Businesses these days receive hundreds and even thousands of customer queries daily. For any customer service representative, it becomes tremendously difficult to keep track of these issues, specifically because of the three Vs

Volume
Velocity and
Variety

With inconsistent similarities between large amounts of incoming data along with the frequency of product updates, it adds exponential complexity to the process of unearthing trends in the data. This data can be created from various data sources including customer-created tickets, service requests, bots, customer reviews, case objects from different CRMs, help articles, or even FAQs. While these datasets share the same ground of belonging to customer interactions, they all can have extreme differences in terms of unearthing actual actions to be derived from them.

At Ascendo, we call these as “Interactions”. What is common across these interactions is that they deal with symptoms/problems/questions/advice that a customer needs. Each of them needs an understanding of what the “root-cause” of the request is. Then the root cause should be mapped to the relevant solution/knowledge to surface it back to the customer.

We will be going into topics like:

Semantic Inference - what is it?

Real world Examples of Semantic Inference

How does this help with Support time and effort, Product teams and Go-to-market teams

How does an AI engine use the above?

What help does this provide to an agent?

How do AI systems comprehend data?

What help does this do to CX leaders?

What if you are starting out with no systems in place?

To read more of this, please read the full whitepaper.