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How To Use AI To Predict User Engagement in Marketing (Guide 2024)

Imagine if your marketing could read minds. Every campaign, blog, and email would feel just right to your audience. That’s what happens when AI and marketing automation team up. You turn casual viewers into active users. 

AI might seem daunting to someone without a tech background or a big budget. But here’s the exciting part — AI is accessible to all. It’s practical and, yes, even fun.

This guide will show you how to make AI work for you. We will cover tactics to make your marketing efforts hit the mark every time. 

10 tips to predict user engagement and implement the findings

Predicting user engagement goes beyond understanding what customers liked in the past. It includes analyzing patterns, predicting future behavior, and making real-time adjustments in your user engagement strategy. Let’s see how. 

1. Collect detailed user behavior

Use data on clicks, views, time spent, and navigation paths to learn how users interact with your website, emails, and social media

This will help you understand how clients use your software, which features they love, and where they might encounter issues. 

So, how do you collect this data? Use Google Analytics. 

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Here’s a concise summary and action plan for each key metric: 

  • Sessions timeline: Track when you get more or fewer visitors. Look at why visitor numbers increase at times — like special sales — and do those things or advertise more for better results.
  • Sessions breakdown: Shows where visitors come from. Spend more where you already do well, like email, and work on improving where you don’t, like improving search engines (SEO), for example.
  • Sessions total: Tells you how many times people visit your pages. If this goes down, check which pages people leave quickly and make them more exciting or faster to keep visitors around.
  • Total users: The number of unique people who visit your site. If this number isn’t going up, try new channels and different types of content — for example, reaching out on social media, teaming up with popular people online, and using fun content to attract more people.
  • User engagement: How long people stay on your site. To get people to stay longer, make your site easier to use and produce better content, taking tips from the parts of your site that people already like. 

2. Track targeted campaigns

To see how your marketing campaigns are performing, keep an eye on key metrics like bounce rates, time spent on a page, conversion rates, engagement levels, and click-through rates. Tools like Google Analytics can help you gather this data, giving you insights into what’s catching your audience’s interest and what might need some tweaking. Here’s a simple way to go about it:

  1. Set clear goals: Decide what you want each campaign to achieve, like a specific number of sign-ups or a sales target.
  2. Collect data: Gather information related to your goals using analytics tools.
  3. Analyze results: Compare the information you’ve collected with your initial goals to identify what’s working and what’s not.
  4. Make adjustments: Based on your findings, tweak things like your call to action, ad copy, or the channels you’re focusing on.

Try experimenting with different parts of your campaigns to dial in what works best. Here’s what you can do: 

Implement A/B testing

Make it a habit to test key elements of your campaigns every few weeks. Focus on aspects like email subject lines, the layout of your landing pages, and the images used in your ads. This will help you see which versions resonate more with your audience and allow you to fine-tune your approach based on concrete data. Here’s what to focus on:

  1. Email subject lines: Test different phrasings or formats to see which generates more opens.
  2. Landing page layout: Experiment with different designs or placements of call-to-action buttons to identify which layout drives more conversions.
  3. Ad images: Switch up the visuals in your ads to determine which images capture more attention and lead to better engagement.

3. Uncover engagement patterns 

Use AI to identify patterns that show low or high user engagement levels. Most AI tools provide visual data insights in dashboards that feature graphs, heatmaps, and other visualization tools. Use these features to understand where users are most and least engaged. 

Heatmaps work by showing you where users click the most so you can identify your page’s most engaging parts. 

user engagement - uncover engagement patterns
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You can also use: 

  • User flow diagrams: These help track user engagement paths in your app or site, highlighting where they drop off or spend the most time.
  • Trend analysis: This is where you observe engagement over time so you can identify whether changes you made are improving user engagement or not.

Most advanced analytics platforms also let you set alerts when specific metrics go above or below set limits. 

Each time you notice a pattern, pinpoint the potential causes. For example:

  • High engagement: Could a new feature, marketing campaign, or product or service update be driving more interaction?
  • Low engagement: Is there a usability issue, performance glitch, or outdated content preventing users from engaging? 

Remember to let the tool run and collect data over a significant period. This duration will depend on your business cycle and objectives, but allowing it to run for at least a few weeks will give you more comprehensive data to measure user engagement.

4. Build models to predict future behavior

Develop predictive models based on historical engagement data to anticipate user behavior. This lets you act early, for instance, by identifying users likely to stop using your service so you can try to keep them engaged before they leave. 

Consider using a predictive model called product-based clustering. This method groups customers based on their purchase habits. For example, one group might only buy meat, while another buys various snacks. 

You can use a similar approach for content to identify customers who prefer certain topics. Use these insights to tailor your product promotions and email content to match each customer group’s preferences. 

Now, how do you build your first predictive model?

  1. Collect historical data, including user behaviors, engagement metrics, transaction records, and other relevant data points. Make sure this information comes from various situations and times to help you predict future trends.
  2. Clean up your data by fixing or filling in missing details and removing any odd or inconsistent data. Choose the most relevant information that affects the results you’re trying to predict. For example, tools like correlation matrices can be used to see which features, like age or purchase history, are closely linked to customer churn.
  3. Start with simple predictive models if you’re new to this. Depending on what you’re trying to predict, basic techniques like logistic regression, decision trees, or linear regression are good first choices. As you get more familiar and your data gets more complex, you can move on to more advanced methods like random forests or neural networks.

5. Customize user experience based on predictions

Use your data to make targeted adjustments to how users interact with your site. There are a lot of ways you can do this; here are examples: 

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Personalize product recommendations

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For instance, one of the core tenets of a successful digital marketing practice is to make your product recommendations more personal. eCommerce sites like Amazon analyze your past buys, what you looked at, and your ratings to suggest products you will likely buy. 

Dynamic content display

News sites and media platforms change what they show based on your likes and past activity to keep you hooked. 

Customize website navigation and design. 

You can enhance your website’s navigation and design to better suit individual users. Tracking past browsing behavior can highlight frequently visited sections for returning users. 

Similarly, tailor the UX/UI design to reflect users’ preferences, making navigating your site easier and more intuitive. This personalized approach makes for a smoother and more satisfying user experience.

This works great for educational content, especially if you want to set up online courses that adjust based on student performance. It keeps learning tailored and effective. To get you started, check out how to create an online course. It’s packed with actionable insights on how to make your courses adaptive and engaging. 

6 Leverage real-time data for immediate interaction

Use AI to instantly process user data to update your marketing messages in real time to match what your users are currently interested in or doing. This direct, timely approach can boost your campaign’s effectiveness. Here’s what you can do: 

Get Real-Time Analytics Tools

Install tools like Google Analytics to keep an eye on user actions as they happen, giving you the ability to react immediately.

Set automated triggers

Configure your marketing systems to respond to specific user behaviors automatically. 

For example, if a user spends more than 2 minutes on a product page, trigger a targeted discount offer or a personalized email to encourage a purchase. The benefits are obvious. 

The most effective type of trigger marketing campaign is automated onboarding emails. When a lead turns into a customer, these personalized onboarding campaigns are 800% more effective than mass emails. The click-through rate is 826% higher. 

Optimize ad placements

Tweak your digital ads based on real-time performance data. For example, if you notice that emails about a new product are getting many opens and clicks in the early evening, use AI to send more of these emails. This way, you hit your audience when they are most likely to engage.

Enhance customer support

Use AI chatbots to reply to customer questions quickly. They learn from user feedback and get smarter over time so they can give faster, more spot-on answers. This helps keep your customers happy because they get the help they need without waiting.

7. Make content more relevant with AI insights

Use AI to understand what your audience likes and how they behave clearly. This insight lets you create content that hits the mark, making it more engaging and relevant. Here’s how you can do this: 

Analyze preferences

Use AI tools like Salesforce Marketing Cloud or Adobe Campaign to show you which topics are hot, which tones and styles get the best reactions, and the best times to post or send content. 

For example, if data shows that your audience loves watching videos in the evening, try releasing your top videos to grab more views and interactions. 

Develop personas

user engagement - develop personas
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To create buyer personas that resonate and reflect your audience, dive deeper into your data analysis. Here’s how you can develop rich, actionable personas and a tailored content strategy:

  1. Construct personas that go beyond basic demographic information. Include details like their preferred content formats, the problems they face daily, and their goals. For example, a persona could be “Tech-savvy Tina, a mid-level manager who prefers quick video tutorials over text because she’s always on the go and wants to stay updated with industry trends.”
  2. Tailor your content strategy based on what you know about each persona. If AI insights show that a segment like “Tech-savvy Tina” engages more with how-to guides, focus on creating more video tutorials that address common industry challenges or introduce new technologies.

Don’t forget the power of social media insights when building your buyer personas. Growing your Facebook page organically will give you clues about what your audience loves. Here is a helpful guide on boosting your Facebook engagement

8. Optimize channel strategy with AI

To optimize your channel strategy using AI, first pick between Google Analytics, HubSpot, or Adobe Analytics. Here’s a straightforward way to use AI effectively:

  • Collect behavioral and demographic data from your Sales CRM, social media insights, and email marketing platforms. Use tools like Adobe Analytics to analyze this data and create specific audience segments, such as users who respond well to email versus those who engage with social media ads.
  • Implement tools like AdRoll or Optimizely that automatically adjust your marketing budget towards the best-performing channels. Set these tools to manage your spending based on real-time performance data, ensuring your budget is always optimally allocated without needing your constant attention.

These steps will help ensure your marketing efforts are smart and targeted, hitting the right spots where your audience hangs out locally and globally. 

9. Implement AI-driven retargeting

Use AI to find and re-engage users who showed interest but didn’t buy Get these visitors to complete a transaction or engage more with your content. Here are 2 simple ways to do this: 

Create personalized retargeting ads for each group. Use insights from AI to design ads that tackle specific reasons users left. For instance, give a discount to users who abandoned their cart, or emphasize product benefits to those who browsed but didn’t purchase. 

How can we ensure that AI tools have what they need to accurately predict and improve user engagement? It all starts with gathering the right data.

10. Use Encharge to engage customers  

Use Encharge to hit the right note with each person in your email audience. This will spark higher engagement and allow you to collect valuable data through your email communication that you can use to predict future behaviors. 

Here’s how you can utilize Encharge’s email personalization features:

  • Design custom user journeys: Use Encharge’s visual flow builder to personalize emails with dynamic merge tags that reflect user actions like choices made or features used. 
  • Create personalized emails: Add specific details with merge tags or use liquid tags for sections that change based on user data to keep every email relevant to its recipient.
  • Advanced customization with liquid: For more complex personalization, use Liquid.  It’s a coding language that helps you tailor your emails to fit your users’ preferences.
  • Reusable modules: Create and save custom content blocks that include user data to save time and keep your messages consistent.

How do I collect the right data for AI?

To get your AI-driven marketing strategies up and running, consider the following steps: 

Define your objectives

Identify what you want AI to help you with and identify the types of data you need. Ask yourself: 

  • What problems do you want to solve with AI?
  • What kind of data will you need? Consider which details are needed to reach your goals.
  • What do you want to improve or understand through AI? Identify key performance indicators (KPIs) related to your goals.
  • How will your data gathering align with your business goals? 

Read more: How to Set Your Marketing Automation Objectives in 2024

Understand data types and sources

Get to know the various kinds of data, such as behavioral, demographic, and transactional. It is important to identify which data sources are most relevant to your specific marketing goals.

With Encharge, you can integrate data from these various sources to automate marketing processes based on user behavior and demographic data.

Remember to align each data type with your AI goal. For instance, if your objective is to improve user retention, transactional data from your CRM will reveal patterns in customer churn, which AI can analyze to predict and preempt future churn. 

Choose the right data-gathering tools 

Pick tools and technologies that align with your specific needs for efficient data collection. Here’s an overview of tools for different data collection requirements. 

  • Google Analytics: Ideal for tracking website visitor behavior, traffic sources, and engagement metrics.
  • Adobe Analytics: Provides detailed analysis of customer pathways, conversion metrics, and in-depth segmentation.
  • Encharge: Great for B2B SaaS companies looking to automate marketing efforts. It helps gather and utilize customer interaction data to fine-tune your AI-driven marketing strategies for better personalization and effectiveness.

Challenges in using AI and how to fix them

Using AI in marketing can spice up how you connect with your audience, but, it’s not all smooth sailing — tackling a few hurdles is part of the journey to making the most out of AI. Let’s dive into typical challenges and how to solve them:

Data privacy and security

AI systems need lots of data to work well, including sensitive information about customers. If this data isn’t handled carefully, it can cause privacy issues and identity theft, hurting a company’s reputation and losing customer trust. 

Solution: Use strong encryption to protect data during transmission and storage. Update your security measures monthly, conduct security checks weekly, and train your team every quarter to prevent unauthorized access.

Data quality and quantity

The data must be accurate, relevant, and current. However, gathering large amounts of clean data is challenging. Often, data can be incomplete, outdated, or riddled with errors, which might cause AI to make wrong assumptions or decisions.

Solution: Implement checking for and correcting inaccuracies, removing duplicate entries, and updating outdated information before feeding the data into your AI systems. 

Bias in AI models

AI models learn from the data they get; if that data is biased, the AI’s decisions will be too. This means an AI could make unfair choices, like targeting ads wrongly or leaving out certain groups. Bias can hurt a brand’s reputation, push away customers, and even cause legal trouble. 

Solution: Gather customer surveys, social media interactions, transaction histories, and third-party datasets from several sources. This variety helps to balance out any biases that might be present in a single source. Also, use debiasing algorithms to adjust and correct any skewed data so that AI’s decisions are fair. 

Integration with existing systems

Adding AI to current tech setups can be tricky. Most companies already use complex systems to manage data and customer relationships. AI often needs special software or infrastructure that might not work well with these existing tools.

This can cause problems like compatibility issues, where systems can’t communicate properly, or increased maintenance costs. Solving these challenges is important to ensure AI works smoothly and helps rather than hinders business operations.

Solution: Use APIs (Application Programming Interfaces) and middleware to help AI blend smoothly into your existing systems. These tools act like translators between different technologies, letting them work together without a hitch. This way, you can get your AI up and running more easily and make sure it plays well with the tech you already have. 

Skill gap

Implementing and managing AI requires specialized knowledge that many teams may not initially have. AI technology involves complex areas like data science, machine learning, and software engineering. 

Finding skilled employees in these areas can be difficult and expensive, and training current employees to meet this level of expertise involves a significant investment of time and resources.

Solution: Offer your staff AI and data science training through workshops and hands-on sessions. Consider creating internal internship programs that serve as a training ground for both your current employees and new graduates. Partner with platforms that connect graduates with internships to bring fresh talent into your organization. 

Consider expanding your team by hiring specialized AI and data science talent from overseas. Platforms like Genius let you tap into a global talent pool. Hiring from a broader talent pool will cut salary costs by up to 88%, and let you find the best experts from cost-efficient locations. This flexibility also makes it easier to scale your team as your business needs change. 

With the right data and people at your disposal, the question becomes: how do we make the most of it? 

Which AI tools work best? 

The AI tool landscape offers a lot, from analyzing data to crafting content, engaging customers, and predicting trends. Match your tool choice to your goals. 

How can you determine which tools are best for you? Consider the following pointers: 

  • The tool should align with specific marketing goals like enhancing engagement, boosting conversions, or improving campaign efficiency. For example, if your goal is to enhance engagement, choose a tool like Hootsuite to schedule and analyze social media activity across different platforms. 
  • It should specialize in areas critical to marketing, such as data analysis, content creation, or customer engagement.
  • Integration with your current marketing tools and systems should be easy, simplifying data use and streamlining your workflow.
  • It should come with user reviews and case studies that prove it works well and effectively.
  • Customer support and educational resources must be readily available if you encounter issues.

Conclusion

As you explore integrating AI into your marketing efforts, ask if this AI strategy aligns with your core goals. This will help you avoid the common pitfall of trying out every possible AI application and wasting valuable resources. 

How will you use this technology to build deeper connections and make your mark in the market?
By leveraging Encharge, you do more than predict user engagement — you make way for remarkable growth and deeper connections with your audience. Are you ready to revolutionize your marketing with precision and insight? Check out Encharge today.

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