A loyal customer means high profit. Do you know that, according to Oracle, 86% customers are ready to pay for better service? This is precisely why, as Gartner reports, in 2017 50% of B2C investment projects globally have been aimed at development of the customer experience. And according to Dimension Data, an IT integrator, 80% of the existing customer loyalty assessment systems do not meet the companies’ needs.
However, such quality assessment systems as phone surveys, test purchase, loyalty button, commercial SMS messages and emailing do not ensure 100% coverage of customers, do not provide information on customer loyalty motives, and bother customers with extra surveys. What can help us? It is video analytics and neuronet!
Scenarios of using video analytics systems are broad-ranging and are based on super useful functionality: detecting and tracking certain objects, identifying and classifying objects by predetermined criteria, recognizing emergency situations.
Smart machines have learned to understand the emotions of people for a while. For example, American Emotient, currently owned by Apple, wrote the advanced software for Google Glass back in 2014 (unadvanced one, a little bit earlier). Affectiva, Realeyes, Beyond Verbal can analyze speech, recorded or live, very well. There are domestic systems as well, for example, Russian HeedBook which uses video stream directly from the employee’s workplace in the background, analyses information using neural networks and receives real-time customer services quality assessment, business process analysis, cross-sell control. And all these are achieved with high-quality audio and video recording and simple integration to the employee’s computer.
The system detects emotions receiving them via webcam. It does this by the pitch of a voice and facial expression, and it understands the dialogue content as well by recognizing key words. The customer satisfaction rating is made based on the five basic parameters, including intonations and dialogue content, positive mimic emotions, negative mimic emotions, and attention.
Public services, banks, stores, and sales offices, transport enterprises, restaurants, automobile sales centers, clinics, insurance and travel agencies, cinemas, repair shops, museums are already using such systems. We are talking about Russia. And across the globe, the application of neuronets in the customer service is much wider.
Features may be provisionally divided into two categories. The first category is aimed at employees, and the second one deals with customers. Both are important and interrelated. “Internal” features include ranking of employees by the service quality and load; real-time reviewing of the customer service process, monitoring of compliance with a script; assessing peak loads and downtimes; recording dialogues for various purposes, collecting data on dialogue number and duration; notifying of front-line events.
Customer “focus” functionality means, for example, defining the customer profile (sex and age); analyzing words and phrases provoking negative and positive emotions of customers; conducting targeted product campaigns, demonstrating target media content, advertisements; analyzing the reaction of a customer to special offers, promotions, discounts; indexing key words by use frequency, and many other things.
People are just working, and the neuronet monitors and continuously analyzes what is going on to the fullest extent. As a result, a huge amount of precious analytical data is received: results may be filtered and viewed at different levels: for example, at the level of a certain customer, employee, business process, office, and the entire company. The analysis is performed based on the internal algorithm and objective estimation. What is particularly important is that such systems do not distract the employees, depersonalize and encode data, may be integrated with CRM and ERM.
The solution structure is a piece of cake: we need an employee’s computer (or smartphone/tablet), webcam/media screen/mobile camera, and specialized SW.
The use of the abovementioned domestic HeedBook may be given as an example of application of such solutions in business. A Russian company applying it operates in the tourism industry. The company is a representative of the large business, and increase in sales and customer loyalty is one of the challenges it faces. Owing to use of analytics based on data from neuronets, sales of insurance policies grew by 5%, sales tunnel expanded, and customer satisfaction indicators increased by 19%.
Reutov Multifunctional Center is one more case in point. In 2017, the automated emotion detection system was installed in the center in order to collect data on the customer service quality. Webcams monitors the applicants’ emotions, voices, face expression, and speech content on a real-time basis, and the resulting analytical data are converted into the report for the management. It becomes clear at once what may be improved and where such improvements should be introduced!