The Solution presented in this article is the winner of the Cisco & Google Cloud Challenge contest in 2018 – achieved with Bucher & Suter and Expertflow:
Cisco Customer Care, now Cisco Customer Journey Solutions (CJS), is by definition the best architecture to ride and support the current highest priority in large enterprises – Customer Experience sales innovation, the #1 priority for 71% of the business leaders (2017 Global CX Benchmarking Report). CJS, very often considered a cost center in the past, is now seen by enterprises as a driver of revenue, able to increase customer loyalty, retention rate, and important financial metrics such as the Annual Renewal Rate (ARR).
Today, 65% of customers prefer Chats versus traditional voice calls to customer care (BT Global services-Cisco-Davies Hickman Partners 2017). Thus, to consider these changes of users habit, a modern CJS has to offer a selection of contact methods, called Omnichannel, and at the same time offer the possibility to move seamlessly between interaction channels bringing the context along.
Conversational self service powered by artificial intelligence
Customers also expect a near instant response time and quick resolution of their needs – both being key business metrics proven to drive customer retention and loyalty. One third of the time it needs two or more interactions to resolve the issue, causing customer dissatisfaction and 40% of them eventually leaving to find a new provider (ICMI, 451 Research). This business ask is setting another mandatory need for a modern CJS: it has to offer Conversational Self Service solutions powered by Artificial Intelligence that are efficient, productive and cost effective.
The four major business needs addressed by the “Hybrid Chat,
Artificial Intelligence solution for Cisco CCE/CCX/HCS”
The next picture describes the architecture of the solution developed by Bucher & Suter and Expertflow, a Cisco Ecosystem partner. The architecture is constituted of several building blocks able to interact, dialogue, and orchestrate through OPEN API’s to allow easy customization of the end customer solution:
- DIGITAL TOOLS (any sort of present and future type of CHAT tools used by end users)
- ARTIFICIAL INTELLIGENCE services and vendors
- Cisco CJS architecture: CCX, CCE, PCCE, HCS and CJP
- A CONVERSATIONAL ENGINE developed by the ECOSYSTEM partner, being the broker, the orchestrator between digital tools, CJS APIs, AI vendors and NLP services, and offering the integration of both end users and agents front ends.
Let’s see the way it works, beginning with a description of its hybrid approach
When implementing a chat bot in a digital CJS you always need a hand-off strategy for all those cases where the BOT isn’t confident enough to answer and thus needs a human agent. This means that in a standard solution a chat is always managed either by a BOT or by an agent, which very often results in very low productivity of the CJS, especially if the chat bot is not powered with AI.
The solution presented in this article features a different innovative approach where the agent, the BOT, and the user are always engaged in a Continuous Chat Conference, and the agent can monitor multiple chats and leverage the BOT during the entire conversation, thereby reducing the workload and response time. After a hand-off to an agent, the BOT remains in the conversation and works as an agent assistant so upon every customer utterance query, the Hybrid Chat presents the most appropriate answers identified by the BOT to the agent.
A colored icon signals the agent which chats demand an intervention (RED), the conversations where the BOT can run independently (GREEN), and those where the BOT has multiple options (including a “strike probability view”) but it is not 100% sure so best would be having the agent picking the right one or overwriting (YELLOW). The agent can let the BOT auto-answer with the highest-scoring answer, intervene and select one among those that the BOT suggests, or even draft a new response to the customer.
A timer displayed with a colored circle around the chat icons indicates timeouts upon which certain configurable actions are taken.
The BOT uses a model created with Machine Learning powered by Google Dialogflow to answers chats, but the solution is quite innovative also because the messages tagged and validated by the agents can be used as new training data to the BOT in order to improve future recognition rates (Natural Language Understanding) and answers (Dialogue Engine).
The chatbot is constantly learning through conversations from person-to-person (clients and agents) making the whole solution self-tuning on the job, where the performances of the BOT are continuously improving in a specific contest further reducing engagement of the agents and therefore raising productivity and lowering costs. The interplay between customer, agents, and the BOT also reduces the response time, increasing the quality of the service delivered and enabling higher customer satisfaction and loyalty.
Let’s now analyze the way this solution interacts and integrates with a Cisco CCX/CCE or HCS CJS.
As highlighted by the diagram above, the Conversational Engine developed by Expertflow is maintaining a status of the chats allocated to each agent, but it also continuously predicts anticipated “intensity and human work volume” with machine learning, based on the type of unserved recognized intents and the type of media (SMS is slower than FB Chat). Based on such analysis, it assigns multiple chats in parallel to agents interacting with Cisco CJS through Open APIs (CTI and UQ API), ensuring that each agent has the same work volume. If an agent is fully charged, the Conversational engine makes a new synchronous media routing request to the CJS to reserve the next full-time agent. Conversely, if a chat session requires a full-time collaboration session (escalation to audio and/or video and screen sharing), all other ongoing chats are given back to the general chat pool and distributed to other agents and that agent is reserved for the full-time session.
The solution presented in this article is showing the incredible potential of combining together the Cisco architectures with Google artificial intelligence to design custom solutions targeting the modern business needs of large, medium, and small enterprises: Customer Experience, customer loyalty, customer retention, increased renewal revenue, decreased costs.
We’d love to hear what you think. Ask a question or leave a comment below.
And stay connected with Cisco DevNet on social!
Visit the new Developer Video Channel