ML Use Cases in Marketing, Media, and Publishing

Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

There is a lot of hype about the use of ML in different industries. But, how are marketing, publication, and media industries making use of technologies and making difference? This article will focus on different use cases of ML in the Marketing, Publication, and Media industries.

Machine?Learning offers new tools for answering questions about your data, but to use it successfully in your business, you need to be sure you’re using the right technologies in the first place. Many companies have used this power of machine learning to support marketing initiatives, make better content, and increase their brand values over time, and we’ve discovered 8 such use cases in the process that really make a difference:

• Better Recommendation Engine
• Predicting Customer Churn
• Text Summarization
• Marketing Automation
• Customized Offers to the Customers
• Word Suggestions to Auto-Complete Text
• Hyper-targeted Advertisements
• Personalized feeds on Social Media Platforms

Recommendation System

How is ML helping companies to recommend the relevant things to their customers?

Recommender systems are the systems that are designed to recommend things to the user based on many different factors. These systems predict the most likely product that the users are most likely to purchase and are of interest to. Companies like?Netflix, Amazon, etc. use recommender systems to help their users to identify the correct product or movies for them.?

The recommender system deals with a large volume of information present by filtering the most important information based on the data provided by a user and other factors that take care of the user’s preference and interest. It finds out the match between user and item and imputes the similarities between users and items for recommendation.?

Types of Recommendation System:

1. Popularity-Based Recommendation System
2. Collaborative Filtering
3. Content-Based Filtering

Predicting Customer Churn

Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service. It is a critical prediction for many businesses because acquiring new clients often costs more than retaining existing ones. Once you can identify those customers that are at risk of canceling, you should know exactly what marketing action to take for each individual customer to maximize the chances that the customer will remain.

Customer churn is a common problem across businesses in many sectors. If you want to grow as a company,?you have to invest in acquiring new clients. Every time a client leaves, it represents a significant investment loss. Both time and effort need to be channeled into replacing them. Being able to predict when a client is likely to leave, and offer them incentives to stay, can offer huge savings to a business.

Text Summarization

With the increase in content on the internet, people are always looking for summarised content which reduces reading time. Automated summarization is a machine learning technique for creating a short, accurate, and fluent summary of a longer text document. When researching documents for publication, summaries make the selection process easier. Automatic summarization improves the effectiveness of indexing. With the improvement in ML in language processing techniques, automatic summarization algorithms are less behind than human summarizers.

Inshorts?is one of the highest-rated Indian news apps. It provides news, infographics, and blogs and?summarizes them in 60 words. Inshorts, a news app set up four years ago, the business model of Inshorts is very simple. Their main aim is to create a buzz and get readers to spend five minutes on the app daily. Inshorts is aggregating the news through push notifications to its more than 4 million subscribers today. Inshorts tells its subscribers what, where, why, when, and a how of all important news of the day in less than 60 words. The brief content is linked to the original story for the people who want to read the whole story. Inshorts is targeting those people who don’t want to track whole news stories but want to take a look once a day.

Hyper-Targeted Advertisements

Thanks to the click-through rate (a metric that measures the number of clicks advertisers receive on their ads per number of impressions) which can help us track the effectiveness of ad campaigns. But, AI can actually go one step further, AI for advertising has the ability to increase your return on ad spend (revenue) and reduce the amount of money you spend on staff time and ineffective ad budget. The tool uses sophisticated AI to analyze ad campaigns, then manage targeting, testing, and budgets.

Personalized feeds on Social Media Platforms

Personalization is a very broad concept. In this case, personalization equates to leveraging engagement data to build an interesting feed for a user. Many of the apps you use every day leverage machine learning to personalize your feeds. Here are 4 examples:

1. Instagram created an “Explore” section that shows you pictures you’re likely to be interested in, based on your past clicks, likes, and engagement.
2. Quora uses your engagement data, like the posts that you search for and comment on, to personalize your feed experience and emails.
3. Facebook uses advanced machine learning to prioritize posts from the friends you engage with most, after starting out with just a basic edge rank algorithm.
4. Etsy analyzes your search and shopping behavior to optimize your newsfeed, email, and e-commerce conversion.

Conclusion?

Machine learning helps marketers solve many problems and has huge potential to automate and optimize marketing streams and funnels to newfound levels of performance. ML works well with big data but it can also collect, clean, and use new data, including unstructured data, to provide marketing insights.?

ML has also become increasingly able to deal with natural language and is able to creatively analyze sentiments and other text, this effectively allows marketers to tap into the internet as a rich resource of customer information, trends, thoughts, and behaviours. From the examples here, you can also see how ML has increasingly creative and out-of-the-box uses.

REFERENCES:

1. A Gentle Introduction to Text Summarization in Machine Learning - https://blog.floydhub.com/gentle-introduction-to-text-summarization-in-machine-learning/
2. How is Machine Learning used in Marketing - https://understandingdata.com/machine-learning-use-cases-marketing/
3. Case study of Inshorts?https://www.whizsky.com/2017/09/case-study-inshorts-number-one-news-app/