AI marketing software: How to choose and optimize AI-driven marketing platforms

Your marketing team probably isn’t short of data. They're more likely short of time to figure out exactly what to do with it. The information exists, but acting on all of it manually, at speed, across every channel simultaneously, is simply not possible.

Enter AI marketing software. These essential tools for modern marketers use machine learning, predictive analytics, and automated decisioning to handle the kind of complex, high-volume personalization and campaign optimization that no team, no matter how skilled, can do by hand.

AI-driven marketing platforms have matured over recent years, with a variety of types available. It’s good that there is software that can be highly in-tune with your brand, rather than a generalised tool, but it also makes choosing which one to use much more difficult.

Before you take the plunge, this guide will help you understand what to look for, the benefits to focus on and how not to get swayed by a snazzy feature list. Backed up by real-life case studies from Braze customers, you’ll see how AI marketing software is a competitive edge you don’t want to miss out on.

TL;DR

• AI marketing software uses machine learning, predictive analytics, and AI decisioning to personalize and optimize campaigns across channels at a scale no team can replicate manually.
• The category spans six main capability areas, from marketing automation and predictive segmentation to multi-channel decisioning, content optimization, and AI-powered analytics that generate measurable insights across every customer interaction.
• Choosing the right platform comes down to four things: how it integrates with your data, how transparent its models are, whether it natively supports your channels, and how it scales with your business.
• Kayo Sports, 24S, and foodora each applied AI marketing software to a specific problem and saw measurable results, including a 105% increase in cross-selling, a 35% uplift in purchase conversion rate, and a 26% reduction in unsubscribe rates respectively.

Key takeaways

• AI marketing software is defined by six core capabilities: predictive analytics, AI personalization, marketing automation, offer and incentive personalization, multi-channel decisioning, and performance analytics. Understanding each one helps you evaluate platforms more clearly.
• Integrating AI marketing software into your cross-channel marketing strategy gives you a unified view of every customer interaction, allowing decisioning to happen across email, push, SMS, in-app, and other channels simultaneously.
• Predictive marketing identifies high-intent and at-risk customers before they act, allowing you to engage at the right moment with the right message.
• AI marketing platforms do more than automate execution. The analytics and insights they generate feed back into campaigns continuously, improving performance over time.
• Data quality is the foundation of everything. A platform with strong AI capabilities will still underperform if the data flowing into it is incomplete or siloed.

What is AI marketing software?

AI marketing software is a category of technology platform that uses artificial intelligence and machine learning to automate, predict, personalize, and optimize marketing activity across channels. It can improve engagement, efficiency and ROI.

Core capabilities of AI marketing software are predictive analytics, personalization engines, and campaign automation. Each feeds the next: predictions inform personalization, personalization drives automation, and automation can execute across every channel a customer uses. Most platforms can connect to existing CDPs, CRMs, and data warehouses, so the AI software can work with customer data that brands already have.

Key benefits of AI marketing software

Adopting AI marketing software gives your team a boost in what they can achieve. The advantages are many and they compound too, but let’s take a look at the top benefits.

AI personalization and real-time adaptive messaging

AI personalization tailors content, timing, channel, and offer for each individual customer based on their behavior and predicted intent, adapting continuously as new signals come in. A recommendation that reflects what someone browsed yesterday, or a message timed to when they typically open their inbox, will consistently outperform a generic campaign sent on a fixed schedule to a broad segment. Running campaigns across multiple touchpoints builds trust over time, and trust is what keeps customers around.

Predictive marketing, automated decisioning, and smarter targeting

Predictive marketing uses machine learning models to anticipate customer behavior, flagging high-intent users before they convert, or customers at risk of churning before they cancel. Automated decisioning acts on those predictions at scale, determining the optimal message, channel, and timing for each customer without you needing to manually configure every scenario. Your marketing budget concentrates on the audiences and the actions most likely to produce results.

Improved campaign efficiency and ROI

Automated marketing workflows handle the execution layer, freeing you and your team to focus on strategy and creative. Campaigns that previously took days to configure can run continuously, adapting based on incoming data without anyone needing to manually reconfigure them. If you're managing multiple campaigns across multiple channels at the same time, you'll feel that time saving quickly.

AI customer engagement and unified insights across channels

AI customer engagement orchestrates interactions across every channel your customers use, creating a coherent and connected experience. Cross-channel AI brings email, push, SMS, in-app, and other touchpoints into journeys that respond to customer behavior in real time. The insights generated across those interactions feed back into your campaigns continuously, giving you a clearer picture of what's driving performance and where to focus next.

Data-driven recommendations for optimization

AI marketing platforms identify patterns, highlight underperforming segments, and recommend where to act next. Those AI-driven customer insights improve with time, as models accumulate more data from your specific audience and channels. The more consistently you act on those recommendations, the more performance lift compounds across campaigns.

Types of AI marketing tools and features

AI marketing tools span six main capability areas. Most enterprise platforms combine several into an integrated suite, but understanding what each one does helps you identify what you actually need and what to prioritize when evaluating options.

1. Marketing automation software and campaign workflows

Marketing automation software manages campaign execution at scale, handling scheduling, triggering, channel routing, and optimization that would otherwise eat up significant time. AI-powered automation makes probabilistic decisions based on everything known about each customer, so your campaigns stay relevant without you manually reconfiguring them every time conditions change. For complex, multi-step customer journeys, this handles the decision points automatically.

2. Customer segmentation and predictive targeting

AI-powered segmentation builds dynamic audience groups that update automatically based on real-time data. Machine learning marketing models identify patterns in behavior, purchase history, and intent signals you'd find difficult to spot manually. Predictive targeting scores customers on the likelihood of specific actions, such as purchasing or churning, so you can focus your campaigns on the people most likely to respond. Your audiences stay more precise and more current than anything built through manual segmentation.

3. AI-powered content optimization

Content optimization tools use AI to test and improve message content based on what resonates with your specific audience. Subject line testing, copy variation analysis, image selection, and send-time recommendations are all informed by behavioral data. AI copywriting assistants help you generate and refine message content faster.

4. Offer and incentive personalization

AI item recommendation engines analyze each customer's purchase history, browsing behavior, and affinity patterns to suggest the products, content, or offers most likely to resonate with them individually. For eCommerce brands, showing a customer a product they're likely to buy can produce measurably better conversion rates. Personalized offers also help you target incentives at customers who need a nudge, rather than discounting to people who would have converted anyway.

5. Multi-channel decisioning and orchestration

AI decisioning acts as an always-on brain that sits above your data systems, automatically selecting the winning combination of message variant, offer, channel, timing, and frequency for each individual customer. You define what success looks like and provide the campaign assets; the AI learns from your customer data and determines the best action for each person at scale.

Because it learns continuously, updating its models as engagement patterns change, cross-channel optimization can be achieved in real time, keeping your campaigns relevant as customers move through different life stages or behaviors.

6. Analytics dashboards and performance tracking

Brands can measure ROI and engagement from AI marketing software through AI analytics. A dashboard will interpret performance data and identify trends rather than simply reporting on what happened. For teams running always-on programs across multiple channels, AI-powered performance tracking means faster iteration cycles. You can course-correct while a campaign is still running, and the connection between analytics and AI decisioning creates a productive loop.

How to select the right AI marketing platform

Picking the right platform is less about finding the most features and more about finding the right fit for your data, your channels, and your team. Here's what to look at.

Assess scalability, integrations, and data management

Check whether the platform integrates natively with your existing CDP, CRM, and data warehouse, and ask specifically how it handles data ingestion, synchronization, and governance. Platforms that process data in real time give your campaigns more current information to work with. Ask directly whether the platform processes in real time or in batches, and what that means for campaign execution.

Evaluate AI/ML modeling capabilities and predictive accuracy

Ask how models are trained, how often they update, and how transparent the platform is about what's driving its recommendations. A platform that can explain its decisioning logic is easier to trust, easier to optimize, and far easier to get stakeholder buy-in on. Ask to see examples of how the platform reports on model performance and what levers you have to influence it.

Look for cross-channel support and orchestration flexibility

Native cross-channel support means email, push, SMS, in-app, and other channels are managed from a single orchestration layer, giving your AI decisioning a complete, real-time view of every customer interaction. Orchestration flexibility allows you to configure journey logic, set frequency caps, define re-entry rules, and adjust campaign paths without needing engineering support every time. The more control your team has over how journeys are structured and adapted, the faster you can respond to what the data is telling you. Ask whether channels are managed natively or through third-party integrations, how customizable the journey logic is, and what your team can configure independently.

Prioritize ease of use, support, and security/compliance

Spend time with the interface before you commit, and evaluate the quality of onboarding, documentation, and ongoing support. Security and compliance capabilities are particularly important if you're operating across multiple regions with different data protection requirements. Look for platforms with recognized security certifications and a clear approach to data governance built in.

Case studies and real-life customer examples

A sports streaming service in Australia, a luxury fashion retailer in France, and a food delivery platform across Europe. Three very different businesses, three very different problems, and one thing in common: measurable results from applying AI marketing software to the right use case.

1. Kayo's Customer Cortex scores a 105% cross-selling increase

Kayo Sports is Australia's largest and fastest-growing sports streaming service, launched as part of the Foxtel Group and now a DAZN company. It gives sports fans instant access to more than 50 live and on-demand sports across TV, mobile, and web. Personalization runs across the entire Kayo customer experience, from tailored landing pages at sign-up to in-app content curation based on favorite sports and teams.

The challenge

Kayo Sports needed to engage a diverse fanbase with personalized content and recommendations across multiple devices and channels. Existing systems limited personalization options, and without predictive insights driving communications, customer experiences were more generic than the data would allow.

The strategy

Using Braze and BrazeAI Decisioning Studio™, Kayo Sports built the "Customer Cortex," an AI-powered personalization engine at the center of their marketing operation. Ten purpose-built reinforcement learning models, trained on first- and third-party data, determine the optimal message, creative, channel, timing, frequency, and promotion for each individual subscriber. Four channel-specific Braze Canvas journeys handle execution across email, SMS, push notifications, and in-app messages, delivering 1.2 million personalized message variations, up from 300.

The wins

• 14% increase in subscriptions in FY24
• 8% increase in average annual occupancy
• 105% increase in cross-selling to BINGE, another streaming service in the Foxtel portfolio
• All achieved while average subscription prices increased by 20%

2. 24S adds to cart: AI recommendations drive a 35% conversion uplift

24S is LVMH's online luxury retailer, bringing Parisian elegance to a global audience. Launched in 2017, it offers a curated selection of more than 60 brands, from luxury houses to contemporary designers. Delivering a premium, personalized digital experience that mirrors luxury in-store service is central to what 24S does.

The challenge

24S wanted to increase purchase frequency and maximize the rate of first purchase, but their previous approach used multiple disconnected platforms for triggered emails, mobile messaging, and recommendations. The result was disjointed customer experiences and significant internal overhead.

The strategy

Using Braze, 24S built eight personalized triggered campaigns designed to turn friction points into engagement opportunities. Their abandoned cart campaign targeted customers who had left items in their cart 30 days prior when those items were running low in stock, using inventory data integrated via Braze Catalogs and Liquid operators to create authentic urgency.

Their back-in-stock campaign used Braze AI Item Recommendations to add four personalized product suggestions to each alert, encouraging continued shopping while customers waited for their desired item. Both campaigns were built within a single platform, consolidating what previously required three separate systems.

The wins

• 35% increase in purchase conversion rate (3-day purchase) in the last six months, driven by the abandoned cart low-stock campaign
• 7% increase in add to cart rate in the last six months, driven by the back-in-stock campaign with AI Item Recommendations

3. foodora delivers on timing: AI optimization cuts unsubscribes by 26%

foodora is a leading food delivery service operating in more than 700 cities across Europe. Their mission is to provide a fast, affordable, and seamless experience that gives people more time for what matters most to them, building customer relationships that grow over time through trust and relevance.

The challenge

foodora's customer communications were fragmented across multiple platforms that lacked predictive insights. Scheduled send times meant messages often landed at the wrong moment for individual customers, reducing their effectiveness and contributing to higher unsubscribe rates than the team wanted.

The strategy

By adopting Braze, foodora unified their cross-channel marketing across email, push notifications, and in-app messaging. Using BrazeAI™ Intelligent Timing, they moved from pre-defined campaign send times to AI-optimized delivery, with each message sent at the moment each individual customer was most likely to engage. The team began with new customers in Austria during the onboarding flow, using unsubscribe rate as an early performance signal, before expanding the approach to additional regions and channels.

The wins

• 41% conversion rate from messages sent
• 26% reduction in unsubscribe rate with Intelligent Timing
• 6% increase in push direct opens

Final thoughts and takeaways: Choosing marketing AI solutions that grow with you

If you've got this far, you probably already know your current setup isn't keeping pace with what you need. That's a good place to be. It means you know what problem you're solving. And the criteria and questions in the selection section above give you a great starting point for evaluating your options.

AI marketing software transforms campaigns from static to adaptive, responding to customer behavior in real time as models learn more about your specific audience. The longer you use it, the more precise it gets.

And this precision is what drives measurable engagement, retention, and revenue. Each interaction generates data, which improves the models, which then improves the next campaign. A platform that works well today should work even better for you next year.

If you're seeking scalable, data-driven personalization, that compounding effect makes AI marketing software an essential part of the stack.