Marketing is the fuel which energizes business to achieve higher growth. It has evolved a lot from what it was initially. A number of new tools and technologies have been implemented to make it more effective and, I must say, relevant. For instance, before a decade you may have been able to convince a client using a physical brochure. However, in this age, a digital brochure or, perhaps, a presentation would work more effectively.
Marketing professionals are expected to be equipped with knowledge about cutting-edge advertising tools. One of the tools which are being increasingly used to optimize marketing efforts is Machine Learning. Being a branch of Artificial Intelligence or AI, it involves automating model building for data analysis.
Scratching your head? Wondering what’s it about?
Well, machine learning is nothing but enabling a computer to learn without programming it. It is a technology which can take decisions without human interference.
So, what does it have to do with marketing?
Machine Learning (ML) helps in improvising general marketing tasks including customer segmentation, communication with customers, and securing and segregating content. This list is not exhaustive. ML is more expansive in the sense that it helps understand, predict, and act on the problems faced by sales prospects in an expeditious manner.
Before proceeding, let’s press the rewind button and look how ML initially began.
- History of Machine learning
History of Machine learning
Arthur Samuel coined the term ‘Machine Learning’ in the year 1959. He referred to it as a tool which empowers computers to learn without any programming.
Later, in the year 1997, Tom Mitchell defined it as
“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”
Now that we have got a gist of machine learning and have a basic idea about how it can be useful for marketers, let’s explore the concept in detail to know how you can boost your marketing campaigns utilizing the powerful tools and technologies of ML.
1) Analysis of Customer Satisfaction Levels:
One of the most powerful applications of ML involves analyzing 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
- Identify customers who are delighted with your product/service so that you can offer them special discounts, deals, or offers
- Read the emotions of users of specific products and make relevant changes wherever possible
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 complain. It gives me instant answers. And, this is what is preferred by most of the clients.
According to a survey, 63% of customers are highly likely to revisit a website if it offers a live chat feature.
One significant point to be noted here is the application of sentiment analysis which can be used by chatbots to respond appropriately. 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 you a number of things. You all must have observed this when YouTube recommends you videos related to the ones you watch. This is a part of ML.
What ML does is to identify customer preferences and make recommendations based on them.
Recommendations help in enhancing customer experiences as customers feel that they are being catered to with a customized platter. 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 behaviours. 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
- Minimizes marketing efforts by showcasing products which are highly demanded or showed interest towards.
- 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 are empowered to predict numerical values depending on the existing features.
This, in turn, empowers them to optimize various aspects of the sales funnel. Further, it can be leveraged for forecasting sales and in determining the marketing budget.
5) Translation for Personalized Customer Experiences:
With the help of attention mechanisms machine translation can be improved 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 their 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
Most of the businesses, catering to markets which are linguistically varied, prefer to get only the review of translated content done by a human translator.
6) Recognizing Products Through Computer Vision
This very less known application of ML 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. This is done by looking for images similar to the products sold by the brand. The algorithm scouts the internet for matching images without any relevant text.
This technique can also be utilized to gauge and compare the popularity of products offered by competitors of the brand. Such comparisons will help marketers make strategic business decisions and introduce changes, if any, in the existing product lines.
Following are the benefits achieved by 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. This comparison is made to identify and then implement a more effective option so as to get improved results.
However, one drawback of this method is the opportunity cost. If the seemingly effective option turns out to give less ROI then expected then it’s a loss for the business.
One of the alternatives provided by ML is bandit tests. These tests help avoid opportunity cost as they explore and exploit the options at the same time. With time, they gradually move towards the option which is more effective and result-oriented than the other.
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 to marketing are endless provided 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.