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What Is Artificial Intelligence & Machine Learning in Marketing?

Artificial Intelligence & Machine Learning

Artificial intelligence in marketing uses online and offline customer data along with concepts such as machine learning, natural language processing, social intelligence, etc. to gauge your audiences’ future actions. It allows you to target audiences with the appropriate message at the right time through the relevant marketing medium to guide them through the marketing funnel.

In this article, we will look at how organizations can use artificial intelligence and Machine Learning (ML) in marketing to their full potential. We’ll start by understanding the fundamental concepts, followed by their use cases and the benefits of AI and ML.

John McCarthy, an American computer scientist, coined the term Artificial Intelligence in 1955. He stated the following vision in the proposal for the landmark Dartmouth conference held in 1956:

The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

The conference touched upon the various aspects of Artificial Intelligence (AI) such as automatic computers, programming a computer to use a language, self-improvement, abstractions, randomness, and creativity.

60 years later, artificial intelligence has made headway in different industries and across multiple departments. Marketing is no stranger to this game-changing technology either. In fact, marketing has been the early adopter of artificial intelligence in the pursuit of improving customer experience.

Graphic Representing Artificial Intelligence in Marketing

What Is Artificial Intelligence in Marketing?

Artificial intelligence in marketing uses online and offline customer data along with AI concepts such as machine learning, natural language processing, social intelligence, etc. to gauge your audiences’ future actions.

AI allows you to target users with the appropriate message at the ideal time through the right marketing medium to advance them through the marketing funnel.

Artificial intelligence in marketing can collect customer data, analyze it to find reason and then act on that information to help drive conversions or influence human behavior.

What Is Machine Learning in Marketing?

Although the term artificial intelligence was coined in 1955, its implementation can be traced back to 1939 during World War II.

Alan Turing, the father of theoretical computer science and AI, developed Bombe, a machine that would decipher the German Enigma messages. Bombe was arguably the first implementation of machine learning in its archaic form.

Machine learning is a subset of artificial intelligence that uses algorithms and statistical methods that enable computers to learn and improve without the need for explicit programming.

Getting Started With Artificial Intelligence and Machine Learning in Marketing

Now that we’ve covered the basic concepts of AI and ML, let’s look at a few examples of artificial intelligence and machine learning in marketing. These examples will help you understand how to get started with AI, especially the machine learning aspect in marketing and how AI helps improve marketing functions.

Representation of the 9 ways to Implement Artificial Intelligence in Marketing and Machine Learning in Marketing

1. Content Creation

Computers creating content on their own would have been a pipe dream a few decades ago, but today we are at the vanguard of this vision becoming a reality. Although AI is not completely capable of writing opinion or editorial pieces yet, significant progress has been made to enable writing data-centered content.

AI-driven content creation tools have been successful in creating news stories, and industry reports, such as financial and sports reports, that sound coherent and cohesive.

2. Conversational AI

The rapid adoption of chatbots and digital assistants in the past couple of years has helped conversational AI gain prominence. Let’s understand how you can use chatbots and digital assistants in your marketing strategy:

  • Chatbots: Chatbots are a huge boon when it comes to delivering top-notch customer service. Chatbots can interact with users/customers conversationally, almost as a human would. Also, their round-the-clock availability allows customer care personnel to focus on more complicated concerns. As consumers continue to embrace messenger apps, the utilities of a chatbot will continue to grow in the future.
  • Digital Assistants: The increasing reliance of consumers on digital assistants such as Amazon Alexa, Apple Siri, Microsoft Cortana, and Google Now has pushed brands to optimize their content for voice search. Search algorithms such as Google RankBrain and Hummingbird encourage content that is conversational and focuses on long-tail keywords. Also, to win at voice search queries, Google recommends making the most of micro-moments.

3. Audience Segmentation

The foundation of personalization lies in accurate audience segmentation. Traditional segmentation criteria are restricted to demographic, firmographic, psychographic, and geographic characteristics. Whereas, AI-driven segmentation allows marketers to segment their audience on a more granular level.

Machine learning algorithms can analyze heaps of data and categorize users into different strata depending on their characteristics, interests, past behavior or purchase pattern.

For example, based on product usage and subscription data, you can identify customers that are discontinuing your services. Using this information, you can initiate a marketing campaign to reduce potential churn.

4. Predictive Analytics

The groundwork to identify customers showing signs of discontinuing product usage lies in predictive analytics.

Simply put, predictive analytics uses customer data to predict their future actions and behaviors. When provided with a large amount of customer data, the machine learning algorithm creates a prediction/propensity model that predicts the outcome.

You can use predictive analytics to predict churn, find upselling or cross-selling opportunities, predict customer lifetime value, identify the right marketing channels and messages, and predict customer behavior that is triggered by certain events.

The key caveat to the success of this approach is the quality of data fed to the algorithm. If the data provided is inconsistent or error-prone, then the output will not be accurate.

5. Personalization

Once a new visitor lands on your website, you need to give them a reason to keep them coming back. 1:1 personalization enables you can connect with your visitors and customers directly through your website, apps, emails, and other digital properties.

For example, if a user leaves your website without completing their purchase, you can send a cart abandonment email and a special offer to motivate them to complete the purchase.

Similarly, when a user registers for your product, you can send them an onboarding email and push notifications to help them understand the different aspects and features of your product.

6. Paid Ads

One of the biggest advantages of using artificial intelligence in marketing is, it helps you optimize your ad spend. Optimizing paid ads is an iterative process, and AI can help you in the following two ways:

  • Programmatic Ad Targeting: Marketers constantly need to keep a tab on ad performance to understand which type of ad is performing better, the optimal time of the day to serve ads and so on. AI can perform these activities on its own, and it can also automate the bidding process to utilize your ad budget effectively.
  • Retargeting and Lookalike Audience: Retargeting campaigns act as a reminder for your visitors and prospects to come back to your website and complete a particular action; or it can also be used to retain, upsell or cross-sell to existing customers. Lookalike audience uses your existing audience base to find new audiences that shares similar characteristics, interests, behavior and so on.

7. Sales Forecasting

Sales forecasting uses past sales data, trends, patterns and information about upcoming events to predict product demand and sales. Accurate sales forecasting directly impacts the short-term and long-term growth of an organization.

AI also strengthens the lead scoring process. Since B2B sales cycles tend to be longer, sophisticated lead scoring can help sales teams identify the leads that are most likely to buy from you.

Similarly, AI also helps sales forecasting by improving deal closure, customer retention rate and increasing customer lifetime value in the process.

8. Dynamic Pricing

Discounts or coupon codes are great motivators to complete a purchase, but improper planning can hinder your sales. Often, such promotional tactics are best used as psychological nudges to complete a purchase, rather than to push general sales.

Dynamic pricing solves this issue. It considers past purchases and purchase patterns, with minimum alterations in pricing to motivate the consumer to buy. If the user history states that the user completes a purchase only if they receive a special discount, you could offer a coupon that would compel them to make the purchase. Dynamic pricing ensures that you’re generating sales without taking a hit on your profits.

9. Recommendation Engines

Recommendation engines are AI-fueled tools that better customer experience and increase engagement by providing content and product recommendations. Recommendation engines are widely used by e-commerce websites, e-learning, online gaming and audio/video streaming services to identify upselling or cross-selling opportunities or to increase product stickiness.

You must have seen suggestions such as ‘people who bought Product X also bought Product Y.’ This is a recommendation engine in action.

Recommendation engines use three different types of algorithms, viz. Collaborative filtering, content-based filtering, and hybrid recommendation algorithms. These algorithms use different principles to make recommendations. For example, collaborative filtering algorithm analyzes the behavior of multiple users and draws similar characteristics to recommend products, whereas content-based filtering algorithm uses the correlation between description, words, brands, colors, etc. to suggest products.

Using Artificial Intelligence Across the Buyer’s Journey

The standard buyer’s journey consists of three phases: Awareness, consideration, and decision.

In the awareness stage, the customer identifies a need or a problem and starts looking for its solution. The awareness stage is followed by the consideration stage where the customer is evaluating the available alternatives. And finally, the decision stage, where the prospect zeroes down on one solution.

You can implement AI and machine learning solutions across the buyer’s journey that would significantly improve your marketing efforts.

For the awareness stage, you can focus on creating and optimizing your content for voice search, using chatbots to deliver content and attracting visitors with personalized top-of-the-funnel content.

When a user is in the considerations stage, organizations can focus on remarketing ads to grab the user’s attention, therefore using lead scoring to reach out to hot leads.

And when the user is in the decision stage, with the help of dynamic pricing and chatbots, organizations can drive the visitor to the purchase. And when the user becomes a customer or reaches the advocacy stage, you can initiate retention activities through marketing automation and recommendation engines.

The Benefits of Artificial Intelligence in Marketing

AI has now become an integral part of marketing. Let’s look at how AI continues to benefit organizations.

Representation of the 3 Benefits of Artificial Intelligence in Marketing and Machine Learning in Marketing

1. Predicts Customer Behavior

AI makes it simple to forecast your customers’ future behavior. Since you already collect data points that help you understand how your users are using your product and interacting with your brand, AI can help you use this data to predict their future actions as well.

You can predict who is most likely to become your customer or who is most likely to drop-out and so on.

You can also predict how your customers will react to trends or seasonal events based on past data.

2. Scales Marketing Campaigns

Before initiating a large-scale marketing program, you can test it out on a focus group to verify its feasibility and response. This is usually called the minimum viable campaign or minimum viable marketing. Once you understand how different marketing channels work for you and which persona is receptive towards certain messaging, you can scale it to its maximum potential.

AI can help you scale up your marketing campaigns and make the necessary tweaks along the way to cater to the changing customer behavior and to streamline your marketing campaign in the process.

3. Improves Customer Experience

In the age of hyper-personalization, customers expect more from brands. AI enhances the marketing efforts throughout the buyer’s journey right from content curation to customer service.

As chatbots have access to the customer’s history, they can help customers more than human agents. Along with customer support, chatbots are also now empowered to deliver content to the user’s choice of messenger app, help customers complete the booking process or complete the payment process. Innovations like these certainly help organizations improve overall customer experience.

Learn More: Minds and Machines: Marketing and Chatbots in 2019

Final Takeaways

The ideas outlined in this article will help you get started with implementing artificial intelligence in your marketing activities. Rather than going all out, start with the most basic application (for example, chatbots), experiment, iterate, and then scale.

Have you tried implementing artificial intelligence in marketing and machine learning in marketing yet? Let us know on Twitter or LinkedIn or Facebook. We’re listening!

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