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Next Best Action Marketing Explained | Braze
2026-03-07 22:26 Diff
https://www.braze.com/resources/articles/next-best-action
Personalization works. Some brands have seen revenue rise by more than 30% simply by understanding customer intent and responding at the right moment. For many marketers, next best action has been a reliable way to do that—using a mixture of predictive models, segmentation and A/B testing to match people with the products or experiences they’re most likely to want next.
The ongoing challenge is figuring out how to keep that relevance alive as customer expectations shift, and how to make every interaction feel like the logical next step, for a more satisfying and successful customer experience.
Contents
What is next best action marketing?
Why next best action matters for personalization
Next best action models explained
Next best action vs. next best everything
How next best action works in practice
Common use cases and examples of next best action and next best everything
Putting next best everything into action
Challenges of next best action
Final thoughts on moving from next best action to next best everything
Next best action FAQs
Next best action is a marketing and customer engagement strategy that decides the most relevant step to take with an individual customer in any given moment. That could be sending a message, presenting an offer, providing service support, or perhaps choosing not to reach out at all.
Early NBA programs often relied on static rules or predictive modeling, where outcomes were forecasted based on past behavior. While useful, those approaches lacked agility and didn’t keep pace with changing customer needs.
Modern NBA programs are more adaptive, but still limited.
Why next best action matters for personalization

Next best action marketing is a first step away from broad, one-size-fits-all campaigns. Instead, it makes every interaction more relevant and more valuable—both for the customer and the brand.
The impact of NBA shows up in three key areas that matter to marketing teams—the ability to personalize at scale, the efficiency of resource use, and the strength of customer relationships over time.
Personalization at scale

NBA models typically make recommendations for segments, rather than individuals. This allows brands to get closer to true personalization by scaling their efforts to make messages more relevant than traditional “batch and blast” methods.
Efficiency and smarter resource use

NBA directs offers and messages where they’re most likely to have impact, reducing wasted spend and saving customers from irrelevant noise.
Retention and loyalty

Segments can be divided into multiple microsegments and manually tested so that each grouping becomes more relevant over time. This strengthens customer relationships and leads to higher lifetime value, stronger loyalty, and more predictable growth.
Next best action models explained

In principle, any rule or model that takes customer data as input and recommends an action as output could fit under the label of "next best action." In reality, the term usually refers to a framework that blends predictive models and business rules to decide which product, offer, or message to show a customer next.
Here’s how a traditional next best action model typically works:
1. Start with raw customer data — such as purchases, web activity, app sessions, or email engagement.
2. Transform that data into customer features — for example, purchases in the last 90 days, average order value, or days since last interaction.
3. Feed those features into predictive models — like churn risk, repurchase likelihood, or projected lifetime value.
4. (Optional) Segment customers — for instance, grouping anyone with a churn probability above a certain threshold into a “high-risk” audience.
5. Apply business rules — deciding which product, offer, or message each segment should receive based on those predictive scores and attributes.
By combining predictive models, a type of machine learning called supervised learning, and manual business rules, models are created that produce a set of "if-then" business rules under which they operate. What they predict might resonate with a customer, using patterns from past data to guide the next step. But while these systems can personalize at the segment level, they’re still driven by static models and manual rules—limitations that AI decisioning is designed to overcome.
Next best action vs. next best everything

Next best action is based on predictive AI and uses past behavior to forecast the likelihood of future outcomes.This is helpful insight, but it’s rigid, brittle, and expensive to run. Even if you have a lot of microsegments, it’s still not truly 1:1 personalization.
“Next Best Everything” takes the next step. It's powered by AI decisioning built on reinforcement learning. There are no rigid rules or occasional A/B tests. This approach constantly learns from every customer interaction, and decides the best combination of timing, channel, frequency, and creative for every individual, adapting as behavior changes.
Because AI decisioning learns and optimizes in real time, brands no longer need to manage endless segments or retrain static models. The system experiments automatically, balancing what’s best for the customer with what drives business results, and delivering the "next best everything" for every customer, every time.
Common use cases and examples of next best action and next best everything

From retail and travel to media, finance, and hospitality, brands use next best action models to guide engagement at key moments. But next best everything, powered by AI decisioning, takes those same strategies and makes them adaptive, real time, and truly one to one.
Retention and win-back

Example use case:A subscription streaming platform wants to reduce churn by re-engaging inactive viewers.
When using next best action:A predictive model identifies customers who haven’t streamed in 30 days. The team builds a microsegment of these users and sends a reactivation email featuring popular new shows in their region—same message, same send time, same channel.
When using next best everything:AI decisioning analyzes engagement signals in real time, selecting the best action for each viewer. A frequent binge-watcher might get a personalized push notification highlighting a sequel to their favorite series, while a lapsed user receives a tailored in-app offer right when they’re browsing for something to watch.
Repeat purchase reminders

Example use case:A retail brand wants to increase repeat orders for products people buy regularly, such as coffee, pet food, or skincare.
When using next best action:A predictive feature identifies customers who made a purchase around three months ago and are likely to need a restock soon. A campaign is created for that group, and everyone receives the same email reminder to reorder.
When using next best everything:AI decisioning learns each customer’s unique purchase rhythm. One shopper receives a push notification just as their order frequency suggests they’re about to run out, while another gets a personalized email offer with related add-ons based on their past basket size and preferences.
Cross-sell and upsell opportunities

Example use case:A travel company wants to increase average booking value by promoting add-ons and upgrades.
When using next best action:After flights are booked, predictive models flag high-value travelers. They’re grouped into a segment and sent an email offering the same business-class upgrade at checkout.
When using next best everything:AI decisioning tests different combinations of offers, price points, and channels for each traveler. It learns which creative and incentive pairings drive action—a frequent flyer might see an in-app message offering bonus miles for an upgrade, while a leisure traveler gets a push notification about family hotel discounts before departure.
Loyalty nudges and rewards

Example use case:A quick-service restaurant or coffee chain wants to boost loyalty program participation.
When using next best action:A churn prediction model highlights customers who haven’t redeemed points in 60 days. They receive a standard “You’re only 20 points away!” email, sent to all members in the segment.
When using next best everything:AI decisioning identifies the motivator that works best for each customer—sending a push notification with bonus points at their usual visit time, or an in-app message with a reward tied to their favorite order. Every nudge is personalized, timely, and designed to inspire action.
Proactive service experiences

Example use case:A fintech app wants to reduce drop-offs by resolving friction before customers churn.
When using next best action:The team sets a rule to trigger a support email after two failed login attempts or a missed payment, offering generic troubleshooting steps to all affected users.
When using next best everything:AI decisioning spots early signals of frustration—like repeated navigation errors or sudden inactivity—and selects the best next step. One user receives an in-app message offering quick support options, while another gets an SMS linking directly to a personalized help article, all timed to prevent churn.
Account expansion and trial reactivation

Example use case:A SaaS company wants to re-engage free-trial users and identify opportunities to expand existing accounts.
When using next best action:A predictive model highlights users whose trial is nearing its end or who haven’t logged in for a week. The marketing team creates a “high-churn-risk” list and sends a single nurture email offering a product demo to everyone in that segment.
When using next best everything:AI decisioning analyzes engagement patterns and usage signals in real time, selecting the best action for each account. A user who explored advanced features might receive a push notification offering a premium upgrade, while another with low activity gets a personalized in-app message with a short tutorial and a free extension to encourage deeper adoption.
Putting next best everything into action

Kayo Sports shows what happens when a brand takes next best action personalization to the next level. Using BrazeAI Decisioning Studio™, the team turned predictive insights into adaptive, one-to-one engagement—bringing “next best everything” to life for millions of sports fans.
Playing the long game: Kayo Sports delivers 1:1 engagement at scale

Australia’s largest sports streaming platform, Kayo Sports, gives fans access to over 50 live and on-demand sports, spanning 30,000 hours of content from FOX SPORTS Australia and ESPN. In a highly competitive market where viewers expect tailored experiences, Kayo set out to create the most personalized streaming environment possible—one that truly understood every fan’s interests, habits, and timing.
The challenge

Kayo Sports needed to engage a diverse audience of fans across channels and devices with messages that felt individually relevant. While their data was rich, their existing systems were rule-based and limited, meaning personalization was still broad and reactive. The team wanted to create a dynamic, AI-driven engagement model that could anticipate each customer’s needs and desires in real time.
The strategy

Partnering with BrazeAI Decisioning Studio™, Kayo Sports built “Customer Cortex”—an AI-powered personalization engine designed to make intelligent, 1:1 decisions at scale. Rather than predicting what might work, Customer Cortex learns from every customer interaction to determine the next best everything: the right message, creative, channel, timing, frequency, and promotion for each subscriber.
This adaptive system runs on three daily cycles:
Data: Ten purpose-built machine learning models process first- and third-party data to understand each individual’s behavior and preferences.
Decisioning: BrazeAI Decisioning Studio™ analyzes those insights and selects the optimal action for each subscriber, personalizing every touchpoint automatically.
Delivery: Braze activates these decisions across channels—email, push, SMS, and in-app—creating more than 1.2 million unique message variations (up from 300).
This setup gives Kayo the flexibility to test, learn, and refine continuously. Whether a viewer is likely to engage with an in-app offer before a major match or respond to an SMS about a new subscription tier, Braze orchestrates the right experience automatically—motivating customers to take the next step and strengthening loyalty at every touchpoint.
The wins

Since launching Customer Cortex, Kayo Sports has seen measurable growth across engagement and loyalty metrics:
14% increase in subscriptions (FY24)
8% increase in annual occupancy
105% increase in cross-sells to BINGE, another Foxtel streaming service
Achieved these gains while increasing average subscription prices by 20%
Challenges of next best action

Traditional next best action models can deliver results, but they come with built-in constraints that make them difficult to scale or sustain. As customer behavior and business needs move faster than static systems can adapt, these challenges become even more visible.
1. Static models that don’t learn

NBA models depend on predictive algorithms trained on historical data. Once deployed, they don’t automatically improve or adjust to new behavior. That means teams must manually retrain and redeploy models—a time-consuming cycle that leaves campaigns lagging behind real customer needs.
2. Brittle systems built on rules

Traditional NBA combines predictive scoring with hard-coded business rules, so it can quickly become rigid. Any change in product lines, offers, or audience segments often requires reworking multiple rules and dependencies, slowing teams down and limiting experimentation.
3. Limited personalization

NBA works well at the segment level, but struggles to scale true one-to-one personalization. It typically evaluates only a few data points or model outputs, missing the nuance needed to tailor timing, content, or creative to individual customers.
4. Heavy operational overhead

Managing multiple models, rules, and tests creates complexity. Each new campaign or data source adds layers of maintenance, making it difficult for marketing teams to move fast or measure results cleanly.
5. Delayed feedback loops

NBA models don’t learn continuously, so performance insights often arrive after the fact. Teams can see what worked, but not in time to adjust while a campaign is live. This limits responsiveness and keeps optimization reactive instead of proactive.
These challenges don’t make next best action irrelevant, but they do highlight why it’s time for something more adaptive. Next best everything and AI decisioning solve these pain points by learning continuously and simplifying operations, to deliver decisions that improve with every customer interaction.
Final thoughts on moving from next best action to next best everything

Traditional next best action approaches marked an important step toward more personalized marketing, but they were designed around fixed predictions and static segments. Today’s customers expect brands to recognize and anticipate not just what they need, but what they want, and to respond in the moment with relevance and empathy.
Next best everything brings that possibility to life through AI decisioning, which learns from every interaction and chooses the most meaningful way to connect—across timing, channel, and message. It discovers what truly motivates each customer to act.
With BrazeAI Decisioning Studio™, brands can create programs that adapt as naturally as their audiences do, showing that intelligence and creativity can work together to make personalization genuinely human.
Take your next best step by learning more about next best everything
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class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="arrow-right" aria-hidden="true"><use href="/icons/sprites.svg#arrow-right"></use></svg></div></span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/brazeai"><div class="flex-none flex items-center justify-center text-white w-12 h-12 bg-brandingPurpleOrange rounded-full"><svg width="24" height="24" role="img" aria-label="magic-wand-01"><use href="/icons/sprites.svg#magic-wand-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">BrazeAl™<span class="inline-block rounded text-xs text-white font-normal leading-none p-1" style="background:#3608D7">UPDATES</span></span><span class="text-sm font-normal text-gray-600">Automate, learn, and personalize with AI</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/braze-data-platform"><div class="flex-none flex items-center justify-center text-white w-12 h-12 bg-purple-400 rounded-full"><svg width="24" height="24" role="img" aria-label="lightning-02"><use href="/icons/sprites.svg#lightning-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Braze Data Platform</span><span class="text-sm font-normal text-gray-600">Unify, activate, and distribute your data</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/cross-channel-messaging"><div class="flex-none flex items-center justify-center text-white w-12 h-12 bg-purple-400 rounded-full"><svg width="24" height="24" role="img" aria-label="grid-01"><use href="/icons/sprites.svg#grid-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Cross-Channel</span><span class="text-sm font-normal text-gray-600">Send all your messages from one place</span></div></a></li></ul></div><div class="nav-list relative z-10 grow flex flex-col gap-4"><div class="flex flex-col gap-1 text-sm text-gray-500"><span class="font-semibold uppercase">FEATURED CAPABILITIES</span></div><ul class="flex flex-col gap-y-4"><li><a class="nav-item flex gap-3" href="/product/brazeai-decisioning-studio"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="route"><use href="/icons/sprites.svg#route"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">BrazeAI Decisioning Studio™<span class="inline-block rounded text-xs text-white font-normal leading-none p-1" style="background:#3608D7">NEW</span></span><span class="text-sm font-normal text-gray-600">Deliver 1:1 personalization, at scale</span></div></a></li><li><a class="nav-item flex gap-3" href="/product/journey-orchestration"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="skew"><use href="/icons/sprites.svg#skew"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Journey Orchestration</span><span class="text-sm font-normal text-gray-600">Create multi-step, cross-channel experiences</span></div></a></li><li><a class="nav-item flex gap-3" href="/product/ai-agents"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="cube-02"><use href="/icons/sprites.svg#cube-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">BrazeAI™ Agents<span class="inline-block rounded text-xs text-white font-normal leading-none p-1" style="background:#3608D7">NEW</span></span><span class="text-sm font-normal text-gray-600">Scale smarter engagement with always-on AI agents</span></div></a></li><li><a class="nav-item flex gap-3" href="/product/reporting-analytics"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="presentation-chart-01"><use href="/icons/sprites.svg#presentation-chart-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Reporting &amp; Analytics</span><span class="text-sm font-normal text-gray-600">Analyze performance &amp; uncover insights</span></div></a></li></ul></div></div></div><div class="nav-dropdown__right-section relative py-8 xl:pl-8 xl:w-3/5 before:bg-gray-50 before:absolute before:z-0 before:top-0 before:w-screen before:h-full before:left-1/2 before:-translate-x-1/2 xl:before:left-0 xl:before:transform-none"><div class="nav-list relative z-10 grow flex flex-col gap-4"><div class="flex flex-col gap-1 text-sm text-gray-500"><span class="font-semibold uppercase">FEATURED CHANNELS</span></div><ul class="flex flex-col gap-y-4"><li><a class="nav-item flex gap-3 items-center" href="/product/email"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="mail-01"><use href="/icons/sprites.svg#mail-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Email</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/mobile-app-messaging"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="phone-02"><use href="/icons/sprites.svg#phone-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Mobile App Messaging</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/web-messaging"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="browser"><use href="/icons/sprites.svg#browser"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">Web Messaging</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/sms"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="message-text-square-02"><use href="/icons/sprites.svg#message-text-square-02"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">SMS/RCS</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/whatsapp"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="phone-call-01"><use href="/icons/sprites.svg#phone-call-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md">WhatsApp</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/product/cross-channel-messaging"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="dots-grid"><use href="/icons/sprites.svg#dots-grid"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-md text-purple-700">View all channels</span></div></a></li></ul></div><div class="nav-dropdown__section-footer flex flex-wrap sm:flex-nowrap p-2 sm:p-3 bg-[#C9C4FF80] relative z-10 gap-3 items-end sm:justify-between xl:justify-start rounded-lg mt-4"><div class="flex gap-2 sm:gap-3"><span class="flex items-center justify-center flex-none size-8 text-purple-700 mr-2 xl:mr-0"><svg width="20" height="20" role="img" aria-label="star-02"><use href="/icons/sprites.svg#star-02"></use></svg></span><div class="flex flex-col justify-center gap-1"><span class="font-semibold text-gray-900 text-md">How It Works</span><p class="font-normal text-gray-600 text-sm">Get a breakdown of our vertically- integrated technology</p></div></div><a class="inline-flex items-center justify-center text-center rounded-full transition-colors sm:w-fit whitespace-nowrap relative z-10 gap-1 font-aribauGrotesk font-bold px-[14px] py-2 text-sm bg-white border border-transparent text-gray-900 shadow-xs hover:bg-[#F9FAFB] active:bg-white active:border-[#D0D5DD] active:border disabled:bg-white disabled:border-[#EAECF0] disabled:border disabled:text-[#98A2B3] w-full" href="/product">Learn More</a></div></div></div></div></div></li><li class="nav-dropdown"><button type="button" class="relative flex items-center w-full h-full xl:w-auto font-semibold text-md ja:text-xs hover:text-primary-600 text-black py-6 xl:py-[10px] gap-[6px] px-0" aria-expanded="false">Solutions<svg width="20" height="20" role="img" aria-label="chevron-down" class="transition-transform !duration-150"><use href="/icons/sprites.svg#chevron-down"></use></svg></button><div id="nav-dropdown-Solutions" class="nav-dropdown__content absolute top-0 xl:top-full left-0 w-full pb-10 xl:pb-0 h-[calc(100vh-72px)] xl:h-auto flex flex-col overflow-y-auto overflow-x-hidden xl:overflow-hidden bg-white xl:border xl:border-gray-200 xl:shadow-lg transition !duration-300 transform origin-top pointer-events-none -z-1 opacity-0 translate-x-full xl:translate-x-0" aria-hidden="true" inert="true"><div class="container transition delay-150 !duration-300 origin-center xl:opacity-0 xl:translate-y-[5px]"><div class="mt-10 mb-4 xl:hidden"><button type="button" class="items-center justify-center text-center transition-colors sm:w-fit whitespace-nowrap relative z-10 font-aribauGrotesk font-bold text-sm flex gap-2 rounded-none border-none p-0 text-gray-600 shadow-none w-fit hover:text-gray-700 active:text-gray-600 disabled:text-gray-300" aria-controls="nav-dropdown-Solutions"><svg width="20" height="20" role="img" aria-label="arrow-left" class="w-5 h-5" aria-hidden="true"><use href="/icons/sprites.svg#arrow-left"></use></svg>Back</button></div><div class="xl:flex gap-8"><div class="nav-dropdown__left-section w-full py-8"><div class="nav-list-container flex flex-col xl:flex-row gap-6"><div class="nav-list relative z-10 grow flex flex-col gap-4"><div class="flex flex-col gap-1 text-sm text-gray-500"><span class="font-semibold uppercase">Industries</span></div><ul class="flex flex-col gap-y-4"><li><a class="nav-item flex gap-3 items-center" href="/solutions/retail-and-ecommerce"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="globe-01"><use href="/icons/sprites.svg#globe-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">Retail &amp; eCommerce</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/financial-services"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="currency-dollar"><use href="/icons/sprites.svg#currency-dollar"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">Financial Services</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/travel-hospitality"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="map-01"><use href="/icons/sprites.svg#map-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">Travel &amp; Hospitality</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/media-entertainment"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="video-recorder"><use href="/icons/sprites.svg#video-recorder"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">Media &amp; Entertainment</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/gaming"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="gaming-pad-01"><use href="/icons/sprites.svg#gaming-pad-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">Gaming</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/on-demand"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="play"><use href="/icons/sprites.svg#play"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">On Demand</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/qsr"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="heart-rounded"><use href="/icons/sprites.svg#heart-rounded"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">QSR</span></div></a></li></ul></div><div class="nav-list relative z-10 grow flex flex-col gap-4"><div class="flex flex-col gap-1 text-sm text-gray-500"><span class="font-semibold uppercase">Use Cases</span></div><ul class="flex flex-col gap-y-4"><li><a class="nav-item flex gap-3 items-center" href="/solutions/optimize-onboarding"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="trend-up-01"><use href="/icons/sprites.svg#trend-up-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">Optimize Onboarding</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/boost-productivity"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="pencil-01"><use href="/icons/sprites.svg#pencil-01"></use></svg></div><div class="flex flex-col gap-1"><span class="flex flex-wrap gap-x-3 items-center font-semibold text-lg">Boost Productivity</span></div></a></li><li><a class="nav-item flex gap-3 items-center" href="/solutions/improve-acquisition"><div class="flex-none flex items-center justify-center text-primary-600 w-5 h-5"><svg width="20" height="20" role="img" aria-label="pie-chart-01"><use 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type="application/ld+json">{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Resources","item":"https://www.braze.com/resources"},{"@type":"ListItem","position":2,"name":"Blog","item":"https://www.braze.com/resources/articles"},{"@type":"ListItem","position":3,"name":"Next best action: Mo...","item":"https://www.braze.com/resources/articles/next-best-action"}]}</script><div class="mt-8 flex flex-col gap-6 sm:mt-12 md:flex-row md:pb-16"><div class="flex shrink-0 grow basis-0 flex-col gap-8"><h1 class="text-display-md font-bold text-white">Next best action: Moving beyond predictions to real-time personalization</h1><p class="flex gap-3 text-xs font-extrabold text-white"><span>Published on November 24, 2025</span><span>/</span><span>Last edited on November 24, 2025</span><span>/</span><span>12<!-- --> min read</span></p></div><div class="shrink-0 grow basis-0"><img alt="Next best action: Moving beyond predictions to real-time personalization" loading="lazy" width="600" height="400" decoding="async" data-nimg="1" class="h-auto max-h-[352px] w-full rounded-xl object-cover" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fd4a0d5623f8a46e818a8ec78f21755df38d915da-1920x1080.png&amp;w=640&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fd4a0d5623f8a46e818a8ec78f21755df38d915da-1920x1080.png&amp;w=1200&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fd4a0d5623f8a46e818a8ec78f21755df38d915da-1920x1080.png&amp;w=1200&amp;q=75"/></div></div></div></div><div class="container py-[50px] md:py-24"><div class="flex flex-col gap-8 md:flex-row"><div class="w-full shrink-0 grow-0 basis-full md:w-[72px] md:basis-[72px]"><div class="flex gap-4 text-gray-500 md:sticky md:top-20 md:flex-col"><div class="w-fit cursor-pointer text-gray-500 hover:text-primary-500"><svg width="24" height="24" role="img" aria-label="twitter-new"><use href="/icons/sprites.svg#twitter-new"></use></svg></div><div class="w-fit cursor-pointer text-gray-500 hover:text-primary-500"><svg width="24" height="24" role="img" aria-label="linkedin"><use href="/icons/sprites.svg#linkedin"></use></svg></div><div class="w-fit cursor-pointer text-gray-500 hover:text-primary-500"><svg width="24" height="24" role="img" aria-label="mailbox"><use href="/icons/sprites.svg#mailbox"></use></svg></div><div class="w-fit cursor-pointer text-gray-500 hover:text-primary-500"><svg width="24" height="24" role="img" aria-label="link-03"><use href="/icons/sprites.svg#link-03"></use></svg></div></div></div><div class="flex shrink-0 grow basis-0 flex-col gap-12"><div class="flex flex-col gap-6 rounded-xl border border-gray-100 bg-gray-50 p-8"><span class="text-sm font-semibold text-gray-600">AUTHOR</span><div class="flex items-center gap-4"><span class="block size-10 shrink-0 grow-0 basis-10 overflow-hidden rounded-full bg-gray-100"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" class="size-full object-cover" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa871ef5dc9e894d320555710664c2e67f03dad70-1000x1000.png&amp;w=256&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa871ef5dc9e894d320555710664c2e67f03dad70-1000x1000.png&amp;w=384&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa871ef5dc9e894d320555710664c2e67f03dad70-1000x1000.png&amp;w=384&amp;q=75"/></span><div class="flex flex-col items-start justify-center gap-4 sm:flex-row md:items-center"><h6 class="text-md font-semibold text-gray-900 sm:text-lg">Team Braze</h6><div class="hidden min-h-[30px] border-r-2 border-gray-300 bg-gray-300 sm:block "></div><span class="text-md text-gray-900">Braze</span></div></div></div><div class="flex flex-col gap-6"><p>Personalization works. Some brands have seen <a href="https://www.mckinsey.com/capabilities/operations/our-insights/the-future-of-customer-experience-personalized-white-glove-service-for-all" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">revenue rise by more than 30%</a> simply by understanding customer intent and responding at the right moment. For many marketers, next best action has been a reliable way to do that—using a mixture of predictive models, segmentation and A/B testing to match people with the products or experiences they’re most likely to want next.</p><p>The ongoing challenge is figuring out how to keep that relevance alive as customer expectations shift, and how to make every interaction feel like the logical next step, for a more satisfying and successful customer experience.</p><p><strong>Contents</strong></p><p><a href="http://www.braze.com/resources/articles/next-best-action#what" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">What is next best action marketing?</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#why" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Why next best action matters for personalization</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#explained" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Next best action models explained</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#everything" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Next best action vs. next best everything</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#how" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">How next best action works in practice</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#use-cases" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Common use cases and examples of next best action and next best everything</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#action" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Putting next best everything into action </a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#challenges" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Challenges of next best action</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#conclusion" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Final thoughts on moving from next best action to next best everything</a></p><p><a href="http://www.braze.com/resources/articles/next-best-action#faqs" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Next best action FAQs</a></p><div id="what"></div><h2 id="h-c6814468a47d" class="my-4 text-gray-900 lg:text-display-lg">What is next best action marketing?</h2><p>Next best action is a marketing and customer engagement strategy that decides the most relevant step to take with an individual customer in any given moment. That could be sending a message, presenting an offer, providing service support, or perhaps choosing <em>not</em> to reach out at all.</p><p>Early NBA programs often relied on static rules or predictive modeling, where outcomes were forecasted based on past behavior. While useful, those approaches lacked agility and didn’t keep pace with changing customer needs.</p><p>Modern NBA programs are more adaptive, but still limited.</p><div id="why"></div><h2 id="h-bca4daf3212e" class="my-4 text-gray-900 lg:text-display-lg">Why next best action matters for personalization</h2><p>Next best action marketing is a first step away from broad, one-size-fits-all campaigns. Instead, it makes every interaction more relevant and more valuable—both for the customer and the brand.</p><p>The impact of NBA shows up in three key areas that matter to marketing teams—the ability to personalize at scale, the efficiency of resource use, and the strength of customer relationships over time.</p><h3 id="h-13a10b3265ad" class="my-4 text-gray-900">Personalization at scale</h3><p>NBA models typically make recommendations for segments, rather than individuals. This allows brands to get closer to true personalization by scaling their efforts to make messages more relevant than traditional “batch and blast” methods.</p><h3 id="h-4dfe99352329" class="my-4 text-gray-900">Efficiency and smarter resource use</h3><p>NBA directs offers and messages where they’re most likely to have impact, reducing wasted spend and saving customers from irrelevant noise.</p><h3 id="h-ad8ba37de111" class="my-4 text-gray-900">Retention and loyalty</h3><p>Segments can be divided into multiple microsegments and manually tested so that each grouping becomes more relevant over time. This strengthens customer relationships and leads to higher lifetime value, stronger loyalty, and more predictable growth.</p><div id="explained"></div><h2 id="h-e671a4e7e842" class="my-4 text-gray-900 lg:text-display-lg">Next best action models explained</h2><img alt="Diagram: Braze powers a cycle of personalization, efficiency, and customer retention, with agents experimenting and learning autonomously." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F0b388038d104fe82aa0815d64df3290ad4ffed8f-2400x1200.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F0b388038d104fe82aa0815d64df3290ad4ffed8f-2400x1200.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F0b388038d104fe82aa0815d64df3290ad4ffed8f-2400x1200.png&amp;w=1920&amp;q=75"/><p>In principle, any rule or model that takes customer data as input and recommends an action as output could fit under the label of &quot;next best action.&quot; In reality, the term usually refers to a framework that blends predictive models and business rules to decide which product, offer, or message to show a customer next.</p><p>Here’s how a traditional next best action model typically works:</p><p><strong>1. Start with raw customer data</strong> — such as purchases, web activity, app sessions, or email engagement.</p><p><strong>2. Transform that data into customer features</strong> — for example, purchases in the last 90 days, average order value, or days since last interaction.</p><p><strong>3. Feed those features into predictive models</strong> — like churn risk, repurchase likelihood, or projected lifetime value.</p><p><strong>4. (Optional) Segment customers</strong> — for instance, grouping anyone with a churn probability above a certain threshold into a “high-risk” audience.</p><p><strong>5. Apply business rules</strong> — deciding which product, offer, or message each segment should receive based on those predictive scores and attributes.</p><p>By combining predictive models, a type of machine learning called supervised learning, and manual business rules, models are created that produce a set of &quot;if-then&quot; business rules under which they operate. What they predict might resonate with a customer, using patterns from past data to guide the next step. But while these systems can personalize at the segment level, they’re still driven by static models and manual rules—limitations that <a href="https://www.braze.com/resources/articles/ai-decisioning" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">AI decisioning</a> is designed to overcome.</p><div id="everything"></div><h2 id="h-87ed434ece1e" class="my-4 text-gray-900 lg:text-display-lg">Next best action vs. next best everything</h2><p>Next best action is based on predictive AI and uses past behavior to forecast the likelihood of future outcomes.This is helpful insight, but it’s rigid, brittle, and expensive to run. Even if you have a lot of microsegments, it’s still not truly 1:1 personalization.</p><p>“<a href="https://www.braze.com/resources/reports-and-guides/ai-decisioning-next-best-everything" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Next Best Everything</a>” takes the next step. It&#x27;s powered by AI decisioning built on reinforcement learning. There are no rigid rules or occasional A/B tests. This approach constantly learns from every customer interaction, and decides the best combination of timing, channel, frequency, and creative for every individual, adapting as behavior changes.</p><p>Because AI decisioning learns and optimizes in real time, brands no longer need to manage endless segments or retrain static models. The system experiments automatically, balancing what’s best for the customer with what drives business results, and delivering the &quot;next best everything&quot; for every customer, every time.<br/></p><img alt="Table comparing the capabilities of &quot;Next Best Action&quot; and &quot;Next Best Everything (AI Decisioning)&quot; across eight criteria, including approach, personalization, and outcome." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fec5feabd741cd6a72ea3312db158240131035c44-2400x2848.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fec5feabd741cd6a72ea3312db158240131035c44-2400x2848.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fec5feabd741cd6a72ea3312db158240131035c44-2400x2848.png&amp;w=1920&amp;q=75"/><div id="use-cases"></div><h2 id="h-7cc7ea16c95c" class="my-4 text-gray-900 lg:text-display-lg">Common use cases and examples of next best action and next best everything</h2><p>From retail and travel to media, finance, and hospitality, brands use next best action models to guide engagement at key moments. But <strong>next best everything</strong>, powered by AI decisioning, takes those same strategies and makes them adaptive, real time, and truly one to one.</p><h3 id="h-7e8da67c0c44" class="my-4 text-gray-900">Retention and win-back</h3><p><strong>Example use case:</strong>A subscription streaming platform wants to reduce churn by re-engaging inactive viewers.</p><p><strong>When using next best action:</strong>A predictive model identifies customers who haven’t streamed in 30 days. The team builds a microsegment of these users and sends a reactivation email featuring popular new shows in their region—same message, same send time, same channel.</p><p><strong>When using next best everything:</strong>AI decisioning analyzes engagement signals in real time, selecting the best action for each viewer. A frequent binge-watcher might get a personalized push notification highlighting a sequel to their favorite series, while a lapsed user receives a tailored in-app offer right when they’re browsing for something to watch.</p><h3 id="h-2cffadd96610" class="my-4 text-gray-900">Repeat purchase reminders</h3><p><strong>Example use case:</strong>A retail brand wants to increase repeat orders for products people buy regularly, such as coffee, pet food, or skincare.</p><p><strong>When using next best action:</strong>A predictive feature identifies customers who made a purchase around three months ago and are likely to need a restock soon. A campaign is created for that group, and everyone receives the same email reminder to reorder.</p><p><strong>When using next best everything:</strong>AI decisioning learns each customer’s unique purchase rhythm. One shopper receives a push notification just as their order frequency suggests they’re about to run out, while another gets a personalized email offer with related add-ons based on their past basket size and preferences.</p><h3 id="h-417278d9b548" class="my-4 text-gray-900">Cross-sell and upsell opportunities</h3><p><strong>Example use case:</strong>A travel company wants to increase average booking value by promoting add-ons and upgrades.</p><p><strong>When using next best action:</strong>After flights are booked, predictive models flag high-value travelers. They’re grouped into a segment and sent an email offering the same business-class upgrade at checkout.</p><p><strong>When using next best everything:</strong>AI decisioning tests different combinations of offers, price points, and channels for each traveler. It learns which creative and incentive pairings drive action—a frequent flyer might see an in-app message offering bonus miles for an upgrade, while a leisure traveler gets a push notification about family hotel discounts before departure.</p><h3 id="h-0faa428cf280" class="my-4 text-gray-900">Loyalty nudges and rewards</h3><p><strong>Example use case:</strong>A quick-service restaurant or coffee chain wants to boost loyalty program participation.</p><p><strong>When using next best action:</strong>A churn prediction model highlights customers who haven’t redeemed points in 60 days. They receive a standard “You’re only 20 points away!” email, sent to all members in the segment.</p><p><strong>When using next best everything:</strong>AI decisioning identifies the motivator that works best for each customer—sending a push notification with bonus points at their usual visit time, or an in-app message with a reward tied to their favorite order. Every nudge is personalized, timely, and designed to inspire action.</p><h3 id="h-eb14242794b1" class="my-4 text-gray-900">Proactive service experiences</h3><p><strong>Example use case:</strong>A fintech app wants to reduce drop-offs by resolving friction before customers churn.</p><p><strong>When using next best action:</strong>The team sets a rule to trigger a support email after two failed login attempts or a missed payment, offering generic troubleshooting steps to all affected users.</p><p><strong>When using next best everything:</strong>AI decisioning spots early signals of frustration—like repeated navigation errors or sudden inactivity—and selects the best next step. One user receives an in-app message offering quick support options, while another gets an SMS linking directly to a personalized help article, all timed to prevent churn.</p><h3 id="h-37012820ab2d" class="my-4 text-gray-900">Account expansion and trial reactivation</h3><p><strong>Example use case:</strong>A SaaS company wants to re-engage free-trial users and identify opportunities to expand existing accounts.</p><p><strong>When using next best action:</strong>A predictive model highlights users whose trial is nearing its end or who haven’t logged in for a week. The marketing team creates a “high-churn-risk” list and sends a single nurture email offering a product demo to everyone in that segment.</p><p><strong>When using next best everything:</strong>AI decisioning analyzes engagement patterns and usage signals in real time, selecting the best action for each account. A user who explored advanced features might receive a push notification offering a premium upgrade, while another with low activity gets a personalized in-app message with a short tutorial and a free extension to encourage deeper adoption.</p><img alt="A table comparing traditional &quot;Next Best Action&quot; marketing strategies with advanced &quot;Next Best Everything&quot; approaches across six common use cases." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F3b2d81c8f21ef5e997bfa1ab3552772dd52eba71-2400x2848.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F3b2d81c8f21ef5e997bfa1ab3552772dd52eba71-2400x2848.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F3b2d81c8f21ef5e997bfa1ab3552772dd52eba71-2400x2848.png&amp;w=1920&amp;q=75"/><div id="action"></div><h2 id="h-57bf547679ca" class="my-4 text-gray-900 lg:text-display-lg">Putting next best everything into action</h2><p>Kayo Sports shows what happens when a brand takes next best action personalization to the next level. Using BrazeAI Decisioning Studio™, the team turned predictive insights into adaptive, one-to-one engagement—bringing “next best everything” to life for millions of sports fans.</p><h3 id="h-1ac98c47d481" class="my-4 text-gray-900">Playing the long game: Kayo Sports delivers 1:1 engagement at scale</h3><p>Australia’s largest sports streaming platform, <a href="https://www.braze.com/customers/kayo-sports-case-study" rel="noreferrer noopener" class="font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black" aria-label="External link">Kayo Sports,</a> gives fans access to over 50 live and on-demand sports, spanning 30,000 hours of content from FOX SPORTS Australia and ESPN. In a highly competitive market where viewers expect tailored experiences, Kayo set out to create the most personalized streaming environment possible—one that truly understood every fan’s interests, habits, and timing.</p><h4 id="h-6c5bf90e6f16" class="my-4 text-gray-900"><strong>The challenge</strong></h4><p>Kayo Sports needed to engage a diverse audience of fans across channels and devices with messages that felt individually relevant. While their data was rich, their existing systems were rule-based and limited, meaning personalization was still broad and reactive. The team wanted to create a dynamic, AI-driven engagement model that could anticipate each customer’s needs and desires in real time.</p><img alt="An email client displaying an inbox on the left and an open Kayo Sports promotional email with sports imagery and a discount offer on the right." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F17010e253c3eba19db393242cd2e8cfdb80f7124-2400x1200.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F17010e253c3eba19db393242cd2e8cfdb80f7124-2400x1200.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2F17010e253c3eba19db393242cd2e8cfdb80f7124-2400x1200.png&amp;w=1920&amp;q=75"/><img alt="A Kayo Sports ad displayed on a smartphone and tablet, featuring three athletes and an offer of $10 off for 2 months, with a QR code on the tablet." loading="lazy" width="800" height="600" decoding="async" data-nimg="1" class="w-auto max-w-full self-start overflow-hidden rounded-xl" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Ff0aa58257e03db87d0fe16bde4842fc33a7a06c0-2400x1200.png&amp;w=828&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Ff0aa58257e03db87d0fe16bde4842fc33a7a06c0-2400x1200.png&amp;w=1920&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Ff0aa58257e03db87d0fe16bde4842fc33a7a06c0-2400x1200.png&amp;w=1920&amp;q=75"/><h4 id="h-51e3e15be0d1" class="my-4 text-gray-900"><strong>The strategy</strong></h4><p>Partnering with BrazeAI Decisioning Studio™, Kayo Sports built “Customer Cortex”—an AI-powered personalization engine designed to make intelligent, 1:1 decisions at scale. Rather than predicting what might work, Customer Cortex learns from every customer interaction to determine the next best everything: the right message, creative, channel, timing, frequency, and promotion for each subscriber.</p><p>This adaptive system runs on three daily cycles:</p><ul><li class="mb-2 ml-10 list-disc"><strong>Data:</strong> Ten purpose-built machine learning models process first- and third-party data to understand each individual’s behavior and preferences.</li><li class="mb-2 ml-10 list-disc"><strong>Decisioning:</strong> BrazeAI Decisioning Studio™ analyzes those insights and selects the optimal action for each subscriber, personalizing every touchpoint automatically.</li><li class="mb-2 ml-10 list-disc"><strong>Delivery:</strong> Braze activates these decisions across channels—email, push, SMS, and in-app—creating more than 1.2 million unique message variations (up from 300).</li></ul><p>This setup gives Kayo the flexibility to test, learn, and refine continuously. Whether a viewer is likely to engage with an in-app offer before a major match or respond to an SMS about a new subscription tier, Braze orchestrates the right experience automatically—motivating customers to take the next step and strengthening loyalty at every touchpoint.</p><h4 id="h-349cb06f4049" class="my-4 text-gray-900"><strong>The wins</strong></h4><p>Since launching Customer Cortex, Kayo Sports has seen measurable growth across engagement and loyalty metrics:</p><ul><li class="mb-2 ml-10 list-disc"><strong>14% increase in subscriptions</strong> (FY24)</li><li class="mb-2 ml-10 list-disc"><strong>8% increase in annual occupancy</strong></li><li class="mb-2 ml-10 list-disc"><strong>105% increase in cross-sells</strong> to BINGE, another Foxtel streaming service</li><li class="mb-2 ml-10 list-disc">Achieved these gains <strong>while increasing average subscription prices by 20%</strong></li></ul><div class="relative flex flex-col justify-center gap-6 overflow-hidden rounded-xl border border-[#C9C4FF] bg-[#FBFAFE] p-4"><div class="flex h-6 flex-row"><img alt="Kayo Sports logo" loading="lazy" width="200" height="24" decoding="async" data-nimg="1" class="h-full w-auto object-contain" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa2ca14b08bd4e307c08c6b38b7d6c07f2bf414c6-285x150.png&amp;w=256&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa2ca14b08bd4e307c08c6b38b7d6c07f2bf414c6-285x150.png&amp;w=640&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fa2ca14b08bd4e307c08c6b38b7d6c07f2bf414c6-285x150.png&amp;w=640&amp;q=75"/></div><blockquote class="font-aribauGrotesk text-lg text-gray-900"><p>&quot;Customer Cortex is the result of four years of dedicated development and represents our commitment to being at the forefront of technology and data science in media. It’s a mature, incredibly powerful capability at the center of our trading engine that understands and anticipates customer needs, driving engagement, loyalty, and maximizing customer lifetime value...Braze has supercharged our 1:1 relationship with customers by increasing the number of potential actions from 300 to 1.2 million.”</p></blockquote><div><div class="flex items-center gap-4"><span class="block size-10 shrink-0 grow-0 basis-10 overflow-hidden rounded-full bg-gray-100"><img alt="" loading="lazy" width="160" height="160" decoding="async" data-nimg="1" class="size-full object-cover" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fbe20a1d3e561f3baa1875b8388426edf3fe00bb2-631x631.jpg&amp;w=256&amp;q=75 1x, /_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fbe20a1d3e561f3baa1875b8388426edf3fe00bb2-631x631.jpg&amp;w=384&amp;q=75 2x" src="/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2Fb7pblshe%2Fmarketing-prod%2Fbe20a1d3e561f3baa1875b8388426edf3fe00bb2-631x631.jpg&amp;w=384&amp;q=75"/></span><div class="flex flex-col items-start justify-center gap-4 sm:flex-row md:items-center"><h6 class="text-md font-semibold text-gray-900 sm:text-lg">Anthony O&#x27;Byrne</h6><div class="hidden min-h-[30px] border-r-2 border-gray-300 bg-gray-300 sm:block "></div><span class="text-md text-gray-900">Managing Director, Revenue, Kayo Sports &amp; BINGE, Kayo Sports</span></div></div></div></div><div id="challenges"></div><h2 id="h-d766a8bb9ce6" class="my-4 text-gray-900 lg:text-display-lg">Challenges of next best action</h2><p>Traditional next best action models can deliver results, but they come with built-in constraints that make them difficult to scale or sustain. As customer behavior and business needs move faster than static systems can adapt, these challenges become even more visible.</p><h3 id="h-7a76a1f4a1ff" class="my-4 text-gray-900">1. Static models that don’t learn</h3><p>NBA models depend on predictive algorithms trained on historical data. Once deployed, they don’t automatically improve or adjust to new behavior. That means teams must manually retrain and redeploy models—a time-consuming cycle that leaves campaigns lagging behind real customer needs.</p><h3 id="h-7675aeece68a" class="my-4 text-gray-900">2. Brittle systems built on rules</h3><p>Traditional NBA combines predictive scoring with hard-coded business rules, so it can quickly become rigid. Any change in product lines, offers, or audience segments often requires reworking multiple rules and dependencies, slowing teams down and limiting experimentation.</p><h3 id="h-125521db90eb" class="my-4 text-gray-900">3. Limited personalization</h3><p>NBA works well at the segment level, but struggles to scale true one-to-one personalization. It typically evaluates only a few data points or model outputs, missing the nuance needed to tailor timing, content, or creative to individual customers.</p><h3 id="h-78e594175556" class="my-4 text-gray-900">4. Heavy operational overhead</h3><p>Managing multiple models, rules, and tests creates complexity. Each new campaign or data source adds layers of maintenance, making it difficult for marketing teams to move fast or measure results cleanly.</p><h3 id="h-a6531a6c9203" class="my-4 text-gray-900">5. Delayed feedback loops</h3><p>NBA models don’t learn continuously, so performance insights often arrive after the fact. Teams can see what worked, but not in time to adjust while a campaign is live. This limits responsiveness and keeps optimization reactive instead of proactive.</p><p>These challenges don’t make next best action irrelevant, but they do highlight why it’s time for something more adaptive. Next best everything and AI decisioning solve these pain points by learning continuously and simplifying operations, to deliver decisions that improve with every customer interaction.</p><div id="conclusion"></div><h2 id="h-8c297d8410e3" class="my-4 text-gray-900 lg:text-display-lg">Final thoughts on moving from next best action to next best everything</h2><p>Traditional next best action approaches marked an important step toward more personalized marketing, but they were designed around fixed predictions and static segments. Today’s customers expect brands to recognize and anticipate not just what they need, but what they want, and to respond in the moment with relevance and empathy.</p><p>Next best everything brings that possibility to life through AI decisioning, which learns from every interaction and chooses the most meaningful way to connect—across timing, channel, and message. It discovers what truly motivates each customer to act.</p><p>With BrazeAI Decisioning Studio™, brands can create programs that adapt as naturally as their audiences do, showing that intelligence and creativity can work together to make personalization genuinely human.</p><div class="p-8 bg-primary-400 rounded-xl gap-8 flex flex-col text-white"><div class="flex flex-col gap-4"><div class="font-bold text-display-sm">Take your next best step by learning more about next best everything</div></div><div class="flex flex-wrap gap-4"><a class="inline-flex items-center justify-center text-center rounded-full transition-colors whitespace-nowrap relative z-10 gap-1 font-aribauGrotesk font-bold px-[18px] py-[10px] text-md border border-[#4411D6] bg-[#4411D6] text-white shadow-normal hover:border-[#1505C9] hover:bg-[#1505C9] hover:shadow-normal active:border-[#4411D6] active:bg-[#4411D6] active:shadow-focused disabled:border disabled:border-gray-200 disabled:bg-gray-200 disabled:text-gray-400 disabled:shadow-xs w-full sm:w-fit false" href="/get-started">Connect with sales</a></div></div><div id="faqs"></div><p><br/></p></div><div class="space-y-6"><h2 id="faq-accordion" class="mb-0 text-display-xs sm:text-display-sm font-bold">Next best action FAQs</h2><section class="bg-white rounded-xl shadow-xl shadow-purple-500/5 border border-purple-200 overflow-hidden" aria-labelledby="faq-accordion"><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What is next best action in marketing?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What is next best action in marketing?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>Next best action in marketing is a predictive approach that uses data and business rules to suggest the most relevant step for a customer—such as sending an offer, message, or reminder—based on past behaviors and patterns.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="How does next best action work?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">How does next best action work?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p></p><p>Next best action works by analyzing customer data, applying predictive models, and following predefined rules to recommend what a brand should do next. It predicts likely responses rather than driving them.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What’s the difference between next best action and predictive AI?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What’s the difference between next best action and predictive AI?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>Next best action is one way predictive AI is applied in marketing. It uses predictive models to anticipate customer needs and guide messaging, while broader predictive AI can support many functions across analytics, forecasting, and automation.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What are examples of next best action marketing?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What are examples of next best action marketing?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>Examples of next best action marketing include restock reminders for retail, cross-sell recommendations after a purchase, loyalty reward updates, or win-back emails to re-engage lapsed customers.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="How do companies use next best action to personalize campaigns?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">How do companies use next best action to personalize campaigns?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>How do companies use next best action to personalize campaigns?</p><p>Companies use next best action to personalize campaigns by segmenting customers based on predictive scores—such as likelihood to purchase or churn—and tailoring messages or offers for those groups.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What is an example of next best action?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What is an example of next best action?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>What is an example of next best action?</p><p>An example of next best action is a fitness app identifying users likely to cancel and sending them a personalized offer or incentive to renew their subscription.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What is the next best action model?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What is the next best action model?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>The next best action model is a framework that uses customer data, predictive analytics, and business rules to recommend the product, offer, or message most likely to appeal to each customer segment.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What is the next best action in CRM?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What is the next best action in CRM?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>The next best action in CRM refers to the system-generated recommendation for the next step in a customer interaction, such as prompting a follow-up call, scheduling an appointment, or sending a retention offer.</p><p><br/></p></div></div></details><details class="group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50" name="faq-accordion"><summary class="flex items-center justify-between cursor-pointer list-none px-6 py-8 [&amp;::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2" aria-label="What is the difference between next best action and next best offer?"><span class="text-lg font-semibold text-gray-900 group-hover:text-purple-700">What is the difference between next best action and next best offer?</span><svg width="20" height="20" role="img" aria-label="chevron-down" stroke="currentColor" class="shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300" aria-hidden="true"><use href="/icons/sprites.svg#chevron-down"></use></svg></summary><div class="px-6 pb-6"><div class="text-base leading-relaxed text-gray-600"><p>The difference between next best action and next best offer lies in scope. A next best offer focuses on sales or promotions, while next best action includes any type of engagement: marketing, service, or retention-related.</p><p><br/></p></div></div></details></section><script type="application/ld+json">{"@context":"https://schema.org","@type":"FAQPage","mainEntity":[{"@type":"Question","name":"What is next best action in marketing?","acceptedAnswer":{"@type":"Answer","text":"Next best action in marketing is a predictive approach that uses data and business rules to suggest the most relevant step for a customer—such as sending an offer, message, or reminder—based on past behaviors and patterns."}},{"@type":"Question","name":"How does next best action work?","acceptedAnswer":{"@type":"Answer","text":"Next best action works by analyzing customer data, applying predictive models, and following predefined rules to recommend what a brand should do next. It predicts likely responses rather than driving them."}},{"@type":"Question","name":"What’s the difference between next best action and predictive AI?","acceptedAnswer":{"@type":"Answer","text":"Next best action is one way predictive AI is applied in marketing. It uses predictive models to anticipate customer needs and guide messaging, while broader predictive AI can support many functions across analytics, forecasting, and automation."}},{"@type":"Question","name":"What are examples of next best action marketing?","acceptedAnswer":{"@type":"Answer","text":"Examples of next best action marketing include restock reminders for retail, cross-sell recommendations after a purchase, loyalty reward updates, or win-back emails to re-engage lapsed customers."}},{"@type":"Question","name":"How do companies use next best action to personalize campaigns?","acceptedAnswer":{"@type":"Answer","text":"How do companies use next best action to personalize campaigns? 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A next best offer focuses on sales or promotions, while next best action includes any type of engagement: marketing, service, or retention-related."}}]}</script></div></div><div class="flex w-full shrink-0 grow-0 basis-full flex-col gap-8 md:w-[280px] md:basis-[280px]"><div class="flex flex-col gap-5"><h6 class="text-sm uppercase text-gray-600">Related Tags</h6><div class="flex flex-wrap gap-3 text-sm"><a class="block rounded-2xl border border-gray-300 px-3 font-bold py-1 text-gray-700 hover:text-primary-700" aria-label="Search AI and Machine Learning tag" href="/search?resource=Article&amp;topic=AI and Machine Learning">AI and Machine Learning</a></div></div><div class="hidden flex-col gap-8 bg-white  md:sticky md:top-20 md:flex"><div class="rounded-xl bg-primary-500 shadow-xl p-6 flex flex-col gap-6 items-start"><div class="w-full flex flex-col gap-4 flex-1"><span class="text-sm text-primary-100 font-bold">SUBSCRIBE</span><h4 class="!text-white text-display-xs font-extrabold">Be 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Some brands have seen \",[\"$\",\"a\",null,{\"href\":\"https://www.mckinsey.com/capabilities/operations/our-insights/the-future-of-customer-experience-personalized-white-glove-service-for-all\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"revenue rise by more than 30%\"]}],\" simply by understanding customer intent and responding at the right moment. For many marketers, next best action has been a reliable way to do that—using a mixture of predictive models, segmentation and A/B testing to match people with the products or experiences they’re most likely to want next.\"]}],[\"$\",\"p\",null,{\"children\":[\"The ongoing challenge is figuring out how to keep that relevance alive as customer expectations shift, and how to make every interaction feel like the logical next step, for a more satisfying and successful customer experience.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Contents\"]}]]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"href\":\"http://www.braze.com/resources/articles/next-best-action#what\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"What is next best action 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link\",\"children\":[\"Common use cases and examples of next best action and next best everything\"]}]]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"href\":\"http://www.braze.com/resources/articles/next-best-action#action\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"Putting next best everything into action \"]}]]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"href\":\"http://www.braze.com/resources/articles/next-best-action#challenges\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"Challenges of next best action\"]}]]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"href\":\"http://www.braze.com/resources/articles/next-best-action#conclusion\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"Final thoughts on moving from next best action to next best everything\"]}]]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"href\":\"http://www.braze.com/resources/articles/next-best-action#faqs\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"Next best action FAQs\"]}]]}],[\"$\",\"div\",null,{\"id\":\"what\"}],[\"$\",\"h2\",null,{\"id\":\"h-c6814468a47d\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"What is next best action marketing?\"]}],[\"$\",\"p\",null,{\"children\":[\"Next best action is a marketing and customer engagement strategy that decides the most relevant step to take with an individual customer in any given moment. That could be sending a message, presenting an offer, providing service support, or perhaps choosing \",[\"$\",\"em\",null,{\"children\":[\"not\"]}],\" to reach out at all.\"]}],[\"$\",\"p\",null,{\"children\":[\"Early NBA programs often relied on static rules or predictive modeling, where outcomes were forecasted based on past behavior. While useful, those approaches lacked agility and didn’t keep pace with changing customer needs.\"]}],[\"$\",\"p\",null,{\"children\":[\"Modern NBA programs are more adaptive, but still limited.\"]}],[\"$\",\"div\",null,{\"id\":\"why\"}],[\"$\",\"h2\",null,{\"id\":\"h-bca4daf3212e\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Why next best action matters for personalization\"]}],[\"$\",\"p\",null,{\"children\":[\"Next best action marketing is a first step away from broad, one-size-fits-all campaigns. Instead, it makes every interaction more relevant and more valuable—both for the customer and the brand.\"]}],[\"$\",\"p\",null,{\"children\":[\"The impact of NBA shows up in three key areas that matter to marketing teams—the ability to personalize at scale, the efficiency of resource use, and the strength of customer relationships over time.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-13a10b3265ad\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Personalization at scale\"]}],[\"$\",\"p\",null,{\"children\":[\"NBA models typically make recommendations for segments, rather than individuals. This allows brands to get closer to true personalization by scaling their efforts to make messages more relevant than traditional “batch and blast” methods.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-4dfe99352329\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Efficiency and smarter resource use\"]}],[\"$\",\"p\",null,{\"children\":[\"NBA directs offers and messages where they’re most likely to have impact, reducing wasted spend and saving customers from irrelevant noise.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-ad8ba37de111\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Retention and loyalty\"]}],[\"$\",\"p\",null,{\"children\":[\"Segments can be divided into multiple microsegments and manually tested so that each grouping becomes more relevant over time. This strengthens customer relationships and leads to higher lifetime value, stronger loyalty, and more predictable growth.\"]}],[\"$\",\"div\",null,{\"id\":\"explained\"}],[\"$\",\"h2\",null,{\"id\":\"h-e671a4e7e842\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Next best action models explained\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/0b388038d104fe82aa0815d64df3290ad4ffed8f-2400x1200.png\",\"alt\":\"Diagram: Braze powers a cycle of personalization, efficiency, and customer retention, with agents experimenting and learning autonomously.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"p\",null,{\"children\":[\"In principle, any rule or model that takes customer data as input and recommends an action as output could fit under the label of \\\"next best action.\\\" In reality, the term usually refers to a framework that blends predictive models and business rules to decide which product, offer, or message to show a customer next.\"]}],[\"$\",\"p\",null,{\"children\":[\"Here’s how a traditional next best action model typically works:\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"1. Start with raw customer data\"]}],\" — such as purchases, web activity, app sessions, or email engagement.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"2. Transform that data into customer features\"]}],\" — for example, purchases in the last 90 days, average order value, or days since last interaction.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"3. Feed those features into predictive models\"]}],\" — like churn risk, repurchase likelihood, or projected lifetime value.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"4. (Optional) Segment customers\"]}],\" — for instance, grouping anyone with a churn probability above a certain threshold into a “high-risk” audience.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"5. Apply business rules\"]}],\" — deciding which product, offer, or message each segment should receive based on those predictive scores and attributes.\"]}],[\"$\",\"p\",null,{\"children\":[\"By combining predictive models, a type of machine learning called supervised learning, and manual business rules, models are created that produce a set of \\\"if-then\\\" business rules under which they operate. What they predict might resonate with a customer, using patterns from past data to guide the next step. But while these systems can personalize at the segment level, they’re still driven by static models and manual rules—limitations that \",[\"$\",\"a\",null,{\"href\":\"https://www.braze.com/resources/articles/ai-decisioning\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"AI decisioning\"]}],\" is designed to overcome.\"]}],[\"$\",\"div\",null,{\"id\":\"everything\"}],[\"$\",\"h2\",null,{\"id\":\"h-87ed434ece1e\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Next best action vs. next best everything\"]}],[\"$\",\"p\",null,{\"children\":[\"Next best action is based on predictive AI and uses past behavior to forecast the likelihood of future outcomes.This is helpful insight, but it’s rigid, brittle, and expensive to run. Even if you have a lot of microsegments, it’s still not truly 1:1 personalization.\"]}],[\"$\",\"p\",null,{\"children\":[\"“\",[\"$\",\"a\",null,{\"href\":\"https://www.braze.com/resources/reports-and-guides/ai-decisioning-next-best-everything\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"Next Best Everything\"]}],\"” takes the next step. It's powered by AI decisioning built on reinforcement learning. There are no rigid rules or occasional A/B tests. This approach constantly learns from every customer interaction, and decides the best combination of timing, channel, frequency, and creative for every individual, adapting as behavior changes.\"]}],[\"$\",\"p\",null,{\"children\":[\"Because AI decisioning learns and optimizes in real time, brands no longer need to manage endless segments or retrain static models. The system experiments automatically, balancing what’s best for the customer with what drives business results, and delivering the \\\"next best everything\\\" for every customer, every time.\",[\"$\",\"br\",null,{}],\"\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/ec5feabd741cd6a72ea3312db158240131035c44-2400x2848.png\",\"alt\":\"Table comparing the capabilities of \\\"Next Best Action\\\" and \\\"Next Best Everything (AI Decisioning)\\\" across eight criteria, including approach, personalization, and outcome.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"div\",null,{\"id\":\"use-cases\"}],[\"$\",\"h2\",null,{\"id\":\"h-7cc7ea16c95c\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Common use cases and examples of next best action and next best everything\"]}],[\"$\",\"p\",null,{\"children\":[\"From retail and travel to media, finance, and hospitality, brands use next best action models to guide engagement at key moments. But \",[\"$\",\"strong\",null,{\"children\":[\"next best everything\"]}],\", powered by AI decisioning, takes those same strategies and makes them adaptive, real time, and truly one to one.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-7e8da67c0c44\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Retention and win-back\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Example use case:\"]}],\"A subscription streaming platform wants to reduce churn by re-engaging inactive viewers.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best action:\"]}],\"A predictive model identifies customers who haven’t streamed in 30 days. The team builds a microsegment of these users and sends a reactivation email featuring popular new shows in their region—same message, same send time, same channel.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best everything:\"]}],\"AI decisioning analyzes engagement signals in real time, selecting the best action for each viewer. A frequent binge-watcher might get a personalized push notification highlighting a sequel to their favorite series, while a lapsed user receives a tailored in-app offer right when they’re browsing for something to watch.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-2cffadd96610\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Repeat purchase reminders\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Example use case:\"]}],\"A retail brand wants to increase repeat orders for products people buy regularly, such as coffee, pet food, or skincare.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best action:\"]}],\"A predictive feature identifies customers who made a purchase around three months ago and are likely to need a restock soon. A campaign is created for that group, and everyone receives the same email reminder to reorder.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best everything:\"]}],\"AI decisioning learns each customer’s unique purchase rhythm. One shopper receives a push notification just as their order frequency suggests they’re about to run out, while another gets a personalized email offer with related add-ons based on their past basket size and preferences.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-417278d9b548\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Cross-sell and upsell opportunities\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Example use case:\"]}],\"A travel company wants to increase average booking value by promoting add-ons and upgrades.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best action:\"]}],\"After flights are booked, predictive models flag high-value travelers. They’re grouped into a segment and sent an email offering the same business-class upgrade at checkout.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best everything:\"]}],\"AI decisioning tests different combinations of offers, price points, and channels for each traveler. It learns which creative and incentive pairings drive action—a frequent flyer might see an in-app message offering bonus miles for an upgrade, while a leisure traveler gets a push notification about family hotel discounts before departure.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-0faa428cf280\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Loyalty nudges and rewards\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Example use case:\"]}],\"A quick-service restaurant or coffee chain wants to boost loyalty program participation.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best action:\"]}],\"A churn prediction model highlights customers who haven’t redeemed points in 60 days. They receive a standard “You’re only 20 points away!” email, sent to all members in the segment.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best everything:\"]}],\"AI decisioning identifies the motivator that works best for each customer—sending a push notification with bonus points at their usual visit time, or an in-app message with a reward tied to their favorite order. Every nudge is personalized, timely, and designed to inspire action.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-eb14242794b1\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Proactive service experiences\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Example use case:\"]}],\"A fintech app wants to reduce drop-offs by resolving friction before customers churn.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best action:\"]}],\"The team sets a rule to trigger a support email after two failed login attempts or a missed payment, offering generic troubleshooting steps to all affected users.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best everything:\"]}],\"AI decisioning spots early signals of frustration—like repeated navigation errors or sudden inactivity—and selects the best next step. One user receives an in-app message offering quick support options, while another gets an SMS linking directly to a personalized help article, all timed to prevent churn.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-37012820ab2d\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Account expansion and trial reactivation\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Example use case:\"]}],\"A SaaS company wants to re-engage free-trial users and identify opportunities to expand existing accounts.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best action:\"]}],\"A predictive model highlights users whose trial is nearing its end or who haven’t logged in for a week. The marketing team creates a “high-churn-risk” list and sends a single nurture email offering a product demo to everyone in that segment.\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":[\"When using next best everything:\"]}],\"AI decisioning analyzes engagement patterns and usage signals in real time, selecting the best action for each account. A user who explored advanced features might receive a push notification offering a premium upgrade, while another with low activity gets a personalized in-app message with a short tutorial and a free extension to encourage deeper adoption.\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/3b2d81c8f21ef5e997bfa1ab3552772dd52eba71-2400x2848.png\",\"alt\":\"A table comparing traditional \\\"Next Best Action\\\" marketing strategies with advanced \\\"Next Best Everything\\\" approaches across six common use cases.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"div\",null,{\"id\":\"action\"}],[\"$\",\"h2\",null,{\"id\":\"h-57bf547679ca\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Putting next best everything into action\"]}],[\"$\",\"p\",null,{\"children\":[\"Kayo Sports shows what happens when a brand takes next best action personalization to the next level. Using BrazeAI Decisioning Studio™, the team turned predictive insights into adaptive, one-to-one engagement—bringing “next best everything” to life for millions of sports fans.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-1ac98c47d481\",\"className\":\"my-4 text-gray-900\",\"children\":[\"Playing the long game: Kayo Sports delivers 1:1 engagement at scale\"]}],[\"$\",\"p\",null,{\"children\":[\"Australia’s largest sports streaming platform, \",[\"$\",\"a\",null,{\"href\":\"https://www.braze.com/customers/kayo-sports-case-study\",\"rel\":\"noreferrer noopener\",\"target\":\"$undefined\",\"className\":\"font-bold underline decoration-primary-700 decoration-2 underline-offset-1 hover:decoration-black\",\"aria-label\":\"External link\",\"children\":[\"Kayo Sports,\"]}],\" gives fans access to over 50 live and on-demand sports, spanning 30,000 hours of content from FOX SPORTS Australia and ESPN. In a highly competitive market where viewers expect tailored experiences, Kayo set out to create the most personalized streaming environment possible—one that truly understood every fan’s interests, habits, and timing.\"]}],[\"$\",\"h4\",null,{\"id\":\"h-6c5bf90e6f16\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"The challenge\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"Kayo Sports needed to engage a diverse audience of fans across channels and devices with messages that felt individually relevant. While their data was rich, their existing systems were rule-based and limited, meaning personalization was still broad and reactive. The team wanted to create a dynamic, AI-driven engagement model that could anticipate each customer’s needs and desires in real time.\"]}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/17010e253c3eba19db393242cd2e8cfdb80f7124-2400x1200.png\",\"alt\":\"An email client displaying an inbox on the left and an open Kayo Sports promotional email with sports imagery and a discount offer on the right.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/f0aa58257e03db87d0fe16bde4842fc33a7a06c0-2400x1200.png\",\"alt\":\"A Kayo Sports ad displayed on a smartphone and tablet, featuring three athletes and an offer of $10 off for 2 months, with a QR code on the tablet.\",\"width\":800,\"height\":600,\"className\":\"w-auto max-w-full self-start overflow-hidden rounded-xl\",\"loading\":\"lazy\"}],[\"$\",\"h4\",null,{\"id\":\"h-51e3e15be0d1\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"The strategy\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"Partnering with BrazeAI Decisioning Studio™, Kayo Sports built “Customer Cortex”—an AI-powered personalization engine designed to make intelligent, 1:1 decisions at scale. Rather than predicting what might work, Customer Cortex learns from every customer interaction to determine the next best everything: the right message, creative, channel, timing, frequency, and promotion for each subscriber.\"]}],[\"$\",\"p\",null,{\"children\":[\"This adaptive system runs on three daily cycles:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Data:\"]}],\" Ten purpose-built machine learning models process first- and third-party data to understand each individual’s behavior and preferences.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Decisioning:\"]}],\" BrazeAI Decisioning Studio™ analyzes those insights and selects the optimal action for each subscriber, personalizing every touchpoint automatically.\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"Delivery:\"]}],\" Braze activates these decisions across channels—email, push, SMS, and in-app—creating more than 1.2 million unique message variations (up from 300).\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"This setup gives Kayo the flexibility to test, learn, and refine continuously. Whether a viewer is likely to engage with an in-app offer before a major match or respond to an SMS about a new subscription tier, Braze orchestrates the right experience automatically—motivating customers to take the next step and strengthening loyalty at every touchpoint.\"]}],[\"$\",\"h4\",null,{\"id\":\"h-349cb06f4049\",\"className\":\"my-4 text-gray-900\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"The wins\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"Since launching Customer Cortex, Kayo Sports has seen measurable growth across engagement and loyalty metrics:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"14% increase in subscriptions\"]}],\" (FY24)\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"8% increase in annual occupancy\"]}]]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[[\"$\",\"strong\",null,{\"children\":[\"105% increase in cross-sells\"]}],\" to BINGE, another Foxtel streaming service\"]}],[\"$\",\"li\",null,{\"className\":\"mb-2 ml-10 list-disc\",\"children\":[\"Achieved these gains \",[\"$\",\"strong\",null,{\"children\":[\"while increasing average subscription prices by 20%\"]}]]}]]}],[\"$\",\"div\",null,{\"className\":\"relative flex flex-col justify-center gap-6 overflow-hidden rounded-xl border border-[#C9C4FF] bg-[#FBFAFE] p-4\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex h-6 flex-row\",\"children\":[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/a2ca14b08bd4e307c08c6b38b7d6c07f2bf414c6-285x150.png\",\"alt\":\"Kayo Sports logo\",\"width\":200,\"height\":24,\"className\":\"h-full w-auto object-contain\"}]}],[\"$\",\"blockquote\",null,{\"className\":\"font-aribauGrotesk text-lg text-gray-900\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"\\\"Customer Cortex is the result of four years of dedicated development and represents our commitment to being at the forefront of technology and data science in media. It’s a mature, incredibly powerful capability at the center of our trading engine that understands and anticipates customer needs, driving engagement, loyalty, and maximizing customer lifetime value...Braze has supercharged our 1:1 relationship with customers by increasing the number of potential actions from 300 to 1.2 million.”\"]}]]}],[\"$\",\"div\",null,{\"children\":[\"$\",\"div\",null,{\"className\":\"flex items-center gap-4\",\"children\":[[\"$\",\"span\",null,{\"className\":\"block size-10 shrink-0 grow-0 basis-10 overflow-hidden rounded-full bg-gray-100\",\"children\":[false,[\"$\",\"$L27\",null,{\"src\":\"https://cdn.sanity.io/images/b7pblshe/marketing-prod/be20a1d3e561f3baa1875b8388426edf3fe00bb2-631x631.jpg\",\"alt\":\"\",\"width\":160,\"height\":160,\"className\":\"size-full object-cover\"}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col items-start justify-center gap-4 sm:flex-row md:items-center\",\"children\":[[\"$\",\"h6\",null,{\"className\":\"text-md font-semibold text-gray-900 sm:text-lg\",\"children\":\"Anthony O'Byrne\"}],[[\"$\",\"div\",null,{\"className\":\"hidden min-h-[30px] border-r-2 border-gray-300 bg-gray-300 sm:block \"}],[\"$\",\"span\",null,{\"className\":\"text-md text-gray-900\",\"children\":\"Managing Director, Revenue, Kayo Sports \u0026 BINGE, Kayo Sports\"}]]]}]]}]}],false]}],[\"$\",\"div\",null,{\"id\":\"challenges\"}],[\"$\",\"h2\",null,{\"id\":\"h-d766a8bb9ce6\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Challenges of next best action\"]}],[\"$\",\"p\",null,{\"children\":[\"Traditional next best action models can deliver results, but they come with built-in constraints that make them difficult to scale or sustain. As customer behavior and business needs move faster than static systems can adapt, these challenges become even more visible.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-7a76a1f4a1ff\",\"className\":\"my-4 text-gray-900\",\"children\":[\"1. Static models that don’t learn\"]}],[\"$\",\"p\",null,{\"children\":[\"NBA models depend on predictive algorithms trained on historical data. Once deployed, they don’t automatically improve or adjust to new behavior. That means teams must manually retrain and redeploy models—a time-consuming cycle that leaves campaigns lagging behind real customer needs.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-7675aeece68a\",\"className\":\"my-4 text-gray-900\",\"children\":[\"2. Brittle systems built on rules\"]}],[\"$\",\"p\",null,{\"children\":[\"Traditional NBA combines predictive scoring with hard-coded business rules, so it can quickly become rigid. Any change in product lines, offers, or audience segments often requires reworking multiple rules and dependencies, slowing teams down and limiting experimentation.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-125521db90eb\",\"className\":\"my-4 text-gray-900\",\"children\":[\"3. Limited personalization\"]}],[\"$\",\"p\",null,{\"children\":[\"NBA works well at the segment level, but struggles to scale true one-to-one personalization. It typically evaluates only a few data points or model outputs, missing the nuance needed to tailor timing, content, or creative to individual customers.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-78e594175556\",\"className\":\"my-4 text-gray-900\",\"children\":[\"4. Heavy operational overhead\"]}],[\"$\",\"p\",null,{\"children\":[\"Managing multiple models, rules, and tests creates complexity. Each new campaign or data source adds layers of maintenance, making it difficult for marketing teams to move fast or measure results cleanly.\"]}],[\"$\",\"h3\",null,{\"id\":\"h-a6531a6c9203\",\"className\":\"my-4 text-gray-900\",\"children\":[\"5. Delayed feedback loops\"]}],[\"$\",\"p\",null,{\"children\":[\"NBA models don’t learn continuously, so performance insights often arrive after the fact. Teams can see what worked, but not in time to adjust while a campaign is live. This limits responsiveness and keeps optimization reactive instead of proactive.\"]}],[\"$\",\"p\",null,{\"children\":[\"These challenges don’t make next best action irrelevant, but they do highlight why it’s time for something more adaptive. Next best everything and AI decisioning solve these pain points by learning continuously and simplifying operations, to deliver decisions that improve with every customer interaction.\"]}],[\"$\",\"div\",null,{\"id\":\"conclusion\"}],[\"$\",\"h2\",null,{\"id\":\"h-8c297d8410e3\",\"className\":\"my-4 text-gray-900 lg:text-display-lg\",\"children\":[\"Final thoughts on moving from next best action to next best everything\"]}],[\"$\",\"p\",null,{\"children\":[\"Traditional next best action approaches marked an important step toward more personalized marketing, but they were designed around fixed predictions and static segments. Today’s customers expect brands to recognize and anticipate not just what they need, but what they want, and to respond in the moment with relevance and empathy.\"]}],[\"$\",\"p\",null,{\"children\":[\"Next best everything brings that possibility to life through AI decisioning, which learns from every interaction and chooses the most meaningful way to connect—across timing, channel, and message. It discovers what truly motivates each customer to act.\"]}],[\"$\",\"p\",null,{\"children\":[\"With BrazeAI Decisioning Studio™, brands can create programs that adapt as naturally as their audiences do, showing that intelligence and creativity can work together to make personalization genuinely human.\"]}],[\"$\",\"$L36\",null,{\"_key\":\"8f76233d3c77\",\"_type\":\"ctaCard\",\"actions\":[{\"_key\":\"6e514f51856c\",\"_type\":\"ctaAction\",\"actionType\":\"link\",\"buttonSize\":\"lg\",\"buttonStyle\":\"purple-button\",\"buttonText\":\"Connect with sales\",\"iconPosition\":\"trailing\",\"internalLink\":{\"blank\":false},\"link\":{\"blank\":false,\"href\":\"https://www.braze.com/get-started\"}}],\"heading\":\"Take your next best step by learning more about next best everything\",\"markDefs\":null}],[\"$\",\"div\",null,{\"id\":\"faqs\"}],[\"$\",\"p\",null,{\"children\":[\"\",[\"$\",\"br\",null,{}],\"\"]}]]}],[\"$\",\"div\",null,{\"className\":\"space-y-6\",\"children\":[[\"$\",\"h2\",null,{\"id\":\"faq-accordion\",\"className\":\"mb-0 text-display-xs sm:text-display-sm font-bold\",\"children\":\"Next best action FAQs\"}],[\"$\",\"section\",null,{\"className\":\"bg-white rounded-xl shadow-xl shadow-purple-500/5 border border-purple-200 overflow-hidden\",\"aria-labelledby\":\"faq-accordion\",\"children\":[[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"What is next best action in marketing?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"What is next best action in marketing?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"Next best action in marketing is a predictive approach that uses data and business rules to suggest the most relevant step for a customer—such as sending an offer, message, or reminder—based on past behaviors and patterns.\"]}],[\"$\",\"p\",null,{\"children\":[\"\",[\"$\",\"br\",null,{}],\"\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"How does next best action work?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"How does next best action work?\"}],[\"$\",\"svg\",null,{\"width\":20,\"height\":20,\"role\":\"img\",\"aria-label\":\"chevron-down\",\"stroke\":\"currentColor\",\"className\":\"shrink-0 ml-4 text-purple-600 group-open:rotate-180 transition-transform !duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"\"]}],[\"$\",\"p\",null,{\"children\":[\"Next best action works by analyzing customer data, applying predictive models, and following predefined rules to recommend what a brand should do next. 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It uses predictive models to anticipate customer needs and guide messaging, while broader predictive AI can support many functions across analytics, forecasting, and automation.\"]}],[\"$\",\"p\",null,{\"children\":[\"\",[\"$\",\"br\",null,{}],\"\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 [\u0026::-webkit-details-marker]:hidden group-open:pb-4 transition-all !duration-300 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-purple-600 focus-visible:ring-offset-2\",\"aria-label\":\"What are examples of next best action marketing?\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-lg font-semibold text-gray-900 group-hover:text-purple-700\",\"children\":\"What are examples of next best action 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!duration-300\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"use\",null,{\"href\":\"/icons/sprites.svg#chevron-down\"}]}]]}],[\"$\",\"div\",null,{\"className\":\"px-6 pb-6\",\"children\":[\"$\",\"div\",null,{\"className\":\"text-base leading-relaxed text-gray-600\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"How do companies use next best action to personalize campaigns?\"]}],[\"$\",\"p\",null,{\"children\":[\"Companies use next best action to personalize campaigns by segmenting customers based on predictive scores—such as likelihood to purchase or churn—and tailoring messages or offers for those groups.\"]}],[\"$\",\"p\",null,{\"children\":[\"\",[\"$\",\"br\",null,{}],\"\"]}]]}]}]]}],[\"$\",\"details\",null,{\"className\":\"group border-b border-purple-100 last:border-b-0 open:bg-purple-50/50 hover:bg-purple-50/50\",\"name\":\"faq-accordion\",\"children\":[[\"$\",\"summary\",null,{\"className\":\"flex items-center justify-between cursor-pointer list-none px-6 py-8 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