While AI technology for content and images has quickly become mainstream, AI marketing is often seen as a more “enterprise” capability.
Tools like Azure AI and the OpenAI codex dominate conversation and can intimidate, making AI marketing feel like a massive undertaking.
But the reality is, you’re probably already using AI for marketing. Functions of AI are embedded into software you already use.
Let me expain:
Gmail’s Smart Compose functionality, which is driven by neural networks, came out years ago.
Anyone running paid ads benefits from the machine learning processes of each ad network.
In practice, AI marketing is less about choosing one “supertool” and more about experimenting with multiple tools, each of which makes your life slightly easier.
AI marketing is accelerating quickly. According to the American Marketing Association’s September 2022 CMO survey, the use of AI in marketing efforts is happening 8.6% of the time, and will grow to 22.9% by 2025.
From data analysis to content generation to real-time personalization, artificial intelligence and machine learning is working its way into every aspect of marketing.
Today’s AI marketing landscape
Solo operators and small businesses may not even know AI is helping them with marketing.
But most businesses using paid advertising, email marketing, or content creation tools are using some aspect of AI or machine learning.
Common artificial intelligence marketing applications include:
- Analytics: AI analyzes vast amounts of data easily, and can surface insights that allow for real-time (and smarter) decision making.
- Content: AI can handle short form content (like sales copy), and long form content (like blogs). It’s able to generate content at scale, especially data-driven content like financial data and weather reports.
- Advertising: Artificial intelligence powers much of the online advertising landscape already. From smart bidding to optimization tools to determining media placements, machine learning is an integral part of media buying.
- Personalization: Increasingly, AI tools allow each customer to have a personalized experience based on their past shopping habits, interests, and demographics. Personalization happens on websites and apps, within chatbots, and via email.
At the enterprise level, AI is frequently deployed to guide customers more effectively along the journey to purchase.
A hyper-personalized message at the right time through a chatbot or email can mean the difference between a sale or lost revenue.
Even before the era of AI, large companies have used analytics to get the right offer to customers at the right time.
As early as 2012, Target was analyzing purchases at scale to determine when a woman was most likely to be pregnant in order to send targeted pregnancy-related offers.
With AI, the ability to crunch huge amounts of data and personalize offers has increased dramatically.
Applying AI to marketing campaigns
Artificial intelligence has spread to every aspect of marketing. Below are some of the most common ways AI is being applied within marketing campaigns.
Chatbots were one of the earliest and most visible marketing applications for AI and Natural Language Processing (NLP) technology. While the first chatbots focused on customer support, today’s chatbots show up in a variety of contexts to encourage upsells or help customers choose the right products.
AI-powered content tools have proliferated in recent years. On a small scale, this allows publishers to pair AI tools with a human operator and quickly generate long-form content (like blog posts) and short-form content (like ad copy, product descriptions, and social media captions).
One AI email marketing function is the ability to send emails at different times based on the best chance of response in each recipient’s time zone. Meanwhile, AI-generated email subject lines and copy helps fine-tune open rates and click-through rates. Email content personalization is also made possible by artificial intelligence. Services like rasa.io allow marketers to send dynamic emails with content each subscriber is most likely to be interested in.
Brands can use AI to personalize a customer’s experience around content or products that are more likely to be relevant to them. Think about features like Amazon’s “recommended products” or Netflix’s content recommendation engine. Personalization can drive more sales, reduce churn, and increase engagement.
Advertisers and agencies use AI to come up with compelling ad copy and unique AI-generated images. Endless ad variants can be quickly generated. Once ads are ready, ad networks use AI and machine learning to manage ad inventory, determine placements, and show the best-performing ad variants.
Natural language processing technology allows trained AI models to analyze social media at scale. This helps companies understand changing consumer sentiment toward their brand. With sentiment analysis tools, you can figure out how your audience is responding to your campaigns, and what trends they’re engaging with.
One of the biggest wastes of resources in retail marketing happens when you promote an out-of-stock product. AI can prevent this from happening. Software like Inventoro helps forecast sales based on historical data. It tells you exactly how much to order to meet likely demand, increasing product availability to 99% or more.
Big data analytics uses artificial intelligence to make sense of the massive amounts of data generated by businesses. This means it’s easier to target customers based on factors like geography, income levels, past purchases, demographics, chats with staff, and more.
Where AI marketing is headed
AI is increasingly powering marketing—and in many cases, customers have no idea that’s what’s happening behind the scenes.
One accessible application of AI for consumers is the Nike Fit experience. Using augmented reality via a phone app, users are able to measure their feet and get a recommendation for exactly what shoe size is best for them to order.
The scan is run through artificial intelligence processes including machine learning and recommendation algorithms. Then, customers are quickly served up with recommended shoes only in their size.
Content that’s built around data is increasingly being powered by natural language processing tools. In 2021, the US Open tennis tournament used IBM’s Watson suite of AI and NLP systems to generate content about players and matches. The content was generated using raw data from player statistics, matches, and media commentary.
Many companies now incorporate machine learning into the core of their content operations. Buzzfeed publishes over 1,200 pieces of content per week and uses machine learning to quickly identify which of those is most likely to drive engagement and go viral. AI also powers Buzzfeed’s process of A/B testing thumbnails and headlines.
Whether artificial intelligence powers content, email, advertising, personalization, or data insights, it’s already a part of most marketing operations.
The key for marketers is to avoid the trap of thinking that applying AI to marketing is a transformational overhaul.
Instead, it’s a series of small process improvements and experiments over time, each of which improves ROI, operations, and customer experience just a bit more.