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6 Criteria for Choosing the Right AI Solution for Your Support Team

Customers today expect companies to deliver smarter, faster, and better support for their problems or queries. Back in 2011, Gartner predicted that customers would manage 85 percent of their non-voice customer service interactions without dealing with a human by 2020. This is because millennials and the Facebook generation prefer automated solutions that don't involve human interaction, and as a result more businesses are starting to leverage the power of Artificial Intelligence (AI) to improve their current customer support system. 
As AI-based support systems become the best way to handle a growing business without the accompanying increase in customer service costs, more enterprise software providers are entering this space. Here are the customer service experience selection criteria to consider when choosing a customer support platform for your business. 
1. Intelligent "Deflection" Tool 
An effective AI-powered customer support system should come with an intelligent deflection tool to reduce incoming ticket volume and deliver an excellent customer experience. Deflection technology enables your customers to find answers or resolve problems on their own, utilizing information available in your content portals. It automatically suggests an answer from your knowledge base when your customers are about to submit a ticket. The key to selecting an intelligent deflection technology is to ensure that the relevance of  results are continuously improved by an intelligent human in the loop, aka customer support agents.   

2. Automated Ticket Triage 
Another feature you should look for in an AI-powered customer support platform is its capability to automate ticketing processes. An automated ticket triage solution fueled by machine learning techniques ensures tickets are assigned priority based on analyzing historical patterns versus "best guess." It can scan new tickets as they come in — looking for the type of issue, historical data, urgency, language, spam etc. and route them automatically by skills to the most appropriate support agent or department.

3. Automated Ticket Response 
One of the keys to streamlining your customer support is responding to help requests as quickly and efficiently as possible. To achieve shorter response times, you should leverage a support platform that automates ticket responses with zero-touch from your agents. It should be armed with a machine learning algorithm to relieve your support agents of handling common issues or repetitive tickets, such as tracking packages and resetting passwords, so that they can focus on what they do best: provide empathetic human support. 

4. Ease of Implementaion
Integration within your existing CXM System, like Zendesk/Salesforce Service Cloud/MS CRM, is key because it's where your cases and agents already reside. Agents should not have to export data and run it in a separate app, learn a new platform, or build new workflows and/or processes that only add to ticket handle time. A turnkey AI implementation should happen within 60 days and that should be a key criteria in your search. The right AI platform for customer support should be so turnkey that it can learn your historical tickets and build AI models that don’t require an inordinate amount of training. It should be as easy as 1. go live 2. make predictions and 3. execute actions - all within two months of an engagement.

5. Scalability & Enterprise Readiness
Because you don’t have the time or budget to hire and train a growing number of support agents, you should look for an AI platform that can handle spikes in ticket/case volumes without having the need to add more agents. Search for an AI-based solution that efficiently sorts, categorizes, and flags incoming tickets and can resolve up to 30% of  high-volume tickets without agent touch. Sophisticated AI systems also need to pass all enterprise requirements from info sec teams around data privacy, security assessments, and compliance. In addition, key analytics reports that make the stakeholder successful are key when choosing an AI platform.

6. Sophisticated Chatbot 
Efficient customer support platforms should be equipped with intelligent chatbots that leverage the power of Artificial Intelligence and Natural Language Processing (NLP) — to deflect and resolve the top 5-10 high volume intents. Sophisticated chatbots can help automatically improve decision flows by observing how agents are resolving similar cases and mimic them. With the help of AI, a chatbot can replicate how a human thinks, read between the lines, and deduce meaning from slang, colloquialisms, and language nuances. This enables your organization to automatically respond to help requests and intelligently route them to the right support agent or department. Smart chatbots can improve the quality, presentation, and efficiency of your content because they learn from previous tickets and detect redundancies or knowledge gaps, which improves the experience for the next customer. 

Take Your Customer Support System to the Next Level  

Today, many companies are using AI to improve customer experience. Because AI-based customer support solutions learn from every single interaction, they can help deliver a much more personalized experience to customers. Without investing a great deal of time and money, you can quickly and efficiently handle customer interactions, making your operations customer-oriented and improving your future growth prospects. 
AnswerIQ, an AI-based customer support platform, helps your company provide your customers an excellent service by leveraging the most advanced AI algorithms to equip your support agents with the information they need to perform their duties efficiently. With the help of AnswerIQ's support technology, you can significantly reduce ticket handle time, improve ticket response times, and increase CSAT score. 
Learn more about how AnswerIQ works with your existing workflow to boost agent productivity and improve CXM efficiency in a short period of time. 

Topics: automation, language support, backlog, nlp, self assist, agent assist, first contact resolution, average handle time, agent response

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