Artificial Intelligence Training Course in Digital Marketing
Reach – Use various inbound technologies to reach visitors
Coverage involves using technologies such as content marketing, search engine optimization, and other “earned media” to bring visitors to your website and start their buyer journey. Artificial intelligence models and trendy apps can be used at this stage to attract more visitors and provide a more engaging experience for your website.
1. AI generates 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.
2. Smart content management
The content policies supported by AI enable you to further attract your visitors by showing relevant content. This technique is most commonly found in the “Buy X Buyers Also Purchase Y on Many Pages” sections, but they can also be used for blogging and messaging content that is more widely available on the web. This is also a good technique for subscribing to business, and more and more people are using this service, the more data to use machine learning algorithms and better content suggestions. Think of the Netflix backup system to continue to recommend you, indicating that you are interested.
3. Voice search
Voice search is another artificial intelligence technology, but when marketing, it uses technology developed by major players (Google, Amazon, Apple) rather than developing its own capabilities. Voice search will change the future of SEO strategies, brands need to compete. As virtual artifacts driven by artificial intelligence adds to voice search traffic, brands that determine voice search can take advantage of high buying intentions to capitalize on the huge profits of organic traffic.
4. Purchase programmed media
Programmatic media buys can use trend models generated by machine learning algorithms to target ads more effectively to the most relevant customers. After Google’s latest brand security scandal, programmatic advertising needs to be smarter. It has revealed that programmatic advertisements through the Google advertising network appear on terrorist websites. Artificial Intelligence can help identify the site and remove it from the list of sites that can be placed.
Action – Attract visitors to tell them about your product
5 tend to model
As previously mentioned, modeling tendencies is the goal of machine learning programs. Machine learning algorithms provide a lot of historical data and use this data to create a trend model that, in theory, can make accurate predictions about the real world. The easy chart below shows the stage of this process.
6. Prediction 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 model, as good as a model of inclinational tendency to provide data, so if there is a high-level error or random data, you can not make accurate predictions.
7. The main score
The trend model created by machine learning can be trained to score potential customers based on a set of criteria so that your sales team can determine how “hot” a given leader is worth production. In the B2B business with the sales process of negotiations, it is very important for the sales team to spend a lot.
8. Advertising targeting
Machine learning algorithms can run large amounts of historical data to determine which ones are best for people and at any level during the purchase process. Using this data, they can give them the most effective content at the right time. By using machine learning to continuously optimize thousands of variables, you can achieve more effective ad placements and ad content than traditional methods. However, you still need humans to do creative parts!
Transform – Raise interests of interested users to customers
9. 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 been without sales.
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.
10. Custom Web & App
Using a trend model to predict the customer phase in a buyer’s journey allows you to provide the customer with the most relevant content on an application or web page. If someone on your site is a novice, the content that informs them and makes them interesting will be the most effective, and if they visit it many times and are interested in the product, the more in-depth content about the benefits of the product will be better .
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.
12. Aim again
Like ad targeting, machine learning can be used to determine from historical data which content is most likely to return a customer to a website. My building is an accurate predictive model and what kind of content is most effective for winning different types of clients, machine learning can be used to optimize your targeted ad to make it work.
Engage – get your customers back
13. Predict customer service
It’s easier to attract new customers by duplicating existing customer groups. So keeping the existing happy customer is the key. This is especially true in subscription-based services, where high churn prices can be very expensive. Predictive analysis can be used to determine which customers are most likely to cancel a subscription service, customers unsubscribe by evaluating who has the most common. It is then possible to reach these customers by offering, encouraging or helping to prevent them from beating.
14. Marketing Automation
Marketing automation technology often involves some rules when these rules force active engagement with customers. But who decides this rule? In general, marketers basically guess what works best. Machine learning runs through billions of customer data points and determines when it is the most effective contact time, and what words in the subject line are most effective. This vision can be used to improve the effectiveness of marketing automation efforts.
15 1: 1 dynamic email
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 using a trend model may be inclined to set up several categories, sizes and colors from users buying through your previous actions, and showing the most relevant products in communication. When opening email, product inventory, trading, price is correct.