# 4 Ways AI Can Be Used for Better Customer Engagement

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## Table of contents

- What is AI customer engagement?
- What should you consider before implementing AI?
- 4 ways to use AI for better customer engagement
- Final thoughts

With [customer acquisition costs](https://www.mailmodo.com/guides/customer-acquisition-cost/) increasing 222% in the last 5 years and businesses losing $29 per new customer, keeping your customers engaged with your business has never been more important.

However, consistently engaging your customers can be tricky. They expect quick responses, personalized support, and a thorough understanding of their past conversations.

Luckily, there is a solution to this problem—AI. It can gather intelligence on key customer trends, provide instant support, and thoroughly engage customers.

Read on to discover different ways you can use **AI for customer engagement**.

## What is AI customer engagement?

**AI customer engagement refers to the use of artificial intelligence tools to plan, execute, and optimize customer engagement efforts and engage customers effectively.**

These tools rely on technologies like machine learning (ML), natural language processing (NLP), and predictive analytics to process big data and provide in-depth support for engaging customers.

## What should you consider before implementing AI?

While the use of AI in customer engagement is both exciting and increasingly beneficial, there are important factors to consider before going all in with the adoption. Here are some things you should factor in:

![What to consider before implementing AI in customer engagement.png](https://res.cloudinary.com/mailmodo/image/upload/f_auto,q_100/v1737352959/strapi/What_to_consider_before_implementing_AI_in_customer_engagement_3df8c70436.png)

1.  ### Cost
    

Adding AI into your business operations can come with significant costs. You may have to invest in new software, upgrade your existing systems, and allocate resources for training your team.

So consider whether your business is financially ready for such an investment. Given limited budgets and resources, the expense of adopting AI might be challenging to justify for smaller businesses or early-stage startups.

Assess your current stage of growth to determine if implementing AI will pay off for your business in the long run.

2.  ### Privacy
    

AI tools need access to large customer datasets—such as [cookies](https://www.mailmodo.com/guides/website-cookies/), browsing behavior, and purchase history—to produce personalized results. However, this also poses a risk to the customer’s privacy.

For example, if that data gets misused or mishandled, you risk losing your customers’ trust. Plus, you could end up in hot water with privacy laws like [GDPR](https://www.mailmodo.com/guides/gdpr/) or [CAN-SPAM](https://www.mailmodo.com/guides/can-spam/).

So, before implementing it’s essential to establish robust security measures to protect customer data. This includes complying with legal requirements and adopting best practices for data management.

For instance, while businesses may use publicly available customer information such as name and gender for AI processing and confidential information like social security numbers or credit card details should remain strictly off-limits.

3.  ### Bias and inaccuracy
    

AI systems are only as good as the data they are trained on, and if the data is flawed or unrepresentative, then AI can produce biased or inaccurate results.

For example, a software company might primarily train their AI models on historical data from large enterprises. As a result, the AI may frequently suggest enterprise solutions to mid-size businesses, which can make these clients feel undervalued and lead to disengagement.

Additionally, AI systems can fail to keep track of ongoing conversations. If a customer asks a follow-up question, the AI might offer a disconnected or incomplete response.

4.  ### Job concerns
    

AI changes how teams work, especially in customer support, sales, and marketing. For example, [AI chatbot platform ](https://joyz.ai/) can now handle a wide range of customer enquiries, 

While it’s great that some work can get done faster, over-automation can raise employee fears about job displacement.

Additionally, some employees might feel overwhelmed by the demand to adapt to new technologies or learn technical skills. It can be hard to re-skill quickly enough to keep up with the industry. In such cases, teams often rely on an [employee engagement software](https://matterapp.com/blog/employee-engagement-software) to understand employee concerns, gather feedback, and support learning efforts more effectively.


## 4 ways to use AI for better customer engagement

There are a number of ways AI can be used for [customer engagement](https://www.mailmodo.com/guides/customer-engagement/), but it ultimately depends upon what works best for your company and your customer. Here are four ideas to help you get started:

![Ways to use AI in Marketing.png](https://res.cloudinary.com/mailmodo/image/upload/f_auto,q_100/v1737352959/strapi/Ways_to_use_AI_in_Marketing_59a034ea19.png)

1.  ### Predictive analytics for product recommendations
    

If you are interested in making more accurate forecasts on upcoming customer needs without the legwork of manual analysis, then predictive analytics might be the tool you need.

These tools can analyze massive amounts of customer data—such as [customer lifetime value](https://www.mailmodo.com/guides/customer-lifetime-value/), propensity scoring, and churn prediction—to evaluate customer’s needs and their likelihood of making specific purchases.

For example, if a client’s usage data indicates they are approaching their software capacity limit, your business can recommend a robust plan or an add-on service for growth.

>One way we use AI at Kratom Earth is through predictive analytics in email marketing. Our system tracks buying behavior, browsing history, and past orders to determine the best time to send product recommendations or replenishment reminders. This has driven higher open rates, more repeat purchases, and fewer unsubscribes.
> 
> \- [Loris Petro](https://www.linkedin.com/in/loris-petro-182a40144/), Marketing Manager, Kratom Earth

2.  ### Conversational AI chatbots
    

Adding an [AI chatbot](https://www.mailmodo.com/guides/ai-chatbot-tools/) to your website and support portals is one of the smartest ways you can engage customers throughout their buying journey.

These modern conversational AI chatbots use ML and NLP models to understand customers’ intent behind a request and generate accurate answers.

The most significant advantage of using an AI chatbot is that it can answer customer queries at all hours of the day. This means even if your service teams don’t have the capacity to provide round-the-clock assistance, your customers will get answers to their queries when they most need them.

Let’s take [Intercom’s](https://www.intercom.com/) Fin as an example. This chatbot pulls information from your business’s existing knowledge base or previous conversations to provide the support customers need quickly.
![AI Customer Engagement intercomm mailmodo.png](https://res.cloudinary.com/mailmodo/image/upload/f_auto,q_100/v1737352959/strapi/AI_Customer_Engagement_intercomm_mailmodo_608c5f190f.png)


3.  ### Content creation
    

AI-powered content creation tools are one of the most popular and practical applications of AI in customer engagement. These tools can produce content such as blog posts, emails, or social media posts in seconds.

For instance, you can use Mailmodo’s AI email generator to generate emails within seconds.

Beyond writing, AI content creation tools can also generate visual content such as images, videos, and audio to appeal to customers. Honestly, it’s no surprise that [35.1%](https://influencermarketinghub.com/ai-marketing-statistics/) of marketers are already using AI for content production, as these robots can serve quality content fast.

>Our most effective AI deployment paired predictive analytics with personalized content generation. The AI learned from high-performing customer interactions and mimicked these behaviors in automated email campaigns. This took our response rate to customer inquiries from hours to minutes without compromising on a personalized experience. The outcome? Customer satisfaction rates soared from 7.2 to 9.1 out of 10.
> 
>\- [Sheraz Ali](https://www.linkedin.com/in/sheraz-haro-link-builder/), Founder, [HARO Agency](https://harolinksbuilder.com/)

4.  ### Personalized on-site experiences
    

AI can also be used for better content targeting. Content-based recommendation systems incorporate machine learning algorithms to analyze customers’ online behavior, such as what they viewed, liked (or disliked), or when they clicked on a link. They then use the insights to suggest content that is highly relevant to customers’ needs and interests.

Depending on where and how you engage your customers, this recommended content could be anywhere from videos to blog posts.

Need an example? Netflix uses a content-based recommendation system to suggest movies and TV shows based on your viewing history and preferences. Let’s say you’ve been watching a lot of action movies; Netflix will start showing you more recommendations in those genres on your homepage.
![AI Customer Engagement netflix mailmodo 1.png](https://res.cloudinary.com/mailmodo/image/upload/f_auto,q_100/v1737352961/strapi/AI_Customer_Engagement_netflix_mailmodo_1_6dfc23a90d.png)
## Final thoughts

Keeping customers engaged isn’t simple, as there are too many initiatives—such as doing customer research and delivering personalized content—that need to be handled simultaneously and it can be pretty difficult to handle all of that together.

However, with AI, you can perform tasks faster. Take a look at the AI use cases mentioned in this blog and adopt these strategies to have deeper connection with your customers and improve their satisfaction.

## Frequently Asked Questions

### What is the difference between rule-based chatbots and AI-powered chatbots? 

Rule-based chatbots follow predefined scripts and respond to specific commands or keywords, which means they can only handle simple, repetitive tasks and can’t learn from past interactions. On the other hand, AI-powered chatbots use machine learning and natural language processing to understand human language, adapt to new queries, and improve over time.


### How can I make sure that AI-powered personalization is ethical and respectful of customer privacy?

To keep things ethical, start by getting clear consent from your customers so they know their data is being used. Next, implement strong security measures, such as encryption, to safeguard sensitive customer data. Lastly, avoid using personal data in ways that could be discriminatory, and regularly audit your AI systems to make sure they are operating in a fair and ethical manner.

### How can I make sure AI can handle complex emotions and escalate to human agents when needed?

To make sure AI handles tricky situations well, set up clear escalation pathways so the AI knows when it needs to pass the conversation to a human agent. For example, if a customer shows frustration or requests to talk to an agent, the AI should recognize that it’s time to trigger the “Talk to agent” option. Additionally, to make sure your chatbot is working smoothly, use the “Testing” feature that comes with most solutions. During testing, simulate scenarios where the AI might not be able to fully resolve an issue. This will let you see if the AI can recognize when it’s time to hand things over to a human.


### Does AI require a lot of technical expertise to implement?

Using AI to engage customers does require some technical expertise, but it largely depends on the complexity of the AI solution. For simple tools like chatbots, you just need a team that knows how to set up the software and add content. However, for more advanced AI solutions, such as sales forecasting solutions, some data science knowledge may be necessary.

### What are the key metrics for measuring the success of AI customer engagement initiatives?

To measure the success of AI customer engagement initiatives, keep an eye on metrics like customer satisfaction score, net promoter score, first contact resolution rate, and average handling time. These numbers give you a clear picture of your customer’s experience, which is essential for refining your AI approach.


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---

**Author: Nishrath**

Nishrath is a content marketer at Mailmodo, where she writes about email, product marketing, and growth. Outside of writing, she loves reading books and attempts to learn new languages.

