Reading Time: 5 minutes

One of the industry’s significant innovations is Artificial Intelligence tools that streamline processes and make businesses more effective. Artificial Intelligence (AI) and Machine Learning (ML) are two separate entities that complement each other well. While artificial intelligence aims to harness certain aspects of thinking, Machine Learning helps people solve problems efficiently. According to QuanticMind, 97% of leaders believe that the future of marketing lies in how digital marketers work alongside Machine Learning-based tools.
Analytics, personalization, automation, and optimization are the four pillars of success for any digital marketing campaign. Exploiting the enormous potential of Machine Learning is no longer a dream for the digital marketing industry. Several companies are already advancing their digital marketing campaigns with the help of Machine Learning. According to Gartner, 30% of companies will use Machine Learning in one part of their sales process.

7 Ways of Using Machine Learning in Your Digital Marketing Campaigns

1) Enhanced Customer Service

One of the most omnipresent applications of AI signifies Bots and Chatbots. But, most marketing bots are entirely scripted and use minimal Natural Language Processing and Machine Learning. The advanced dialog systems can adapt to unusual questions and reference external knowledge bases. Several companies have already adopted chatbots to provide exceptional customer service throughout a customer’s journey. According to research, 79% of customers prefer live chat for getting their questions answered quickly, and 63% of consumers are more likely to return to a webpage if it offers a live chat feature.
Chatbots can help you enhance your live chat features. Below are reasons why customers prefer live chat.
• Most efficient method of communication.
• 24*7 support.
• You can multi-task.
• Ever-expanding knowledge database.
• Eliminates customer wait time to talk to a representative.
Machine Learning improves chatbots’ operation that uses sentiment analysis to judge customer’s mood. If used alongside social media, Machine Learning can gather more customer information. This will improve your consumer targeting and product recommendations. The eBay chatbot built for Google Assistant is the most advanced e-commerce chatbot available out there and is also the most used.

2) Text Classification for User Insight and Personalization

Brands that care for their customers’ needs are more likely to be their favorite. So much so that 52% of consumers are anticipated to switch brands if they feel a company isn’t making enough efforts to personalize the experience. The Machine Learning system can probe text or voice-based content using Natural Language Processing (NLP) and then classify each content based on sentiment, tone, or topic to generate customer insight or curate relevant materials.
Personalization makes consumers feel valued, and they are more likely to shop from the brands that cater to their needs. According to research, 44% of customers will return to make future purchases after having a personalized shopping experience. IBM Watson’s Tone Analyzer is a great example that can parse through online customer feedback and analyze the general tone of users reviewing a product.

3) Content Optimization

Machine Learning doesn’t strive to outsmart and usurp human intellect. Instead, it concentrates on analyzing problems and processes and finding ways to optimize them. A popular method that most marketers use to find out content options such as email tone, visual elements in an ad, web page layout, article headline, etc., is A/B testing.
A/B tests are the most effective ways of finding content options that resonate well with your audience. However, A/B testing involves a “regret’ period where you lose revenue while using the less optimal option. You need to wait and finish the countdown before learning which option is the best. Whereas bandit tests mitigate regret (opportunity loss) through dynamic optimization where it simultaneously explores and exploits options, automatically moving towards the better option.
Whether it is Facebook ad graphics, email subject lines, or an article headline, A/B tests help marketing departments to try out various options and collect the results to determine which connects best with the audience. Curata and Vestorly are the tools that are able to send the right content to the right person at the right time. These tools wrench together articles from preferred online destinations and personalize the content experience for your customers.

4) Automation for Marketing Operations

Digital marketing needs automation to make work easier for hard-pressed practitioners. Digital marketing persons can stay ahead of the curve by embracing automated processes for reading emails, data entry for templated reports, opening and analyzing email attachments, and tracking or engaging social media. According to Forrester, marketing automation tool spend will reach $25 billion by 2023, and HubSpot, Marketo, and Pardot make up 50% of that market.
Marketing automation makes your strategy exceptional using Machine Learning to learn from patterns and past outcomes to deliver insights on content targeting, customer segmentation, and prescriptive suggestions to simplify decision making. For online ads, the AI platform Albert eliminates the need for human involvement in large-scale media buying, optimizing the pace of required analytical computations and paid ad campaigns.

5) Automated Email Marketing Campaigns

When the market is hyped with artificial intelligence, virtual reality, video, and chatbots, it seems we are getting over email. But, if you don’t consider email in your digital marketing strategies, you are missing out on the real metrics. According to a study in 2018, email marketing ranked as the most effective marketing channel, beating out social media, SEO, and affiliate marketing. And is probably the best strategy for your business.
Many marketers are in search of email marketing automation software to improve the return of investment. With the help of Machine Learning, email marketing can leverage customer segments and personas, a library of content, and data about prospects to personalize email campaigns. The use of Machine Learning in email marketing can help marketers improve content creation to drive user engagement—also, data segmentation and the right timing to send emails to prospects. Automizy and MailChimp are the tools that have made it possible for marketers to use Machine Learning in their email campaigns.

6) Computer Vision for Branded Object Recognition

This technology might not be familiar to you, but computer vision is a rapidly advancing field in AI that lends itself to a broad range of applications. Marketers can use Machine Learning-powered computer vision for product recognition and extract customer insights from unlabeled videos and images.
GumGum is the tool that allows marketers to identify when their brand logos have appeared in user-generated content and immediately calculate earned media from video analysis. Marketers can also use an API like Clarifai to build custom solutions for content moderation, search and recommendation engines based on visual similarity.

7) Powerful Social Media Management

Social media holds an indispensable position in digital marketing. Several crucial marketing functions, right from content marketing to customer service, are performed through Facebook, Instagram, Twitter, and YouTube. And Machine Learning helps marketers use big data to optimize their social media resources.
Machine Learning can help marketers analyze the user communications, reviews, and complaints on social media that need priority responses to build a strong brand image with social media reputation management. For instance, Yext uses Machine Learning to help brands identify mentions on Facebook, Google, and Yelp to track what’s being spoken about their companies, which helps marketers respond more promptly.
Machine Learning not only helps you decide what to post but also the perfect time to post it. By analyzing hundreds of thousands of profiles, tools like Cortex help companies determine the perfect time to post on Instagram, Facebook, and other social media platforms.

Conclusion

Due to big data and the enhanced computational power, artificial intelligence and its subfields such as computer vision and Natural Language Processing (NLP) have become astonishingly powerful over the years. Marketers are already using Machine Learning to leverage their strategies. You need to follow the latest trends to provide exceptional customer service. Define your business goals, create the strategy and use Machine Learning to your advantage.

Pooja Patil is a versatile Content Writer with proven writing capabilities in all genres. She holds more than 2 years of experience in the content marketing field, working closely with B2B, B2C, and entertainment businesses providing unparalleled content with enhanced search engine visibility. Content writing expert by day and technophile by night, Pooja enchants her readers with insightful information displayed artistically.

Leave a Reply

Your email address will not be published.