7 Machine-Learning use cases in marketing automation


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If you want to manage marketing today, you need to not only possess information but also track valuable data for your business. Knowing that a company has 100,000 faceless customers is not enough; It is important to understand what these people are interested in and what they can offer them.

An effective way to improve marketing performance and increase sales is to use machine-learning (ML) technology to help improve and automate your marketing.

The marketing automation software market will almost triple by 2026, reaching $ 19.66 billion, according to Mordor Intelligence. Martech solutions and technologies will be a priority in the coming years.

Seven areas where machine learning algorithms are useful

1. Marketing Analytics

Imagine a marketer who is tasked with analyzing a huge amount of customer information. The marketer can use a descriptive, diagnostic, predictable or prescriptive analysis method, but these are not enough for modern business.

Thanks to ML-based analysis, specialists can more quickly assess the effectiveness of marketing campaigns, improve them and make predictions for the future.

Applications: MIT’s ZyloTech platform uses machine learning to sort customer data and create relevant recommendations. Converseon, which partners with companies like Google, Cisco and IBM, uses ML to select and analyze social media insights so companies can better respond to customer needs and requirements.

2. Content Marketing

Machine learning enables marketers to forget repetitive, routine tasks such as choosing and analyzing keywords, searching for suitable topics, publishing posts on social networks, sending emails, and so on.

AI can collect popular topics and search queries and predict which ones will be relevant to your audience in the near future. Manual searches are time consuming; ML speeds up the process significantly.

Usage Cases: Netflix understood the benefits of AI and ML a long time ago, and now it engages viewers with personal movie and TV show trailers tailored to their preferences. ML algorithms also help Optimail improve its email marketing campaigns. Mailings are automated in terms of personalization: templates are created, product recommendations are created, payment confirmation emails are sent, and so on.

3. Advertising

Many people get annoyed with irrelevant and poorly designed ads. AI-powered tools create engaging offers for each user, so ads reach the right people at the right time and place.

Use case: Dynamic Creative Optimization (DCO +) technology adapts ads according to design and color to clients based on their tastes. The brand’s style is retained, but each specific buyer sees an individual banner.

Such technologies are expected to revolutionize sales by inspiring more people to make a purchase.

4. SEO

Machine learning can help find relevant web queries and customize textual content.

Use case: ML algorithms allow you to quickly perform technical revisions, optimize content, arrange linking, etc. The resulting technical and non-technical improvements attract more users, so the search engine recognizes your page as interesting and gives it a higher rank.

ML tools allow you to predict which SEO improvements to your site are realistic and help you implement them.

5. Account-based marketing

AI-assisted account-based marketing (ABM) increases the company’s revenue by up to 40% per year, according to Salesforce, whereas traditional ABM approaches increase it by only 10%.

Usage Cases: Using AI, marketers can identify accounts that convert the most and predict peak sales periods.

6. Dynamic sites

Dynamic websites are generated in real time. When they open dynamic websites, users see pages generated for their unique needs.

Usage: Through ML / AI, everything on a web page can be customized: headings, colors of elements and page backgrounds, recommended products, sorting by price, etc. Users can not visually distinguish them from standard static pages, and they are more interested in spending time on these sites, as well as more willing to make purchases.

7. Branding

What do IBM, Google, Facebook, Tesla, Lenovo, Amazon, Microsoft and Uber have in common? They all use AI in brand-building.

Personal user experience, better SEO and marketing strategies, targeted advertising, accurate sales and risk predictions, round-the-clock customer support – everything that helps build a brand, and it’s all driven by automation and machine learning.

Improved performance with AI

Machine learning is a fundamental part of the strategy of modern marketers. It is estimated to improve the company’s productivity by up to 40%.

Such technologies help companies find an approach to customers, adapt content and services to their needs, segment the audience and perform other useful actions – without creating impossible expectations for human workers.

More resources for marketing automation and machine learning

How to implement artificial intelligence in marketing: Rajkumar Venkatesan on marketing smarts [Podcast]

The benefits of marketing automation [Infographic]

Four Ways to Strengthen Your Email Marketing Strategy with AI

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