How to Integrate AI Into Your Customer Support Team

Integrating Artificial Intelligence (AI) into your business might seem daunting at first, but taking steps towards innovation usually pays off. Many industries have already started incorporating this technology into their systems. There is a lot of value that AI can provide for customer support teams. Far from replacing agents, AI systems are here to assist them in providing the best customer experience.

AI’s capabilities continue to grow as research progresses, but two, in particular, provide benefits for customer support: Machine Learning and Natural Language Processing (NLP)

Machine learning allows AI to collect and analyze large amounts of data and respond accordingly. Netflix’s recommended algorithm and Google Maps’ route suggestions are examples of machine learning.

Meanwhile, Natural Language Processing (NLP) allows technology to understand the complexities of human language from text or voice. From recognizing typos to holding a conversation out loud, NLP enables easy communication between humans and AI.

Most AI technology employs both to achieve consistent and data-based results. To better understand how AI can help your team, here are ways you can best weave the technology into your customer support systems.

Customer Response Chatbots

Websites like HubSpot have a convenient live chatbot the moment you open their page! (Image: HubSpot)

Fast response to queries is something customers expect. Utilizing chatbots is one way to quickly and accurately respond to customers’ concerns.

Microsoft found that two-thirds of customers try self-service before contacting a live agent. Meanwhile, a HubSpot survey revealed that 57% of customers are interested in getting information from bots when browsing a business’s website.

Chatbots that customers can message and receive an immediate response from make the customer experience more pleasant. In addition, employees do not have to address simple and easy-to-resolve issues. Chatbots can help:

Answer repeated questions like “Are you open today?” or “Is there a sale currently?”
Point customers to the right information quickly and accurately
Reduce wait time and enhance customer satisfaction
Answer simple concerns 24/7

Predictive Analysis

AI that employs predictive analysis looks into past data and suggests next steps. Having information in advance makes communication smoother and more effective. As per Aberdeen and IBM, 33% of users are more likely satisfied with the personalized experiences offered by AI.

AI with predictive analysis can help:

Identify customers about to leave, therefore improving retention
Identify customers who would likely be more receptive to follow-up calls
Suggest words and phrases to the customer support agent for quicker and more consistent responses
Forecast trends and demands

The key element in AI used for predictions is data. The more data it has access to, the more accurate its predictions can be.

Personalization Based on Data

If you have a social media account, you are already familiar with AI that focuses on personalized experiences.

On Instagram, the Explore page shows you posts based on who you follow and where you are posting from. On TikTok, the For You page features content based on factors like hashtags and song clips. In these cases, AI accesses information you provide via the actions you take.

Personalization can support your business by storing data that is easily accessible to your customer support agent. Customers appreciate a more personalized experience based on their past actions.

76% of customers expect personalization using data
20% of respondents think it is important not to have to repeat the same information when transferred; similarly, 31% consider a knowledgeable agent important

With AI analyzing vital information and presenting it in a way that agents can easily understand, the customer does not need to keep repeating themselves during a support call. Personalization can help:

Improve your Customer Relationship Management (CRM) System
Inform your agent who the customer has talked to and what details they have provided
Refine customer journeys based on segmentation and key groups

Intent Detection

The organization is essential to analyzing data. However, when there are hundreds or thousands of data to sift through, it is inefficient for humans to do it manually. Using AI focused on intent detection, you can quickly identify who is interested, who needs more information, and who is ready to purchase. Intent detection can help:

Read and classify a large number of inquiries through channels like email, messaging apps, and form responses
Prioritize concerns based on factors like importance and complexity
Allocate an agent’s time more effectively, guided by the perceived intent of a customer

Having an AI with intent detection capabilities helps your team by empowering them with data to better resolve problems.

Alleviating Fears Related to AI

Currently, some customers are uncertain about businesses using AI— 28% of respondents in a global study claimed that they were uncomfortable about interacting with AI when conducting business. However, a number of these respondents were not aware that they were already interacting with AI:

“The survey found that 84% had recently used at least one AI-powered service or devices, such as virtual home assistants, intelligent chatbots, or predictive product suggestions. That’s a knowledge gap of 50 points.” – Pegasystems

Despite their hesitation, they had already encountered AI through systems like email filters and predictive search. This indicates that a lack of knowledge can lead to distrust. Knowing that AI integration must bring with it a dedication to trust and transparency. With the rise of AI, more and more businesses are looking into incorporating technology into their processes. Continuing advancements allow for more efficient and better-informed customer support teams with AI assistance.