Published on 10|6|2019 by David Radosta & Aleksandra Adamczyk
Managing a lively customer service centre is certainly not the easiest of tasks. There are often struggles with constant stress, low motivation of the team. On top of that, a huge amount of work is tedious and repetitive. Does this mode of operations result in high efficiency? Is there a way to help companies optimize the process in terms of both costs and staff satisfaction?
Working in a customer service centre is demanding. You are supposed to empathize with the client's situation, understand their expectations and learn to deal with everyday monotony, because the work usually gets quite repetitive. At the same time, it can be overwhelming for employees, who often end up performing of their tasks negligently. It is estimated that almost 25% of administrative and customer service employees are dissatisfied with their work .
Many managers do not notice problems in customer service until something is broken and will not result in the customer leaving the company. There may be several reasons: they either do not have time to analyze or have not received sufficient measurement tools. But how to analyze a process that is very complex and often contains many unknowns?
The buzz about artificial intelligence that supports corporations in a variety of ways has been intensifying. But what is artificial intelligence really and how can it affect the situation of customer service offices?
Artificial intelligence is basically a system that analyzes data and uses it while learning and drawing conclusions. Some branches of artificial intelligence, such as machine learning, aim to create an automatic system that can improve using collected data and acquire new knowledge on this basis. In customer care terms, this means optimizing entire customer service processes, thanks to machine learning. Text data generated by customers, i.e. e-mails, sms messages, social media messages or phone call transcripts, are processed by an algorithm that understands and classifies the issues customers raised. That opens opportunities for automation and insight generation. The consultant's work is also analyzed, so we can assess whether they are good at customer service or need further training.
Interestingly, AI is now able to work independently of language. Until recently, such solutions could only be implemented by companies serving English-speaking clients; now it is also possible for those operating in Polish.
Another important advantage provided by NLP (natural language processing) AI is knowing which clients might cancel the service in the near future. That gives companies an opportunity to change customers’ mind before they decide to leave.
Acquiring a new client is more expensive than maintaining the current one. So, we decided to look at the process of keeping the client using artificial intelligence - says David Radosta, Product Owner at SentiSquare, a start-up specializing in the processing of text data by artificial intelligence. - We analyzed the archival group of connections of the Czech branch of T-Mobile, in which clients talk about the termination of the contract, with words such as ending, period of notice, expiration and so on. AI has revealed deep connections between these expressions and issues mentioned by the customers. We used this dataset as a training material for the algorithm we developed. Thanks to this, our system could then determine which clients are close to giving up services and contribute to persuading them to stay.
Retention is crucial as an indicator of good customer service and a predictor of profit. According to Bain & Company, an increase in maintaining the number of customers by 5% results in a profit increase of over 25%. One way many companies try to gain competitive advantage is the analysis of customer behavior. Prediction models are key to creating a solid customer retention strategy; deploying NLP to customer-generated text is essential to building an effective model.
But where does the implementation of NLP-type artificial intelligence work best? The amount of data is a key metric here; wherever a huge amount of text data is to be processed, there is business case of AI deployment. Currently, AI-powered analytics most often utilised by corporations in sectors such as finance, retail, IT and call-centers around the world.
It is estimated that nearly 38% of fast-growing companies are already actively working on the introduction of artificial intelligence into their own business, of which 9 out of 10 companies are planning to invest in the development of artificial intelligence in their processes within three years - shared Stanislav Rejthar, CEO SentiSquare. - The market is certainly growing, the only question is which companies will implement solutions faster and overtake competitors, and which will be left behind.
SentiSquare is a technology company that deals with customer-generated text analysis. As one of the few companies uses artificial intelligence based on principles of distribution semantics, which provides many competitive advantages, such as language independence. The company was founded in 2014 as a spin-off by team of researchers at the Faculty of Applied Sciences of the University of West Bohemia in Pilsen. SentiSquare currently supplies technology to the contact centers of large companies such as T-Mobile, E.ON or Albert.
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