In todays world every sectors is leveraging the power of Machine Learning to beat their competition and expands their business.
Let’s take a look at what is machine learning ?
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.
In a nutshell, ML is a way with which Machine Learn with the help of data.
There is a lot of hype about the use of ML in different industries. But, how are marketing, publication, and media industries really making use of technologies and making difference?
This article will focus on different use cases of ML in the Marketing, Publication, and Media industries.
Let’s Clarify First What is Marketing, Media & Publishing?
Marketing : Marketing is the process of getting potential clients or customers interested in your products and services. The keyword in this definition is “process.” Marketing involves researching, promoting, selling, and distributing your products or services.
This discipline centers on the study of market and consumer behaviors and it analyzes the commercial management of companies in order to attract, acquire, and retain customers by satisfying their wants and needs and instilling brand loyalty.
Media : The word media refers to any form of communication that delivers information. Common media outlets include newspapers, radio, television, magazines and internet sources like blogs or online publications. Today, ‘social media‘ is a term that many people are familiar with; this describes information distributed on social networking websites.
In the past, the media was mostly limited to newspapers, magazines and other printed publications. Technology helped to advance the media and expand the options for people to get their daily news and information, first on the radio and then on television. Now, many millions of people rely on the internet to get their news and online media outlets have become very popular all over the world.
Publishing : It is the activity of making information, literature, music, software, and other content available to the public for sale or for free. Traditionally, the term refers to the distribution of printed works, such as books, newspapers, and magazines.
Machine Learning offers a various technique for marketing Products find the potential customers and cross sell to the existing customers also with the help of machine learning technologies companies can also increase users engagement , cross sell and upsell relevant products to the customers , providing lead magnets and increase their brand values overall by which companies get higher ROI.
we’ve discovered 5 such use cases in the process that really make a difference:
- Recommendation Engine
- Real Time Analytics
- Chatbots
- Marketing Automation
- Hyper-targeted Advertisements
How ML works in case of recommendation engine to recommend relevant things to the customers.
Now companies are able to capturing the past activities of the customers and using them to recommend relevant products to the customers also they recommend products which are more likely to their purchasing power.
Also they are applying cross sell and up-sell strategies by forming the recommendation engine with the help of Machine learning.
Daily Hunt’s goal is to become the first point of access for local language users by providing content in their own language. India is a diverse country with 22 official languages and with an English literacy rate of 10% so there are 400 million mobile users in India who rely on their local language to operate and consume material in the local language. Daily Hunt has already built a strong recommendation engine to aggregate news for its user base of over 90 million with 28 million active monthly users and aiming to increase the number to 150–200 million in coming years. After its inception, Daily hunt has become India’s biggest avenue for local language content with more than 28 million page visits every month and more than 90 million user base.
Trendyol, a leading e-commerce company based in Turkey, faced global competitors like Adidas and ASOS, particularly for sportswear. To help gain customer loyalty and enhance its emailing system, it partnered with vendor Live clicker, which specializes in real-time personalization. Trendyol used machine learning and artificial intelligence to create several highly personalized marketing campaigns. It also helped to distinguish which messages would be most relevant to which customers. It also created an offer for a football jersey imposing the recipient’s name on the back to ramp up personalization. By creatively using one-to-one personalization, the retailer’s open rates, click-through rates, conversions, and sales reached all-time highs. It generated a 30% increase in click-through rates for Trendyol, a 62% growth in response rates, and an impressive 130% increase in conversion rates. It has now also employed strong marketing functions like social media utilization, mobile app, SEO blogs, celebrity endorsement, etc to reach its customer base.
Real-Time Machine Learning is the process of training a machine learning model by running live data through it, to continuously improve the model. This is in contrast to “traditional” machine learning, in which a data scientist builds the model with a batch of historical testing data in an offline mode.
Real-time machine learning is useful in scenarios when there is not enough data available upfront for training, and in cases where data needs to adapt to new patterns. For example, consumer tastes and preferences change over time, and an evolving, machine-learning-based product recommendation engine can adjust to those changes without a separate retraining effort. Therefore, real-time machine learning can provide a more immediate level of accuracy for companies and their customers by recognizing new patterns and adapting to reflect those.
In a real-time ML deployment, the system replies to a request within milliseconds of the request being made. There are two general workflows for making prediction requests with a real-time system:
- Web Requests
- Streaming Workflows
In the first case, the system or client that needs a prediction makes an HTTP request to an endpoint that responds directly to the request with a prediction. Other protocols, such as GRPC, can be used for this type of workflow.
The second workflow can be implemented in a variety of ways. For example, a request can be made to a Kafka topic, where it is processed with Spark Streaming, and the result is published to a separate topic. Other streaming frameworks such as Flink or GCP Dataflow can be used to respond to prediction requests in near real time.
