While I was attending the Business Rules Forum a few weeks back I got my first chance to learn about Convergys. A major sponsor of the event, Convergys is focused on improving the customer experience and customer relationships using decisioning technologies. Building on a history in customer care and billing, Convergys is now a nearly $3Bn/year company with more than half the Fortune 50 as clients. Besides the technology group, which sells customer experience software, the company has 70,000 employees most of whom provide outsourced customer care services for clients through 80+ centers around the world. The technology is used by Convergys to support clients and licensed directly to end user clients. Convergys is focused on customer relationships and on what it calls the relationship economy. In this view of the world, companies must be able to be transparent, focus on individual customers and copy with rapidly changing circumstances and customer needs.
Convergys’ technology is very focused on supporting multiple channels. Over the last few years consumer preferences have moved with voice dropping from 77% to 60% (though the number of interactions has risen by more than 10% in absolute terms). Web chat, email and web self service have all doubled or more than doubled in terms of preferences, making multi-channel support a necessity for most companies. Convergys wants to help companies deliver intelligent interactions across all these channels for self-service and assisted-service as well as proactively. They call these three pieces, Intelligent Self-Service and Intelligent Assisted Service and Intelligent Notification, respectively.
Supporting these pieces is the Dynamic Decisioning Solution that acts as a framework for capturing events, making decisions by evaluating and executing the business rules or policies that apply and then tracking the effectiveness of these decisions. This Dynamic Decisioning Solution is built on a customer-data integration framework and an open interface to allow easy integration of event listeners (what they call “sensors,” such as calls to the call center or account postings) and action-taking “actuators” (everything from sending email to recommending offers and call routing). A policy manager controls the rules that drive a real-time decisioning engine that responds to the events, applies policies and customer data and then takes actions. The whole engine runs in-memory around an in-memory database core.
A quick look at the technology shows a core policy management interface for defining business rules that is clearly aimed at business analysts. Business rules can be managed at multiple levels and can be included by reference and overridden locally when and if this is allowed. Different levels of editing are allowed and access can be controlled. The real-time decisioning engine also offers support for analytic models with the ability to bring in models using PMML. Scoring is done in real-time, as you would expect given the in-memory architecture, and the results are available to business rules handling the decision. The ability to have “what-if” policies means a company can see what a set of rules would have done and compare this with what was actually done, supporting some impact analysis and what-if scenario testing. I only got a quick look but I am looking forward to learning more.