Decrease the cost and increase the quality of your customer support center by automatically processing a significant percentage of incoming requests in a fraction of a second with a custom built intelligent AI.
If the algorithm is confident in the answer (the predicted probability of the correct answer is above 98%), the answer is presented to the customer instantly.
For complex, rare, or new requests, the algorithm will provide relevant recommendations to be approved in one click by a customer support operator. Over time, the algorithm will learn, gain confidence and expand the set of questions it can cover with human-level quality.
We use advanced NLP, topic modeling, and deep learning techniques and our proprietary algorithmic library to accurately estimate the probability of the correct answer. This score is available for the operators to view in the user interface.
By integrating the solution with your CRM, you gain additional benefit as your suggestions and auto-responses will be tailored to each individual client based on all available data. For example, for a user using Mac OS, the algorithm will suggest an article with the MacOS instruction rather than for Windows or Linux.
With each new customer reply or feedback event from the operator, the model gets better. The quality of answers it can provide increases and it can automate more and more incoming requests by building up the confidence of its predictions.
By analyzing customer support log at its entirety, we can accurately identify emerging problems before it is too late and quantify their impact. Now, operators answer the requests in isolation and, hence, this crucial intelligence is lost.
Existing solutions provide very simplistic full-text keyword search functionality. We assign each request to a set of fine-grained topical categories enabling powerful semantic search and filtering capabilities. Plus, a separate module is available to enable sentiment-based search.
Generate reports about the conversations, customer needs, and the performance of your customer support staff based on the entire customer support log. Know exactly the distribution of request categories instead of doing a low sample manual quality control, which adds extra cost due to human annotation. Customize by choosing different chart types, date ranges, and filters.
Thanks to our distributed implementation of the underlying algorithms and a new theory of additive model regularization, we can train a machine to recognize an unlimited number of categories without quality degradation.
By using active learning techniques specifically designed for the customer support and questions answering domain, we significantly reduce the data annotation cost. Rather than training the model in two steps, we connect the data annotation step and the model training steps together achieving efficiency and reducing the cost for the solution adoption.
A subset of the incoming requests is processed fully-automatically and, hence, the response will be instant. The rest of the data is processed by humans but even they are provided with a ranked list of best answers and quick replies. They can simple approve the answer without typing in one click.
Having access to the intelligent answer suggestions and search, the customer support operators can process dramatically more requests per unit of time.
Due to automation, you can significantly reduce the number of operators required to support a given volume of requests. Moreover, since machine can answer a subset of the requests better than humans, both you and your customer win.
Know exactly the distribution of request categories instead of doing a low sample manual quality control, which additionally requires human costs on annotation. View trending topics and derive insights from conversations to create better content guidelines and agent training. There is a possibility of conducting detailed statistics on incidents without the need for manual analysis of hundreds of thousands of incidents
Rather than processing repeated boring one-off requests, your employees will be able to refocus their attention to more challenging tasks providing superior customer service where it is really necessary. Let us take care of routine.
By leveraging the power of our cognitive engine based on advanced NLP and text mining techniques, a new employee can become productive from day one.
Inevitably, people make typos and their performance decreases closer to the end of the day due to physical or attention fatigue (repeated exposure to the same question). The machine will never get tired and will never make errors.
Rather than adding a new request to a generic queue, which might be forgotten, or routing following the first-in first-out policy to the first available operator, we identify the most knowledgeable operator for the problem. It helps distribute the load more effectively leading to higher customer satisfaction.
The algorithm predicts a topic, confidence score, and detects a sentiment of the request. All these indicators can be combined to decide whether to answer it automatically, force the request to the human operator, or escalate the request to the manager before it is too late.