According to Gartner, the Customer Engagement Center (CEC) is one of the fastest-growing application software markets. Have you already jumped on the bandwagon of contact center chatbots, but didn’t get the expected results yet? Most consumers switch to a competitor after one bad customer experience. Can automation actually improve service? Stop wasting time and start making your chatbot work!
24/7 availability, clear understanding, a smooth handoff and quick resolutions are everything your customers could want when calling your contact center. A conversational chatbot should be able to meet them all but, in most cases, they don't. If a virtual agent can interpret the intent behind your chat or phone request, chatbots can answer you more quickly and efficiently than human agents. But if your chatbot doesn't understand your customers well, let us give you a few words of advice.
The key of having a Knowledge Base
One basic premise is to have a knowledge base at your disposal – this base is mainly composed of frequently asked questions the chatbot must be able to answer.
There are plenty of retailers using chatbots, but not all enterprise chatbots are built on the same knowledge base. Depending on the company, there are several sources of information you can extract to feed your chatbot:
- Checking the FAQ-section in your website to get out the more common inquiries.
- Analyzing previous conversation logs, either via audio or as a transcription.
- Gathering insights from the call center team.
At Bitext, we could even help you download any information available on your website to draw out the most used concepts and words. Thus, a stronger knowledge base can be easily built. After that, this base is transformed into questions grouped by topic, also known as intents, and different ways to utter a question, also known as seeds. From these intents and seeds, our technology can generate thousands of different variants to automatically train your chatbot in a matter of secs. That’s possible thanks to techniques based on deep learning for chatbots.
Human in the Loop
Bots can improve with human supervision. In cases where virtual agents can’t figure things out, human supervisors can be very helpful. When a virtual agent gets stuck, more experienced humans can step in and solve the problem. Not only that, but the human agent can tag the problem when the virtual agent bogs down, which allows the AI system to learn from that scenario and become smarter for the next question.
Take a look at the following chart to get a better idea on how our NLG technology could optimize the performance of any call center, making chatbots understand with a 90% accuracy:
The future of customer service is human-machine collaboration. Do you want to know how to integrate this technology with your existing chatbot? It’s easy-peasy! Just contact us for more info and you’ll receive help in a blink of an eye!
- Try our API: Boost the capabilities of your chatbots. Available in 9 languages.
- Request a Demo to reach 90% accuracy despite any mistake made by the user.
- Check out our FAQs about Chatbots and Virtual Assistants.
- Download Use Case: how TechCrunch has made its chatbot more conversational.
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