What You Will Learn
- 1 Artificial Intelligence in Marketing
- 2 Introduction
- 3 1. Online Advertising
- 4 2. AI-powered Chatbots
- 5 3. Personalized User Experience
- 6 4. Web Designing
- 7 5. Predictive Analysis
- 8 6. Content Generation
- 9 7. Email Marketing Campaigns
- 10 8. Content Curation
- 11 9. E-commerce
- 12 10. Voice Search Optimization
- 13 Conclusion
Artificial Intelligence in Marketing
Artificial intelligence is widely used in current advanced markets to service their customers. The Artificial Intelligence in Marketing reduces human involvement to perform the jobs better and helping humans. It processes a large amount of data to perform specific marketing tasks such as Personalization, Chatbots, Web generation, Web curation, Programmatic advertising, Voice search optimization, product recommendation, predictive analysis and, etc. These all applications and tools required a large amount of data, the learning model learns the data patterns and data characteristics to perform the specific task. We will modify the data to perform better tasks, makes faster learning, and make it easier for users.
Artificial intelligence is used in many real-life applications such as social media, Consumer Electronics, Robotics, Travel and Transportation, Finance, Healthcare, Security, Surveillance, E-commerce, etc. The AI reduces human involvement to perform the jobs better and helping humans .
The new AI technologies are wieldy implemented in digital markets to facilitate their customers and seller. In the current market, there is a massive requirement to process a large amount of data. The current market used the AI application to process the data faster, provide 24 by 7 services to their customers and improve the digital market strategy [1, 2]. Several AI ways are implemented in digital marketing such as online advertising, personalized user experience, AI chatbot, predictive analysis, web designing, content generation, content curation, email marketing Campaigns, Voice Search Optimization, and E-commerce.
1. Online Advertising
Now a day digital marketing mainly used online advertisements to promote their products to reach their customers quickly. There are many online platforms to do online marketing such as websites, advertising applications, Facebook, Instagram, and YouTube channels increases day by day. It is difficult to select the best place to put the advertisements by using different online platforms and also to target specific customers .
AI technologies provide better decisions from a large amount of data. AI technology is integrated with the digital market to make it easy and more helpful in businesses.
i. Programmatic Advertising
It is used in the current digital market to improve online advertisement. Programmatic advertising uses AI technologies to improve efficiency and automates the buying and selling of online advertising and make better budget decisions for advertisers.
The programmatic advertising platform identifies online consumers according to:
- Demographics (male / female / 18 – 45)
- Purchase history
- Appropriate time (summer/winter)
Advertisements are personalized to get more customers according to their unique interests and behaviors. Programmatic advertising facilitates the seller and buyer of ad spaces to target the right channels at right time and at a good price. It conducts auctions where these ad spaces are sold and bought under milliseconds. Programmatic buying illustrates that machine learning makes the digital market more flexible by adding the customer needs and their interests [2, 3].
Before AI dealers were strongly searching for the right platform to market their business. Now AI reduces the load from marketers to allow them an easy and effective way to select the appropriate platform to market their business.
- Make strong bounding between customer and buyer.
- Getting better targeting customers.
- Filtering ads to determining the best ads to show to users.
- Getting the best price and managing the budget in the best possible way.
2. AI-powered Chatbots
A chatbot is an AI application that provides human-like conversation by using machine learning and natural language processing (NLP). It simulates a user conversation through messaging applications and is available 24/7 to increases the user response level on the website
AI-powered chatbots are an advanced version of standard Chatbots. It has overcome the issues of standard Chatbots and acts like a human to users. When a user asks any question, it does not get short-tempered. It also responding several client queries at the same time . The automatic response is personalized to pursue any user to purchase the products or services. The Xenioo, Hyro, ManyChat, Landbot, and REVE chat .
3. Personalized User Experience
Personalization is the process to make a strong relationship bonding between the customer with dealer. It provides a significant customer experience.
AI learns the customer experiences from its previous data that figure out his/her interests according to their behavior patterns, likes, and dislikes. We collect and analyze this data given demographics, location, geographics, devices, etc. .
AI improves the personalization process to build strong relationships with buyers. Higher personalization gives better chances of conversion. Many eCommerce companies using this technique to target more customers. The goal is to show the relevant products to each customer according to their preferences and interest history.
Retailers and eCommerce companies are using AI to personalize online experiences. The goal is to display relevant, context-sensitive products and offers that will appeal to each customer, based on their history and preferences.
It also gives the prediction of the next destination by using a recurrent neural network (RNN). The RNN model analyzes the sequence of the user’s location in his route and suggests a new destination that he likes more. The multi-class prediction models are integrated with personalization to show the right content to the right person [2,5].
4. Web Designing
5. Predictive Analysis
Predictive analysis is a meaningful technique in marketing mainly use in baking, insurance, and investment sectors. The AI performs the predictive analysis by enabling the genetic algorithm to establish an intelligent part that optimizes the collecting data and detects its irregularity. The predictive analysis offers a stage for rising interactive models in advising data collection, investment customization, practices, and analysis for investors. The genetic algorithm understands the characteristics of data to predict the analysis. AI optimizes the data pattern to make a correlational pattern for the investors to suggest them a better prediction .
6. Content Generation
Content is used in the majority of online platforms such as blogs, mobile applications, and websites the valuable content grasps several users and target audience quickly. Good content needs a professional content writer with expert-level writing and language skills. It is very difficult to make better content without a professional content writer. AI technology resolves this issue by generating content for services, products, websites, etc. Furthermore, it can also write the content of a movie review for a news website .
AI generates Human-like content by dealing with many Terabytes of a dataset to analyzing thousands of contents that help to engage the audience. Wordsmith, Quill, and Articoolo are well-known marketing tools for a content generation . It makes content writing more efficient and easier for us. It creates highly targeted content on websites to attract its customers.
7. Email Marketing Campaigns
In the current technology world, people are expecting concerning/perfectly relevant emails from buyers. The AI helps us to offer auto-generated emails that automatically creating relevant emails according to user behaviors and preferences and sent a modified email to their users. The auto-generated emails analyzing customers’ behavior and likings .
AI learns from numbers of GB data to make the prices title and appropriate subject line that attract the customers. It also helps to find the acuate day, time, and frequency to send the email, which also increases the communication level between buyer and seller. It makes the mailing process faster, better, easier, and improves transfer times, manages an email list, automates the reporting for structuring the data, and automatically personalizes the email body in every step. Customers are more relaxed with this application .
8. Content Curation
It is used in all marketing strategies to offers the maximum arrival on investment. Content generation and content curation are two main parts of content marketing. the content generation is generation content by combining the similar contents, it also learns the relevance of over research topic.
Content is usually generated by getting inspired by other similar pieces of content. AI can also used to search the content relevant to our topic of interest. But the content curation is the process to select the appropriate set of content to share with their customers. It shows the relevant content to each user by learning their interest from its history. AI offers Concurred and BuzzSumo tools to search current trending content, recreate the current content, plan future content, schedule, and allocate the content [2,10].
Netflix, Amazon used the content curation technique to recommends a particular movie/TV show and a particular product to particular users. The AI learns the user’s interest to make a better recommendation. Content curation also helps to produce faster and accurate content on large scale. It provides user-friendly customer support .
AI provides an effective way of marketing that improves online business. The e-commerce business owners used this technique to change their marketing style. AI provides the facilities to automatically build web content, website, and storge communication between customer and company. It also gives 24/7 services to each customer at a time. It improves the personalization process to recommends suitable products according to customer interest and behaviors. AI also plays a significant role in e-commerce research for competitor markets, sales prediction, observing user interest trends, etc. .
E-commerce majority used various AI applications, AI tools such as Personalization, Chatbots, Web generation, Web curation, Programmatic advertising, Voice search optimization, product recommendation, predictive analysis and, etc. The AI reduces human involvement and saves the buyer and seller time.
10. Voice Search Optimization
Voice search is a speech recognition technique that used the (Natural Language Processing) NLP model to recognize the given voice. Google used this application where the user just saying the search term in the search section instead of typing each word. Google usually used voice search for searching purposes. It improves the voice search by optimizing the necessity of voice search .
The AI provides revolutionary changes in the market strategies by facilitating their sellers and buyers. Many advanced technologies used voice search optimization to optimize the platform for voice search. Google’s RankBrain used this tool to optimize their website for voice search and reduce the upcoming traffic during a regular search.
AI and machine learning used the NLP technique to meaningfully improve the Google voice search. The speech recognition of AI-powered Google Assistant is increased to 95% to understand the English language, where the error rate is as low as 4.9% .
There are many voice search devices some are the most popular such as:
- Amazon Echo/Alexa: Bing
- Google Home: Google
- iPhone/Siri: Safari
- Google Assistant: Google
- Microsoft Cortana: Bing
- Android phones and devices: Google
The new AI technologies are wieldy implementing in digital markets to facilitate their customers and seller. In the current market AI process, a large amount of data to perform the specific tasks such as Personalization, Chatbots, Web generation, Web curation, Programmatic advertising, Voice search optimization, product recommendation, predictive analysis and, etc. These all applications and tools required a large amount of data, the learning model learns the data patterns and data characteristics to perform the specific task. We will modify the data to perform better tasks, makes faster learning, and make it easier for users.
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