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Marketing Automation: How to Build ML Solutions That Really Work

Marketing Automation: How to Build ML Solutions That Really Work

Today, if you want to rule the world of marketing, you need to not only possess information but also find data that are valuable for your business. Knowing that a company has 100,000 faceless clients is not enough – it is vital to understand what they are interested in and what can be offered to them. An easy and effective way to improve marketing performance and increase sales is to use Machine Learning technology.

Machine Learning (ML) is a promising solution in marketing. According to Mordor Intelligence, the marketing automation software market will almost triple by 2026, reaching $19.66 billion. MarTech solutions help to extract insights from the data stream, which can be used to build profitable sales. Such technologies will become a priority in the coming years.

Where ML algorithms are needed

In marketing, ML-based technologies are used in the following seven cases.

1. In marketing analytics.

Imagine an average marketer tasked with analyzing a huge volume of customer information. The marketer can apply a descriptive, diagnostic, predictive, or prescriptive method, but all these are not enough for modern businesses.

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

For example, MIT’s ZyloTech platform uses ML to sort customer data and create relevant recommendations based on it. Converseon uses ML to select and analyze social media insights to help businesses better respond to customer needs and demands. And given the fact that the company partners with such influencers as Google, Cisco, and IBM, ML tools must really help Converseon in its marketing analytics.

2. In content marketing.

With ML, marketers can forget about repetitive routine tasks: selecting and analyzing keywords, searching for suitable topics, publishing posts on social networks, sending emails, etc. AI collects popular topics and search queries and predicts which ones will be relevant to your audience in the near future. Manual searches are time-consuming, while ML significantly speeds up the process.

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 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. In AI advertising.

Users are annoyed by irrelevant and poorly designed ads that have no value to them. 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.

For example, Dynamic Creative Optimization (DCO+) technology adapts ads by design and color to each client, based on their taste. The style of the brand is preserved, but each specific buyer sees an individual banner. These technologies are expected to revolutionize sales by inspiring more people to make a purchase.

4. For SEO.

ML helps in finding queries for the website and personalizing text content. ML algorithms make it possible to quickly conduct technical audits, optimize content, arrange interlinking, etc. These improved technical and non-technical aspects attract more users, so the crawler recognizes your page as interesting and gives it a higher rank. ML tools enable you to predict realistic SEO improvements for your website.

5. In account management.

According to Salesforce, AI-assisted account-based marketing (ABM) also increases corporate revenue by up to 40% a year, while traditional approaches only increase it by 10%. With AI, marketers identify accounts that convert the most and predict peak sales periods.

6. For dynamic websites.

Dynamic websites are generated in real-time. Users can€™t visually distinguish them from standard static ones. When opening dynamic websites, users see pages generated for their unique needs. Everything has been adapted: headers, colors of elements and page backgrounds, recommended products, sorting by price, etc. Users are more interested in spending time on these websites and more willing to make purchases.

7. In branding.

What do IBM, Google, Facebook, Tesla, Lenovo, Amazon, Microsoft, and Uber have in common? They use AI in brand building. Personalized user experience, better SEO and marketing strategies, targeted advertising, accurate sales and risk predictions, 24/7 customer support using ML – all this helps to build a brand. Forbes estimates that by 2025, 95% of customer interactions will be driven by AI.

ML is a basic part of the strategy of modern marketers, which improves 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 actions that are useful for the development of companies.