We’ve got a surprise! Today our team would like to introduce a new offering that we’re calling Omnichannel Intelligence. The idea is to bring all the contextual customer information from across systems together in one place for real-time use. It’s designed to help customer service agents help customers faster and bring a layer of intelligence to routing and agent selection that ensures a customer is matched with the most appropriate agent or bot for the situation. The goal is to reduce the cost of service and improve the customer experience at the same time. Here’s how we got here and what it’s all about:
Back at Rackspace in 2011, when we first began building the platform that was to become HelpSocial, we were trying to solve what seemed like a simple problem. When someone reached out for help, we needed to know the answers to two very important questions, very quickly: Who is this person we’re talking to? And, is there anything important in their history with us that we need to know?
Sounds like it should be a simple problem to solve, but trying to answer those two questions in a real-time conversation can be amazingly difficult. The answers are often spread across many systems around a large company and the longer it takes to find the answers, the more upset the customer becomes. Solving this problem is what led to the creation of HelpSocial and it’s one of the things that made our platform special so early on in the social customer care space.
We’ve observed agents in contact centers all over the globe. We’ve noticed that trying to find the answers to these same questions are often the biggest wastes of time in the service process for other communication channels as well – it’s not just a social media/messaging thing. And with other channels like voice, wasting time not only hurts the customer experience, it’s also very expensive for the business. We want to help. The set of systems we’ve built for Omnichannel Intelligence go back to our beginnings with social media, but this is now open to work with every communication channel.
The way it works is when a new email/phone call/live chat/text message/etc is received by our platform, we immediately identify the customer and pair relevant contextual data with the inbound activity. This is done by making connections with the different CRMs, ticketing systems and customer databases around the business, to bring information into one place at the instant that it’s needed.
The service acts like a filter that sits in between the customer who sends the activity and your system that receives it. Only, instead of filtering things out, this middleware service is adding information that keeps agents from having to search for it. But it goes beyond that.
Typically, the first thing that happens in a contact center is a routing engine receives the inbound activity and selects an available agent to handle it, based on pre-determined agent skill sets. Many of these routing engines are incredibly capable and boast an ability to match THE most appropriate agent to the customer situation. Technically, many of them are definitely capable of doing that but the problem is, inbound customer calls/emails don’t come in with much contextual data. If the routing engine doesn’t understand the full context of the situation, it can’t ever truly be intelligent at finding the THE most appropriate agent.
In the routing process, our Omnichannel Intelligence service adds all the relevant contextual data to improve the decision process. It can even add pieces of information from machine learning systems such as accurate sentiment analysis and cognitive data points like the intent of the customer’s inquiry.
Packaging all this together, for each communication channel, makes AI advancement in the contact center a tangible thing. Whether you’re using bots or agents or both, you can now make very intelligent decisions on which/who is the most capable for handling each inbound activity.
Once the activity reaches an agent (or bot), they are immediately presented with the most relevant contextual data about the customer and the situation they’re inquiring about. The agent doesn’t have to search for and review customer history to see what happened last time. They don’t need to look for routine data points or metrics – they’ve got it already, right there in front of them. Immediately, with the first response, they’re helping the customer to a resolution.
In social customer care, we saw this reducing handle times by an average of 30% per interaction. If you applied that average across your contact center, how much would you save? A lot. 😉
There’s so much more to this that we have discussed here yet, but we will in upcoming posts. If you’d like to learn more or see a demo, please contact us here. 2018 is going to be fun – Happy New Year!