Marketing automation has become much smarter with AI, moving from simple rules-based systems to more intelligent tools.
A Statista survey found that 90% of marketers use AI to automate customer interactions, and 88% say it helps personalize the customer journey across channels.
In this blog, we’ll look at 6 AI automation examples in marketing you can use to make your marketing better.
What Is AI Automation?
AI automation handles repetitive, time-consuming tasks to streamline workflows. This helps businesses cut costs and be more efficient, freeing up people for strategic work.
Humans are good at complex decisions. Now, AI and automation let machines mimic this, but unlike traditional automation with fixed rules, AI automation can learn. AI agents can analyze results and adapt processes for better outcomes.
AI automation uses machine learning and NLP to understand and respond to human language, analyze data, and make smart decisions. Machine learning lets AI see patterns in data and predict future ones to make decisions.
Large language models have greatly improved these techniques. Adding generative AI offers endless possibilities for AI to create, not just predict or analyze.
For example, when a customer asks a virtual agent a question. A regular chatbot gives a set answer, but an AI automation model, trained to understand language, can give a more relevant solution.
6 AI Automation Examples in Marketing
Let’s explore 6 AI automation examples in marketing.
Predictive Analytics & Customer Segmentation
AI’s biggest strength is analyzing massive amounts of data, which saves time and money and helps make predictions, called predictive analytics.
For example, you can understand customer behavior by looking at past purchases. You can see individual patterns, like if an email or social media promo led to an interaction.
These insights help you group customers by behavior or interests and predict when they’ll buy or what offers they’ll like. This lets you target them at the right time with the right offer on the best channel to convert.
Brand Example:
Natural Cycles, a birth control app, uses an algorithm based on body temperature to track fertility.
To target users better and personalize messages, they used the AI marketing platform Optimove to analyze user data and behavior.
By grouping users based on real-time activity, the app could tailor messages. They could also schedule more campaigns faster with fewer people.
Chatbots & Conversational AI
Using conversational AI, businesses can offer more personal and efficient customer service and free up their human agents’ time.
That’s why Gartner predicts that by 2027, chatbots will be the main customer service channel for about 25% of businesses.
And chatbots are getting smarter. AI chatbots with advanced NLP can handle complex questions and adjust responses based on customer tone. They can also provide 24/7 support without sacrificing quality.
Brand example:
Digital insurance company Lemonade created a chatbot, Maya, to help users buy insurance.
Maya can gather info, give quotes, and process payments. The bot ensures customers get insurance in 90 seconds and get paid in three minutes.
Maya also chats with customers to give personalized answers and helps the company make policy changes. As a machine learning system, it gets smarter with each customer interaction.
Lemonade says Maya now handles a quarter of their inquiries and has sold 1.2M policies in just three years.
Personalized Content
ChatGPT has really changed marketing, especially for content creators and companies wanting to scale content production.
Many other tools like Claude, Perplexity, Google NotebookLM, and Midjourney can also create content from clear instructions.
Recent improvements in text, image, and video generation help marketers make targeted ads, product descriptions, and email campaigns. AI tools like Adobe Creative Cloud Express and Canva can generate graphics from text.
Some marketers are even creating content that changes in real-time based on context and audience feelings for super-personalization.
Brand example:
Spotify uses AI to create playlists and recommend artists based on what users listen to.
This lets Spotify give customers more of the music they like and recommendations tailored to their taste.
Spotify also has an AI DJ for premium users and is testing AI voice translation for podcasts.
Campaign Optimization & Performance Measurement
For marketers, it’s key to optimize campaigns and track how they’re doing. AI tools can help by tracking KPIs, giving real-time feedback, and providing useful insights, making this easier.
With this understanding, marketers can optimize campaigns to find the best channel (like email) and spot trends or issues to maximize results.
Some AI tools, like Google Analytics 360 and Zoho Analytics, can even automatically adjust campaigns based on engagement, click-through rates, and conversions.
Brand example:
The North Face wanted to know what shoppers in different markets were looking for to improve their experience. They often watch how people search on their website.
By using Google Tag Manager 360 and Analytics 360, they found customers were searching for “midi parka.” So, they renamed a product and saw a 3x increase in sales and revenue.
Visual Identity for E-commerce & Social
AI can now analyze images to find brand-related content, user-created content, or product matches.
Because of this, visual search engines are in high demand for retail and e-commerce, and the global AI e-commerce market is expected to hit $16.8 billion by 2030.
For example, image classification is getting popular for phone and social media shopping. Facial recognition could even be used to understand emotions for better sentiment analysis.
Automated image recognition helps marketers see how good and relevant their visual content is. It can also help optimize images and create tags or keywords for better SEO and accessibility.
Brand example:
L’Oreal created Beauty Genius, an AI personal beauty assistant, for personalized advice, routine suggestions, and Q&A.
It uses AR, computer vision, and Gen AI for an immersive and secure experience, aiming to feel like talking to a beauty expert.
Customers can also virtually try on looks with AR and get suggestions from makeup artists.
Sales Automation
Salesforce reports that 98% of sales teams believe automated lead scoring improves how they prioritize leads.
AI lead scoring uses algorithms to track and rate how users or clients interact. This helps predict which leads will lead to more profitable sales and improves how leads are passed to sales teams.
Sales teams can also automate lead nurturing with AI-triggered campaigns that change based on what a lead does, making them more personal and likely to get engagement.
AI can also give better insights into customer behavior and automate customer processes like sending emails or reports.
Brand example:
U.S. Bank wanted to use predictive lead scoring to help their sales team focus on the best leads.
They used Salesforce’s Einstein, which has integrated AI and machine learning.
Using Einstein’s lead scoring helped U.S. Bank see 25% more closed deals, a 260% jump in lead conversion rates, and 300% more marketing qualified leads.
Conclusion
As AI keeps improving, marketers will have more chances to use automation for personalized, efficient, and scalable campaigns that drive results.
These practical AI automation examples will empower you to design and implement an end-to-end, achieving more ROI with less time and effort.
