How Artificial Intelligence Is Changing Business Marketing Strategies
From deep learning algorithms that help you save money to software that recognizes exactly what television show you should watch next based on preferences you didn’t even know you had, artificial intelligence is changing and influencing nearly every facet of our lives. Artificial intelligence – also commonly referred to as deep learning or machine learning – refers to the suite of computer-based tools that analyze huge amounts of data, processing it to learn exactly what the underlying patterns among all that data are. Then, this knowledge is used to either determine the outcomes of new data or generate entirely new content.
One application of deep learning that is rarely discussed is its up-and-coming role in marketing. Traditionally, marketing has been deeply influenced by the content that marketing specialists and advertisers can create. But with the rise of Google and its search engine optimization parameters, marketing has become more and more technical. Understanding how to use the Internet to its maximum potential as a company is important, as advertising needs to attract new customers and keep old customers through whatever platforms necessary to maintain a successful, revenue-driven business.
Here, we’ll dive deep into just how machine learning is already changing marketing for businesses, and how that’s influencing advertising and marketing strategies going forward.
When someone Googles a company, the top result that usually pops up is a link to that company’s website. When you click on the company’s website, you’re faced with the full details of a company, delivered to you through web design meant to attract you and educate you on company values. In most cases, the way company websites are designed is through highly specialized web designers who are able to code in HTML, CSS, Java, and a range of other languages that help users interact with the webpage. But recently, machine learning has crept in as a way to create a more intuitive web design.
Uses for Deep Learning in Web Design
- Creating GUIs: User interfaces are key for marketing, as they allow users to interact with websites. Deep learning algorithms can take in huge amounts of data, primarily of screenshots of website GUIs, then process them to identify the most marketing-effective GUI for a website.
- Dark data processing: Dark data is a term for the data that a company generates but doesn’t necessarily use. In terms of web design, this could be data on how long a user hovers over a certain button, or how often a user interacts with a specific part of a GUI. Experts in machine learning like @hemant.pal Hemant Pal, a professional data analyst, believe that dark data processing could help companies refine their marketing processes significantly.
Influences on Web Design Strategies
Web design is just beginning to be influenced by machine learning. While most websites are still custom design jobs built for specific purposes, a few companies like AirBnB have generated full web designs using training data and relatively little human input. In the future, this artificial intelligence-based approach could be used to design targeted web designs for specific demographics.
Search Engine Optimization
Search engine optimization, or SEO, is essential for business success in today’s world. Where a company ranks on Google searches and keywords related to a specific industry can directly impact revenue, so many companies seek the help of SEO experts to boost rankings. Deep learning is constantly being applied to search engine results and inputs, helping users find exactly what they’re looking for and refine the rankings of quality businesses.
SEO Machine Learning Feedback Loop
While it’s quite obvious that Google has accumulated a huge amount of data, it’s less obvious how Google processes all of this data to deliver accurate results. Google has been a key developer in machine learning algorithms that process natural language to process its petabytes of accumulated data. By looking at words and phrases, the search engine is able to understand the intent of a webpage by comparing it against the neural net of data it’s built up over time. And as this artificial intelligence has become more refined, it’s become harder to input keywords in a way that isn’t natural – meaning that Google will filter these results out in favor of more natural text.
Keyword naturalization directly influences how SEO experts work – instead of stuffing webpages with hundreds of keywords, experts must work to include these keywords in a subtle way that actually fits naturally in the company’s market context. The changed SEO strategies result in a loop, where more natural content is consistently favored. And while search engines constantly strive to find the best answer for any given question, this likely isn’t the final solution for optimization algorithms. SEO expert Darryl Stevens, CEO of Digitech Austin, emphasizes that consumers want multiple result options based on their media preferences, highlighting that marketing for companies still needs to be diverse enough to bring in new customers through different media types.
Much like web design, marketing content creation has been a hands-on job for professionals in the space. Teams of advertising experts will sit down for brainstorming sessions, crafting the perfect set of ads that will entice new customers. But with the advent of machine learning and data analytics, some big changes have come to these deep-rooted strategies.
One of the most time-consuming aspects of content creation is the creation of targeted content. Generally, marketing teams will come up with one generalized ad. Then, small parts of this ad will be tweaked to target specific customers. For example, an ad may mention the name of a city – but ten variations of that ad may exist, each with a different city named just to target multiple cities. Machine learning can do this directly, rather than a person having to input variations manually. With a few input lists of the potential demographics a company is targeting, machine learning algorithms can output hundreds of potential variant ads that can then be refined by content marketing teams.
Potential in Content Creation
Beyond personalization, machine learning is just beginning to be used in other areas of content creation. Some of these include:
- Custom video content: AI can auto-generate short videos, potentially creating entirely new marketing content without any human input.
- Keyword research: Algorithms can scrape huge amounts of data from the Internet constantly, learning in near-real-time what the optimal content keywords will be for future marketing efforts.
- Real-time changes: Content can be tweaked in real-time based on how users are interacting with that content, automatically updating in a way that increases customer acquisition. Machine learning experts like @remi.astier Remi Astier have documented ways to directly link attributes with people using a website or loaded into a database, implementing individualized links to make sure content changes with information known about a person on the fly.
Today’s advertising landscape is already heavily focused on Internet users. From the online content a potential customer interacts with to company website user interfaces those same customers experience, each aspect of marketing and advertising is informed by massive troves of data. Deep learning is accelerating change in this sphere, informing advertisers on the content they make and the ways they optimize search content – and this change is just beginning. As deep learning integrates more marketing data, even more traditional advertising paradigms will likely shift.