Solving Customer Issues with Intelligent Answers and Insights

When it comes to customer support, the entire process is built on a simple idea: when someone communicates a problem, the representative dealing with the case uses their dashboard to review, work, resolve, and close a ticket. This ticket then remains open until the issue is closed, along the way acting as a repository for information about that particular request or problem. 

From the point of view of the people here at Attivio, a ticket is just a repository of information that, when combined with existing information repositories, adds to the understanding of the customer relationship.

During a recent internal hackathon we took a deep dive at support tickets and looked at how they fit into the full customer lifecycle. A ticket in a customer support situation is just one point of information, but our technology is adept at looking at everything – from license and renewal information, to CRM and sentiment data. We unify information, enrich it with natural language processing and analytics, then use machine learning to gain intelligence over time. In other words, we want to make support reps more adept at solving tickets faster and more accurately. (In fact, we want to help prevent tickets from being submitted in the first place by making self-service more effective.)  

Today a support rep has a limited window of information with which to help a customer, usually contained in an approved knowledge base. But not all requests can be answered quickly and efficiently just through that information alone, which leads to a fundamental question: how do we make the whole process better of the customer?  

The answer lies in finding macro trends in the customer support tickets and then using that information to help customer support personnel in real time. When a ticket comes in a system should be able to read the structured and unstructured information entered by the client and the support rep, then proactively make suggestions on how to solve the issue. That may mean providing access to the right information or it could mean re-routing the request to a person who is in a better position to answer it. 

Imagine a system that begins to understand how Bob is great at handling queries about a problem with mobile while Mary excels at database issues. In that case a support ticket can be directed to the right person in real time based entirely on an intake call or email. And what if a series of tickets start to come in on a particular issue, and the answer isn’t in the official repository at all, but instead resides in a SharePoint document within engineering? Identifying that information, putting it through a vetting process and putting it into the support repository should be a high priority. 

All that is only possible if the system has the intelligent search technology in place to find answers and deliver insights. But the bottom line is helping the person with the request get what they need faster enabling them to remain a satisfied and happy customer. 

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