Bring 1:1 decisioning to every marketing journey with BrazeAI Decisioning Studio™ Go

Marketers know that it's easier and more effective to keep a customer engaged than to acquire a new one. But today's consumers expect experiences that feel relevant and valuable to them as individuals. That's made 1:1 personalization a longtime "holy grail" for lifecycle marketing—one that finally feels in reach, thanks to AI's rapid evolution. At the same time, many marketing programs are still settling for rigid segments, static rules, and manual A/B tests, resulting in cumbersome campaigns that leave consumers cold.

A year ago, we released a new feature designed to make that vision of 1:1 personalization a reality. BrazeAI Decisioning Studio™ Pro uses AI decisioning agents to make per-customer decisions for all aspects of a marketing journey. Marketers work with forward-deployed data scientists to configure these highly customizable agents, ensuring that they are consistently performant and effectively leveraging first-party data to optimize the core KPI of their choice.

Over the past year, we've talked to many marketers who want nothing more than to leverage the power of AI decisioning across all of their marketing programs. That's why we're announcing BrazeAI Decisioning Studio™ Go, now in beta, to bring 1:1 decisioning to every customer journey. With Decisioning Studio Go, marketers can quickly configure AI decisioning agents, and deploy them to their core lifecycle campaigns without data science support or an engineering ticket. These agents choose the message variants, send times, days, and frequencies designed to optimize engagement for each individual, making 1:1 decisioning available as a standard practice, rather than a special project.

Putting AI decisioning to work in your customer journeys

So what does 1:1 decisioning look like in practice?

Marketers start with the engagement data they already have in Braze, choose the audience that the agent will look to engage, and then pick the actions the agent is allowed to take, determining the base creative, variants, allowable times of day, days of the week, and message frequencies, while also setting guardrails (like how often a particular base creative can be used with a particular customer). The AI decisioning agent then uses a type of AI called reinforcement learning to autonomously test and learn within marketer-defined limits, empirically discovering the optimal actions for each individual. Initially, the release of Decisioning Studio Go includes agents that are designed to optimize email CTA clicks, with more channels planned to be added over time.

Here are some examples of use cases for self-serve AI decisioning agents:

• An eCommerce brand could use Decisioning Studio Go for an evergreen campaign which promotes a rotating catalog of seasonally-appropriate products, giving each customer an individualized journey.
• A quick-service restaurant (QSR) brand could use Decisioning Studio Go for a first-purchase journey. Customers enter the agent’s audience when they create an account on the website and exit when they make a first purchase.
• A travel brand could use Decisioning Studio Go to encourage customers to redeem reward points. The audience for this agent would be loyalty program members who had not redeemed rewards in 60 days.
• A bank could use Decisioning Studio Go to drive direct deposit sign-ups, targeting customers who have opened a checking account but not yet set up direct deposit.
• A streaming brand could use Decisioning Studio Go to win back lapsed viewers, giving each individual a personalized cadence of nudges encouraging them to return.
• A retailer could use Decisioning Studio Go to optimize a seasonal campaign, with a broad audience of active shoppers.

In each case, the agent builds a personalized journey for each customer in the audience, learning which messages, creatives, send times, days, and frequencies of communication work best for each individual.

The role of experimentation and optimization in 1:1 decisioning

We see new announcements nearly every week about breakthroughs in the reasoning capabilities of generative AI, powered by large language models (LLMs). Given that, it's natural to wonder why Braze uses proprietary reinforcement learning models to power AI decisioning in addition to leveraging publicly available foundational LLMs.

These models are, after all, extremely powerful, and Braze employs them to power key capabilities such as the BrazeAI Agent Console™ and the BrazeAI Operator™. An agent built using LLMs can engage in human-like decision-making, but at a speed and scale that’s impossible for a human marketer. These agents are generalists: They rely on foundational models trained on a vast corpus of human knowledge, and can be flexibly deployed to solve a wide variety of problems. For example, an agent step embedded in a Braze Canvas might route users down a journey path based on real-time signals, or compose personalized messages at the moment of send.

In contrast, AI decisioning agents are specialists: They are purpose-built for autonomously experimenting and optimizing toward a goal. While LLMs are trained on general data, reinforcement learning models are able to learn empirically from a brand’s own first-party data, and find patterns in that data that were previously unknown.

For example, a home security brand discovered that some customers would prefer a higher rate when renewing a contract in return for locking that rate in for a longer period of time. The agent identified this pattern by learning from the behavior of individual customers. The marketer would not have had the confidence to offer higher prices to customers at risk for churn without an AI decisioning agent that could quickly identify who was likely to find that offer of genuine value. Crucially, an LLM would be very unlikely to make such an offer for the same reason a human marketer would not—namely, because the outcome is not intuitive and not suggested by existing patterns.

AI decisioning agents are not merely making educated guesses; they’re truly getting smarter with every send. These agents are able to empirically discover what works for each individual by looking at the outcomes of each choice they make and then adjust their reasoning the next time they try to engage that customer.

Final thoughts

As AI capabilities accelerate, AI decisioning will be an ever more important tool in a marketers arsenal for 1:1 personalization that delivers measurable uplift on a defined KPI. For years, marketers have used Decisioning Studio Pro (and its predecessor OfferFit) to optimize their customer journeys. Decisioning Studio Pro offers brands tremendous flexibility, with agents that learn from data from any source, personalize any aspect of customer communication, and optimize for custom financial KPIs. Today, Decisioning Studio Go gives marketers the ability to deploy self-serve agents that optimize engagement across a broad range of customer journeys, without relying on technical teams.

Brands can use Decisioning Studio Pro for their most important journeys, which merit highly customizable agents fine-tuned by forward-deployed scientists. For their other journeys, marketers can get started in minutes with Decisioning Studio Go. Taken together, these BrazeAI™ decisioning capabilities give marketers highly customizable agents for their most critical journeys, and self-serve 1:1 personalization for all the rest.

At Braze, we believe that marketers were made for this moment, and Decisioning Studio Go was made to help.

Interested in learning more about Decisioning Studio Go? Check out our documentation.