Marketing is the fuel that energizes businesses to achieve higher growth. It has evolved a lot from what it was initially. Businesses are implementing new tools and technologies to make it more effective and, I must say, relevant.
Marketing professionals are now implementing cutting-edge advertising tools and one of the best tools to optimize marketing efforts is Machine Learning. Being a branch of Artificial Intelligence, it involves automating model building for data analysis.
Let’s explore the concept in detail to know how you can boost your marketing campaigns utilizing the powerful tools and technologies of ML.
Contents
1) Analysis of Customer Satisfaction Levels:
One of the most powerful applications of ML involves analyzing the sentiments of customers. Through an email or even a phone call, it might be difficult to gauge the emotions of your customer. However, with the application of ML technology, you can analyze the text to judge if the sentiment behind the message is positive or negative.
This helps your customer care executive in responding with empathy. Sentiment analysis can help marketers with the following;
- Understand the overall attitude of customers towards a business
- Gauge the level of reputation a business has on the online platform
- Get alerts on any negative or debasing content on social media
- Identifying customers who show interest in your product/service to offer special discounts, deals, or offers
- Read the emotions of users of specific products and make relevant changes wherever possible
2) Chatbots:
This is one of the most popular applications of ML technology. Personally, I too prefer to go for Live Chat whenever I have a product/service-related query or complaint. It gives me instant answers.
According to a survey, 63% of customers are highly likely to revisit a website if it offers a live chat feature.
Apart from this, the following is what a chatbot helps in achieving.
- Improve average session duration for your website
- Enhances customer satisfaction by improving engagement levels
- Reduce wait time for customers who are seeking customer service
- Helps in delivering personalized customer service
3) Implement Recommendation Systems:
Based on the searches you perform on search engines; Google recommends a number of things. You all must have observed this when YouTube recommends videos related to the ones you watch. This is a part of ML.
ML improves marketing efforts by helping explore the types of products their customers want. This discovery is based on the client’s browsing history and shopping behaviors. By recommending relevant products, eStores can enhance conversions.
Following is what marketers can achieve through recommendation engines:
- Create personalized and relevant content
- Establish consistent brand experience
- Enhances lead scoring
4) Regression Models for Progressive Pricing
The right price can help marketers establish a market monopoly for a product. With the help of regression strategies in machine learning, marketers can predict numerical values depending on the existing features.
This, in turn, empowers marketers to optimize the sales funnel, forecasting sales and determining the marketing budget.
5) Translation for Personalized Customer Experiences:
With the help of attention mechanisms, marketers can improve machine translation and this, in turn, helps in empowering marketing efforts at the international level.
When a brand enters a linguistically novel market, a major part of its expense was attributed to marketing efforts. However, with the development of AI, machine translation is almost akin to the spoken language.
Following are the ways in which machine translation pushes marketing efforts:
- Hits the right chord of the customer by using a native language
- Minimizes translation cost
- Helps cater to customers who have limited linguistic capabilities
6) Recognizing Products Through Computer Vision
Machine learning also aims at gauging the online or social media popularity of a business or a brand. Utilizing computer vision, businesses can track the number of posts related to the brand. The algorithm scouts the internet for matching images without any relevant text.
This technique can also help marketers to gauge and compare the popularity of products offered by competitors. Such comparisons will help marketers make strategic business decisions and introduce changes, if any, in the existing product lines.
Following are the benefits of using machine learning to trace brand-related user-generated content:
- Identify the brand popularity on social media
- Increase marketing efforts for less popular product lines
- Make relevant changes in the features of products/services
- Identify the most popular brand-related videos which can help marketers generate more similar videos
- Recognize the platforms where the brand is getting more popularity
- Increase marketing efforts to augment the popularity of less known or less popular products
- Perform market research before introducing new product lines
7) Content Optimization Through Multi-Armed Contextual Bandits:
Businesses perform A/B testing to measure and compare the effectiveness of two products or options. It can also be a comparison between two web pages, email tones, visual elements of an ad, article headline, etc.
If the seemingly effective option give less ROI than expected then it’s a loss for the business. One of the alternatives provided by ML is bandit tests. These tests help avoid opportunity costs as they explore and exploit the options at the same time.
Following are the benefits of utilizing bandit tests:
- Time and resource-saving
- Negligible opportunity cost
- Opens up possibilities of exploring other options
The benefits and applications of ML in marketing are endless and marketing specialists utilize it optimally and effectively. With more developments in AI technology, machine learning will benefit more than what it is doing currently.
Which application of ML did you find to be most useful for your marketing campaigns? Please share your experiences in the comment section below.
