Business ecosystems are full of jargon, because that’s the way your organization’s teams talk, and that’s the way customers talk to you. To achieve true customer success, learning from data is crucial. However, customer service is one of the foremost pillars of great customer journeys, and customer service data is always messy. Consider these two complaints received by two different teams of a company on everyday basis:
“The ac in my car stopped working, and the mileage is deteriorating too.”
“looks like my a/c has been reset . . . can you fix it quickly?”
These two problems belong to two different teams and call for very different expertise. However, telling that to a machine is a wholly different story, and a computationally expensive one.
Most language capabilities that exist today would identify the problem in two parts – mapping same words to different meanings on the basis of context, and using training data to finally map the problem to the right team or knowledge base. Traditional language processing algorithms fail to efficiently perform the distinction, because of two reasons. First, the language of customer service channels (and most business interactions, for that matter) can’t be mapped onto a dictionary. Moreover, business teams and customers often use acronyms that derive meaning from the context rather than the word itself, and most of them don’t exist in a formally defined notion of language that we call the dictionary.
Secondly, if the resolution of difference was successful, it means that the machine was able to capture the intent behind the speech/text rather than just meaning. Such a capability could accurately classify your service tickets into meaningful labels without human support. Defined as a problem of relationality in the context of machine language learning, this task is computationally expensive – the second reason why traditional models are unsuitable for use in the everyday B2B context, where the pressure on the bottom line is high, and competition is driven (often exclusively) by costs.
Sainapse replaces the dictionary with your organization’s data, and learns from it, instead. Therefore, if your teams refer to the server rooms with the word ‘box’, Sainapse won’t direct a ‘gear box’ problem to your IT support teams. By using proprietary algorithms designed specifically for the language of everyday business, Sainapse is language-agnostic and can extract the intent behind text with minimal computing power, from within your enterprise’ firewall. With such a capability, the possibilities are endless! Talk to us and find out how Sainapse can make your customers happier from day 1.