Applications of Artificial Intelligence in Marketing

 The most effective AI – Artificial Intelligence technology mapping for digital marketing in the commercial life cycle today and future!

Artificial Intelligence and Machine Learning Marketing
“Artificial intelligence” focuses on machine learning and prediction analysis

AI Generates Marketing Content

This is a very attractive AI area. Artificial Intelligence can not write a political opinion or blog article on industry-specific best practices advice, but content generated by artificial intelligence in some areas can be useful and help attract visitors to your site.

For some features, AI content compilers can select elements from the dataset and build a “human exploration” article. AI author named “WordSmith” produces 1.5 billion copies in 2016 and is expected to continue to be popular in the coming years.

The author of AI is very useful for reporting organized events and data. Examples include quarterly earnings reports, sports events and market data. If you operate in areas such as financial services, the resulting AI content may be part of your content marketing strategy. The good news is that the automation vision that the companies behind Wordsmith have announced is a free beta version of its AI writing application so you can try out the technology and see if it might be useful for your brand.

Smart Content Curation

AI powered content curation allows you to better engage visitors on your site by showing them content relevant to them. This technique is most commonly found in the ‘customers who bought X also bought Y’ section on many sites, but can also be applied to blog content and personalizing site messaging more widely. It’s also a great technique for subscription businesses, where the more someone uses the service, more data the machine learning algorithm has to use and the better the recommendations of content become. Think of Netflix’s recommendation system being able to consistently recommend you shows you’d be interested it.

AI Voice search

Voice search is another AI technology, but when using it for marketing, it’s about using technology developed by major players (Google, Amazon, Apple) instead of building your own capabilities. Voice search will change the future SEO strategy, and brands need to compete. Brands that search for voice search can capitalize on huge profits in organic traffic with high purchasing intentions due to increased voice search traffic caused by AIs that push virtual personal assistants.

Buy media programming

Programmatic media sourcing can use trend models generated by machine learning algorithms to target ads more effectively to the most relevant customers. Programmatic ads need to be smarter in Google’s latest brand safety scandal. It exposes programmatic ads through the Google Network appearing on terrorist websites. Artificial Intelligence can help you by identifying problematic websites and removing them from your site’s ad list.

Tendency modeling

As mentioned earlier, propensity modeling is the goal of machine learning programs. Machine learning algorithms provide a wealth of historical data and use this data to create a propensity model that, in theory, can make accurate predictions of the real world. The simple chart below shows the stages of this process.


Much like with ad targeting, machine learning can be used to establish what content is most likely to bring customers back to the site based on historical data. My building an accurate prediction model of what content works best to win back different types of customers, machine learning can be used to optimize your retargeting ads to make them as effective as possible.

Predictive analysis

Propensitive modeling can be used in many different fields, such as predicting the possibility of a specific customer conversion, predicting prices that can be changed by customers, or customers who are likely to repeat the purchase. This app is called forecasting analysis because it uses analytics data to predict customer behavior. The important thing to keep in mind is that the trend model is the same as the data provided to make the data, so if the data is wrong or the random level is high, precise predictions can not be made.

The main score

Trend models created through machine learning score metrics against specific criteria, so your sales team can set how to give “hot” clues and whether it’s worth the time. In a B2B business, this can be very important to the negotiated sales process, where each sale takes a considerable amount of time on the sales team. By calling the most relevant clues, sales teams save time and focus on the most effective areas. Insight into buying leads can also be used to target sales and discounts in the most efficient way.

Ad targeting

Machine learning algorithms can run a large amount of historical data to determine which ad and stage of purchase process are doing best. Using this data, they can provide the most effective content at the right time. By using machine learning to optimize thousands of variables on an ongoing basis, you can achieve more effective ad placements and content from traditional methods. However, you still need humans to do creative parts!

Dynamic price

All marketers know that effective sales lead to more products. Discounts are very powerful, but they can also hurt your bottom line. If you double your sales with two thirds of your small profits, then you will have less profits than you would have if you did not.

Sales are very effective because they allow people to buy your product and never think they can justify the purchase cost. But they also mean that people who pay higher prices pay less than they pay.

Dynamic pricing avoids this problem by targeting specific bids only to users who need to convert. Machine learning can create a trend model whose characteristics indicate that the customer may need to convert quotes and may change without quoting. This means you can maximize sales by increasing sales without sacrificing profitability.

Customize Web & App

Using a trend model to predict customer stages in a buyer’s journey can let you serve the customer, either on the app or on a web page, with the most relevant content. If someone is new on the site, the content that informs them and makes them interested will be the most effective, while if they have visited many times and are interested in the product, the deeper content of the benefits of the product will be better.

Chat robot

Chatbots mimic human intelligence by explaining the user’s problems and completing their orders. You might think chat robots are hard to develop and only big budget big brands can develop them. But in fact, using the open chatbot development platform, it’s fairly easy to make your own chat robot without a lot of developers.

Facebook is interested in promoting the development of branded chatbots. He wants his Messenger application to be where people talk to brand ambassadors. The good news with this brand is that this means they can use some of Facebook’s powerful bot development tools. Using the lessons they learned from the “M” beta (Facebook Messenger’s own chat robot), Facebook created a smart boat engine that lets you train ships through example dialogues and keep your ship engaged with your customers. If you are interested in creating a chat robot for your brand on the messenger platform, Facebook has created useful tutorials that you can find on their Facebook page.

Predict customer service

Repeat sales of existing customer base to attract new customers easier. Therefore, maintaining existing customer satisfaction is the key. This is especially true in subscription-based services, where high churn rates can be prohibitively expensive. By evaluating the most common features of unsubscribed customers, you can use predictive analytics to determine which customers are most likely to unsubscribe from the service. You can then reach out to these customers by offering, asking, or assisting to prevent them from pretending.

Marketing automation

Marketing automation technology often involves a series of rules when these rules trigger active engagement with customers. But who decided these rules? In general, marketers basically guess what is most effective. Machine learning can run through billions of customer data points and determine when it is the most effective contact time, and which terms in the subject line are the most effective. These insights can be used to increase the effectiveness of marketing automation efforts.

1: 1 Dynamic email automation

In the same way as marketing automation, applying the insights generated by machine learning can create a highly effective 1: 1 dynamic email. Predictive analysis uses a trend model to build a consumer’s tendency to purchase certain categories, sizes, and colors through past behavior and to show the most relevant products in communication. When opening email, product inventory, trading, price is correct.