automation tools. Integration is engineered into the build from scoping forward, so the recommendation lands at the moment of decision.
Every engagement includes MLOps design and model monitoring. We build the retraining triggers and performance tracking that keep recommendations accurate as behavior and context shift.
Segmentation and rules work when the action depends on a handful of fields and the logic can be written down. Next Best Action handles the cases they can't: many variables interacting, behavior that shifts over time, and decisions where the right answer for one individual differs from the right answer for a similar individual. The model finds patterns that rules miss.
Through controlled comparison against the current baseline: a holdout group that gets standard outreach versus a group that gets model-driven recommendations. The lift in the metric you care about (conversion, retention, engagement, intervention timeliness) is measured continuously as the model evolves.
