What is OfferFit? AI-Driven Personalization Explained
Published on November 05, 2025/Last edited on November 05, 2025/16 min read
Published on November 05, 2025/Last edited on November 05, 2025/16 min read
Personalization has become one of the biggest levers marketers can pull for growth. But delivering truly individualized experiences and AI-driven personalization has always come with trade-offs—long testing cycles, manual segmentation, and campaign fatigue.
BrazeAI Decisioning Studio™, previously known as OfferFit by Braze, changes that equation. Built on reinforcement learning, it’s an AI decisioning engine that automates experimentation and makes one-to-one personalization possible at scale. Acting as the “brain” between data systems and customer engagement platforms, BrazeAI Decisioning Studio™ continually learns the right message, timing, channel, and incentive for each individual customer to personalize and customize every aspect of customer communication.
Now integrated into the Braze customer engagement platform, BrazeAI Decisioning Studio™ complements the platform’s journey orchestration and real-time delivery capabilities. Together, they give marketers a powerful way to replace guesswork with continuous optimization—unlocking more revenue, stronger relationships, and campaigns that adapt in real time.
Contents
OfferFit personalization vs. traditional A/B testing
The benefits of OfferFit marketing automation
OfferFit and Braze in action with real-world results
Kayo Sports personalizes the playbook for 1:1 fan engagement
4 High-impact use cases for OfferFit AI
Considerations for scaling with OfferFit personalization
Getting started with OfferFit AI
OfferFit, which is now the BrazeAI Decisioning Studio™, is an AI decisioning engine designed to take the guesswork out of personalization. Instead of relying on broad segments, static rules, or one-off A/B tests, BrazeAI Decisioning Studio™ uses AI agents and reinforcement learning to make individualized decisions for every customer.
The solution acts as a decisioning layer in your martech stack. It ingests customer data from sources like CDPs or data warehouses, evaluates the available actions—whether that’s an email, push notification, discount, or timing—and chooses the best option for each person. With every interaction, the system learns and refines, continuously improving campaign outcomes.
This “self-learning campaign” approach utilizes self-learning algorithms and means marketers no longer need to manually test every variable. BrazeAI Decisioning Studio™ experiments across dimensions like channel, subject line, frequency, creative, and offer type, optimizing against the key performance indicators (KPIs) that matter most—whether that’s conversions, revenue per user, or customer lifetime value.
BrazeAI Decisioning Studio™ engine is powered by reinforcement learning, a type of machine learning where agents learn by experimenting and adjusting based on results. Instead of running a handful of static A/B tests, BrazeAI Decisioning Studio™ continuously runs automated experimentation across multiple variables at once, finding the right action for each individual customer.
Getting started involves three main steps:
Under the hood, BrazeAI Decisioning Studio™ relies on contextual bandits, an advanced form of reinforcement learning. Unlike traditional multi-armed bandits that look for one “winner” across a group, contextual bandits determine the optimal choice for each individual, using context from every available data point.
What you get is a system that runs millions of micro-experiments in real time, constantly improving.
Marketing teams face testing fatigue, resource bottlenecks, and the pressure to deliver personalization at scale. Budgets tighten while expectations rise, and CFOs demand a clear link between AI investments and measurable financial outcomes.
Three shifts make this the moment for BrazeAI Decisioning Studio™:
The payoff is clear. According to research from McKinsey, personalization can reduce acquisition costs by up to 50%, lift revenues by 5-15%, and increase marketing ROI by 10-30%. Companies with faster growth rates derive 40% more revenue from personalization than slower-growing peers.
Most marketers know the grind of traditional testing. A/B tests pit two variants against each other, dividing the audience in half and waiting until a clear winner emerges. It’s simple, but painfully slow. To test more than two variants, teams either run multiple sequential A/B tests or move to multivariate testing—where several variants are tested in parallel.
Multivariate testing is faster, but it carries the same limitations:
To address these constraints, multi-armed bandit (MAB) algorithms emerged. Instead of splitting audiences evenly, MABs allocate more traffic to better-performing variants while still leaving room to explore others. This makes them more efficient and adaptive than static tests—but they still only optimize at the segment or whole customer population level.
Contextual bandits take the next step. By incorporating customer attributes (purchase history, preferences, location), variant metadata (style, price point, timing), and even environmental factors (seasonality, holidays), contextual bandits learn the right action for each individual in context. Unlike MABs, they don’t just chase the overall best variant—they tailor decisions to the person.
BrazeAI Decisioning Studio™ builds on contextual bandits with a “community of bandits” approach, breaking down decisions into separate dimensions—such as channel, timing, subject line, creative, or offer. Dedicated agents optimize each dimension, then work together to determine the optimal combination for every customer.
BrazeAI Decisioning Studio™ personalization learns continuously, adapts in real time, and personalizes at scale—replacing the cycle of A/B fatigue with AI decisioning, and resource bottlenecks with self-learning campaigns that improve outcomes with every send.
Faster testing is great, but what marketing teams really want are outcomes that compound over time. With self-learning algorithms, BrazeAI Decisioning Studio™ unlocks that potential, by turning every campaign into an ongoing cycle of learning and improvement. The benefits include:
Together, these benefits transform marketing automation from a rules-based workflow into an AI-driven personalization system that gets sharper with every interaction. Instead of chasing short-term wins, marketers build a foundation where performance naturally improves as the system runs.
BrazeAI Decisioning Studio™ solves two sides of the personalization challenge: it acts as the decisioning brain, continuously experimenting and selecting the right action for each customer and, natively integrated with your engagement and orchestration layer on Braze, delivers those actions instantly across email, SMS, push, in-app, and web.
The combined reinforcement learning and real-time customer journey orchestration creates a system where every customer journey is both adaptive and scalable. BrazeAI Decisioning Studio™ agents determine the best message, timing, and channel, while Braze executes those decisions as part of coordinated, cross-channel journeys.
One example of this in action is Kayo Sports, Australia’s largest and fastest-growing sports streaming service. By pairing BrazeAI Decisioning Studio™, Kayo has built a personalization engine capable of delivering 1.2 million daily variations of customer communications—a leap from just 300 previously.
Launched as part of the Foxtel Group (now a DAZN company), Kayo Sports is Australia’s largest and fastest-growing sports streaming service, offering instant access to more than 50 sports live and on demand. With over 30,000 hours of sports, documentaries, and entertainment shows from FOX SPORTS Australia and ESPN, Kayo has built a reputation for delivering a cutting-edge streaming experience and cultivating a customer-first culture.
In the crowded streaming market, Kayo Sports knew that retention and loyalty would depend on delivering truly personalized experiences. Early efforts included tailored sign-up flows and curated in-app content, but customer engagement campaigns were limited by manual rules and workflows. The team wanted to move beyond segmentation and one-size-fits-all tests to build a system capable of making personalized decisions for every fan across channels.
Kayo Sports built its “Customer Cortex,” a personalization engine powered by AI agents and integrated with Braze. The Cortex analyzes user behavior, preferences, and engagement patterns to create 1:1 subscriber experiences at scale.
https://www.braze.com/customers/kayo-sports-case-study
This approach now gives Kayo an automated cycle of real-time personalization and customer journey orchestration that adapts daily, ensuring every fan gets the right message across every channel.
Here are four of the most effective ways brands use BrazeAI Decisioning Studio™ with Braze to drive key customer actions.
This may sound familiar: a customer signs up, downloads your app, or starts a free trial…and then disappears. Without the right nudge, they never finish setting up or activating, which means they never see the real value of your product.
BrazeAI Decisioning Studio™ personalizes onboarding journeys by testing different cadences, channels, and creative in real time. One customer might get an SMS reminder to finish setup in the morning, another might receive an in-app message that highlights a key feature in the evening. Braze delivers these tailored journeys automatically, so every new user gets the right message at the right moment.
Pro tip: Use a mix of channels in early onboarding. Customers who ignore email might respond to push or SMS, and BrazeAI Decisioning Studio™ agents will quickly learn which combination works best.
Retaining customers is difficult and many marketers resort to offering blanket discounts. But overspending on promotions for people who would have stayed anyway is a risk.
With BrazeAI Decisioning Studio™, retention campaigns get smarter. AI agents learn which subscribers need an incentive, which are likely to renew without one, and which should be engaged with a different type of message. Braze then delivers those individualized decisions through email, push, or in-app, ensuring every renewal message is both timely and cost-effective.
Pro tip: Combine BrazeAI Decisioning Studio™ with Braze Predictive Churn. Use churn scores to identify at-risk users, then let BrazeAI Decisioning Studio™ optimize the timing and type of retention outreach.
Not every customer is ready for an upgrade, and sending the same offer to everyone can create churn risk.
BrazeAI Decisioning Studio™ personalizes cross-sell and upsell campaigns by analyzing which customers respond best to leapfrog offers, who requires a discount, and who simply needs more time. Within Braze Canvas, these decisions translate into personalized paths across email, in-app, SMS, and push, and result in a higher average revenue per user without wasted spend.
Pro tip: Don’t just test offers—test timing. BrazeAI Decisioning Studio™ may learn that some customers are more likely to upgrade right after a purchase, while others need a quiet period before considering add-ons.
Every brand has customers who stop showing up. Sending the same “come back” message to everyone rarely works.
BrazeAI Decisioning Studio™ turns re-engagement into an ongoing learning process. Agents test different creative, offers, channels, and timings to discover what works for each individual. With Braze handling delivery, one customer might receive a playful push notification, another a discount code by email, and another a reminder of new features via SMS. Over time, winback campaigns get sharper and more cost-efficient.
Pro tip: Set a re-eligibility window in Braze Canvas so lapsed users can re-enter campaigns if they show signs of churning again. BrazeAI Decisioning Studio™ will continue adapting as their behavior changes.
AI decisioning can be transformative for marketers, and with the right preparation, adopting BrazeAI Decisioning Studio™is both achievable and rewarding. Success comes from recognizing the key areas that make the biggest difference—data quality, organizational alignment, transparency, and compliance—and planning for them early. Here are some of the most important considerations for teams getting started.
BrazeAI Decisioning Studio™ learns from customer-level data, so the more high-quality inputs you can provide, the faster and more effective the system will be. Most brands already unify this information in a warehouse or CDP, but it’s worth auditing your data pipelines before launching. The goal isn’t perfection—it’s ensuring key events (purchases, logins, cancellations, renewals) are accurate and available.
Marketers need to understand not only what the AI is doing, but why. BrazeAI Decisioning Studio™ reveals insights that show which variables matter most (like timing, channel, or creative) and how they affect performance. These insights help teams build confidence in the system, communicate results to stakeholders, and apply learnings across campaigns.
Personalization must always align with data protection standards. BrazeAI Decisioning Studio™ works with pseudonymized customer-level data and integrates into existing martech stacks without requiring sensitive information to leave your systems. When used with Braze’s compliance and data governance features, this makes it possible to scale personalization while staying aligned with regulatory requirements.
AI decisioning is a cross-functional effort. Marketing, data, product, and compliance teams all play a role, and each comes with their own priorities. Building alignment early helps prevent bottlenecks later. Education is also critical. Explaining how reinforcement learning works, what guardrails are in place, and how performance will be measured makes it easier to get executives and cross-functional partners on board.
Getting started with BrazeAI Decisioning Studio™ doesn’t require a full-scale transformation. Most brands begin with one high-impact use case—like onboarding, retention, or winback—and expand from there. BrazeAI Decisioning Studio™ natively integrates with Braze, so once the decisioning engine identifies the best action for each customer, Braze delivers it in real time across email, push, SMS, and in-app messaging.
With the right data foundation and cross-functional support in place, marketers can quickly prove ROI, build internal momentum, and scale one-to-one marketing across the entire customer journey.
OfferFit, now BrazeAI Decisioning Studio™, is an AI decisioning engine that uses reinforcement learning to deliver one-to-one personalization at scale. It continuously tests and learns to optimize messaging, channel, timing, and offers for each individual customer.
BrazeAI Decisioning Studio™ works by deploying AI agents that run automated experiments across multiple campaign variables. These agents learn from customer behavior in real time, making better decisions with each interaction.
Unlike traditional A/B testing, BrazeAI Decisioning Studio™ doesn’t wait for tests to finish before acting. Instead, it uses reinforcement learning to continuously optimize, personalizing at both the segment and individual level while adapting to changes in customer behavior.
The benefits of BrazeAI Decisioning Studio™ for marketers include faster campaign optimization, improved customer engagement and retention, reduced testing fatigue, and the ability to scale true one-to-one personalization. These gains translate into stronger ROI and customer lifetime value.
BrazeAI Decisioning Studio™ uses reinforcement learning through contextual bandits, which allow agents to personalize decisions for each customer. This means the system not only finds what works best overall but adapts to individual preferences and context.
Braze and BrazeAI Decisioning Studio™ work together by combining decisioning and orchestration. BrazeAI Decisioning Studio™ determines the best action for each customer, while the Braze customer engagement platform delivers that action instantly across channels like email, SMS, push, and in-app messaging.
BrazeAI Decisioning Studio™ is used by leading brands across industries including telecom, energy, retail, streaming, travel, and financial services. Customers include brands like Brinks Home, Canadian Tire, Chime, LATAM Airlines, MetLife, Foxtel/Kayo Sports, Wyndham Hotels, and Yelp.
BrazeAI Decisioning Studio™ is a solution that provides a decisioning layer on top of your customer engagement platform to enable 1:1 AI-powered personalization. The solution is natively integrated with Braze to deliver on personalized recommendations.
Examples of BrazeAI Decisioning Studio™ in action include Kayo Sports, which scaled from 300 to 1.2 million personalization variations daily, boosting reactivation and cross-sell, and Brinks Home, which grew contract extension value by over 450%. These real-world cases show how BrazeAI Decisioning Studio™ personalizes at scale to drive measurable business impact.
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