The ultimate guide to sentiment and emotion analysis
Sentiment and emotion analysis enables businesses to keep track of how customers feel about products and services. This blog shares how these insights...
The Team at CallMiner
February 16, 2017
Twenty years ago or even ten, if we were seen talking to our laptops and smartphones, we would have gained the stare from quite a few people around us, not anymore. Devices these days especially smartphones come equipped with technology that allows us to voice our demands and generate the necessary results. So, asking Siri, Cortana, Alexa, or even Google Now the best route to get to work in a jiffy and make a coffee stop while at it is a cakewalk.
Most tech enthusiasts will say that with the use of natural language processing that the aforementioned technologies use, technology has come of age. But, has it? Well, to be honest not quite. Though, natural language processing has a very promising future, with a McKinsey report claiming that 80% of technology could be automated through the use of natural language processing; its current state presents an opportunity for vast improvement. This is especially true, for call center software that relies on natural language processing.
It is a scientifically established fact that our voices are as unique, if not more, than our fingerprints. The number of spoken languages in the world keeps growing year-on-year, with the current number of spoken languages in the world is being capped at 6909. The most advanced technologies such as Google are investing more and more resources to enhance their natural language understanding capabilities.
With more and more customers relying on self-service and automation such as quality management, meeting customer expectations is the only edge that contact centers can have. There are three common mistakes that most businesses are making when relying on software that offers natural language processing. Let’s analyze what these mistakes are.
1. Treating language as data
Though data is the basis of analytics that can help businesses understand their customers better, when it comes to natural language processing, this is not the case. Each data string needs to be treated individually instead of clubbing it together. Like each voice is unique, each request is unique, so should its results. Computational statistics and pattern recognition is not the best approach when it comes to artificial intelligence that uses natural language understanding. Simply transforming language into data and not understanding the context is a flaw that will need to be addressed.
2. Not a set formula
The context of a language is integral to its meaning. Language cannot be compartmentalized to a fixed set of formula. What most artificial intelligence models are missing out on is understanding the context in which the search request is being made. With customers having little patience to voice their concerns and even little time to repeat themselves, contact center software has to have the ability to understand the context of the query to completely and correctly address it.
3. The rationale behind language
The ‘customer is king’ is still the ruling phrase of the contact center industry and will be for a long time to come. The AI model that a contact center relies on should be facilitated with the ability to rationale a request. Our voices represent a myriad of emotions and often we forget that we are talking to a machine instead to a fellow human. To start with, a query can be resolved better if factors such as the time and location of the person making the request can be taken into consideration. Building trust and ensuring that eventually, the customer becomes dependent on the assistant should be the long term goal of any business.
Though technology is advancing at a rapid pace, growing customer expectations are matching their speed. Natural language processing has to understand all the nuances that language presents. Artificial intelligence models will need to be empowered in a way so as to understand not just the words being spoken but also the emotions represented by them.
Businesses can either blame movies such as the Star Wars and Iron Man series or actually use them as benchmarks that they need to achieve and even surpass. Research and development with the adoption of every achievement are necessary when it comes to the evolution of technology. Results that are effective, accurate, and efficient in the eyes of the customer need to be commonly achieved by all businesses.
Natural language processing has the power that will enable our customers to directly interact with machines the way they do with fellow humans. Machines will need to evolve training and skills that will help them cater to the request of a baby boomer and a millennial differently.
The interactions between machines and humans are only likely to strengthen efficiencies of all parties involved. A McKinsey report clearly spells out a bright future by saying, ‘rethink how workers engage with their jobs and how digital labor platforms can better connect individuals, teams, and projects’ through natural language processing.
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