- Contact center queuing and routing based on traditional automatic call distribution (ACD) technology has always been a very linear process in which the next customer is typically matched with the next available agent. However, “who is next” never really translated to “what is best” for the customer or the enterprise.
- PureMatch, an innovative application in the newly released PureCloud customer service offering of Interactive Intelligence, takes a new approach to matching customers to agents, which could prove to be better for customers and agents – or not.
Interactive Intelligence’s PureCloud – the company’s new cloud-based communications, collaboration and customer engagement offering, due out in Q4 of this year – is provided via the Amazon Web Services (AWS) cloud. PureCloud reinforces the company’s thought leadership image in the customer service industry by offering several interesting and innovative applications, including: PureCloud Social Customer Service (SCS), an application that enables customers to view agent profiles and performance prior to selection of the agent; PureMatch, a system that automatically pairs customer interactions with contact center agents, based on multiple attributes and criteria; and PureCloud Directory, a corporate directory that makes enterprise user profile content available including skills, work experience, location, etc. Although all these applications are relevant to customer service operations, I believe it will be the criteria-based matching of PureMatch that will get the most attention in the contact center space.
The intent of PureMatch is to enable contact centers using PureCloud to collect extensive data on their customers from multiple sources – an obvious big data application. Rather than have each customer fill out an extensive form, PureMatch collects and assembles the data from multiple sources over time, improving as customer interactions grow in number. The information sources may include customer conversations with agents, as well as several data streams from the outside world including public social networks. Of course, to work best, customers must be willing to share such information for the potential of better, more personalized service.
The collected data could be extensive depending on the amount and kinds of information enterprises have about their customers and what customers are willing to share, have indicated in a self-service environment, or input during web interactions. The end result will be a list of criteria that the customer has defined in order to conduct his/her ideal interaction with the customer service operation. Then, as in online dating and in real time, PureMatch presents the customer with pictures and profiles and any other chosen information on selected contact center agents with whom they may choose to interact. The customer selects his ideal agent, and customer and agent are linked together.
In order to be most effective, enterprise‐grade services of this type must include powerful data analytics capabilities that allow organizations to spot trends and unlock value in the vast amount of data they collect. Overall, PureCloud and PureMatch will log every aspect of customer interactions as well as critical information for document access and workflow. Ideally, advanced visualization technologies and analytic tools such as Hadoop and Google Analytics will be used to make sense of this valuable information and even make predications based on it.
This sounds ideal. However, I believe some customers may misuse the newfound freedom to choose their ideal agent. Don’t you think, at times, there will be a proclivity to choose agents based on looks or some other criteria such as ethnic background, race, or even their name? Human nature being what it is, I suspect that some agents may become overworked and others will be utilized infrequently, making workload balancing and employee scheduling more difficult than it is today. I would be interested in what you think. Will PureMatch make conditions better or worse for customers and agents?