Machine learning in marketing

Machine learning (ML) is changing how companies engage customers and drive growth in the marketing realm. In 2024, Gartner predicted that leading organizations would use ML in their sales process, a forecast now coming true. In fact, according to Salesforce, 75% of marketing organizations have already implemented or are experimenting with AI (artificial intelligence) solutions, and 92% of marketers say AI has affected their role. Marketers use ML for personalization, real-time customer support, and making sense of big data. Unlike manual analysis, ML sifts through large datasets quickly to uncover patterns and predictions for faster and more informed decisions. The result is that ML now touches nearly every aspect of digital marketing, from customer segmentation to content creation. Here are the most important ways ML is changing marketing and what it means for businesses.

Customer segmentation and targeted marketing

One example of ML in marketing is customer segmentation, or grouping customers into clusters based on shared attributes or behaviors. Algorithms analyze demographic data such as age or gender, psychographic data such as interests and lifestyle, geography, past purchases, and web behavior to create granular audience segments. This far exceeds what can be done with traditional manual segmentation. With ML-driven segmentation, marketers then target each group with personalized messages or offers best suited to their needs, increasing relevance and engagement.

Effective targeting matters because personalized offers drive conversion. For example, 65% of customers cite targeted promotions as a top reason to make a purchase. ML models help marketers optimize who sees which promotion. In addition, machine learning supports contextual advertising as an alternative to cookie-based targeting. ML-powered ad platforms scan webpage content for keywords to infer its topic, then place ads relevant to that context. This means even if a visitor hasn’t been tracked with cookies, the system shows, say, a sports gear ad on a sports news site, improving ad relevance while respecting privacy. With privacy regulations tightening, such ML-driven contextual targeting is important.

Personalization and recommendation engines

Today’s consumers demand personalization. According to McKinsey, 71% of consumers expect companies to deliver personalized interactions, and 76% are frustrated when the results they get aren’t relevant. ML supports one-to-one personalization that meets these expectations. By analyzing individual customer data, from browsing history and purchase behavior to real-time context signals, ML models tailor the content or offers each person sees. 

In practice, this might mean a retail website dynamically reordering products to show a shopper’s preferred styles, or an email campaign sending different messaging to each recipient based on predicted interests. Some systems even adapt to a user in process, using contextual signals and a unified customer profile to present the most relevant next action or content piece. This real-time personalization helps customers feel understood and valued.

The impact of ML-driven personalization is evident in the successes of major brands. Coca-Cola, for example, uses AI to analyze consumer data such as purchase history, social media engagement, location, and even local weather patterns in real time. Insights from these models help Coca-Cola deliver hyper-personalized marketing campaigns that resonate with local preferences and individual behaviors. 

A related application is the recommendation engine. Recommender systems have become the backbone of many e-commerce and streaming media platforms. Companies such as Amazon and Netflix popularized ML-based recommendation engines that analyze user behavior, purchase/viewing history, ratings, and other data to suggest products or content a user is likely to love. These systems help customers navigate huge catalogs by finding relevant items, which improves user satisfaction and engagement. Recommendations also boost the bottom line: By tailoring suggestions to individual tastes, businesses see higher conversion rates, larger average basket sizes, and better retention.

Machine learning drives recommendation engines through various techniques. Some systems use collaborative filtering, which finds look-alike users or items, such as “users who liked X also liked Y.” Others use content-based filtering, recommending items with similar attributes to those a user has liked before. The most effective engines often combine approaches in hybrid models. The common thread is that ML processes large matrices of user-item interactions to predict new favorites for each person. Every time you see a “Recommended for you” list that feels spot-on, chances are an ML algorithm has crunched the data behind the scenes to personalize that experience.

Predictive customer analytics

Beyond personalization, machine learning is invaluable for predictive analytics in marketing. Predictive models use historical and current data to forecast consumer behaviors or outcomes, allowing marketers to plan. One example is customer lifetime value (CLV) prediction. ML analyzes a customer’s past interactions and purchases to estimate the total value they will bring over their relationship with the company. With accurate CLV predictions, marketing and sales teams identify which customers are likely to be the most valuable, and allocate resources to retain and upsell those individuals.

Another application is churn prediction, or identifying which customers are at risk of leaving. By examining usage patterns, engagement levels, support inquiries, and other signals, an ML model flags high-risk customers before they actually leave. This helps marketers intervene with targeted retention efforts, such as special offers or personalized outreach, to try to win those customers back. 

Similarly, B2B marketing and sales use ML models for lead scoring, or evaluating incoming leads to predict their likelihood of converting. Recent advances in natural language processing help ML analyze textual data, such as email messages and inquiries, and estimate a lead’s conversion probability. That helps companies prioritize the leads deemed most promising, improving conversion rates and sales efficiency.

ML-driven predictive analytics aren’t limited to individual customer metrics. They also reveal broader market and trend insights. One of machine learning’s biggest advantages is making sense of unstructured data such as social media posts, reviews, or open-ended survey responses. By mining such data, ML finds emerging market trends and opportunities that would be hard for humans to detect. 

For instance, algorithms might analyze millions of social posts and detect a rising interest in a new fitness movement or fashion style, giving marketers early insight into a cultural shift. Smart companies use these ML findings to inform their product development and campaign strategies, letting the data tell them where consumer demand is heading. In today’s fast-moving landscape, foreseeing trends and acting on them quickly is a game-changer for marketing teams.

Marketing automation and customer engagement

Machine learning also powers marketing automation, streamlining and improving how brands interact with customers across channels. Marketing automation involves using software, which is increasingly ML-driven, to handle repetitive tasks and to engage customers with minimal human intervention. 

A prime example is the proliferation of AI chatbots and virtual assistants for customer engagement. These bots, often deployed on websites or messaging apps, use natural language processing to converse with customers, answer common questions, and guide purchases. ML helps chatbots understand customer queries and respond in a human-like way, providing real-time assistance 24/7. Marketers use conversational AI solutions to gather customer information and feedback from site visitors and to attract them with personalized messages, such as offering a product recommendation or a discount via chat, based on the user’s browsing behavior. This kind of automated yet personalized interaction captures more leads and makes customers happier without requiring constant input from support staff.

Today’s email marketing and campaign management tools also use ML for automation. Algorithms determine the best times to send messages to each user, select the content most likely to appeal to them, and personalize subject lines. 

In fact, CRM and marketing platforms such as Salesforce Marketing Cloud and Microsoft Dynamics 365 now include AI features, such as Salesforce’s Einstein AI, that handle tasks such as audience segmentation, message personalization, and campaign scheduling. For instance, Salesforce Einstein churns through customer data and segments audiences or generates campaign performance reports, tasks that are time-consuming manually. By automating these processes, ML lets marketers focus more on marketing strategy and creative work while routine decision-making runs on autopilot.

ML-driven automation is not limited to just triggering messages. It’s increasingly involved in content creation for engagement as well. Chatbots and marketing platforms now incorporate generative AI to produce content on the fly. This means an AI assistant drafts personalized email copy, generates a tailored landing page, or composes a social media reply, based on understanding the customer’s context and past interactions. 

For example, if a user asks a chatbot a complex question, the underlying ML system pulls relevant info from a knowledge base and formulates a detailed, context-aware answer, instead of a generic reply. Some companies use artificial intelligence to generate dozens of variants of an ad or email message and then test which content performs best. 

As generative AI models such as GPT-4 or DALL·E continue to advance, we’re seeing more of this automated yet creatively agile marketing content. Customizing both the timing and substance of customer communications is a leap forward for marketing automation, all made possible by machine learning.

AI-driven content creation and optimization

Content is king in marketing, and ML is helping marketers reign supreme by improving both content creation and content optimization. On the creation side, AI algorithms generate content that previously required human creativity. ML now helps copywriting, graphic design, and video production. 

For example, natural language generation models draft blog posts, product descriptions, or ad copy given a few prompts. These models analyze text data to learn how to produce fluent, persuasive language. Marketers use them to get a first draft of content that they then tweak, producing content faster. AI image generation similarly creates custom visuals or ad banners. 

We already saw how Coca-Cola’s marketing team produced thousands of personalized video ads via AI, a task that would have been impossible to do manually in a short time. In another case, an asset management firm used a generative AI system to produce written market commentary tailored to different client segments, freeing their content team for higher-level work. These examples illustrate how ML is augmenting human creativity, helping marketers produce more content, faster, and often personalized to each viewer.

Even when humans create the content, ML helps make it more effective. Content optimization tools use machine learning to analyze what kind of material performs well and how to improve it. For instance, ML-driven SEO tools can study top-ranking pages and identify which topics, keywords, and even phrasing are trending, then suggest improvements to your own content. A content marketer gets AI recommendations on which keywords to include in a blog post or how to adjust headlines and meta tags to rank higher in search results. ML models also predict what blog topics or video subjects are likely to engage your target audience by analyzing past engagement patterns and external data. This takes a lot of the guesswork out of content strategy.

Machine learning improves content in real time as well. Websites use algorithms that continuously test and adjust content based on user behavior. For example, an e-commerce home page could use ML to rearrange itself on the fly, showing different banner images or prioritizing certain product categories, depending on the profile of the visitor and how they navigate. If the system detects that a user is lingering on a particular section, it might highlight related content or promotions. ML also gauges subtle cues such as mouse movements or reading time to infer interest. Using these signals, it adapts page layouts or content blocks to better suit each visitor’s preferences. 

Beyond layout, AI refines messaging by analyzing how people react emotionally to different wording. By treating consumer emotions and responses as data points, ML figures out which phrasing or creative style best results in action. In short, machine learning helps marketers create content at scale, and that content's topic, timing, format, and wording are as engaging as possible for the target audience.

Marketing analytics and campaign improvements

The analysis side of marketing has also been assisted by machine learning. Marketing analytics traditionally involves tracking campaign performance and customer interactions, then interpreting the data to make better decisions. ML takes this to the next level by handling the heavy data crunching and revealing insights that might be hidden from human analysts. 

For example, machine learning identifies which factors have the biggest effect on marketing KPIs, such as click-through rates or conversion rates, by sifting through multiple data dimensions. An ML system might analyze an email campaign and discover that time of day and subject line length affect open rates, insights a human might miss. It then recommends the best email send times or suggests more effective subject line wording based on patterns in the data.

One particularly valuable area is marketing attribution and budget optimization. In multi-channel marketing, consumers might interact with a brand through ads, social media, email, and websites before making a purchase. Attribution is about figuring out which touchpoints deserve credit for the sale. Machine learning algorithms analyze customer data across all these channels and touchpoints to determine which ones most drive conversions. 

They might learn, for instance, that customers who see a certain sequence of ad -> blog post -> email are likelier to buy. With that knowledge, marketers allocate budgets more effectively, doubling down on high-impact channels and cutting investment in underperforming ones. ML also automates media buying, adjusting bids and budgets across dozens of running ad campaigns in real time for a better return on investment. Instead of marketers manually tweaking campaign settings, ML models continuously learn from performance data and improve bidding strategies to achieve the best outcomes. This is the engine behind many programmatic advertising platforms that adjust ad buys on the fly.

ML-driven analytics extends to understanding customer sentiment and brand health. Sentiment analysis uses natural language processing to comb through social media posts, product reviews, and customer feedback, determining whether the sentiment is positive, negative, or neutral. Marketers learn how customers feel about their brand or a specific campaign quickly, as the ML processes new data in real time. This alerts teams to PR issues, such as spotting a surge in negative sentiment about a new ad campaign on X, so they respond promptly. On the flip side, it highlights positive reactions or viral trends that marketers might capitalize on. 

In summary, machine learning turns marketing data from ad metrics to social chatter into actionable intelligence. It not only saves analysts time by automating data analysis but also often uncovers non-intuitive insights to improve campaign effectiveness and strategy.

Challenges of implementing ML in marketing

While the benefits of ML in marketing are compelling, organizations often face several challenges when adopting these technologies. It’s important to be aware of these hurdles and plan for them.

Data quality and integration issues

Machine learning is data-driven, which means data quality and accessibility are critical. Marketing data comes from many sources, such as CRM systems, websites, mobile apps, social media, and email platforms. A challenge is ensuring all this data is accurate, up-to-date, and consolidated for analysis. Many companies struggle with data trapped in silos, such as the email team’s data being separate from the web analytics data, making it hard for ML models to get a complete view of the customer. If input data is inconsistent or incomplete, the model’s output will also be unreliable. 

To tackle this, businesses often need to invest in data integration, such as building a central data warehouse or customer data platform that aggregates information from all marketing channels into one place. They also need processes for ongoing data cleaning and validation. Preparing the data foundation is one of the hardest parts of implementing ML in marketing.

Lack of expertise

Another common barrier is the talent gap. Taking advantage of machine learning requires specialized skills in data science, ML engineering, and analytics, which many marketing teams don’t have in-house. Traditional marketers may not be trained in building or interpreting ML models. As a result, organizations looking to implement ML for marketing must address this skills shortage. 

Some approaches include hiring dedicated data scientists or ML engineers to work with marketing, training existing staff, or using automated ML tools that require less technical know-how. There is also the option of partnering with external vendors or consultants to develop and manage ML solutions. Any of these paths involves investment, whether in recruiting, training, or purchasing AI software, which can be a hurdle, especially for smaller firms. Nonetheless, as AI becomes more central to marketing, companies increasingly recognize the need to build at least basic ML competency in their marketing teams.

Model transparency and trust

Machine learning models, especially complex ones such as deep neural networks, sometimes behave as “black boxes,” meaning they produce results without an obvious explanation of why. This lack of transparency impedes trust in the insights or decisions the model provides. 

For example, if an ML model tells you that a certain customer is likely to churn, a marketer might reasonably ask: What factors led to that conclusion? If the answer isn’t clear, it can be hard to trust the recommendation or to explain it to stakeholders. This is a challenge, particularly in marketing use cases that may require justification for decisions, such as why a high-value offer was given to Customer A but not Customer B. 

To improve model reliability and acceptance, organizations are adopting practices such as using more interpretable models where possible, tracking model performance metrics, and implementing explainable AI techniques that shed light on how a model makes decisions. It’s also wise to keep a human in the loop. Marketing teams should review and sense-check ML outputs rather than blindly relying on automation. Over time, as a model proves its accuracy and value, trust will build, but achieving that first requires validation and communication about how the ML system works.

Security and privacy concerns

Marketing ML initiatives often involve handling large volumes of personal customer data, from purchase histories to click behaviors. This raises security and privacy concerns. Data breaches or misuse of personal data not only harm customers but also lead to regulatory penalties through laws such as GDPR and reputational damage. 

Thus, companies must keep their AI-driven marketing solutions secure and compliant from the beginning. Challenges include implementing robust cybersecurity around ML systems, which might be juicy targets for attackers, given the personal data involved, and ensuring that data usage respects privacy regulations and customer consent. Techniques such as data anonymization or masking help protect sensitive information when training models. 

Companies should also enforce strict data governance policies, clearly defining what data can be used for ML, who has access to it, and how it’s stored and shared. Another aspect of this challenge is ethical: Marketers need to avoid the “creepy factor” when AI gets too personal or appears invasive to consumers. Striking the right balance between personalization and privacy is an ongoing tightrope for marketers as they deploy machine learning. By being transparent about data usage and giving users control where possible, businesses mitigate some of these concerns while still reaping ML’s benefits.

Trends in AI marketing

The intersection of AI and marketing continues to evolve. Looking ahead, several trends shape the future of marketing through machine learning:

Generative AI transformation

The rise of generative AI, such as large language models and advanced image generators, continues to change marketing content and creative work. We’ll likely see more brands using AI to generate marketing materials, from countless variations of ad copy tailored to micro-segments to on-demand creation of personalized videos or interactive content for each user. This will provide one-to-one content personalization that was previously unimaginable. Companies using these tools could increase their content output and relevance. 

In fact, IDC projects that by 2029, the adoption of generative AI could boost enterprise marketing productivity by 40%, thanks to efficiency gains and automating creative processes.

Real-time and omni-channel AI

Speed will be an enduring competitive differentiator. Consumers are coming to expect instantaneous, context-aware experiences, such as a shopper receiving a personalized offer on their phone the moment they walk into a store. To power these experiences, marketing AI must operate in real time and across all channels. This will drive greater investment in streaming data pipelines, real-time customer data platforms, and low-latency ML models that make split-second decisions, such as deciding which ad or product to show in a mobile app. Real-time databases and feature stores will become important, feeding fresh data to ML models for both training and inference. We may also see more use of edge AI, where certain AI computations happen on devices or at edge servers closer to the user, for faster response times in marketing interactions.

Vector databases and deeper personalization

As AI techniques advance, marketers will tap into new data representations to understand customers better. One emerging trend is vector embeddings, or numerical representations of data such as customer behavior or content attributes for similarity search. Vector databases store and query these embeddings efficiently. 

In marketing, this means an AI might encode a customer’s interests or a piece of content into a vector and find similar customers or content via nearest-neighbor search. This results in more sophisticated recommendations and personalization, beyond simple demographic or keyword matching. 

For example, a fashion retailer could use vector embeddings to recommend apparel that “feels” similar to what a shopper has liked in the past in terms of style, even if it’s a new brand or category. We are already seeing the incorporation of vector search in personalization, and this will grow as more companies realize they need AI infrastructure that handles these complex data types. In short, the future of personalization might rely on understanding the semantic relationships between products, content, and customers, which vector-based ML does.

Improved marketing decisions 

Beyond customer-facing applications, ML will increasingly support marketers’ strategic decision-making. AI will get better at simulating outcomes, such as predicting how sales would change under different pricing strategies, improving marketing mixes, and suggesting new campaign ideas based on past results and market data. AI assistants will help marketers brainstorm, such as analyzing a brand’s previous campaigns and market trends to propose the best campaign calendar for the next quarter. As businesses collect more data on what works and what doesn’t, ML models will become more adept at guiding planning and budgeting decisions. This doesn’t mean AI will replace human marketers, but it will advise them, crunching numbers and offering data-backed recommendations that humans then refine with creativity and domain knowledge.

Ethical and responsible AI in marketing

With great power comes great responsibility. As ML permeates marketing, companies need to follow ethical AI practices. Expect to see more transparency in how AI models make decisions that affect customers, more options for customers to opt out of AI-driven experiences if they wish, and stricter guidelines to avoid biases in marketing AI. Regulatory bodies may also start paying closer attention to AI in advertising and customer data usage. Forward-thinking organizations are likely to establish internal AI ethics boards or guidelines specifically for marketing use cases, so the drive for personalization and automation does not lead to discrimination or privacy violations. In the long run, maintaining consumer trust will be at least as important as technological innovation for successful AI-powered marketing. 

Overall, the future points toward more integrated, intelligent, and faster marketing powered by AI. Marketing teams that thrive will be those that not only adopt the latest ML technologies but also invest in the data infrastructure, skills, and governance needed to use AI responsibly and effectively.

Machine learning in marketing and Aerospike

Machine learning has undeniably become a cornerstone of modern marketing, supporting everything from micro-targeted marketing campaigns to automated customer service. But taking advantage of its potential requires the right foundation. This is where Aerospike comes in. Aerospike is a real-time data platform designed to power AI-driven applications at a global scale. It provides the high-performance database infrastructure needed to support many of the ML examples discussed here. 

For example, Aerospike’s database serves as a fast feature store, supplying fresh customer data and predictive features to your ML models with sub-millisecond latency. This means your personalization or ad targeting models make millisecond decisions on streaming data without lag. Aerospike’s technology is built for extreme scale, delivering reliable performance even when handling billions of events and terabytes of data efficiently. In fact, marketing industry leaders have used Aerospike to adapt customer experiences in real time using unified profile data, to serve hyper-personalized recommendations quickly, and to drive smarter ad bidding using live audience signals.

Many global companies already trust Aerospike for their marketing and AI workloads. For instance, advertising intelligence firm Quantcast switched its feature store to Aerospike and saw query latencies drop from ~25ms to ~5ms, speeding up its ad targeting algorithms. Similarly, fintech leader Barclays implemented an Aerospike-powered ML platform for an 80% reduction in latency and 4× higher throughput in detecting fraud in real time, a performance that just as easily applies to real-time customer analytics or personalization. By providing a unified, low-latency data backbone, Aerospike trains your marketing ML models on up-to-the-second data and serves quick decisions to every channel. 

The bottom line: If your organization is looking to enhance its marketing with AI, a robust, scalable database like Aerospike is key to making it all work.