Industry News & Trends

5 Ways AI Can Take Your Emails to the Next Level 

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Now that artificial intelligence (AI) and machine learning are becoming mainstream, there are more and more opportunities for senders to improve their email programs and better engage with customers. 

However, few email marketers fully understand how AI works or how the technology can improve their campaigns. 

As a marketer, your goal is to find the best ways to reach your audience. But with so many new marketing technologies hitting the market, how can you determine if AI tools are really worth the investment?  

To help, we’re here to explain five key benefits—and limitations—of AI in email, and why senders may want to take advantage of it now.

 1. Personalization  

Artificial intelligence doesn’t know everything—yet.  

But it does know how to make your customers feel like the brands they love pay attention to them and take their preferences into account. 

Email personalization is no longer a nice-to-have for marketing campaigns—it’s a must. Seventy-two percent of consumers now say they will only engage with brands that deliver personalized messaging.  

Using AI technology to personalize campaigns helps brands ensure their messages are relevant, engaging, and effective for the intended customer. 

Think of how Netflix uses account activity to personalize email campaigns. The company leverages AI to analyze behavior patterns (e.g., viewing history) to deliver individualized recommendations for what to watch next.

For marketing teams, AI can analyze content downloads, customer service tickets, and browsing patterns to keeps tabs on what your customers have done, what they’ve said they like (and don’t like), and even the time of day they prefer to receive emails or look at promotions. 

Armed with this information, it’s easier than ever to send the right message to the right person at the right time. 

2. List segmentation

 Marketers have long used list segmentation to engage with customers.

It’s easy to see why: Research shows segmented marketing campaigns generate 14.64 percent more opens and almost 60 percent more clicks than non-segmented campaigns.

But the segmentation process is typically time-consuming and prone to human error.  

With AI, marketers can go beyond simple segmentation by location or age, and segment lists using more specific attributes—like a customer’s purchase history and interests. 

This enables marketers to send more targeted emails and increase those all-important open rates and conversions. 

3. Creating subject lines
 

In the past, marketers sent emails with generic subject lines and blindly hoped they’d get noticed in subscribers’ inboxes. 

In 2022, this doesn’t fly. Especially now that inboxes are more crowded than ever.

Now that AI is being used to analyze massive volumes of customer data, marketers can personalize their email campaigns and boost the chances of their emails being opened.

This starts with writing a clickable subject line.

Writing good email subject lines is tough. Luckily, our robot friends make the process easier for us.  

Tools like Cloud Natural Language and Tone Analyzer give senders insights into the tone, structure, and sentiment of their subject lines and offer suggestions for improvement. You might be surprised to learn that your text comes across as negative or overly formal.

Other tools like StoryLab.ai generate content ideas for you. All senders need to do is enter their company name, describe their email content, and hit the ‘Inspire me” button to get a list of subject line ideas. 

4. Analyzing performance data
 

When it comes to analyzing performance data, the marketing industry faces a significant challenge.

The volume of data generated by campaigns and customer behavior has grown massively in recent years. According to current estimates, humans generate over 1.1 trillion megabytes of data every day

Humans can’t realistically manage these vast volumes of data on their own. As a result, the insights that can be derived from this data are limited.  

This is where AI can play a decisive role. 

When analyzing campaigns, AI tools follow the same cognitive process as the human brain. The system learns independently how to recognize patterns, predict behaviors, and generate conclusions.

To do so, systems must be trained using statistical methods.

But the prerequisite for training such algorithms is that they have access to as much high-quality data as possible. This is the only way will they be able to distinguish between meaningful and irrelevant information, and thus make precise conclusions about customer behavior and campaign response.  

5. Automating copywriting
 

Keeping brand language interesting and engaging can be a struggle.

To overcome pesky writer’s block, some companies are using artificial intelligence tools to conduct surveys or tests, and then write content based on the results.  

This lets marketers easily tailor content for different audiences, without having to write copy themselves. 

Tools like Copymatic, Copy.AI, Persado, and Phrasee focus on writing marketing emails and social media posts. They’re particularly excellent at generating short sentences and headlines.

This is extremely valuable for the average email marketer. After all, when you write for these mediums, your goal is usually to write short, snappy sentences that attract people to your content.

As a bonus, AI writing tools like these free up traditional copywriters to focus on bigger-picture creative thinking, strategy, and campaigns. 

Challenges and limitations of AI in email 

Artificial intelligence is a powerful tool for improving email processes and increasing efficiency. But it has its limitations. Organizations should be aware of the following hurdles before they implement AI-powered tools. 

Data quality 

An organization’s data can be inaccurate, which can lead to flawed or biased AI algorithms or applications. Others have to search through the various silos at their companies to find the data they need to train AI models, and come up with incomplete data sets. This is a problem. After all, feeding an AI system low-quality data generates low quality outputs.

It’s absolutely essential that marketers clean and consolidate their CRM data before using it to train AI models.  

Lack of expertise 

Since AI is still relatively new, the talent pool of workers familiar with AI is limited.Many companies don’t have employees with the necessary skills required to develop and deploy AI systems. To fill this gap, some have to hire temporary consultants or contractors, or send staff members to training programs where they can learn new skills on the job. These knowledge gaps can delay marketing initiatives as users get up to speed on the technology.

Privacy and regulations 

AI is still a new and evolving industry, so there aren’t many regulations governing its use. However, given the growing concerns about consumer privacy, signs indicate that AI regulation is coming soon. Since 2017, over 60 countries have adopted some type of artificial intelligence policy.  

In the U.S., agencies like the Food and Drug Administration and the Department of Transportation have been working for years to incorporate AI considerations into their regulatory agreements. Across the pond, the European Union is expected to adopt new AI regulation in the near future.

Organizations must consider the impact that future regulatory shifts might have on their AI strategies for email marketing.  

Explainability 

Usually, organizations don’t need to understand every aspect of how their software systems work to use them effectively. However, “explainability” becomes important when dealing with AI systems.  

Why? Because these tools can sometimes make decisions that seem illogical or even irrational. 

Especially since these tools can be prone to biases, which can occur if an organization trains a model based on data collected under certain conditions. (For example, training a tool based solely on organizational data collected during COVID-19 conditions.) 

Developers need to find ways to gain user trust by ensuring AI systems are explainable enough that users can see how and why decisions are made. Otherwise, serious problems can arise when users lack confidence in the system’s decisions.

Enter Explainable Artificial Intelligence (XAI), a set of tools and frameworks designed to help users understand and interpret predictions made by machine learning models. The goal of XAI is to ensure humans can fully understand the “why” behind the “what” in AI-driven decision making. 

The road to AI 

The email marketing landscape has changed dramatically over the past few years.  

Your emails are now read by advanced software bots and algorithms before they ever reach your potential customer’s inbox.  

Not to mention, global sending volumes are at all-time highs, competition in the inbox is fierce, and new pressures like Apple’s Mail Privacy Protection have made go-to performance metrics less reliable. 

In this email climate, marketing teams that don’t have artificial intelligence in their arsenal risk falling behind competitors. 

AI is just one way in which the email landscape is changing. For more tips to master email in 2022, read Validity’s new report “State of Email 2022: Mastering the New Email Landscape.”