Artificial Intelligence (AI) has been reshaping many fields, and digital marketing is no exception. IBM data reveals that 35% of companies already use AI in their business, while 42% are exploring AI. If you’re looking into how to use AI in digital marketing, you’ve landed right where you need to be! Penji educates modern brands on how to use AI to fortify their business.

Here are some of the ways AI can assist you in marketing, with real-world examples and actionable tips you can apply to get closer to your audience.

digital marketing AI guide

Table of Contents

1. Personalized Content Recommendations

Imagine a browsing experience where you’re bombarded with content that you neither find interesting nor relevant. In such a noisy digital maze, you’d probably quit browsing altogether.

Now, contrast that with a scenario where every piece of content you see feels handpicked for you, resonating with your interests and preferences. The second scene reflects the power of AI-driven personalized content recommendations.

Data from Google says nine out of 10 marketers acknowledge that personalization plays a major role in enhancing business profitability. AI has made it a lot easier to offer personalization by using algorithms that analyze user behavior to provide tailored content suggestions.

Real-World Example: YouTube’s Personalized Recommendations

YouTube utilizes AI to offer personalized content recommendations. The platform analyzes each user’s interaction data, including the videos watched, duration of views, likes, dislikes, and comments, each action contributing to building a profile of their preferences.

Over the past weeks, I’ve been watching videos about productivity and writing. I’ve also developed a habit of playing jazz music in the background whenever I’m working.

Here’s what my YouTube recommendations when I opened it on my browser as I’m writing this article:

These recommendations piqued my interest, and I would probably watch them if I wasn’t too busy doing deep work. This only goes to show how YouTube’s AI understands the content and context of videos, helping curate tailored recommendations for me. Because the recommendations are spot-on, I consume more videos and spend longer on the platform.

Applying it to Your Brand

Consider using AI personalization tools in your ecommerce business. For instance, a tool like Intellimaze allows websites to show unique page variations to each website visitor using AI, thereby increasing conversion rates.

Dynamic Yield also offers personalized product recommendations using deep learning algorithm, to predict the next product each visitor is most likely to click.

2. Predictive Analytics for Lead Scoring

Lead scoring refers to the marketing methodology used to rank prospects against a scale that represents the perceived value of each lead. 

The strategy involves assigning numerical values, scores, or descriptors (such as hot, warm, or cold). Traditionally, marketers establish specific criteria for scoring leads, such as:

  • Demographic information
  • Company size
  • Job title
  • Engagement level (like website visits or email interactions)
  • Where they are in the marketing funnel

Predictive analytics, meanwhile, is defined by Harvard Business School as the “use of data to predict future trends and events.” 

AI in digital marketing allows for predictive analytics in lead scoring by using historical data to forecast potential scenarios. After all, AI can integrate and analyze vast amounts of data from various sources, including:

  • CRM systems
  • Social media
  • Website interactions, and
  • Email engagements.

This comprehensive data analysis provides a more complete picture of each lead.

Real-World Example: Salesforce’s Einstein Lead Scoring

Salesforce, a customer relationship management (CRM) software, allows users to track their leads. In the CRM, each lead is created when they receive new contact information in their database.

The platform uses a technology called Einstein Lead Scoring to qualify leads. This feature harnesses the power of AI to evaluate leads against the user’s established patterns of successful conversions.

Einstein Lead Scoring uses AI to analyze historical leads as it identifies which current leads are most similar to past converters. Based on this data, the system assigns scores to these leads based on various information. The higher the score, the more the leads have in common with past customers.

This feature allows marketers to see more detailed insights and prioritize leads according to their score.

Applying it to Your Brand

Consider using AI-powered software to get predictive analytics for lead scoring. Not only will this streamline your marketing efforts, but it will also enhance your brand’s overall effectiveness in engaging potential customers. 

3. Dynamic Email Marketing

How often do you open marketing emails in your inbox? Data from Mailmunch reveals an average email open rate of 21.33% across industries – that’s just two out of ten emails.  

Without a doubt, email marketing can be tricky when you’re not armed with experience, skills, and the right tools. Fortunately, artificial Intelligence in marketing allows businesses to optimize their campaigns by leveraging machine learning to craft the right messages to send to the right audiences at the right time.

In the context of email campaigns, AI in digital marketing allows users to:

  • Analyze past email performance to craft attention-grabbing subject lines and identify optimal send times
  • Aggregate email analytics, providing valuable insights into the overall campaign performance
  • Initiate email workflows automatically based on specific actions taken by recipients
  • Enhance email list management by cleaning and organizing your contact lists
  • Generate tailored email copy that resonates with your target audience
  • Personalize email content to cater to distinct audience segments

Real-World Example: MailChimp’s AI Marketing Tool

Mailchimp leverages its AI growth assistant, Intuit Assist, to revolutionize how clients approach their email campaigns.

The tool allows users to automate and optimize various aspects of marketing and enables them to create personalized content at scale. Mailchimp’s AI functionalities include generating automation for welcoming new contacts, recovering abandoned carts, and re-engaging lost customers. 

The tool also uses AI in drafting on-brand emails and marketing copy alongside AI-driven custom design capabilities that align with brand aesthetics.

Applying it to Your Brand

Consider using AI-powered email marketing tools to optimize your email campaigns. Doing so will help you send the right messages at the right time, improving your email open rate and click-through rate.

4. Chatbots for Customer Support

Chatbots have been around for a while now. However, when chatbots are not thoughtfully configured, the limited capabilities of traditional ones can trap customers in a frustrating loop.

Enter conversational AI chatbots – the newest revolution in AI in digital marketing and customer service. 

Traditional chatbots, also called rule-based chatbots, rely on a predetermined set of rules and responses. These tools run based on “if-then” logic, responding to specific keywords or phrases identified in the user’s input. 

On the other hand, conversational AI chatbots are powered by technologies like ML and natural language processing (NLP). That means they can understand, learn from, and respond to human language in a more nuanced and context-aware manner. 

Real-World Example: KLM Royal Dutch Airlines 

KLM Royal Dutch Airlines’ customer base spans continents. Rightfully so, it implements an AI-powered multilingual chatbot on Facebook Messenger, accessible 24/7. The chatbot manages 15,000 weekly social conversations in various languages.

I’ve tried chatting with the bot in Spanish, and it understood my query and did the necessary actions:

By having a better understanding of inquiries across multiple languages, these bots not only enhance customer engagement but also save time for customer service representatives, allowing them to focus on more complex tasks.

Applying it to Your Brand

If you receive a lot of complex inquiries that may be hard to handle with rule-based chatbots, consider using AI chatbots such as LiveHelpNow, Ada, Tidio, and many others.

5. Behavioral Targeting in Advertising

The use of AI in digital marketing also includes behavioral targeting, which allows brands to create highly targeted and personalized ad campaigns.

If you’ve ever searched for a backpack online, and immediately after, ads for backpacks start popping up on your apps and browsers, then you’ve seen the power of behavioral targeting.

This technique uses data collected on a user’s web-browsing behavior, such as searches, pages visited, and products checked out, to select which ads to display to that individual. 

For instance, I make it a point to keep updated about the latest in AI and business, and this is what sponsored ads on my Instagram page look like:

Real-World Example: Facebook Personalized Ads

Facebook uses ML to enhance its personalized ad delivery, creating value for both consumers and businesses. 

Advertisers define their target audience through Facebook’s self-service tools, utilizing data such as:

  • Age
  • Gender
  • User actions on the platform
  • Custom information like email lists or website visitor data 

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Ads targeting these audiences enter an auction. In this process, Facebook uses ML to calculate each ad’s total value score based on its bid, estimated action rate (the likelihood of a user taking the desired action), and ad quality score. 

This system ensures that not only the highest bidder wins but also ads that are predicted to be more engaging or of higher quality, allowing businesses of all sizes to reach their desired audience effectively.

Applying it to Your Brand

Use platforms like Facebook and Instagram, which offer advanced ML algorithms to pinpoint your ads to users based on their online interactions. Doing so can improve the relevance of your ads and help boost conversion rates.

6. Semantic Analysis for Content Optimization

Let’s say you were tasked to create content for the keyword, “rock.” What topic will you center your content on?

Well, there are a few avenues you may take. For one, you could create content on rock music and discuss its history and the major players in the industry. On the other hand, you might also do a piece about solid mineral materials, their types, and their uses.  

Either way, because you didn’t know the right context in the first place, half of the people searching for the term “rock” might find your content irrelevant. This is where semantic analysis comes into play.  

In the context of AI in digital marketing, semantic analysis tools work to understand the context and nuances of language within content. This process allows for the creation of content that is relevant to the user’s intent and better aligned with search engine algorithms.

Real-World Example: Google BERT

Google BERT (Bidirectional Encoder Representations from Transformers) is a neural network-based technique for natural language processing (NLP).

This tool is designed to grasp the context and nuances of searched queries, enabling the search engine to deliver more relevant results for users’ searches.

Unlike previous models that understood the text in a linear fashion (one word after the other in order), BERT processes words in relation to all the other words in a sentence, bidirectionally. 

Applying it to Your Brand

Use AI-powered SEO tools that offer semantic analysis capabilities to identify not just keywords but also related concepts, terms, and questions your target audience is interested in. Create content that addresses those areas and focus on providing value and answering queries in a contextually relevant manner.

7. Social Media Listening and Sentiment Analysis

Monitoring customer sentiment has always been a part of good marketing practices. But AI in marketing took this method to a whole new level.

Algorithms now enable ventures to better understand customer moods and public perception and identify real-time trends. AI can analyze vast quantities of social media data, providing users with actionable insights into consumer preferences and behaviors.  

Real-World Example: Airbnb’s Review Sentiment Analysis

Airbnb uses AI algorithms to glean important insights from customer feedback. By using NLP methods, the platform can parse through text in guest reviews to gain a comprehensive grasp of client sentiments, likes, dislikes, and areas of concern.

This AI approach allows Airbnb to extend past basic sentiment analysis and pinpoint precise improvement opportunities.

Applying it to Your Brand

To use AI in sentiment analysis and social media listening, consider using an AI-enabled social media management platform.

For example, Hootsuite allows users to access data on insights such as the frequency of mentions and commonly used words within a search.

The emotional context of conversations regarding the search terms is analyzed to understand the public’s perception. For instance, consistently negative feedback about a product should be communicated to the product development team.

Identifying “top authors” can also uncover potential influencers or detractors engaged with the search terms, offering opportunities for collaboration or engagement.

8. Automated Campaign Optimization

When it comes to implementing and monitoring campaigns,  AI-driven strategies enable marketers to move beyond guesswork.

AI in digital marketing can facilitate continuous testing and real-time adjustments, allowing users to get the most return on investment (ROI).

How does it work? With AI, you can automate the allocation of ad spending to the best-performing creative elements and channels. By doing so, your campaigns have more precise targeting, and you can enjoy increased conversion rates. 

Real-World Example: Google Ads’ AI-Powered Campaign Solutions

Google Ads allows users to take advantage of its automated bidding technologies called Smart Bidding. This feature utilizes ML to optimize campaigns for conversions or conversion value.

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With a lot of Google Ads users already using automated bidding, the focus is now shifting towards holistic cross-channel optimization.

This tech aims to transcend traditional single-channel strategies by integrating bidding across Search, Display, Video, and more, adapting to the multifaceted consumer journey that spans numerous channels.

For instance, the Holiday Shopping Study cconduced by Google/Ipsos in October 2021 to January 2022 revealed that more than half of US shoppers used five or more channels like social media and video when they shopped within a two-day period.

Applying it to Your Brand

Consider using automated bidding the next time you run a campaign on advertising platforms. In Google Ads, for instance, over 80% of users already opt for automated bidding, so it’s a good idea to see if this will also work for your brand.

It’s also worthwhile to investigate if your cross-channel optimization could be a good strategy to guide your audience through the funnel as they engage with multiple platforms daily.

9. Predictive Customer Lifetime Value (CLV) Modeling

Customer Lifetime Value (CLV) is a metric that estimates the total amount a business can expect from a customer throughout the business relationship.

Here’s a basic formula for this metric:

CLV = Average Value of a Sale x Number of Repeat Transactions x Average Retention Time

Let’s say that the average value of a sale is $100, the number of repeat transactions per year is five, and customers are retained for three years in average. If we use the formula above ($100 x 5 x 3), the CLV would be $1500.

AI in digital marketing allows ventures to compute this metric at scale without needing to do manual computation. With predictive CLV, users can forecast the future value of customers by analyzing their past and current behaviors. 

Real-World Example: Microsoft Dynamics 365

Microsoft’s Dynamics 365, a suite of enterprise resource planning (ERP) and customer relationship management (CRM) software applications uses AI to predict CLV by aggregating transaction history and customer activities.

In so doing, it allows users to identify high-value customers and strategize accordingly, segment customers based on potential value, and uniform product development decisions.

Dynamics 365 uses a scoring system to evaluate the performance of the AI model in identifying high-value customers. These grades are:

  • Grade A – AI model predicts at least 5% more high-value customers accurately compared to the baseline model
  • Grade B – AI model’s performance in predicting high-value customers falls within 0-5% more accurately than the baseline model
  • Grade C – This AI model predicts fewer high-value customers accurately compared to the baseline model

This grading system allows users to assess the effectiveness of their predictive CLV models and decide whether to rely on the AI model’s predictions for strategic decisions.

Applying it to Your Brand

Consider implementing an AI solution to integrate data into predictive models that can forecast CLV. You can then use these insights to fine-tune your marketing efforts on high-value customers and personalize their experiences by offering tailored rewards.

10. Visual Search and Image Recognition

Have you ever used image search to look for an item online? If you answered yes, then you know just how helpful this feature can be for consumers.

Coupled with AI tech, visual search and image recognition allow for a more efficient shopping experience. After all, customers would only need to upload photos of products, and they can get relevant results automatically.

Algorithms can also analyze the images to identify colors, shapes, patterns, and other characteristics, making it easier for customers to find what they want.

Real-World Example: Amazon Lens

Amazon Lens is a good example of AI-powered visual search technology in the e-commerce sector. 

Users can access the tool directly via the Amazon app. You only need to select the Lens feature at the search bar and upload an existing image or take a photo of an item. Image recognition algorithms will then identify products matching the visual characteristics of the item in the picture.

For instance, I took a photo of my computer mouse, and Amazon Lens came up with results that look similar to it:

Applying it to Your Brand

Integrate a visual search feature into your e-commerce website. Shopify users, for example, can choose from several apps on the Shopify app store that offer this capability. It would also be smart to use ecommerce platforms that already use this feature, such as Amazon and eBay.