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If you want to master marketing today, you not only need to own 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 can be offered to them.
One effective way to improve marketing performance and increase sales is to use machine learning (ML) technology to improve and automate your marketing.
According to Mordor Intelligence, the marketing automation software market will nearly triple to $ 19.66 billion by 2026. Martech solutions and technologies will become a priority in the years to come.
Seven areas where machine learning algorithms are useful
1. Marketing analysis
Imagine a marketer tasked with analyzing a huge amount of customer information. The marketer can use a descriptive, diagnostic, predictive, or prescriptive analytical method, but these are not enough for modern businesses.
Thanks to ML-based analytics, specialists can assess and improve the performance of marketing campaigns and make predictions for the future much faster.
Use Cases: MIT’s ZyloTech platform leverages machine learning to sort customer data and generate relevant recommendations. Converseon, who works 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 demands.
2. Content Marketing
With machine learning, marketers can forget about repetitive, routine tasks like selecting and analyzing keywords, searching for appropriate topics, posting on social networks, sending emails, and so on.
AI can collect popular topics and searches and predict which ones will be relevant to your audience in the near future. Manual searches are time consuming; ML speeds up the process considerably.
Use cases: Netflix recognized the benefits of AI and ML a long time ago and is now delighting 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 created, product recommendations are created, emails with payment confirmation are sent, etc.
3. Advertising
Many people get annoyed by irrelevant and poorly designed ads. AI-powered tools create engaging offers for every single 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 customers’ tastes. The style of the brand is retained, but each individual buyer sees an individual banner.
Such technologies are expected to revolutionize sales by inspiring more people to buy.
4. SEO
Machine learning can help find relevant website searches and personalize textual content.
Use case: ML algorithms make it possible to quickly carry out technical audits, optimize content, arrange links, etc. The resulting technical and non-technical improvements attract more users, so that the search crawler recognizes your page as interesting and gives it a higher ranking admits.
ML tools can help you predict which SEO improvements are realistic for your website and help you implement them.
5. Account-based marketing
According to Salesforce, AI-supported account-based marketing (ABM) increases company sales by up to 40% per year, while conventional ABM approaches only increase it by 10%.
Use case: Using AI, marketers can identify accounts with the most conversions and predict peak sales times.
6. Dynamic websites
Dynamic websites are generated in real time. When opening dynamic websites, users see pages that have been created for their individual needs.
Use Case: Through ML / AI, everything on a webpage can be customized: headers, colors of elements and page backgrounds, recommended products, sorting by price, etc. Users cannot visually distinguish them from standard static pages, and they are more interested in spending time on it 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 branding.
Personalized user experience, better SEO and marketing strategies, targeted advertising, accurate sales and risk forecasting, 24/7 customer support – all of these help build a brand and are powered by automation and machine learning.
Improved performance with AI
Machine learning is a fundamental part of modern marketers’ strategy. It is estimated that business productivity is improved by up to 40%.
Such technologies help companies to find access to their customers, to adapt content and services to their needs, to segment target groups and to carry out other useful actions – without awakening impossible expectations of human employees.
More resources on marketing automation and machine learning
How to Implement Artificial Intelligence in Marketing: Rajkumar Venkatesan on Marketing Smarts [Podcast]
The (many) advantages of marketing automation [Infographic]
Four ways to empower your email marketing strategy with AI
