Most customer service and contact center executives are honing in on bots because they can handle large volumes of queries. Thus, their service center staff can focus on more complex tasks. As the technology behind bots has improved in terms of natural language processing (NLP), machine learning (ML), and intent-matching capabilities, companies are increasingly willing to trust them to handle direct customer interaction.
Building effective customer support agents requires large amounts of data to understand every query made by the user. Most people believe that chatbots are plug & play technology. Just a couple steps and the chatbot is ready. However, chatbot development doesn’t work that way. Developing a chatbot, it needs proper time, research, and resources. The first step is understanding what your target audience typically need help with, and how much human interaction is needed to provide that support. Also, once the chatbot is ready, you have to continuously grow the knowledge base and make your chatbot more efficient. You also need to promote the chatbot effectively so that people know about it.
How Chatbots Impact Contact Centers
Bots can start handling the initial conversation, gathering relevant information about the customer before passing the query to an agent. This reduces the wait time and improves customer experience. Look for highly repetitive, easily defined problems. As long as the queries are simple, bots can satisfy them more quickly than humans, 24x7, reducing customer resolution time with a cost effective solution.
Customer service staff shouldn´t fear chatbots as Gartner suggests companies working on Artificial Intelligence and Machine Learning should employ human-in-the-loop crowdsourcing as an enabler of Artificial Intelligence solutions since this approach gives a wider access to problem solving, model training, classification and validation capabilities compared with traditional Machine Learning processes. Therefore, when rules are too complicated for automation or the Machine Learning algorithm can't get more accurate results, humans must step in.
The diversity of customer contact channels will complicate the job of contact center agents. Moreover, the lack of consistency interacting with the brand across different channels is a fear for every CX expert, however, automation will ensure consistent experiences across channels.
Benefits of Conversational AI in Contact Centers
Conversational AI can reduce the operational costs for contact centers but there are more benefits:
- Greater ability to achieve higher customer satisfaction (NPS score) as every company needs to accommodate fluctuating customer interests.
- A significantly higher likelihood of exceeding customer retention targets.
- Reducing customer task completion time.
- Reduce overall contact center cost through operational efficiencies: handling multiple tasks and queries at the same time.
- Creating an innovative and distinctive brand by delivering a differentiated experience that modern customers demand.
- According to Oracle Research Report "The Impact of Emerging Technology on CX Excellence": 45% of firms using Artificial Intelligence for CX have increased their ASPs or total spent by customers.
- CX leaders report that 24-hour service is increasingly important. Many organizations are implementing automated chatbots into their web services and mobile applications so customers can get real-time assistance, even when call centers are closed.
- AI for CX is about knowing your customers and using that knowledge to make the customer experience smoother from offer to ongoing support and service.
- According to a recent Accenture report, 80% of live-chat sessions can be automated with a well-designed virtual agent.
Causes and types of conversational AI failure
Some of the most common errors in creating customer experiences are:
- Unsupported requests that chatbots can't handle (e.g. "sorry, I did not get that").
- Misunderstood requests - the virtual agent misclassifies the user intent and answers a slightly different request.
- Missed requests: the virtual agent identifies the correct intent but fails to recognize certain phrases or words.
The far more efficient and less risky alternative to manual training is by using Bitext's Artificial training data, also called synthetic data. We introduce a new way to speed up the deployment of new domains and languages for any bot platform. If you are interested in learning more about artificial data for chatbots, read more:
- Try real examples from domain leaders in retail, home and news.
- 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 Dialogflow Benchmark: increase accuracy up to 40%.
- Download LUIS Benchmark: increase accuracy up to 40%.
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