Machine learning is transforming the marketing industry and can come into play in all aspects of digital marketing, from search engine optimization and marketing to social media, email marketing, paid advertising and even content marketing. It is a must for digital marketers to learn how to apply machine learning to their digital marketing strategies.
Many companies already use machine learning to solve problems and drive customer engagement by collecting campaign data to create personalized marketing journeys that maximize consumer happiness. According to the McKinsey Global Survey in 2021, 49% of companies are exploring or planning to use ML.
Companies use machine learning to stay ahead of their competitors by tackling some of marketing’s toughest challenges, such as personalization, instant customer support, and big data, content, and online channels to increase productivity and better understand their target audience.
Machine learning (ML) and artificial intelligence (AI) tools enable digital marketers to identify patterns and create predictive models, offer content, and products and better understand target audiences and optimize interactions with them.
For example, Amazon Web Services (AWS), which is a cloud service, uses data collected through machine learning. Amazon reports that personalized product recommendations derived from machine learning account for 35% of its total revenue. Organizations that use ML tools have more time to specialize in other areas and use ML results to get new, deep insights to optimize their marketing strategies.
In this article, we discuss 10 ways to use machine learning to supercharge your digital marketing efforts.
Related Content: 20 Best AI Marketing Tools to Grow Your Online Business
What Is the Difference Between Artificial Intelligence And Machine Learning?
Machine Learning (ML) and Artificial Intelligence (AI) are often referred to as if they are the same term, but in reality, they are two distinct entities that complement each other. Machine learning is the science of computing, a section of artificial intelligence (AI) that uses data and algorithms to mimic human learning.
While artificial intelligence (AI) aims to harness aspects of the thinking mind, machine learning (ML) refers to a machine capable of learning to help humans solve problems more efficiently. Machine learning, as a subset of AI, allows devices, computers, and gizmos to automatically learn and improve from experience and helps humans solve problems more efficiently.
Machine learning algorithms start by building a model based on sample data, called training data, and then use the data combined with other experiments to make predictions or decisions without being explicitly programmed.
In other words, they are self-taught. The more data the machine analyzes, the more accurate the algorithm or model is.
What is Machine Learning for Marketing?
Machine learning as an advanced tool that uses data to provide effective solutions to a multitude of complex digital marketing problems.
At its core, machine learning refers to the ability of computers to learn without being explicitly programmed. Machine learning uses data to automatically make decisions, rather than simply providing insight into what is happening at a macro level.
In other words, machines begin to learn on their own how to optimize results without the need for manual human labor. In marketing, is means that a computer can identify patterns in the data and apply them to predict future outcomes with a high degree of precision.
For example, a machine learning model may have the ability to predict which prospects will convert based on their past behavior, and then take specific actions to optimize their experience for better results.
Why You Should Use Machine Learning On Your Marketing Strategy?
Machine learning helps solve digital marketing problems by sifting through customer data to find new insights and streamline marketing processes. A machine’s ability to identify trends and patterns from data can easily surpass a human’s ability to do the same.
Machines can analyze huge amounts of data, quickly and efficiently, without human error.
By removing the need for your marketing team to analyze data, you free team members from one of their most time-consuming tasks.
Consequently, many industry leaders have created their own custom ML systems to achieve better results for their clients. One of the key uses of ML in marketing is that it reveals trends in customer behavior.
Using these data, companies can develop new products, target their audience more effectively, and provide relevant offers that generate more revenue. The most common use of machine learning by marketers is to find patterns and predict future customer behavior.
They use it for audience segmentation, cost-effective media buying, process automation, optimization and personalization. For example, you can use machine learning to determine a user’s preferred language in emails based on their location, or whether they are more responsive to unwanted pop-ups or banners based on the device they use.
The outcome of all this is more effective marketing that can improve business growth and profitability. So let’s look at specific ways in which marketers can exploit machine learning.
10 Effective Ways to Use Machine Learning in Digital Marketing
Below are some of the ways machine learning can help you stay ahead of the curve in your marketing efforts.
1. Improve Personalization
Machine learning allows you to provide your customers with the most personalized experience possible. ML enables you to process large amounts of data, from information collected by data science and IT staff to sales, customer service, and customer retention sources.
So the software learns as it takes in all this information, and continues to learn as new data gets added.
Personalized marketing requires that every touch point in the customer’s buying journey – from mobile marketing to email to product offers and promotions – be tailored to each individual, and this is not possible without machine learning.
Customer Segmentation
Using machine learning in marketing campaigns helps brands learn more about their customers over time by measuring their activities in a more granular way to better target their messages through customer segmentation.
ML allows you to use an algorithm that tracks user behavior at a granular level, learns which products they like and thus creates a personalized landing page and recommendation list or generate personalized offers for each customer and email them when they are most likely to buy.
With a large amount of shopping data available, machine learning can help you understand what products are often purchased together and what types of items consumers may splurge on, so you can offer more effective promotions.
Machine learning can also segment customers into groups of high-value prospects, who share characteristics with existing customers and will be most likely to become customers. Better segmentation allows for a more personalized experience. If you understand where your customer is in the funnel, you can offer them the ad best suited to their stage and preferences.
Using metrics such as previous purchases, frequency of purchase, time of day, demographics, progress through the funnel, etc., the algorithm is able to aggregate broader audiences and segment them into highly granular, mutually exclusive audiences for more effective targeting and messaging, and ultimately a better return on ad spend (ROAS).
Customer Behavior Prediction
With machine learning you can anticipate a customer’s wants or needs before they know it by gathering information about customer preferences and determining what they will do next. Integrating machine learning with predictive analytics can help you see individual profile data such as CLV (customer lifetime value) history and predicted CLV, total customer lifetime value, churn risk, average time between orders and predicted gender.
Such insights can help a brand understand if a customer is likely to leave its website to seek a better offer elsewhere, or if they are a conversion candidate, and then react accordingly. Major brands like Amazon and Netflix use machine learning and predictive models to refine product recommendations.
Using machine learning that takes into account users’ purchase history, items in their shopping cart, and viewing habits, they predict product demand and optimize inventory to ensure it is ready to meet seasonal or trending consumer demand or suggest content that the viewer is most likely to enjoy, based on everything they have previously watched, ignored, and rated.
2. Customer Experience Improvement
Producing a premium customer experience is one of the most important jobs that any business does. Research suggests that 79% of customers prefer live chat to get a quick answer to their questions. So here’s where AI and ML-based chat software can be the lifesaver.
– use of intelligent chatbots
A chatbot is a virtual robot capable of holding a conversation with humans through text, voice commands or both. Integrating a chatbot ensures 24/7/365 availability, as well as reducing customer wait times to zero. Chatbots powered by machine learning and with their natural language processing (NLP) capabilities, can conduct a human conversation and automatically answer consumer questions with a surprisingly high accuracy rate.
Once trained and successfully implemented, chatbots can also expand the database without manual intervention and because they are constantly learning and improving, they will provide an even better customer experience by multiplying the conversations. chatbots also allow calls or chats to be transferred to human agents at any point in the conversation or help brands with outbound marketing by sending follow-up messages to customers.
One of the most advanced and widely used e-commerce chatbots is eBay’s chatbot designed for the Google Assistant that helps customers find the best deals for their favorite products using voice search.
Thus, chatbots with ML not only help digital marketers save money, but also ensure better business results.
3. Improve Content Creation and Curation
Content drives all forms of marketing. No brand can be marketed without content. Creating and curating content requires hours and hours of thought and research. Machine learning tools can save you a lot of time, which you can spend on other crucial business areas that need your attention. They can help you improve what you write and publish.
There are many AI and machine learning based content creation tools, such as Vestorly, Flipboard, Pocket , Curata frase.io, and Quill, that can help you research a topic and quickly summarize long texts. With these machine learning-based content curation solutions, marketers can find and publish the most relevant and engaging content for their audience
4. Design the User Interface (UI) and User Experience (UX) of the Website
The user interface (UI) and user experience (UX) of a website are the most critical aspects of digital marketing because they are directly related to the website’s ranking and visibility in search engines. Machine learning can gather data on user preferences, website heat maps, design best practices and A/B testing to create a more personalized and effective user experience and enhance a brand’s image while meeting the needs of its customers.
The main areas in which ML improves the user interface are:
Content personalization and further optimization
ML may automate the UI process by modifying a homepage based on the shopper’s history, and suggesting products that interest them the most.
Recognition of scanned images
A web-based ML program can identify the image when a customer scans an item using a mobile application and suggest the same or similar items for purchase.
Adaptative User Interface
Adaptive UI can improve website design by hiding irrelevant content and displaying products or advertisements based on the shopper’s search or previous searches.
Voice-activated interfaces
Voice-activated interfaces such as Siri or Alexa are two of the best-known virtual assistants that use machine learning technology to recognize voice patterns to meet customer needs and are able to improve as people use them.
Related content: 9 Highly Effective Website Design Tips
5. Improve Marketing Automation
Machine learning can also learn to perform tasks without human oversight and automate your marketing efforts, thereby significantly improving customer engagement. Using machine learning to automate tasks in marketing campaigns, many of which are conveniently mundane and repetitive, frees up marketers to focus on issues that require human intervention.
The bottom line is a more streamlined and efficient process, less human error and more ability for marketers to handle complex and strategic tasks. In addition, the automation achieved through machine learning can quickly and accurately process and analyze campaign data and trigger notifications when certain trends or unusual peaks occur and customize content and offers based on consumer browsing history.
80% of the most successful companies that have been using marketing automation technology for at least three years have already seen the benefits of automation in terms of increased revenue and customer engagement. Marketers can also use machine learning to improve the effectiveness of email campaigns and improve their ROI.
Machine learning helps you with lead segmentation, writing custom subject lines to drive user engagement, finding the right time to send emails to prospects and automatic email delivery.
6. Optimized Advertising
Leveraging the power of machine learning, you can optimize the performance of your existing marketing campaigns through testing multiple variables at once like choosing the best ad channel, best ad time, ad campaign duration, optimal placement, color, size, etc. of a CTA button or image is something we can experiment with using A/B testing and more.
AI-based advertising tools, such as Facebook Ads or Google’s Smart Bidding use machine learning to automate bidding to optimize conversions and can save you time and improve your ROI.
7. Empowering Social Media Management
ML and AI are valuable tools for marketers across all social media channels, because that’s where customers generate the most data by sharing products, purchases and reviews. ML-based listening tools provide insight into what customers think about the brand by mining huge amounts of data, keywords, mentions and posts.
Marketers can use this data to provide engaging social media content, improve lead generation and manage a brand’s reputation. For example, tools like Yext use machine learning to help brands identify mentions on Facebook, Google and Yelp to track what’s being said about their business, allowing marketers to react more quickly and create content that resonates with their social audience on a deeper level.
Using tools like Cortex, brands can help determine the perfect time to post anything on Instagram, Facebook and other social media platforms, based on analysis of hundreds of thousands of profiles. As a result, machine learning algorithms help perform NLP-based segmentation of the massive variety of social media posts on user demographics, preferences and behaviors.
Related Content: How to Create a Social Media Marketing Strategy in 7 Steps?
8. Prove Influence Marketing
Using natural language processing (NLP), machine learning-based marketing tools can make more sense of video content posted by influencers, help brands identify good brand advocates, and stay on top of how the brand’s message is being delivered by the influencer.
Machine learning uses artificial neural networks (ANNs) to analyze past performance to develop the most beneficial incentive system for influencers or rank images and videos to match influencers with brands based on their personas.
Social monitoring and listening tools such as Sprout Social help brands understand who is talking about their brand and what they are saying about it, and promotion optimization tools such as Clearmob and Data Gran help understand which content formats and posts work best for a brand’s audience. It provides actionable insights to influencers.
9. Website Optimization for SEO
Search engine optimization (SEO) is still a major player in a well-rounded digital strategy, and the machine learning-based component of Google’s search algorithm, called RankBrain, is at play in at least 30% of web searches. Similarly the Caffeine update has changed the traditional method of indexing content, relying on fresher content. It clearly indicates the importance of machine learning for the future of SEO and the need for digital marketers to adopt it.
To stay in the race, SEO companies such as SEM Rush, Hubspot and Moz have developed ML-based tools that collaborate with Google SEO, which have the power to automate SEO analysis at scale to track large amounts of data and suggest the best keywords to target, choose voice search optimized keywords, links to build or pages to optimize for higher rankings.
Machine learning powered tools can also help create SEO content for on-site SEO such as title tags, meta descriptions and image alt tags.
Related content:
On-Page SEO:10 Essential Factors for Ranking on the First Page
8 Steps to Build an Effective Website Structure for SEO
10. Enhanced PPC Campaigns
Pay per click (PPC) advertising is the most data-intensive marketing channel. Its scope and complexity have increased since Google made several machine learning-based improvements to Google Ads. Where PPC managers and executives used to have to spend hours analyzing huge data sets to get actionable insights, machine learning can now intervene in many areas of the process.
In-market audiences, predictive click-through rates, ad rotation optimization – all of these features mean that marketers can get more out of their PPC spend. Google’s Smart Bidding, which leverages machine learning to optimize each campaign for conversations to increase ROI.
It combines machine learning and contextual signals to optimize your bids, and uses billions of data points to estimate the probability of conversions, making your bids more targeted. ML tools can help you level your PPC campaigns by providing insights that demonstrate:
– Identify low-competition, low-cost keywords with the right monthly traffic volumes.
Analyze campaigns at the search query level rather than the keyword level.
– Analyze search contexts to understand the likelihood of conversions
– Include user search behavior in determining the offer
– Aggregate information across data points such as location of use and device to help you bid better.
Machine Learning in Digital Marketing – Conclusion
Integrating machine learning into your marketing toolbox can help augment, optimize and automate your marketing campaigns such as identifying relationships between disparate data. Machine learning becomes smarter in conjunction with marketing analytics to improve engagement, increase customer lifetime value (CLTV) and brand loyalty, and refine offers to provide each person with the most impactful customer journey.
Don’t hesitate to leverage the power of machine learning and artificial intelligence to improve your online marketing to grow your business.
