Marketing Automation: Seven Machine-Learning Use Cases


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If you want to rule marketing today, you have to not only possess information but also track valuable data for your business. Knowing that a company has 100,000 faceless clients is not enough; it is vital to understand what those people are interested in and what can be offered to 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 become a priority in the coming years.

Seven Areas Where Machine-Learning Algorithms Are Useful

1. Marketing Analytics

Imagine a marketer tasked with analyzing a huge volume of customer information. The marketer can apply a descriptive, diagnostic, predictive, or prescriptive method of analysis, but those are not enough for modern business.

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

Use cases: MIT’s ZyloTech platform uses machine-learning to sort customer data and create relevant recommendations. Converseon, which partners with companies such as Google, Cisco, and IBM, uses ML to select and analyze social media insights so businesses can better respond to customer needs and demands.

2. Content Marketing

Machine-learning allows marketers to forget about repetitive, routine tasks such as selecting and analyzing keywords, searching for suitable topics, publishing posts on social networks, sending emails, etc.

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 significantly speeds up the process.

Use cases: Netflix understood the benefits of AI and ML a long time ago, and now it engages viewers with personalized movie and TV show trailers tailored to their preferences. ML algorithms also help Optimail improve its email marketing campaigns. Mailings are automated with regard to personalization: templates are compiled, product recommendations are created, emails with payment confirmation are sent, etc.

3. Advertising

Many people become annoyed by irrelevant and poorly designed ads. AI-powered tools create engaging offers for each individual user so that ads reach the right people at the right time and in the right place.

Use case: Dynamic Creative Optimization (DCO+) technology adapts ads by design and color to clients based on their taste. The style of the brand is preserved, 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 queries for websites and personalize text content.

Use case: ML algorithms make it possible to quickly conduct technical audits, optimize content, arrange interlinking, etc. The resulting technical and nontechnical improvements attract more users, so the search crawler recognizes your page as interesting and gives it a higher rank.

ML tools enable you to predict which SEO improvements for your website are realistic, and help you implement them.

5. Account-Based Marketing

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

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

6. Dynamic Websites

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

Use case: Through ML/AI, everything on a webpage can be adapted: headers, colors of elements and page backgrounds, recommended products, sorting by price, etc. Users can’t visually distinguish them from standard static pages, and they are more interested in spending time on those websites, 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.

Personalized user experience, better SEO and marketing strategies, targeted advertising, accurate sales and risk predictions, 24/7 customer support—all that helps to build a brand, and it’s all driven by automation and machine-learning.

Improved Performance With AI

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

Such technologies help companies find an approach to customers, tailor content and services to their needs, segment audiences, and perform other useful actions—without creating impossible expectations of human workers.

More Resources on Marketing Automation and Machine-Learning

How to Implement Artificial Intelligence in Marketing: Rajkumar Venkatesan on Marketing Smarts [Podcast]

The (Many) Benefits of Marketing Automation [Infographic]

Four Ways to Empower Your Email Marketing Strategy With AI

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