4 Ways AI Can Be Used for Better Customer Engagement

With customer acquisition costs 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:

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, 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 or 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-powered chatbots can now handle a wide range of customer enquiries, from answering FAQs to processing orders.

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.

4 ways to use AI for better customer engagement

There are a number of ways AI can be used for 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:

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, 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.

2. Conversational AI chatbots

Adding an AI chatbot 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 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.

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% of marketers are already using AI for content production, as these robots can serve quality content fast.

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.

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.