As CSPs constantly strive to stay ahead of the competition, they draw upon their customers’ perception – positive or negative – for their products which, perhaps, is the ultimate satisfaction indicator. Customers, in turn, often do realize the power that their feedback has and opt to express their feelings in writing, either to draw the operator’s attention on their opinion or even to have their acquaintances involved.
In some occasions, customer care online forms, emails, chatbots or IVR are the digital touchpoints that convey textual feedback spontaneously offered by clients, directly to the CSP. In other instances, it is the CSP that encourages customers to provide feedback via questionnaires or surveys. There is, also, a third option, where a happy or dissatisfied customer may resort to uploading posts on all types of social media, acting like a social influencer.
Evidently, if an operator was able to sift through the abundance of internal and external customer produced texts referring to its products, then immense business value would come out of it!
Making sense of textual customer feedback has been a long-standing pursuit for CSPs, usually addressing it on a best effort basis and through manual labor. Not long ago, however, the advances in both Natural Language Processing (NLP) and Machine Learning (ML) have boosted Sentiment Analysis: the automatic process that helps identify the topics for which opinions are expressed in texts, and classify them into the proper sentiment tag (positive, neutral or negative).
In simple terms, Sentiment Analysis follows two processes:
Once properly deployed, a Sentiment Analysis solution can automatically process large quantities of text in near real-time, unearthing otherwise hidden, hard to detect insights. Having said that, two key challenges cannot stay unnoticed:
Intracom Telecom’s Cognitiva™ Sentiment Analysis is an NLP-based solution that analyzes textual information in Greek and extracts meaningful information regarding customer feedback. It empowers CSPs to deep dive into customer-generated textual feedback, ‘listen’ to their conversations and take into account their messages.
Cognitiva™ Sentiment Analysis addresses the aforementioned key challenges, by making use of a native language corpus into the training process, either annotated with accuracy using highly automated tools or reusing the already annotated textual CSP data verified by experts. This process can be applied not only to Greek language, but to other non-English ones, such as Bulgarian and Serbian.
Cognitiva™ Sentiment Analysis is an approach that can be applied on a wide product range of vertical industries, such as Telecommunications, Banking and Retail. In abstract terms, the solution is structured along three pillars:
The deployment of Cognitiva™ Sentiment Analysis is based on five conceptual steps, each executed in different fre-quency.
Network degradations, billing dissatisfactions or bad customer support experiences are often among the top reasons for churn. In those cases customers may choose one of the following means to interact with the CSP in free-text form: send a message directly to the CSP describing their dissatisfaction, respond to a CSP-initiated survey providing their feedback, or even post their experience in public through social media.
Cognitiva™ Sentiment Analysis receives customer input, regardless of its origin, and performs the following tasks:
For example, Cognitiva™ Sentiment Analysis will automatically identify frustration in a user message and detect that the reason is a network issue affecting IPTV. Based on the user’s location, Cognitiva™ Sentiment Analysis will initiate a trouble ticket and route it to the corresponding network Operations/Monitoring Department for immediate corrective actions. At the same time, the ticket will also be routed to the Campaigns Department to propose a recovery offer to the customer.