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Date

Tuesday, March 24, 2026 at 4:30 p.m. ET

Call participants

  • Chief Executive Officer — William Magnuson
  • Chief Financial Officer — Isabelle Winkles

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Takeaways

  • Total revenue -- $205 million, up 28% year over year and 8% sequentially from the previous quarter.
  • Organic revenue growth -- 24.3% year over year, reflecting ongoing acceleration for the third consecutive quarter.
  • Braze AI Decisioning Studio revenue -- $5.7 million contributed in the quarter.
  • Annual recurring revenue (ARR) -- Exceeded $800 million early in fiscal 2027, indicating strong platform demand.
  • Remaining performance obligations (RPO) -- Surpassed $1 billion, up 30% year over year and 16% sequentially.
  • Dollar-based net retention (DBNR) -- 109% overall and 110% among large customers, both increasing or stable sequentially.
  • Customer count -- 2,609 as of January 31, up 14% year over year and 81 sequentially from the prior quarter.
  • Large customers ($500,000+ ARR) -- 333, up 35% year over year and 30 sequentially, comprising 64% of total ARR.
  • Million-dollar plus customers -- Rose 28% year over year, outpacing last year's Q4 growth rate of 18%.
  • Q4 bookings -- Increased over 50% year over year, with a new high for average sales price and strong enterprise momentum.
  • Non-GAAP gross profit -- $138 million for a gross margin of 67.2%, compared to 69.9% in the prior year quarter.
  • Non-GAAP operating income -- $15 million (7% of revenue), up from $8 million (5% of revenue) in the prior year quarter.
  • Non-GAAP net income -- $11 million ($0.10 per share), affected by a $5 million OfferFit deferred tax liability adjustment; excluding this, $16 million ($0.15 per share).
  • Full-year non-GAAP operating income -- $28 million with an approximate margin expansion of 400 basis points year over year.
  • Full-year non-GAAP net income -- $42 million, up from $18 million in the previous year.
  • Free cash flow -- $14 million in the quarter; $58 million for the full year.
  • Cash, cash equivalents, restricted cash, and marketable securities -- $416 million as of quarter-end.
  • Share repurchase program -- Board authorized a $100 million program, including a $50 million accelerated share repurchase to be executed before the end of Q1.
  • Geographic revenue -- International revenue accounted for 45% of total, unchanged from previous periods.
  • Guidance — Q1 revenue -- $204.5 million to $205.5 million, representing approximately 26% year-over-year growth, with a midpoint non-GAAP operating margin of 5%.
  • Guidance — fiscal 2027 revenue -- $884 million to $889 million, or 20% year-over-year growth at the midpoint; non-GAAP operating margin targeted at 8% (400 basis points higher than the prior year).
  • Guidance — fiscal 2027 non-GAAP net income -- $69 million to $73 million; per share guidance of $0.61 to $0.65 based on 113 million weighted-average diluted shares.
  • Go-to-market productivity -- Noted "meaningful improvement in sales productivity" following leadership change implemented in late Q2, with Q4 pipeline generation described as "strong."
  • Product launches -- Both Braze AI Operator and Agent Console launched to general availability ahead of schedule; Agent Console demonstrated immediate consumption of Flexible Credits.
  • Premium messaging and cost impact -- Year over year non-GAAP gross margin declined due to higher premium messaging volumes and hosting costs, partially offset by improved personnel cost efficiency.
  • Contract duration -- Cited a small increase in overall contract duration contributing to RPO growth.
  • Pricing model -- Consumption-based pricing, with uptake in Flexible Credits among renewals and new business driving adoption for AI products like Agent Console.

Summary

Braze (BRZE 4.65%) delivered a quarter marked by revenue of $205 million, accelerated organic growth, significant customer expansion, and higher enterprise deal volume, all with disciplined operating leverage and substantial free cash flow.

Product innovation advanced rapidly, highlighted by the early general availability of AI Operator and Agent Console, with management emphasizing immediate and persistent customer uptake of these features and rising monetization via consumption-based credits. The company initiated its first share repurchase program, reflecting long-term confidence and financial flexibility, and issued guidance for fiscal 2027 that targets both continued revenue expansion and over 400 basis points of operating margin improvement, while cautioning that near-term gross margins remain pressured by premium messaging volumes. Management asserted that AI-driven capabilities, context-centric data architecture, and deep integration across a composable platform are proving material in recent competitive displacements, notably with customers migrating from legacy marketing clouds during the period.

  • Braze stated contract renewals accelerated in Q4, contributing to the notable increase in remaining performance obligations and average contract size.
  • Management highlighted that large enterprise customers represent 64% of total ARR and that over 90% of $500,000-plus customers employ the software development kit (SDK), with 50% now adopting Cloud Data Ingestion.
  • Isabelle Winkles emphasized the scalability of R&D spending, noting that non-GAAP R&D expense remained constant as a percentage of revenue while absolute investment increased to support product innovation.
  • The company cited ongoing momentum in AI workload upsells and deeper platform integration among existing customers as key drivers of its "land-and-expand" motion.
  • William Magnuson referenced, "over 25 trillion data points, executed 3.1 trillion AI decisioning inferences, and made 8.7 trillion updates to our user profile system," to underscore platform scale in calendar 2025.
  • Management attributed the year-over-year decline in gross margin primarily to increased demand for premium messaging channels, with new AI product uptake mixing in at marginally higher gross margin, though currently from a small base.
  • Braze maintained that international revenue remained steady at 45% of total revenue, indicating broad geographic stability.
  • The guidance for Q1 reflects seasonality with three fewer days in the period, which management acknowledged as affecting revenue comparability.
  • The accelerated share repurchase is expected to be completed before Q1 end, and EPS guidance incorporates only the impact of this $50 million transaction.
  • OfferFit (now Decisioning Studio) was stated to be contributing primarily via upsell to the existing customer base, with further improvements anticipated as more self-service product tiers are introduced.

Industry glossary

  • Agent Console: Braze's AI-powered interface enabling marketers to build, deploy, and manage marketing agents through conversational prompts and automation, consuming Flexible Credits.
  • Braze AI Operator: An AI tool within Braze's platform that assists users in automating campaign and agent creation and generating data insights, leveraging internal documentation and customer-specific data models.
  • Cloud Data Ingestion: Braze's reverse ETL integration enabling direct connectivity with third-party data warehouses for real-time data sync and orchestration.
  • Decisioning Studio (formerly OfferFit): AI-driven reinforcement learning engine that optimizes complex customer journeys by personalizing decisions at scale across key conversion moments.
  • Flexible Credits: A Braze unit-based billing model tied to consumption of premium features such as Agent Console and multi-channel messaging volumes.
  • RPO (remaining performance obligations): The total value of contracted future revenue yet to be recognized, inclusive of both current and long-term commitments.
  • SDK (software development kit): Braze's developer toolkit enabling integration of customer engagement capabilities across client mobile and web applications.

Full Conference Call Transcript

William Magnuson, and our Chief Financial Officer, Isabelle Winkles. We announced our results in a press release issued after the market closed today. Please refer to the Investor Relations section of our website at investors.braze.com for more information and a supplemental presentation related to today's earnings announcement. During this call, we will make statements related to our business that are forward-looking under federal securities laws and the safe harbor provisions of the Private Securities Litigation Reform Act of 1995.

These statements include, but are not limited to, statements regarding our financial outlook for the first quarter and the fiscal year ended January 31, 2027, the anticipated benefits from and product advancements due to the combination of Braze, Inc. and ongoing developments in Braze AI technology, our expectations concerning new customer verticals, our anticipated customer behaviors, including vendor consolidation and replacement trends and their impact on Braze, Inc., our potential market opportunity and our ability to effectively execute on such opportunity, the execution and anticipated benefits of our share repurchase program, and our long-term financial targets and goals, including our expectations regarding our profitability framework.

These statements are subject to a variety of risks and uncertainties that could cause actual results to differ materially from expectations and reflect our views only as of today. We assume no obligation to update any such forward-looking statements. For a discussion of material risks and uncertainties that could affect our actual results, please refer to the risks identified in today's press release and our SEC filings, both available on the Investor Relations section of our website.

I would also like to remind you that today's call will include certain non-GAAP financial measures used by management to evaluate our ongoing operations and to aid investors in further understanding the company's fiscal fourth quarter 2026 performance in addition to the impact these items have on the financial results. Please refer to the reconciliations of our non-GAAP financial measures to the most directly comparable financial measures calculated in accordance with U.S. GAAP included in our earnings release under the Investor Relations section of our website. The non-GAAP financial measures provided should not be considered as a substitute for or superior to the financial measures of financial performance in accordance with U.S. GAAP.

I will now turn the call over to William.

William Magnuson: Thank you, Chris, and good afternoon, everyone. Today, we reported outstanding fourth quarter results that further validate our product leadership, go-to-market approach, and financial strategy. In Q4, we generated $205,000,000 of revenue, up 28% year over year and 8% from the prior quarter. Organic revenue growth accelerated year over year for the third straight quarter while we continued to drive operating efficiency in our business. Trailing twelve-month dollar-based net retention showed strength as well, inflecting positively during the quarter to reach 109%. Two milestones underscore this momentum in our business. During the quarter, we surpassed $1,000,000,000 in remaining performance obligations as customers increasingly commit to Braze, Inc. for their long-term customer engagement needs.

And early in fiscal 2027, we passed $800,000,000 in annual recurring revenue, demonstrating continued strong demand for the high ROI delivered by our platform. We are incredibly proud of these achievements, and I thank our team across the world for their tireless efforts over the past year. For the full fiscal year 2026, we delivered 24% year-over-year revenue growth and $28,000,000 of non-GAAP operating income, with operating margins expanding nearly 400 basis points over the prior year. This performance demonstrates our ability to deliver on our profitability framework even while accelerating our investments in Braze AI and completing the successful transformation of last summer's acquisition of OfferFit into the rapidly growing Braze Decisioning Studio.

We also realized $42,000,000 of non-GAAP net income in FY 2026, up from $18,000,000 last year, and generated $58,000,000 of free cash flow, providing us with the financial flexibility to invest thoughtfully in shaping the future of customer engagement. Our financial strength has also enabled Braze, Inc. to initiate its first share repurchase program, a milestone that reflects our high conviction in our long-term growth opportunity. Isabelle will provide more details on this program later in the call. Our business momentum accelerated in the fourth quarter as brands look to transform their businesses with AI and further leverage their ongoing investment in first-party data and direct-to-consumer relationships.

Q4 bookings rose over 50% year over year, as we established a new high watermark for average sales price and saw particular strength in the enterprise. Net customer additions increased by 81 sequentially, up 14% year over year. $500,000-plus customers increased by 30 sequentially, up 35% year over year. Additionally, million-dollar-plus customers rose 28% year over year, up from 18% year-over-year growth in Q4 of last year. Large deal velocity was also impressive as we signed 29 deals of $500,000 this quarter, including 7 $1,000,000-plus deals and an expansion that increased our eight-figure customer count to four.

Notable new business wins and existing customer expansions include Dis-Chem, GoodNotes, ID.me, King, Life360, Mytheresa, Power Us, realestate.co.nz, Shell Mobility and Convenience, and ThriftBooks, along with many others. While new logo wins were strong, upsells also showed strength, as existing customers expanded across channels, adopted new Braze AI capabilities, and deepened their integrations with the Braze Data Platform. This expansion of our land-and-expand strategy to include growth in data integrations and AI workloads is a testament to both Braze, Inc.'s composable design and our position as mission-critical infrastructure for our diverse and demanding global customer base.

Competitive takeaways from the legacy marketing clouds in the fourth quarter continue to validate the market's preference for Braze, Inc.'s AI-driven omnichannel approach to deliver on modern customer engagement use cases at scale. This quarter, brands across diverse industries and geographies migrated from legacy platforms to Braze, Inc., including a global heritage footwear brand, a global genealogy company, a leading American cybersecurity company, an American department store chain, an American financial solutions company, a European travel insurance provider, a European national lottery, a luxury hotel group based in APAC, and a large Latin American bank. Looking ahead, we are well positioned to capitalize on the momentum we have been building over the past several quarters.

Our go-to-market motion under the leadership of Chief Revenue Officer, Ed McDonald, who joined in late Q2, is operating at a high level, delivering a meaningful improvement in sales productivity. Pipeline generation was also strong in the fourth quarter, indicating robust market demand for our AI-driven solutions, particularly in the enterprise. The field of customer engagement is moving faster now than it has in years, and Braze, Inc. continues to be perfectly positioned to turn AI disruption into opportunity. By driving platform innovation in tandem with the evolving craft of engagement, Braze, Inc. has been actively redefining the front door to marketing technology for most of the last decade.

This innovative leadership continues to drive share gain in the enterprise and it is why both our customer community and broader partner ecosystem continue to compound with ambition and optimism. Our competitive position rests on four foundational strengths that position us to capitalize on AI-driven disruption. First, the Braze Data Platform and customer engagement stack serve as critical infrastructure for our customers, delivering secure and reliable performance at massive scale. Unlike applications that manage workflows and tasks that are ultimately executed by humans, Braze, Inc. has always been a platform wielded by small teams of builders to directly execute massive, complex workloads.

During calendar year 2025 alone, we powered 4.5 trillion messages and Canvas actions, processed over 25,000,000,000,000 data points, executed 3,100,000,000,000 AI decisioning inferences, and made 8,700,000,000,000 updates to our user profile system of record. This execution capability provides brands with confidence to deploy business-critical programs for entire global audiences, confidence that no point solution can match. Second, our vertically integrated data and decisioning architecture allows for capabilities that no one else can replicate. The Braze Data Platform feeds real-time context into control planes like Canvas and Decisioning Studio, then serves as a substrate for agentic AI execution.

This tight integration between data infrastructure and AI decisioning combined with the most comprehensive set of marketing, conversational, and product channels on the market delivers differentiated outcomes rooted in real customer data, not just generic LLM outputs. Third, our composable AI architecture is compounding the value of our deep infrastructure and comprehensive platform. At our Forge customer conference in September, we announced the upcoming betas for both Braze AI Operator and the Agent Console to be available in Q1 and Q2. Last month, we beat those delivery timelines by months and launched both Operator and Agent Console into general availability.

Leveraging the flexibility of composable data and the power of composable intelligence, these products have been able to quickly spread their wings and capability because they wield the differentiated power, performance, and scale that Braze, Inc. has delivered for years. The excitement from customers and partners around both launches has been palpable. Agent Console is seeing rapid uptake across a wide array of use cases, and Braze AI Operator is accelerating and evolving for thousands of Braze dashboard users every week, automating campaign, Canvas, and agent creation, deepening quality assurance checks, and uncovering data insights through simple conversational prompts.

Operator is first trained with Braze, Inc.'s documentation, use case libraries, and source code and then enhanced by a comprehensive knowledge of each customer's data models, brand strategy, and integration details, enabling each Braze user's Operator to answer difficult questions and execute complex tasks as it navigates the dashboard in front of their eyes. Operator also integrates with Snowflake's Cortex to drive analytics insights that feed back into campaign strategies, and its skills continue to advance rapidly, including the recently trained capability to build directly inside the Agent Console.

While eager beta testers of the Agent Console repeatedly asked us for more templates, we leaped over their request for faster horses and instead delivered a powerful prompt-to-agent capability that turned simple inspiration into sophisticated agents, each specifically configured according to a customer's Braze integration and existing marketing programs. These agents are already being deployed to enhance customer journeys in Canvas and to drive data enrichment workloads in the Braze Data Platform's Catalog feature. And all of this works in tandem with Braze AI Decisioning Studio, which harnesses modern reinforcement learning to achieve maximum performance in the most important parts of the user journey.

Just as we delivered the most comprehensive solution for cross-channel marketing in the age of deterministic automation, we are building Braze AI to combine the flexibility of a composable architecture with the power of frontier AI across multiple fields of research to deliver a comprehensive solution for AI-driven customer engagement. Fourth, we believe Braze, Inc. occupies a unique position in the software landscape as a rare hybrid of a revenue-driving engine and mission-critical operational capability.

Whether it is the urgency of responding to an evolving emergency, the pressure of publishing a message that will be read by hundreds of millions of people, or the criticality of executing deeply optimized marketing programs that drive a business's most important quarterly results, brands trust Braze, Inc. with their most important workloads because we provide the agility, observability, and reliability that business-critical infrastructure demands. In an environment where companies must maximize every dollar of uplift, this proven ability to deliver measurable ROI at scale makes Braze, Inc. an essential and highly optimized component of the modern enterprise stack, not a discretionary tool.

We look ahead, Braze, Inc. will continue to invest with focus to remain at the frontier of consumer and technological change, turning disruption into opportunity as our customers transform their jobs, their businesses, and the experiences that they deliver to consumers. We are rapidly advancing our platform and enhancing our global customer community to scale agentic use cases across marketing programs, customer conversations, product experiences, and data workloads, enabling brands to turn context into connection and achieve in the AI era what they have been striving for all along: stronger business performance built on durable customer relationships.

I will wrap my remarks by emphasizing what a great position we are in as we enter our next phase of growth in fiscal 2027 and beyond. Thank you for your interest and support. I will now turn the call over to Isabelle.

Isabelle Winkles: Thank you, William. And thank you, everyone, for joining us today. We reported an outstanding fourth quarter with revenue increasing 28% year over year to $105,200,000, driven by a combination of existing customer contract expansions, renewals, and new business. Braze AI Decisioning Studio, formerly known as OfferFit, contributed $5,700,000 of revenue in the quarter. This implies an organic revenue growth rate of 24.3% year over year. We are excited to see continued strength from our core business as organic revenue growth accelerated for the third sequential quarter, and we realized robust bookings and strong demand signals for Decisioning Studio and our other AI products.

As William mentioned, in February, Agent Console transitioned to general availability, and we are pleased to report immediate and persistent consumption of our Flexible Credits in its first month of release. In Q4, our total customer count increased 14% year over year to 2,609 customers as of January 31, up 313 from the same period last year and up 81 from the prior quarter. Our total number of large customers, which we define as those spending at least $500,000 annually, grew 35% year over year to 333, and as of January 31, contributed 64% to our total ARR. This compares to a 62% contribution as of the same quarter last year.

As a reminder, a customer is counted when their service date is effective, not when a contract is signed. As such, some new logos won in the fourth quarter will appear in our customer count in 2027. Measured across all customers, dollar-based net retention was 109%, up from 108% in the third quarter of this year. Dollar-based net retention for our large customers was 110%, in line with the third quarter of this year. Expansion was again broadly distributed across industries and geographic regions. Revenue outside the U.S. contributed 45% to our total revenue in the fourth quarter, consistent with the prior quarter of this year and the prior year quarter.

In the fourth quarter, our total remaining performance obligations were just over $1,000,000,000, up 30% year over year and up 16% sequentially. Current RPO was $642,000,000, up 27% year over year and up 12% sequentially. The strong year-over-year growth in RPO and CRPO was driven by four factors: strong Q4 bookings, healthy Q4 renewals, a large quarter for available renewal dollars, and a small increase in overall duration of contracts. Non-GAAP gross profit in the quarter was $138,000,000, representing a non-GAAP gross margin of 67.2%. This compares to a non-GAAP gross profit of $112,000,000 and non-GAAP gross margin of 69.9% in the fourth quarter of last year.

The decrease in year-over-year margin percentage was driven primarily by higher premium messaging volumes and hosting costs, partially offset by improved efficiencies in personnel costs. Non-GAAP sales and marketing expenses were $70,000,000 or 34% of revenue compared to $60,000,000 or 37% in the prior year quarter. While the dollar increase reflects our year-over-year investment in headcount costs to support our ongoing growth and global expansion, the improved efficiency reflects our disciplined approach to investment as we continue to scale and expand our go-to-market organization. Non-GAAP R&D expense was $29,000,000 or 14% of revenue compared to $23,000,000 or 14% of revenue in the prior year quarter.

The dollar increase was primarily driven by increased headcount costs to support the expansion of our existing offerings as well as to develop new products and features to drive growth. Our R&D expenditures reflect our intentional yet disciplined technology investment and are in line with our long-term non-GAAP R&D percent of revenue targets of 13% to 15%. Non-GAAP G&A expense was $25,000,000 or 12% of revenue, compared to $21,000,000 or 13% of revenue in the prior year quarter. The improved efficiency reflects increasing scale across public company expenses and the benefit of leveraging strategic locations for headcount expansion.

Non-GAAP operating income was $15,000,000 or 7% of revenue compared to a non-GAAP operating income of $8,000,000 or 5% of revenue in the prior year quarter. Non-GAAP net income attributable to Braze, Inc. shareholders in the quarter was $11,000,000 or $0.10 per share, compared to $12,000,000 or $0.12 per share in the prior year quarter. Non-GAAP net income was negatively impacted by a $5,000,000 purchase accounting adjustment related to the deferred tax liability from OfferFit, the reinforcement learning engine company acquired in June. Excluding this one-time adjustment, non-GAAP net income and earnings per share were $16,000,000 and $0.15, respectively. Now turning to the balance sheet and cash flow statement.

We ended the quarter with approximately $416,000,000 in cash, cash equivalents, restricted cash, and marketable securities. Cash provided by operations during the quarter was $19,000,000 compared to cash provided by operations of $17,000,000 in the prior year quarter. Including the cash impact of capitalized costs, free cash flow in the quarter was $14,000,000 compared to $15,000,000 in the prior year quarter, and as we have noted in the past, we expect our free cash flow to continue to fluctuate from quarter to quarter given the timing of customer and vendor payments. Before turning to guidance, I would like to take a moment to highlight the Board's $100,000,000 share repurchase authorization.

This authorization reflects our confidence in our fundamentals, outlook, and disciplined approach to capital allocation. We believe the share repurchase represents an efficient and meaningful way to drive shareholder value. As we noted in our earnings release today, the repurchase program includes a $50,000,000 accelerated share repurchase transaction with respect to our stock, which we plan to enter into before the end of the first quarter. In addition, our guidance for share count and EPS includes only the estimated impact of the $50,000,000 ASR. For the first quarter of 2027, we expect revenue to be in the range of $204,500,000 to $205,500,000, which represents a year-over-year growth rate of approximately 26% at the midpoint.

As a reminder, our first quarter contains three fewer days compared to the other three quarters of the year, which each contain 92 days. First quarter non-GAAP operating income is expected to be in the range of $10,000,000 to $11,000,000. At the midpoint, this implies a non-GAAP operating margin of approximately 5%. First quarter non-GAAP net income is expected to be $11,000,000 to $12,000,000 and first quarter non-GAAP net income per share in the range of $0.10 to $0.11 based on approximately 112,000,000 weighted-average diluted shares outstanding during the period.

For the full fiscal year 2027, we expect total revenue to be in the range of $884,000,000 to $889,000,000, which represents a year-over-year growth rate of approximately 20% at the midpoint. Fiscal year 2027 non-GAAP operating income is expected to be in the range of $69,000,000 to $73,000,000. At the midpoint, this implies a non-GAAP operating margin of 8%, a more than 400 basis point improvement versus fiscal year 2026. Non-GAAP net income for the same period is expected to be in the range of $69,000,000 to $73,000,000 and net income per share is expected to be $0.61 to $0.65 per share based on a full-year weighted-average diluted share count of approximately 113,000,000 shares.

It is an exciting time at Braze, Inc. We remain committed to delivering industry-leading customer engagement solutions powered by AI as we continue executing against our long-term financial targets. And with that, we will now open the call for questions. Operator, please begin the Q&A.

Operator: We will now begin Q&A. For today's session, we will be utilizing the raise hand feature. If you would like to ask a question, simply click on the raise hand button at the bottom of your screen. Once you have been called on, please unmute yourself and begin to ask your question. Please limit to one question and one follow-up before jumping back in the queue. Thank you. We will now pause a moment to assemble the queue. Our first question will come from Ryan MacWilliams with Wells Fargo. You may now unmute and ask your question.

Ryan MacWilliams: Hey. Thanks for taking the question. For William, great to see three straight quarters of organic revenue reacceleration in the business. I know last year has benefited from some go-to-market changes along with moving past COVID-era customer renewals. But how do you feel about the underlying growth trajectory of Braze, Inc. from here? Has it improved more sustainably? And I know it is early, but how do you envision AI layering in to support growth trends?

William Magnuson: Yes. Absolutely. Thanks, Ryan. It has been a great back half of the year heading into Q4. I think that the biggest difference in Q4 was also the differentiation of our AI roadmap, especially coming out of our Forge conference in September. It is clear that customers can see where we are going. They can see how our longstanding advantages are being made more and more capable with further investments in Braze AI. That helped with both win rates and deal velocity in Q4, as a lot of the competitor FUD just did not hold water against both our offering and our pace of new product delivery.

We are also seeing stronger momentum with the partner ecosystem, including across both the global agency groups and the more focused regional players that are growing super fast through partnership with us. And our global sales leaders are moving with high velocity. And so I think that when we look at it all coming together, you have a robust product roadmap that is moving at pace. There is really exciting AI innovation that is not only bringing new capability, but it is also making our existing capability more accessible and more leverageable by our customer base.

And we are out in front with that R&D advantage also being combined with a pricing model that has always been consumption based with a global go-to-market organization that operates in all the world's major markets. And a global customer community that has always been the world's most ambitious and creative marketers who have been on the leading edge of rapidly building with Braze, Inc. from the beginning. And so I think that this is just a great moment for all of our existing scale, performance, and innovation advantages to come together, and we are excited for this year.

Ryan MacWilliams: Really appreciate the color there. And then for Isabelle, it seems like the initial full-year revenue guide is slightly stronger than past years. I would love to hear if there is any change to guidance philosophy here, and what were some of the key points that helped you build up to the full-year guide?

Isabelle Winkles: Yes. Thanks for the question. So first of all, no change in the guidance philosophy. Really excited about the momentum that we saw in the business coming into Q4, coming out of Q4 in particular, as William mentioned, across a number of different dimensions. We are seeing more two-year contracts. We are seeing larger in-quarter contract sizes. Upsells continue to be really strong. There is real excitement around our AI capabilities, as William mentioned. Ongoing strength in the enterprise, ongoing strength in the Americas, which has been something we have been working on. And so there is just a lot of momentum across a number of different dimensions.

I think as we mentioned, bringing Ed on in the middle of last year, he has been furthering our efforts across a number of different things that we have already put into motion. And it has been really great to see some of that success across verticalization. So, really excited for that, and you are seeing that in our guide. We are really comfortable with how we have guided for the year.

Operator: Our next question will come from Scott Berg with Needham.

Scott Berg: Hi, everyone. Really nice quarter, and my apologies, dialing in from an airport in case there is background noise. But first question I wanted to ask was off of a channel check or customer conversation, I guess, that we had during the quarter. We got to speak with one of your largest customers, and they noted to us that they had an internal project that spanned 18 months and over $10,000,000 in cost to try to actually replace their entire Braze, Inc. deployment at this well-known brand. But they killed the project because they were only able to achieve about a third of your functionality even after an 18-month time frame.

I guess, William, as you think about a customer in this situation that might want to try to custom code their own platform with one of the generative AI, LLM models, what is most difficult to replace at the end of the day that makes a customer's approach to probably not feasible?

William Magnuson: Yes. I touched on this in the prepared remarks, but I think that the meat of this answer lies in the combined requirement of, one, a tightly integrated high-performance infrastructure that encapsulates both the context and the intelligence layers; and, two, the comprehensiveness required to handle both the vastness of the modern enterprise data landscape on the ingestion side and then the complexity of customer journeys on the output or the interaction side. Within Braze, Inc., the Braze Data Platform is the dedicated context layer and Canvas provides the control plane. They work together to engineer the context that the Agent Console and other Braze AI capabilities harness to drive higher-performing personalization and orchestration decisions.

For B2C audiences, which, of course, is where we primarily work, this has to happen at massive scale, and performance needs to be able to drive real-time interaction across an ever-growing set of channels and direct-to-consumer product interfaces. Over a third of Braze, Inc. customers use us for five or more channels. More than half of them use us for four or more. And amongst our $500,000-plus customers, more than 90% use our SDK. Over 80% use Currents to export the data that Braze, Inc. generates. And already 50% are now using Cloud Data Ingestion, which is our reverse ETL product that connects directly to cloud data warehouses like Snowflake, Databricks, and Google BigQuery.

You overlay that with privacy, security, and regulatory concerns that are related to first-party data and communication consent. Then you add the operational demands that I also mentioned in the prepared remarks of things like demanding marketing schedules, the urgency of capitalizing on cultural moments or managing through emergencies. And then finally, just consider how much investment is already going into building these direct-to-consumer audiences and first-party datasets in the first place. The combined customer acquisition costs and the product investments for major consumer brands, consider the amount that represents. That is the investment that is already made.

Then customer engagement is a multiplier on that investment, and that means that even small basis points matter when it comes to performance. In finance, you understand the importance of having an edge in data, especially when it is driving decisions on large positions. I think sophisticated customer engagement is that same edge on a brand's customer acquisition investment. And we see time and again that settling for good enough just to save a little bit of money on the software line item is really throwing away enterprise value optimization.

And so I know that is multifaceted, but I think that what you see in that anecdote that you shared is that there is a combination of the need for vertical integration, for reliability, for performance, and for comprehensiveness. And then you need to interface that with privacy, security, regulatory complexity, and the need for this to be operated in real time, and doing so with an external environment and complicated businesses and complicated consumer journeys. All that complexity needs to be managed. And that requires, I think, a professional focus on building the tooling and the platforms that address this problem.

Scott Berg: Excellent answer. Well understood. Thank you. And then I guess from a follow-up perspective, I have been at the ShopTalk conference yesterday and today, and all have a big presence here, obviously. But there is a significant amount of brand momentum with new universal commerce protocol in a couple of different areas. I know you all had released your SDK for ChatGPT last fall to take advantage of some of the apps they were embedding in their platform. But as your customers use more of this UCP to capture transactions on these new channels and platforms, how do you all benefit? How do you capture some of that first-party data within that workflow or process?

William Magnuson: Yes. So I think two things. One is that Braze, Inc. will always invest in every new consumer interface that helps us understand the customer and the customer journey better—any source of first-party data. And we will also invest in areas where we can communicate with customers and where we can help drive better personalization for the product experiences that are delivered to them. And, no matter what happens with consumer devices or app stores, the most valuable customers to a brand are going to continue to be those that they have a direct relationship with.

Braze, Inc.'s bread and butter and focus has always been about helping brands expand and strengthen those audiences that they can access through direct-to-consumer interfaces, or other messaging channels that have low marginal cost that are dictated based off of user consent and the right to communicate with them, instead of needing to pay to acquire the right to put a message in front of their eyes over and over again, which of course is a great way to acquire customers, but cannot be how you run a business over the long term. And also managing the first-party data that contextualizes them and then orchestrating the product and messaging interactions that enrich those relationships over time.

And so I think we are always on top of new innovations and developments and new direct-to-consumer interfaces of all kinds. We are always interested in how they can help us learn about customers better, communicate with them in new ways. And, of course, the more complex that landscape gets, the stronger the answer that I provided to your first question is. Because it means that there is even more complexity for where the data comes from. There is even more complexity for where the interactions are. And as I mentioned in response to Ryan, I think that when we look at agentic workflows, they are also characterized by moving even faster.

And that is a place where our focus on performance that we have always historically had is going to be even more important competitively.

Operator: Your next question will come from Raimo Lenschow with Barclays.

Raimo Lenschow: Hey. Thank you. Can you hear me okay?

Operator: Yes. We can hear you great.

Raimo Lenschow: Okay. Perfect. Thank you. Can I start with DBNR? It got better to 109. You talked about it the last few quarters that it is a lagging indicator that kind of takes some time to improve, but it is really nice to see the improvement now. Can you talk a little bit about the journey we should expect from here? That is my first question. Second question is with Ed joining in Q2, normally, big changes to go-to-market happen more at the beginning of a new year. Is there anything we should be aware of as we go into this year in terms of changes that we should expect there? Thank you, and congrats from me as well.

Isabelle Winkles: Yes. So on the DBNR, one thing that I had been providing is at least a directional view on the in-quarter organic number. And so over the last couple quarters, we had talked about it being a little bit below 107 and then a little above 107 and then continuing to kind of trend up from there. What I can say here is that the in-quarter organic is above where we are reporting. So I think the direction of travel here, we are very comfortable with what we are seeing. We are really excited about the momentum in the business that is driving this.

And so it is a lagging indicator but I think we are comfortable that some of the troughing that we have experienced over the last couple quarters—we have talked about being through the belly of the beast—and we are, in fact, through the belly of the beast. So hopefully, that is some helpful color, though we do not specifically guide on the metric. And then with regards to Ed, look, I think in my last set of answers to questions here, I was indicating that Ed has been driving forward a number of initiatives that we had already put into motion. And so not a lot of massive changes.

He is trying to be more effective and efficient about bringing on the new headcount and being rapid in the right areas, being disciplined about where that is being deployed, building out internal capabilities to help our sales team be more enabled and move more quickly. And he—you know, I think we said in the beginning, not only has he seen the movie, but he has seen the remake, and we are definitely seeing the impacts of that, and leveraging his relationships across the potential hires and prospects here. So nothing material that we need to call out, and he is moving things forward in a way that we feel really happy about.

Operator: Your next question will come from Parker Lane with Stifel.

Parker Lane: Hey, guys, thanks. Isabelle, maybe one for you on gross margins. If you look at premium messaging channel growth, you look at some of these new products you have and your comments about the immediate consumption of credits you see from Agent Console, what is the impact to the predictability of gross margins that you are seeing in the business? And what is the right way to think about not only the near-term picture, but sort of the mid-range picture for gross margins as well?

Isabelle Winkles: Yes. So, look, we have talked about the evolution of the premium and how that mixes in. And just keep in mind that, really over the last couple of years, the only new channels that we have introduced are, in fact, these premium channels. So now we are introducing, certainly with the advent of Agent Console and some of the other features here, things that mix in with a slightly better margin. That said, it is starting off of a small base, and so it is going to take some time for all of that to kind of work itself in. And certainly, the premium messaging is still in demand by our customers.

And so I do not think there is so much of an issue necessarily with predictability because we continue to look at that on a fairly detailed basis. But, certainly, we have got eyes on the direction of travel. And really, I think what is important is the 8% operating income margin that we feel really comfortable with for the year, and we are going to continue to manage to that.

Parker Lane: Got it. Thanks. And, William, one for you. You talked earlier about AI not just helping in the form of new product, but making your existing capabilities more approachable and accessible. I was wondering if you could provide a concrete example or two of what you are seeing there. And where do you expect that to translate into business results? Is that better win rate, better utilization, less churn, all of the above?

William Magnuson: Yes. So I think that when you look at the history of Braze, Inc., we more often are held back by making sure that our customer base can really flex into the full power and sophistication that Braze, Inc. has to offer. And one of the things that is most exciting to me about the Braze AI potential right now is that we are both making Braze, Inc. smarter and more powerful, and we are making it easier and faster to use. And so as we redefine that new front door to marketing technology again, the door is both easier to open and when you get to the other side, there is a lot more excitement and value generation there.

And we are already seeing this in the Agent Console. I mentioned an example in the prepared remarks where we had a lot of customers who were working on building new agents within the beta test and were asking for more templates, more ideas, etc. And we actually went through and we had already been working on prompt-to-campaign, prompt-to-email generation, prompt-to-Canvas, where people can actually vibe-code their Canvases directly from the Braze Operator. And not only is it providing you advice, it is literally grabbing control of the dashboard and you watch it happen in front of your eyes.

And so you are both having the Operator act for you, but you are also learning how to use it at the same time as you watch it. And that allows for marketers to then immediately go in and check things, tweak them, refine them, etc.

I think as a platform that is used for publishing at massive scale or where there is a professional skill set of high consequence, when you are running marketing programs that you are relying on to hit your quarterly numbers or that need to go out to a 100,000,000 people around the globe in response to a critical emergency or to take full advantage of an evolving cultural moment or what have you, these are all places where you need to combine both rapid usability with high confidence. And being able to see the Operator building more confidence for people, speeding up their own workflows, and providing that inspiration with our existing feature set is incredible.

Then when we go over to the Agent Console, where people are getting used to prompting, but there is still a lot to do there, and building a good agent does have a skill set around it. There is still some work that you need to do around the outputs from the agent and helping make sure that there is consistency and that you are getting the right context in the context window. And we have built incredible capability in Agent Console to manage that. Even more exciting is that Operator is helping write and inspire those agents for people. And so it is just driving faster adoption.

It is driving higher levels of ability for people to be able to use new features that they maybe had not looked at before. And helping really build stronger confidence for them to use a system like Braze, Inc. that has always been a small number of builders and a small number of marketers wielding it at massive scale. And that has a stress and a pressure and a consequentialism to it that having that Operator assistant there with you really helps increase confidence and we think is going to drive a lot more usage.

Operator: Your next question will come from Brett Huff with Stephens.

Brett Huff: Good afternoon, and congrats on seeing in the financials stuff that I know you all have been working on kind of in the background for a long time. So nice to see that. Two questions. One big picture—I think this is for you, William. As you are hearing—as your conversations with folks you are selling to on AI, our checks tell us that data heterogeneity, lack of AI talent, governance issues are all roadblocks. At the same time, companies seem to want quick-hit ROI things that are happening in order to justify continued spend. How are you—how are those conversations going with Braze, Inc.?

I think your point—more features, easier to get to—but is there some anecdotes there that give us a little bit of meat on the bone on that we might be able to sort of get our head around?

William Magnuson: Well, I think, first of all, every software investment decision being made right now, you have to have confidence that the company that you are spending the time to integrate with, to enable your teams on, and to build with and commit to has their arms around taking advantage of AI innovation. And so I think that is table stakes for everyone even if you, as a team, do not have confidence that you are going to be able to use it all on day zero, or maybe the incremental budget to drive new use cases is not there yet.

There is simply no one that is making a switch in a software vendor right now without having the confidence that it is the right vendor for them to bet on as they move into this future being transformed by AI. And that is why, if you go back to my answer to Ryan's first question and talking about the strength in Q4, so much of that just came out of the confidence in the roadmap and the confidence in the beta test. We were able to show live demos of Operator and Agent Console. Now, obviously, they are out there in general availability, so it is even more palpable for everyone.

And I think that a lot of the things that you just said are true. There is a lot of sources of anxiety around there. A lot of this is still dynamic. It is changing really fast. But at the end of the day, we already see Agent Console driving stronger performance in ongoing campaigns. These are not brand-new experimental use cases. The same customer journeys are actually just being executed on with higher performance. It is driving real revenue, real performance uplift. It is doing so in an environment that is easy to test and experiment and scale with. I mentioned Canvas as an important control plane.

I think when you look at the difficulty of deploying AI in a lot of enterprises, a lot of it has to do with context engineering. It has to do with observability and governance and that control plane. And when you look at Braze, Inc., the Braze Data Platform is providing that context engineering. Canvas is providing that control plane. We have Decisioning Studio as another angle of being able to bring in agentic decision making over deep data science, when that reinforcement learning approach is the right AI technique to apply depending on where you are in the customer journey.

And we have this full spectrum of the right solution and the right approach for the complex problem space of customer engagement, and we can help guide customers to that. And the vast majority of it is relying on the combination of a reliable, high-performance, stable, and secure infrastructure that Braze, Inc. has always maintained as a competitive advantage in our space. And now we are multiplying the value of that with our investments in Braze AI.

Brett Huff: That is super helpful. Thanks for the insight. And, Isabelle, we are hearing more and more about verticalization, and we also got an update on the gross margin—sort of maybe we are going to get some tailwinds on that given the new AI products. Can you talk a little bit about long-term—any change in long-term sort of puts and takes on the gross margin pressures? And then should we think about any step change for verticalization spending, or is that just a matter of course?

Isabelle Winkles: Yes. So I think on verticalization, I would just consider that kind of a matter of ordinary course. We are going to continue to expand slowly but methodically, just deepening our focus on some of the verticals. We already started this over the last couple of years, and I would just continue to expect that to expand. And then from a gross margin perspective, yes, look, we have been talking about the impact of some of the premium messaging. And then I did indicate that in my response to one of the last questions that some of these new products—and Agent Console—do mix in with a better-than-company-average margin.

But it is obviously starting off from low and so will take a bit of time for that to kind of mix in more meaningfully. And so what we are really focused on is the operating income down to the bottom line, and we feel really good about that 8%.

Operator: In the interest of time, please limit to one question. Our next question will come from Arjun Bhatia with William Blair.

Arjun Bhatia: Perfect. Thank you, guys. William, maybe can we touch on—it seems like Agent Console is obviously getting a lot of traction, and I am just curious if you can kind of put that into perspective of when that might help monetization, which types of customers do you think will adopt that first? And then in the broader scheme of things, we are hearing a lot about obviously third-party agent proliferation. So I am curious how that mixes in with Agent Console and if you have any views on what access third-party agents would have to Braze, Inc. or not have to Braze, Inc., and the data that you store for your customers.

William Magnuson: Yes. So I will just hit those topics one by one. So regarding Agent Console and pacing of adoption and revenue, as I shared in the prepared remarks, both Agent Console and Operator went into general availability months ahead of schedule, and we are already seeing great uptake on both. After just a few weeks, more than two-thirds of our customers are now actively using Operator, and we are watching Agent Console adoption grow week over week. But I think we are going to have a lot more to share about both of those at City x City London, which is our second-largest event of the year, just one month from now on April 23 at Olympia, London.

And we are also lining up the entire company and our ecosystem to help push adoption. Agent Console is already showing material results for its beta testers and early adopters, and we are excited for it to spread rapidly across the customer base. But also remind you and everyone else quickly that Agent Console consumes Flexible Credits. So it is consumption-based pricing, but the revenue is recognized the same way that it is for messaging volumes, which is to say that it is ratably over the length of the contract.

So we expect usage of Agent Console to be supportive of early renewals and upsells, but keep in mind that the consumption of credits does not lead to immediate revenue recognition in our contracting model. We do have the benefit that we have been shifting to the Flexible Credits model over the last few years, and the customers who have adopted the new credits plan—which has basically been the default for all renewals and new business over the course of the last couple of years, so it is already the majority of our customer base—already have credits that are ready to be used for Agent Console.

We do have a portion of our customer base that is still on older pricing that we are working hard to move into this new world so that they can also rapidly adopt Agent Console. And that is something that we will be focused on throughout the year. With respect to looking at other tools, I think Braze, Inc. has always been built for composability and built for change. The combination of our composable architecture, our high-performance infrastructure, and our Flex APIs are not just a strong foundation that we use to build our own innovation. I think they are also really well suited for marketer workflows that are both transforming and inflecting through the use of other AI tools.

We have always been architected to be composable, to be ecosystem-neutral, and to integrate with other best-of-breed tools across the modern marketing stack. And that is both for plugging in to enhance things like orchestration and predictive analytics decision making as well as personalization. It is also for evolving new channels, new use cases, new AI capabilities, etc. A few other things. I think we are also seeing that performance in the context layer is more important than ever with agent-to-consumer interactions. Agents move fast and they are tireless, and we think that is a perfect match for the performance and reliability advantage that Braze, Inc. has always maintained over competing and homegrown products.

And, as I mentioned earlier, we have always been a platform where leading-edge marketers with a builder mindset can deploy and optimize sophisticated strategies. And so I think what we are doing with Operator and Agent Console is simultaneously putting more power in their hands and making it easier to use. There are also big advantages to a tool like Operator being inside the Braze, Inc. dashboard information environment, having access to our internal use case libraries, the skills that we have built, the customer's dashboard information architecture, so that it can adapt recommendations and run the dashboard for them with knowledge of their specific Braze, Inc. integration.

We actually had a really great anecdote on Braze Operator that was shared by a customer recently: they were working through a difficult challenge with Liquid that they had spent over an hour on using one of the big chat AI products, and Operator solved the problem for them in a minute because Operator had full knowledge of the way that Braze, Inc. uses Liquid, where it was in the dashboard, and the information architecture. And so I think being able to adapt both the context and the semantic layer and be able to train the Operator with the skills and the knowledge of the dashboard architecture is going to provide differentiation.

But we have always been composable, built for change, and extensible. And so we are also already seeing a lot of customers that are using the Braze MCP Server and using our powerful and flexible APIs in order to innovate their own workflows outside of the ecosystem, and we embrace both of those through our composability.

Operator: Your next question will come from Taylor McGinnis with UBS.

Taylor McGinnis: Yeah. Hi. Thanks for taking my question. William, so I think there is a view out there that customer engagement and marketing is more workflow-heavy and lacking data moats that could make it more vulnerable to AI. You talked a lot about the context layer and what Braze, Inc. is doing there. But could you just maybe unpack that for us? What proprietary data moats does Braze, Inc. have, and does that give you an edge in creating some of the AI solutions you talked about, like Decisioning Studio and Agent Console?

William Magnuson: Yes. So let me answer that on two dimensions. First, talking about context engineering with respect to Agent Console. And then second, talking about when I talk about us having a full spectrum of AI technologies and how we can compose them together to drive more innovation in the future. So first on the context engineering point, I think context engineering, of course, requires comprehensive rapid access to data. I think that underscores the criticality of the Braze Data Platform. And we were very happy to share some of the scale numbers on the Braze Data Platform recently—over 25,000,000,000,000 data points processed last year, and widespread adoption of the multitude of integration options that we have.

And I think that is the beginning of the story because context engineering requires not just access to huge amounts of data quickly, but also deliberate design. And not just because of the cost and performance considerations of large context windows, but also because of the deliberate management of the attention of agents, which is a relatively new concept. We generally tend to think about data as just storage costs and latency and throughput. This idea of attention is really important as well because you can actually have context windows rot. And unless you can keep the agents focused, you start to see outcomes go down and the quality go down.

And it sounds great to be able to dump a 200-page PDF brand book and every historical campaign result and every raw data point that you have ever seen about a user into a large context window and hope for the best, but that is not only slow and expensive. It also leads to lower-quality outcomes and higher volatility that creates both brand risk and lower performance. And so by taking the environment that we built in Braze AI with the Braze Data Platform, with Canvas and Agent Console, they are designed to solve that problem for customers.

It lets them and their Braze AI Operator rapidly build, test, and scale new agent ideas that they have with tremendous promise to improve consumer experience, enhance their own bottom lines, and do so in a way where the context is being managed and governed in a way that is privacy- and regulatory-compliant, and it is being engineered in a way that it is managing the attention of the agents and it is doing so in a way that keeps performance, quality, and consistency top of mind. And so I think that whole problem space has a lot of additional complexity in it.

We are working really hard to solve that for customers, and that is what is going to be able to drive both defensibility and rapid adoption. And then, going from there over to Decisioning, you have heard me speak about the full spectrum of AI technologies that Braze, Inc. is investing in. And I think that this is another advantage of both our R&D scale and the composable high-performance infrastructure that we are built on top of. When we look at Decisioning, there is another paradigm rising that relies on agentic intelligence overseeing deep data science that relies on reinforcement learning.

And for those that maybe have not looked at Decisioning Studio closely in the past, this approach is similar to what personalizes your Instagram feed and injects ads into it. And it is the best way to enable the decisioning system to rapidly learn from past interactions across the rest of your customer base. You cannot just take, “Here are the last 10,000,000 push notifications that I sent, and how everyone responded to them and everything about them,” and jam it all into a context window and expect an LLM to be able to keep its attention in the right place and make sense of that.

But by using decisioning and reinforcement learning around that, you are actually able to find those hidden points of resonance between the content and the engagement strategy and the individual customers and be able to drive that interaction. And that is also a field that is rapidly advancing. I have talked about how today it is best deployed to optimize the most valuable transition points in the customer journey, like when a free-trial streaming subscriber is upgrading to premium or when an on-demand or a banking customer adopts a new service or an add-on product that makes them more valuable and secure at the same time.

So you want to bring that kind of heavyweight data science approach into those problems exactly because they are your most valuable and they are where you want to have the best performance uplift. But over time, we plan to continue to use decisioning science combined with agentic reasoning to increase the applicability of both approaches across more and more of the small moments in a customer journey as well so that customers can continue to harness these different approaches to AI and combine them together to get the best outcomes for consumers and for their businesses.

And so, when we look broadly across the space, I think there is so much opportunity for additional value to be created out of depth. And, if you go back to the question from earlier about why it is hard to build Braze, Inc. and where that incremental value comes from, and think about the leverage that you get out of the investment made in building first-party audiences, combining together these optimizations and being able to compound them over time and to be deliberate about it is exactly how you drive additional bottom line. It is how you drive higher loyalty in your customer relationship and it is how you get competitive edge in these ruthless consumer markets.

And so, we just believe that the brands that win these markets are going to be the ones that are arming themselves with the most sophisticated tooling and the strongest context engineering, not just trying to throw a whole bunch of data into a context window with a frontier model and hoping for the best.

Operator: Your next question will come from Brian Peterson with Raymond James.

Brian Peterson: Congrats, guys. Thanks for letting me take the question here. So, given the really good bookings this quarter, I am curious, has that changed your thoughts on sales hiring as you enter fiscal year 2027? And, Isabelle, if you could unpack some of the individual margin drivers by OpEx line and gross margin as we think about ramping into that 8% number for fiscal year 2027? Thanks, guys.

Isabelle Winkles: Yes. So, just in terms of hiring—and we talked about this as we were closing out, getting into the end of the year last year—as we have seen rep productivity continue to improve through last year, we already put into our plan that we were going to hire incremental sales capacity. So that is underway and has been underway and continues to work productively. So definitely excited about that. And then as we think about kind of the pathway here to the 8%, look, I mean, the place where it is going to come out of mostly is, in fact, sales and marketing. We continue to expect to get efficiencies of scale there.

And then G&A, as we continue to lean on some of these strategic locations, that is going to be helpful as well. R&D, we have said, is already kind of just operating where we expect it to. So we are really excited about the continued scale we are going to continue to get out of the sales and marketing piece.

Operator: Your next question will come from David Hynes with Canaccord.

David Hynes: Hey, guys, congrats on the nice quarter and the strong guide for fiscal 2027. Isabelle, I am going to pull on that bookings thread as well. When you talk about a 50% year-over-year increase in bookings, obviously, the timing of renewal cohorts can impact that math. I normally would not ask a bookings question, but since you shared the metric, I will a little bit. Any way to help us think about net new ACV growth? Is that growing faster than run-rate revenue growth? I am just trying to get a handle on the magnitude of the strength you saw in the quarter.

Isabelle Winkles: Yes. I mean, look, I think both from renewal cohorts that then kind of added to through upsells as well as kind of the net new business, I think both were really strong. So I think overall, the momentum in the business in Q4 definitely kind of accelerated within the quarter. The renewals that we saw were very, very strong. And as we continue to work on the down-sell pressure that we had been seeing in years past, I think it is a combination of all of those things mixing together. Obviously, that strength in Q4 was certainly a part of the storyline going into this year and what helped us with the guide and our confidence in the outlook.

Operator: Your next question will come from Nick Altmann with BTIG.

Nick Altmann: Awesome. Thank you. Isabelle, can you just talk about what drove the strength in revenue this quarter? And just how much of the outperformance from Decisioning Studio is in subscription revenue versus professional services? Thanks.

Isabelle Winkles: Yes. I mean, they mix in a little bit more with a little bit more professional services, but the reality is the proportion of professional services writ large across the company—the mix shift is not changing dramatically. And so we really sell professional services in order to sell more software. And as the bookings strength continues to be strong, there is some element of implementation and onboarding that is mixing into that. Obviously, we are also trying to bring in more partners to bring that in. So I would not read too much into the distribution between the two. The reality is that we truly sell professional services in order to sell more software.

Operator: Your next question will come from Matthew Bensley with Cantor Fitzgerald.

Matthew Bensley: Great. Thanks for taking the question. Maybe touching on the question about build-it-yourself earlier from a different angle. Are you guys using some of these AI coding tools internally to keep your advantage from a technical perspective and just evolve just as quickly as anybody else can? How is that, I guess, impacting the rapid adoption of some of this functionality? And, if anything, how does it impact your cost structure?

William Magnuson: Yes. So I think that when we look at Braze, Inc.'s R&D overall, my major takeaway is just how excited I am that we have got in place a cadre of long-tenured leaders that have a ton of experience navigating through disruption. We were born in the disruption of smartphones. We have been in probably one of the most competitive software categories our whole existence.

We have got a team that knows how to navigate disruption and knows how to win together, including both of our technical co-founders still here, both of the OfferFit co-founders still here, and a bunch of long-tenured R&D leaders that are driving ahead innovation pace and urgency, combined with experience of navigating disruption and really understanding our deep global customer community. And so, with respect to the adoption of agentic coding, you see it in the results: we released the Operator and the Agent Console months ahead of schedule. That was due to a combination of a strong beta test but also because of the velocity increases that we are seeing.

Braze, Inc. engineering is also at all-time highs in pull requests per engineer per week and lines of code per week. But just like AI slop is not producing value for differentiated investment analysis, the volume of code is not the whole story there. The craft of building and scaling valuable software applications for professional workflows and enterprise workloads that are also transforming themselves is going to remain incredibly valuable—one that we are really excited to apply to customer engagement at scale. And I think that we are right in the throes of this, having fun with it.

We are moving at pace, and we are really excited about what this means for our entire software category to go through another reinvention. A team that was born exactly because of the opportunity that came out of the disruption of mobile gets to see our product space transform again, find new opportunity. We get to do it this time with a global customer community, with a global go-to-market organization, with a lot more experience, but we are moving faster than ever. So it is just really exciting all around.

Operator: Your next question will come from Brian Schwartz with Oppenheimer.

Brian Schwartz: Thanks for taking my question and congrats on a strong finish to the year. William, I wanted to ask you a question again on the moat with AI. Maybe I would ask you the question in the form of the origin of the data model. So if you think about the outputs that are coming in your AI products and the decision engine, is it possible to think about what percentage of those—of the AI output—is coming from signals that are being trained on data specific and proprietary to Braze, Inc. versus those third-party foundation models in the market? Thanks.

William Magnuson: Yes. So when you look at, for instance, everything going on in Decisioning Studio, those are reinforcement learning models that are proprietary to Braze, Inc. They are trained with our customers' data. The data is being fed through the Braze Data Platform. When you look at Agent Console, that is a combination of context engineering that is being done by Braze, Inc. that I spoke about earlier, but of course it is relying on the foundational models to be able to provide the broad-based reasoning and personalization capability. That is a big part of the distinction that I made earlier as well because there is no one size fits all.

And while there is a lot of work that the foundational models can do and a lot of great opportunity for them to be able to do things like personalization once you already have a recommendation algorithm that has narrowed down the choices and you want to write an email that is maybe comparing the top choices for someone so that they can compare and contrast and you can drive up the conversion window, we have also found that being able to combine reinforcement learning with the intelligence that is in the foundational models is actually the best approach to not only get the highest performance, but to be able to improve it over time.

I am sure you have seen in your own experimentation with LLMs that being able to continue to understand what is driving their performance and improve it over time is more of an art than a science. The explainability and observability within them and the attribution of what data really drives better outcomes for them is still a very hard, unsolved problem. Within Decisioning and within the reinforcement learning engine, we are able to actually see what data is moving the needle, and what can be thrown out, what can be optimized, and then go and search for more signals that are along the lines of the ones that are really driving better uplift, etc.

So I think that is why we think that the right approach here is multifaceted. It is multifaceted both from a data source perspective, which is why you see the investment and the scale and the composability in the design of the Braze Data Platform, and then also one where you need to be using multiple approaches to assemble context and utilize your own bespoke training alongside, of course, the formidable intelligence that exists in the frontier models.

Operator: Your next question will come from Sitikantha Panigrahi with Mizuho.

Sitikantha Panigrahi: Thanks for taking my question. It is good to see some of this AI momentum. I want to ask specifically on OfferFit. It has been a year almost since acquisition. What kind of discussion you are having with your installed base? What kind of traction are you seeing cross-selling to the installed base? And then specifically, on the margin side, I know there are some plans there to improve it. What kind of progress are you making on improving margin for OfferFit?

Isabelle Winkles: Yes. So I think just on the installed base, that is the primary area where the sales and the upsells are happening—is, in fact, within our installed base. There is a lot of momentum. The pipeline is strong. There is a lot of interest. And then on the margin front, yes, look. There is a growth element here where, as we bring on the necessary staff to enable the implementations and onboardings for customers buying it, we have to handle that expense. And that does mix into margins as well. But we are very focused on that, and we have been continuing to work on the product.

And we are also working on expanding product tiers to include products that are a little bit more self-service, and those will also mix in with higher margins as well. So a number of things in flight to continue to work on that. But we are just excited overall for the momentum with our existing customers and how it is shaping up.

Operator: There are no more questions at this time. I will now turn the call over to William for closing remarks.

William Magnuson: I want to thank everybody for joining us today. As I mentioned, we are excited for City x City London in about a month, and then we will see you after Q1.