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Top 5 Successful Examples of AI in Contact Centers

ai in contact centers

There is no question that Artificial intelligence has taken the world by storm. However, no matter where you go, whatever you do, Artificial intelligence has some minor or significant role to play. Artificial intelligence dramatically decreases employees’ workload by letting computers take over; that’s pretty exciting. Even implementation of ai in contact centers helps agents to ease their tasks and help them perform better.

The use of machine learning coupled with Artificial intelligence and automated voice responses in a Contact center also helps the agents assist customers by making the calls interactive. According to statistics, AI-powered chatbots will handle 20% of all customer support requests by 2022. The modern age is full of technology and AI that improve everything we do.

The market size of Conversational AI is expected to reach USD$1.3 billion by 2025, growing at a CAGR of 24% 

Importance of AI in Contact Centers

When customers face some problem with some technology, the first thing they think to do is call a contact center and ask the agents for the solution. However, in reality, listening to every customer’s minute problem will take a great deal of time, and the agents cannot help as many people as possible.

AI is important here, which is a prime example of Conversational AI. Therefore, when a customer calls the customer service center, they hear an automated voice that asks them a few questions about their problem. In the case of highly minute problems, AI solves them by itself.

More than 50% of enterprises will spend more per annum on bots and chatbot creation than traditional mobile app development

Still, when the problem is beyond the scope of that automated voice, it transfers the call to the expert in the field of the customer’s tech problem, which saves much time. The agents can assist more customers this way, so the Contact center adopted AI.

Apart from this, AI is considered important in Contact Centres due to the following reasons:

How has AI changed the Contact centers?

Bots are certainly an important use of AI in contact centers, but their applications extend far beyond them. Artificial intelligence can be used in ACDs, IVRs, and workforce management software. It can be used to improve routing interactions, forecast volumes, etc.

These transformational capabilities are a few to consider:

1. AI Optimizes Contact Centers

In contact centers everywhere, AI is used to streamline the experience by facilitating agent information collection, providing context to customer interactions, and speeding up call times. Agents are also redirected based on the context provided by AI to agents with the skills they need to solve a problem.

2. AI Helps with Call Prediction

Many contact centers use artificial intelligence to predict their customers’ behavior,  including how many calls they can expect to receive during a shift. Contact center solutions that use predictive analytics are focused not only on helping agents determine the intent of a call but also on preparing for the interaction

3. AI Helps Discover Previously Unknown Data

AI plays a key role in driving analytics and discovery in Contact Centers. An interaction’s efficiency can be determined and replicated or dispensed with the help of technology that combs through gazillions of call data. In this manner, it is possible to identify patterns and create newer tactics and policies that can be implemented across the organization.

Top Successful Examples AI in Contact Centers

1. Chatbots

In the past decade, the number of customers has grown, but the need for services has risen. Consumers who experience problems usually try to contact the contact center, but this isn’t easy at midnight. So whenever an emergency occurs, the customer is forced to resolve it themselves. To resolve the issue, most companies are now using chatbots to assist customers in solving their technical problems any time of the day.

Companies can use bots such as these to chat with customers and assist them in solving their problems, allowing them to get in touch with the customer a little later after the problem has been temporarily resolved. Moreover, artificial intelligence makes the chatbot easy to use, enabling the best interactivity possible. Customers also get satisfied because they have received some explanation of their problems.

2. Voicebots

A customer can become frustrated when the same agent repeatedly answers the same question. Another customer must wait for the agent to connect with a call. There is a long waiting period, and then the customer has to connect with an agent, which is more frustrating because they only hear beeps. To avoid these problems, engineers designed voice bots.

Voicebot can take multiple calls simultaneously and interactively with each caller. In addition, the bots can be designed to optimize indicators such as the Hold Time (HT) or First Time Resolution (FTR). Upon calling the contact center, the customer is prompted to hear an automated voice and reply according to the problem they are facing, and the bot then connects them to the expert in the field of their problem.

3. Sentiment Analysis

Using artificial intelligence technology, contact centers can do sentiment analysis like it can monitor customer emotions, thoughts, and attitudes to determine how they feel about the company. This technology runs based on natural language processing (NPL), machine learning, and computational linguistics to mine the customer’s data from their social media comments and reviews for related products.

All these inputs are then scored as positive, negative, or neutral, and these scores are made available through the reporting tools.

4. Data-driven Decision Making

A data-driven approach to decision-making focuses on making the best decision possible for the company and is based on using past data to help support decisions. That’s where data-driven decision-making comes in. Past information is used to support data-driven decisions. If someone is supposed to decide on behalf of the company, they must survey the market and use the automated tools designed using AI to determine.

These data-driven decisions can increase the business and the performance of the companies. For example, when a company makes their findings using the previously stored data, their profit is increased up to 10%, and they even have reduced costs for the customers by 10%.

Bottom Line

From the preceding, we can conclude that artificial intelligence is progressing day by day and that the application of artificial intelligence to contact centers has also proved to be productive. By using AI-based bots, agents can improve their performance and be more satisfied with their work.

Companies can use AI to analyze customers’ sentiments and increase customer satisfaction. In addition, AI helps the contact center make their calls with the customers more interactive and easy to find problems.

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