A.I. for marketing, explained

What every marketer needs to know about artificial intelligence in order to succeed in the next few years

Time for changes

Digital marketing changes with each year passing by, and staying up-to-date on the latest best practices and growth opportunities is a full time job of every successful marketer.
 
Thanks to the extreme growth in the usage and adoption of the Internet in early 2000s, we have seen how drastically client communication and business transaction were accelerated.
 
After the release of the first iPhone in 2007, mobile has become the primary platform for marketers to build up effective communication with desirable consumers.
 
Now, it is artificial intelligence that will revolutionize digital marketing in the next few years.
We need to constantly be open to new ideas and approaches, such as artificial intelligence (AI), and be willing to challenge assumptions."​
marc benioff photo
Marc Benioff​
Chairman at SalesForce​

Only 28% of digital marketers realize what AI really means

4 main types of AI for marketing

The growing role of AI in marketing might seem far-fetched for many people.

In early 2019, eMarketer.com reported that only 28% of digital marketers realize what artificial intelligence really means and how to apply it for their businesses.It is still hard for most of us to understand an idea behind AI, and in most cases we even don’t recognize actual AI when we see and interact with it.

The reality is that AI is already here, and there are enough cases in the industry when early adopters use machine learning techniques to improve customer relationships and campaign performance. Now it is a great time to learn the basics of AI for marketing and benefit from this business revolution.

Start with NLP, Image recognition, Predictive analytics, and Recommender systems

Natural Language Processing

Natural Language Processing (NLP) enables computers to understand the way we write and talk. AI-enabled NLP teaches machines for real-time recognition of sounds, words and phrases, as well as the tone of a conversion or article.

To see NLP in action, say “Ok Google” to your Android device or ask Siri a question on your iPhone. The AI engine on your smartphone will use NLP to “read” your voice or typing, understand your words, and provide a solution or answer.

Apple’s Siri, Google Assistant, Amazon’s Alexa, Yandex’s Alice are the popular examples of intelligent personal assistants, or chatbots that marketers use to streamline customer experiences and enhance brand recognition.

nlp

Image recognition

Image recognition allows machines to capture, extract and analyze content within an image to identify what’s in pictures and find visual patterns people can’t detect on their own.

For example, e-commerce websites use image recognition algorithms to help customers identify needed products and find similar items. Similarly, social networks like Facebook and Instagram use their proprietary AI engines to recognize pictures of your friends in online photos and suggest you names and tags.

For marketers, AI-powered image recognition allows to analyze tons of image data to figure out what pictures can provide actionable insights. Imagine, you can capture and recognize millions of pictures on the web for your product photos to find out what photos are shared most often, when and on what devices.

Predictive analytics

Predictive analytics lets machines estimate the likelihood of future outcomes based on historical data. It combines large datasets and advanced algorithms to learn what has happened and why and then provide a best assessment of what will happen in the future.

For example, an AI-powered CRM system analyzes incoming customer data to find and prioritize leads that are likely to make their first purchase within the next week. Predictive analytics turns raw data into meaningful insights for marketers, and ultimately increases customer ROI.

More accurate predictions are made with larger datasets analyzed. But as data grows and becomes enormously big, marketers have to use AI to even make sense of it all. AI-powered predictive analytics engines lets break down the silos of customer data into insights that matter and to deliver more satisfying customer experiences.

predictive analytics

Recommender system

To see recommender systems in action, open almost any popular ecommerce resource on your favorite web browser or mobile app. They use AI to recommend things you might want now.

For example, every time Flipkart offers you a product to purchase or Youtube suggests you videos you might want to watch next, AI behind the scenes analyzes the data about your previous content experiences to figure out what else you might like now.

AI-powered recommendation engines help marketers boost dynamic ads effectiveness on mobile and web. By analysing the content a target user has engaged with in the past, AI manages to serve up personalized ad units based on the predicted likelihood of a tap or click at any given time. Dynamic ads personalization will ultimately improve consumer experience and increase the return on ad spend respectfully.

By 2023, a hybrid human-bot customer service will be considered a priority channel by 69% of customers

10 Use cases for sales & marketing AI

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