AI Marketing: What, Why and How to use Artificial Intelligence in Marketing

Machine learning and marketing automation is revolutionizing how companies reach their customers and sell their products.

3 months ago   •   3 min read

By Colby Tunick
Marketing is ubiquitous and overwhelming if it is not tailored to every person who views it.

Marketing and marketing automation is revolutionizing how companies sell. While some companies divide marketing and sales into different responsibilities, almost everyone can benefit from understanding how the two intersect to make deal closing possible.

Marketing has traditionally been the top or beginning stage of the sales funnel. But this is changing. As consumer buying behavior evolves, so too does how marketers reach them.

The marketing and sales funnel then and now.

Marketing is playing a large role in helping guide a prospective buyer through the sales sequence. This takes significant workload off of sales staff and helps them focus on what they do best - closing the sale. One of the biggest evolutions is marketing automation. Salesforce describes this as “technology that manages marketing processes and multifunctional campaigns, across multiple channels, automatically.” Whereas before we took marketing from concept to development, and finally, deployed manually, now businesses can target customers with automated messages across email, web, social, and text.

How marketing automation helps business reach their customers

Marketing automation itself is rapidly developing. One big change has been the use of machine learning (ML) to make automation ‘smarter.’ We break down how this happens in “How Machines Go From Dumb to Smart by Learning.” Below, we break down the areas where automation and machine learning are driving new revenue and productivity increases for tech-forward companies.

Machine learning makes marketing relevant at scale.

Why use machine learning in marketing?

Machine learning and marketing automation help content marketers reduce manual work, improve customer experience, and boost the performance of marketing campaigns. With ML, companies can:

  • Improve content with data-based recommendations
  • Automatically classify content with tags
  • Recognize the content in images and videos
  • Create personalized messages for different audiences
  • Automate repetitive tasks: social media posting, keyword research, email sending.
  • Auto-optimize marketing messages and emails
  • Auto-generate different content for simple stories
Target your customers with content they actually want to experience.

Account-based marketing

In B2B marketing, account-based marketing (ABM) is a key driver to increase sales. With the help of machine learning and marketing automation, companies can optimize their budget, streamline account prioritization by identifying the accounts that are most likely to convert. ML helps project the best times for sales outreach. As contacts move along the buyer’s journey, it is important to approach the customer at the right time with the right message before it gets outdated. Also, thanks to ML, marketers can predict the lead behavior and tailor their messages depending on the existing data they have about the lead.

Build a brand that stays relevant anywhere and everywhere.

HR and brand marketing

Machine learning and marketing automation help brands target the right people with the right content to increase awareness. In brand marketing, ML helps gauge brand sentiment by analyzing mentions of their business in social media. Also, it identifies gaps and areas of opportunities in the positioning compared with competitors. In HR marketing, ML helps attract and keep talents. Thanks to AI and ML, it is possible to develop personalized marketing campaigns and build the right plans for employees to learn and grow within the company.

Marketing automation is helping companies 'future-proof' their revenue growth.

The Future

Sales and marketing are rightfully obsessed with the future. The next big trend or product feature that will excite customers and drive new revenue. Doing this at scale is a challenge, especially with consumers expecting more relevant (read niche) advertising specific to their interest. To do this well requires leveraging new technology, such as machine learning and marketing automation.

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