Machine Learning: Marketing, Media and Publishing Use Cases

The Machine Learning Market is expected to have $ 30.6 billion by 2024. The world is increasingly driven by the Internet of Things (IoT) and Artificially Intelligent (AI) solutions. ML plays an important role in the development of such solutions.

Today, almost every industry is leveraging the power of ML to improve in one or another way!

Now let’s deep dive into the use cases of ML in Marketing, media, and Publishing.

ML in Marketing:

Advertisers have been given the task of being the “Customer Voice” in the Company and have played a very important role in allowing deeper personal perceptions of consumers. The growth of a marketing organization is closely related to 3 factors.

1. Data
2. Marketing technology
3. Machine Learning (AI-ML)

In this article, we will explain how Artificial Intelligence and Machine Learning (especially related to Customer Analysis) will accelerate the growth of organizations.

a. ? ? Target Marketing & Customer Segmentation: Forecasting target and decision making a relatively newer segment of the marketing where the marketing is specified for the dedicated segment of customers who can be potential buyers. This helps companies to segment customers and make strategy more precisely personalized to the specific segment and thus could help in achieving higher conversion rate. ML could help in identifying the? potential customer segment as well as the approach based on the prior data and its outcome.

b. ? ? Analyzing Customer Behavior: To strategize a better marketing approach, the most crucial part is to understand the customer behavior. This helps derive customer interest and would increase the success yield. Data can be collected via feedback forms, social media, polls etc. to analyze the trend of the user interest and perception.

c. ? ? Recommending Systems: Recommender systems have helped the companies as well users to identify which product a user wants to keep the customer engagement and for customers recommenders helps in identifying their requirement and making decision. These recommenders are machine learning supervised and unsupervised algorithms which help in recommending the user based on other user’s preferences or the content they like to explore. Recommenders was a boon especially for e-commerce like Amazon, Flipkart, OTT

ML in Media and Publishing:

According to Statista, digital publishing generates worldwide $ 22.05 billion.

Globally, countries accessing social media have seen a tremendous increase in popularity. However, with global access comes the challenge of continually producing high-quality content at high volumes.

AI’s ability to mimic human capabilities, such as reading and thinking, has made it the next major technology in the field of digital marketing. Needless to say, AI and machine learning (ML), its largest branch, can change the way we strategize and produce digital content.

With in-depth learning and natural language processing skills, AI and ML can truly transform the digital publishing processes significantly. Here are some of the key ways in which this happened.

Search: Most publishers have gone online with a user-focused digital forum.AI or specific machine learning algorithms when used for search can help your end-users find the right information in just a few seconds. The key will be to build machine learning algorithms that learn from user behavior and provide information in a variety of formats including text, pdf, images, videos, and other digital assets. This is a sure winner to increase customer experience and gain the credibility of your product.

SEO : One of the major challenges publishers face today is to produce marketable content and make the impact they want. Digital marketers and publishers view SEO (search engine optimization) as an important tool for achieving this. Creating SEO for digital content includes doing keyword research, analyzing competitors, and improving image search. By using in-depth reading, a sub-branch of machine learning, publishers can take care of the stressful SEO processes with relative ease and great accuracy. For example, if you want to caption all your photos and categorize them, it is much easier and less expensive to do the process automatically using a tool than human hands have done for you.

?Customer service: AI chatbots, voice search, or human-assisted AI agents improve the quality of customer service. Be it a researcher, editor, librarian or student, questions may vary based on user and other demographics. With the help of an in-depth study of ML and NLP, algorithms can be developed to provide the right answers quickly. Combining this with text analysis and computer languages, you can take a step forward by analyzing emotions to understand your user’s situation and respond appropriately.