12 Ways to Use Machine Learning in Digital Marketing

Last updated: September 28th, 2021

Analytics. Personalization. Automation. Optimization. These are the pillars of success for digital marketing campaigns.

What if a single suite of technologies could multiply the effectiveness of all these aspects of marketing manifolds? That’s exactly what machine learning does. Gartner predicts that by 2020, around 30% of companies will be using machine learning and AI in at least one of their sales processes.

From digital advertising to email marketing, from social media marketing to content creation, machine learning is literally rewriting the rules of marketing success. Share on X

In this guide, I will cover 12 ways in which you can leverage the power of machine learning to improve your digital marketing.

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1) Better Personalization

Arguably, brands selling similar products and services can differentiate themselves purely by the quality of their customers’ experiences. And machine learning is helping marketers deliver a superior customer experience – at scale.

One of the most obvious examples is, of course, Netflix, which has over 100 million members in 190 countries and thus has had to go “beyond rating prediction and into personalized ranking, page generation, search, image selection, messaging and much more.”

They use machine learning to suggest content that the viewer is most likely to enjoy, based on everything they previously watched, ignored and rated. Marketers can leverage the strategy adopted by Netflix to optimize and personalize email marketing campaigns or to boost overall engagement metrics.

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There are dozens of examples, but here are a few other companies that are using AI and ML to enhance the customer experience:

The North Face uses machine learning to help shoppers find the best outdoor recreation product.
Yelp uses machine learning to organize images in the right categories.
Starbucks uses machine learning to recommend drinks via its app that a customer is likely to enjoy.

The common thread among all of them is the use of machine learning to manage customer journeys across various digital experiences in a way that maximizes the experiential value and thus engagement.

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2) Faster Customer Service

Research suggests that 79% of customers prefer live chat for getting their questions answered quickly. Here are the benefits of customer service chatbots:

Zero customer wait time
24X7 availability
Ever-expanding knowledge database
Plus they have the ability to route complex queries to human counterparts

And it’s not just customer service where chatbots shine; they can even assist brands with outbound marketing by sending follow-up messages to customers.

The eBay chatbot built for Google Assistant, which is “the most advanced e-commerce chatbot out there. And is also the most used,” helps customers find the best deal on their preferred products by using voice search:

Bots can also announce new product launches and share information about discounts and coupons to drive user engagement up. The data collected by these chatbots throughout customer interactions helps marketers develop insight on consumer behavior and mindset – and, again, they do this at scale.

Thus, ML-powered chatbots not only help digital marketers save money, but also ensure better business outcomes.

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3) Curating More Precise Content

If your content team curates posts to prepare roundups or other “best of” content, you know how time consuming that can be. Fortunately, there are machine learning-based curation tools available.

Curata and Vestorly are two such tools that can send the right content to the right person at the right time. These tools pull together articles from preferred online destinations (blogs, social media channels, etc.) and personalize the content experience for your customers.

Curata, for example, can:

Easily organize, annotate and create content to engage and inspire yourself and your audience
Suggest content and prioritize by relevancy via self-learning recommendation engine
Intelligently pre-populate the curated post

Companies like JP Morgan, Aramark, Zendesk and SendGrid already use Curata for content curation.

The ROI improvements in your content marketing, with the help of these tools, can be mind-blowing: Vestory suggests that marketers can expect up to a 600% increase in website leads and a 300% increase in engagement by using their AI-powered content marketing platform.

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4) Smart Content Creation

Check any list of jobs that AI is expected to automate, and you’ll see that the ones with a predictable and repeatable set of tasks, like tracking, for example, are right up there. Content creation does not make this list because it’s more in the domain of creativity and, hence, difficult to automate. Or is it?

Conversational AI – Frase.io is a tool that showcases how machine learning is knocking at the doors of content creation already. This tool uses conversational AI – which enables computers to communicate with people in natural language, i.e. have conversations – to generate content briefs and answer customer questions.

Think of it as a tool that can:

Help your content team research topics and prepare summaries in less time
Scale content creation by focusing on topic gaps
Use a customer question-driven content strategy
Continuously optimize your existing website content

And that’s not the only smart automation tool.

Natural Language Generation (NLG) – Take Quill, for example, a tool from Narrative Science that automatically creates descriptions from data to “help you improve the scale, quality and consistency of your reporting. That means more insights for your stakeholders.”

Quill uses NLG technology to generate short content pieces, helping companies drive user engagement up, free up content creators’ time, and create more accurate reports.

AI-Powered Copywriting – If you’re looking to simplify email marketing, there’s Phrasee, a tool that helps you write better email subject lines, ad copy and all kinds of push messages:

The technology behind Phrasee uses “world-leading natural language generation and deep learning algorithms to generate marketing language that sounds human” – and always in your brand’s voice:

Virgin Holidays and Gumtree, among other brands, have been the early adopters of this technology, and have already enjoyed positive ROIs – increased email revenue by a few million pounds for the former, and a 44% email click uplift for the latter.

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5) Stunning Website Design & UX

Web design, although instrumental for successful digital marketing, is a major problem for marketers. But machine learning is a lifesaver, as it can bring together data that is related to user preferences, website heat maps, design best practices and A/B tests.

The result? You can create web designs based on a lot more practical data, rather than on a designer’s guesswork.

One machine learning-powered web design tool is Wix ADI™ (Artificial Design Intelligence), which uses data collected from 86 million users’ preferences to build stunning websites (including its own) within minutes:

AI-powered design assistant tools such as Wix are meant to help human designers create superlative web designs in a jiffy, not replace them. The results of using such tools can be pretty amazing which, of course, makes for better customer engagement.

Dive Deeper: How to Design the UX of a Website or App to Increase Conversions

6) Easier Marketing Automation

According to Forrester, marketing automation tool spend will reach $25 billion by 2023, and HubSpot, Marketo and Pardot make up 50% of that market.

Here are a few key marketing automation stats:

Brands that manage the user experience with marketing automation tools achieve 451% higher qualified leads rate:
Brands that manage their leads using tools for marketing automation experience 10%+ revenue rate jump after 6-9 months.
49% of businesses use marketing tools for their email automation.
79% of renowned brands have used tools for marketing automation for over 3 years.

Marketing automation takes your strategy to the next level. It uses machine learning to crunch numbers, learn from patterns and past outcomes and deliver dependable insights – on customer segmentation, prescriptive suggestions, content targeting and follow ups – that simplify your decision making. As it keeps learning, it keeps getting better. That’s your entire marketing value chain, automated, in principle.

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7) Optimized Advertising

Advertising is a major cost for digital marketers. Traditional campaign optimization is based on manual decision-making such as:

Which advertisement channel to choose
How much ad space inventory to buy
The timing of advertisement
The duration of ad campaign

Of course, like all manual processes, this process’ effectiveness is constrained by the human limitations of brainwork and number crunching. Leveraging the power of machine learning can optimize the performance of your existing marketing campaigns.

For example, using the Lookalike Audience feature of Facebook, marketers can reach out to potential customers who are similar in attributes to your existing customers (but the similarity may be hard for you to pinpoint):

Additionally, Google’s Smart Bidding uses machine learning to automate bids in order to optimize conversions and can save you time and improve your ROI (more on this below).

8) Automated Email Marketing Campaigns

Seasoned marketers are always on the lookout for email marketing automation software to improve their ROI. ML-powered email marketing can help leverage nuanced customer segments and personas, a library of content, and data about prospects. The result? Marketers can hyper-personalize their email campaigns with ease.

Here are four ways machine learning specifically helps marketers improve email campaign effectiveness:

Content creation: Writing tailor-made subject lines and messages to help drive user engagement (what to send).

Data segmentation: Defining rules for sending emails to prospects (whom to send to).

Timing: Using previous responses to determine the right timing for sending emails to prospects (when to send).

Delivery: Enhancing the reputation of the sending domain, to ensure reliable delivery of all emails (how to send).

Apart from this, machine learning also allows marketers build split testing right into email marketing, which helps continually drive ROIs up.

Tools like Automizy and MailChimp have made the power of machine learning for email marketing accessible to marketers.

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9) Powerful Social Media Management

Social media marketing has become the new epicenter for all digital marketing. From content marketing to customer support, several crucial marketing functions are now performed via FaceBook, Instagram, Twitter and YouTube and machine learning helps marketers use the power of big data to optimize the their social media resources.

Here are the different ways in which machine learning is reinventing social media and making it a powerful marketing cockpit for brands:

Reputation Management: Social media reputation management is integral for building a robust brand image, and machine learning can help marketers identify the user communications, reviews and complaints on social media that need priority responses. Yext, for instance, is using ML to help brands identify mentions on Facebook, Google and Yelp to track what’s being said about their companies, which helps marketers respond more quickly.
Social Listening: Machine learning can analyze terabytes of data to understand how audiences engage with specific content themes and types. Social listening tools powered by ML not only automatically track brand mentions, keywords and hashtags across multiple social media sources, but the insights drawn from such analyses help brands create content that resonates with their social audience at a deeper level.
Perfect Timing: Not only does ML help you decide what to post, it also tells you when to post. Tools like Cortex help brands determine the perfect time to post anything on Instagram, Facebook and other social media platforms based on analysis of hundreds of thousands of profiles.
Better Data Segmentation: Machine learning algorithms help categorize the massive variety of social media messages into clusters. These algorithms are made to work with unstructured data and can use this to deliver tremendous insights into user demographics, preferences and behaviors, via advanced data segmentation. This NLP-based segmentation is a much better alternative to stats-based segmentation that is used traditionally.

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10) Transformative Influencer Marketing

MediaKix predicts that global spending on influencer marketing will be $5-$10 billion by 2020:

However, digital marketers know better than to invest blindly in influencers. They are looking for better ROIs, and influencers themselves are looking for ways to demonstrate their potential for marketers.

Machine learning uses Artificial Neural Networks (ANN) to categorize image and video content, even though it may not be accompanied with metadata or hashtags. This enables the process of matching influencers with brands, based on their personas:

Using Natural Language Processing (NLP), machine learning-based marketing tools can make more sense of the video content posted by influencers, helping brands identify right brand advocates and also stay in the know regarding how the brand messaging is being done by the influencer. ANNs can be trained to analyze past performances to develop the most beneficial incentive system for influencers.

Machine learning helps fight the biggest problem with influencer marketing – influencers with fake followers and those inflating their performance via adding their fake followers to a brand’s account. Just take a look at how leading brands have ended up with fake followers after influencer marketing campaigns:

Social monitoring and listening tools such as Sprout Social help brands understand who is talking about their brand, and what they’re saying about it, helping them provide actionable insights to influencers.

Influencers themselves can use promotion-optimization tools such as Clearmob and Data Gran to understand which content formats and posts are working best for a brand’s audience.

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11) Website SEO at Scale

Over the years, Google’s machine learning-based search algorithm component called RankBrain has drawn a lot of awe, and RankBrain is at play in at least 30% web searches.

This clearly indicates the importance of machine learning for the future of SEO and the need for digital marketers to embrace it. Let’s see how can you do that:

SEO Analysis at scale: With machine learning, you have the power to automate SEO analysis at scale. Tools like SEMrush, Ahrefs, WordTracker and Moz already use ML to track vast amounts of data and suggest the best keywords to target, links to build or pages to optimize for better rankings. Instead of manually targeting each and every page, these tools allow you identify pages that need to be optimized within minutes. You can even prepare reports with loads of data easier and faster than manually scanning the site.
Creating content for personalized user journeys: SEO content creation for on-site SEO (such as title tags, meta descriptions, and image alt tags) can be created at scale with machine learning-powered tools.
Voice search: Google is using NLP and machine learning in tandem to optimize search results for voice queries. SEO analysts can use a variety of tools that bring together search stats right from Google to your dashboard, helping you choose the most lucrative voice search-optimized keywords.
Link building with a difference: Machine learning has been Google’s weapon of choice in penalizing websites that use suspicious link-building tactics, and rewarding those that actually put in the hard work to secure quality backlinks. ML is the only way marketers can bring together a variety of attributes (niche, domain authority, social shares, etc.) to determine the value of a backlinking opportunity. Tools like SEMrush, Ahrefs and Moz are already using machine learning to present specialized SEO metrics for marketers to improve the performance of their marketing campaigns.

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12) Improved PPC Campaigns

Pay-Per-Click (PPC) advertising has grown in scope and complexity ever since Google introduced several machine learning-powered improvements to Google Ads. In-market audiences, predictive click-through rates, ad rotation optimization – all these features mean that marketers can get more out of their PPC spend.

Smart Bidding is one such strategy which leverages machine learning to make your PPC campaigns more lucrative:

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This technique combines ML and contextual signals to optimize your bids, and uses billions of data points to estimate the likelihood of conversions, thus making your bidding more focused:

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The capabilities of machine learning in optimizing a PPC campaign are endless. You can:

Analyze campaigns at search query level rather than keyword level
Analyze search contexts such that you understand the likelihood of conversions
Adjust bids in each auction, freeing up the auction manager’s time
Identify low-competition and low-expense keywords with decent monthly traffic volumes
Include user search behavior in the bid determination
Aggregate information across data points such as use location and device to help you bid better

Opteo is a great tool that helps you automate most of your Google Ads tasks to get more out of your campaigns with more than 30 improvement ideas:

Opteo monitors your Google Ads accounts around the clock to identify patterns that point to better ROIs and then automatically suggests improvements based on observations and helps you to evaluate and implement them. This enables better spend tracking, performance monitoring and higher campaign results.

Final Thoughts

Machine learning has eliminated the wastage of human brain power on trivial matters. This essentially means that marketers who use machine learning to augment, optimize and automate their marketing campaigns actually get to invest their intelligence in strategy over operations. Embrace machine learning in all your marketing ventures, because that’s the way forward.