Artificial intelligence (AI) is becoming one of the standards in business, with implementations ranging from customer service, workflow automation and of course, marketing.
It’s also a major game-changer: by 2035, AI will increase overall profitability by 38% and generate $14 trillion of additional revenue for global businesses!
The top-performing companies are already first in line, being twice as likely to use AI in marketing, while other companies are starting to explore its potentials.
In this article, we define AI marketing and its main components, discuss its benefits to businesses and share specific ways to better integrate AI in your marketing efforts.
AI marketing is a form of marketing that uses artificial intelligence technology like data analysis, natural language processing and machine learning to analyze target audiences and economic trends that impact advertising decisions.
Using customer data, AI tools learn how to communicate with your clients and provide tailored messaging at the right time to improve the marketing campaign’s efficiency.
Artificial intelligence in marketing is particularly useful in campaigns that require speedy decision-making and quick results.
Some examples and use cases of AI marketing include:
These are the three main components of artificial intelligence in marketing that accelerate the time-consuming and expensive process of data collection and process repetition:
Artificial intelligence has many uses in marketing and each produces different benefits, from better customer satisfaction to a boost in revenue.
Here are the most common benefits that overlap across different AI marketing use cases:
There are numerous ways businesses can apply artificial intelligence to their marketing strategies.
Below are the ones that yield the best results and the most commonly used by top-tier companies who boast a great success rate with their campaigns.
Not all of your customers will respond the same way to your messages. Content with a high emotional appeal may resonate with some, while others may find a professional tone more appealing.
A personalized messaging based on the user’s profile and customer journey drives best the results and in real-time, making it among two of the most common ways marketers use AI.
With AI, you can monitor which messaging proved to be the most successful with your customers. It allows you to create more complete user personas and provide highly customized messages to these users.
For example, Spotify uses AI algorithms that analyze user behavior to understand the types of music they listen to the most. They then create personalized playlists and music suggestions that even use specific album covers that match the user’s interests.
Using AI and machine learning to gather valuable customer data and improve your messaging may likely result in increased conversion rates and a better overall user experience.
Marketing specialists that decide where and when to place ads are not quick and agile enough to adjust their strategies to the latest information in real-time.
AI-based programmatic advertising mitigates this challenge by bidding on the most relevant ad space for targeting your users in real-time. AI uses data like user’s location, interests, buying intent, purchase history and others to inform this ad bid, allowing you to target the correct audience at the right time, for the best price.
Simply put, programmatic ad buying improves marketing flexibility to meet your customers’ changing needs and habits.
Owing to another artificial intelligence staple — natural language processing (NLP), live chatbots are replacing or supplementing human support agents to provide round-the-clock customer service.
Users that want to ask basic questions about a brand and its services can turn to chatbots that provide instant answers. Chatbots also deliver highly personalized answers based on past questions and historical data about each user.
This enables support agents to focus on more complex queries and elaborate communication with other clients that require human assistance.
Modern users expect a very high level of personalization in their interactions with brands. For that reason, your users’ pain points, location, interests, buying intent and other data points should inform your marketing copy.
With artificial intelligence in marketing, you can go above and beyond this standard set of demographic data to learn more about your users on a very personal level. The end result is a highly curated brand experience formed around your customer’s unique personality.
For example, similar to but also going beyond the Spotify playlists example above, Netflix displays different artwork of the same show to different users. Their hyper-personalization works so that one person will see the show artwork that depicts the main actor that the user often watches in different shows, and another will show an image that captures the feel and atmosphere of movie genres they typically watch.
Another facet of this highly individualized user experience is atomic content with a customized offer and relevant content based on the user’s unique preferences.
AI-enabled dynamic pricing can make your business more competitive by recommending optimal product prices in real-time. Artificial intelligence system estimates a massive amount of competitor data, allowing you to modify your prices according to existing demand for certain products.
This strategy, particularly efficient for small and retail brands, can help you increase your sales and outperform your competitors.
Artificial intelligence can automate the sorting of marketing data, answering basic user queries and other strategic processes in order to grow their efficiency, providing more time for marketing personnel to conduct analytical and strategic work.
Artificial intelligence has significantly accelerated the process of extracting user data – but with so much user data, it has become difficult for marketers to derive valuable insights from it.
Predictive analysis uses algorithms, machine learning and datasets to forecast future user behavior. This helps marketers “predict” what types of services or products the users will look for in the future and when.
With this knowledge, market teams can target their end-users and steer their campaigns with much more accuracy. The most well-known example of predictive analysis is the “suggested products” section on eCommerce websites like Amazon. They suggest products a user is likely to be interested in based on their past purchases and searches.
Artificial intelligence in marketing accelerates the collection of user data, boosts personalization of messaging and enhances market analysis, resulting to process efficiency, more accurate targeting, as well as greater ROI.
AI in marketing can be used to: