The next section will address use cases centered around sentiment analysis and use case #1.A specific customer’s text input could be used as the basis for search against an existing index, and recommendations/answers provided for past complaints of a similar nature could be used as a way to handle this complaint. Similar customer queries and complaints may have been raised in the past and also addressed.

In such cases, chats may be sent to an analysis engine to get categorized, useful information extracted and could be routed to other places (a workflow to escalate an issue), including the chatbot for a response.

Analyzing complaints through a categorization model helps indicate the broad area around which the customer had a complaint, and that topic can be then be addressed within a dialogue.Ĭustomer support centers may have off-hours and may sometimes ask customers to write in their complaints, saying someone will get back to them.

The chatbot could treat negative feedback, as determined by a text analytics engine, by directing the customer to a dialogue that seeks to mitigate the risk due to such negative sentiment. This may then get analyzed for the sentiment.

Customers may wish to provide their feedback, review, or complaint in freeform text.

Here are some initial thoughts around likely objectives customers may have, which involve text analytics accessed from a chatbot. Both enable a connection to other SAS capabilities which may exist on the same, or on other environments. The SAS Code Data Provider Node and the Web Service Data Provider Node are examples of such existing functionality.

How do we access such capabilities? Once a purpose is clearly established, we can then look at the steps required, which can be based on existing functionality.

What do we want to accomplish? Overlapping capabilities between SAS Conversation Designer and SAS Visual Text Analytics (for example, detect entities or match user input to intents) make it important to first establish the purpose so that efforts are not redundant (accomplishing something that was already within SAS Conversation Designer’s ambit).To address this, let us consider the following questions: Consumers expect to easily access VTA capabilities from SAS Conversation Designer with minimum additional configuration. SAS Conversation Designer is available with every offering that also provides SAS Visual Analytics (and this includes VTA). As the SAS Conversation Designer (CD) user community grows, expectations arise with regards to its abilities to access capabilities from other offerings, one such being SAS Visual Text Analytics (VTA).

Sas chatbot