AI and Machine Learning in Marketing

AI and Machine Learning in Marketing

Marketing is a conversation between your brand and your audience. To have a conversation with your audience, you need to know who they are, which means getting to know them before they fill out a form and hand over data like their name, number, or email. This is where AI and Machine Learning can help!

Thanks to companies like Facebook and Google, we have access to more data about your prospective audience than ever before. Useful since your audience's identity is more than just demographics. It's their browsing history, the pages they visit, the content they view, and so much more. AI and machine learning analyze all that information and match it to a profile of your target audience.

For example, machine learning can link the multiple devices a user has used to visit your website from the same location. This provides better clarity to your website analytics, allowing you to see entirely what the user is doing on your website and ads in real-time.

What is Machine Learning?

Before we get too deep, let's first discuss what AI and machine learning are.

  • Machine learning is a technology that builds algorithmic systems based on data trends and patterns. With this data, machine learning simulates the human decision-making process by using predictive analysis.
  • Artificial intelligence, or AI, is the broader concept of machines performing tasks in a way that humans consider intelligent. In other words, AI is when a machine can undertake a job as well, if not better than a human would.

Machine learning is a subset of AI; instead of being programmed to do something, machines are given the just information they need to do it themselves. Because machine learning is being used to solve a massive set of diverse problems with the data, we stand to benefit from this information as marketers. As the information we gather grows, digital marketing as we know it is set to change.

Machine Learning and Digital Marketing

Content Marketing

While still vitally important, the internet has become inundated with too much content. To succeed, you need to create content that is valuable to your readers. And to do this, you need to understand consumer trends. Machine learning allows you to spend less time tracking and deciphering data so that you can focus on creating actionable tasks.

  • User-generated content is highly valuable despite the amount of work required to curate it. Machine learning can filter out undesirable language or spam without the cost of a real person reading through each piece of content.
  • Content recommendation is the most user-facing benefit of machine learning in content. Companies like Pinterest use their users' interests, online behavior, shopping patterns, and history to help their users find relevant content and recommend what's best.

Search Engine Optimization

Search engines receive more revenue for ads when they provide each user with content that will serve a specific purpose, rather than packed with the right keyword density. This is why there is such a focus on the quality of content as a ranking factor on search engines.

Paid Media Pay Per Click Campaigns

Ad platforms like Facebook Ads Manager and Google Ads (formerly Google AdWords) utilize machine learning to dive into keyword queries, social media profiles, and other data to develop better insights, improving your ads' efficiency.

For example, Facebook's Ads Manager can optimize your ads to reach users who are likely to click on them, thereby reducing your cost per click.

Another example is Google Ads bid strategies that analyze ad performance, make alterations to placement, and give you recommendations designed to improve performance.

Not to say that machine learning is a cure-all; it can't fix lousy design or poorly written copy. But it can help ads that are performing poorly because of circumstances like placement.

Personalized Marketing

Personalized marketing uses the consumer's behavior to modify the user experience to present buyers with customized offers carefully crafted to cater to their needs and interests.

  • For example, Amazon's success with e-commerce personalization is built upon machine learning. They leverage large amounts of data on their customer's behavior to tailor the shopping experience.
  • Another example is Spotify, continually refining its service with its consumers in mind. Spotify has built a tailored experience filled with daily personalized playlists, artist suggestions, and weekly recommendations based on their consumer's listening habits. Customers can even rate the tracks on these playlists based on whether they enjoyed them or not—this breads brand loyalty.

Marketing Automation

Marketing automation helps with lead gen, segmentation, lead nurturing, lead scoring, customer retention, and more. It can cause an increase in performance while properly segmenting your data so that you're reaching the users most likely to become customers.

  • Customer support is one of the areas that machine learning that is already well on its way to full automation. Machine learning models can decern the meaning and intention in a customer's request and route and route it to the right team, saving significant time and money. According to Forbes, "57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support."
  • Chatbots automate the customer service processes either fully or partially. With the help of chatbots, companies can automate routine tasks, answer frequently asked questions, and create personalized experiences- without human interaction. Even U.S. Government agencies are looking to deploy chatbots to reduce staff workloads.

It used to be impossible for all but the largest businesses to harness AI in their marketing efforts. Even the smallest companies can now utilize publicly available algorithms and off-the-shelf machine learning services to generate useful insights and create prediction models based on their customer's behaviors.

Posted in Marketing on Jan 31, 2021.

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