AI customer service has become a profound need for thriving business across the globe, making it important to know what it takes to provide successful AI Customer Support. Here’s all you need to know about AI customer services.
AI customer service?
We know traditional Customer service agents can realistically only handle one customer service call or problem at a time. Artificial Intelligence, in its various forms, gives customer service departments the ability to do more, thus improving the customer experience. For eg. One of the most common AI customer service Chatbot can handle multiple queries at once. This is saving grace for busy call centres and struggling to deal with all the customers at a time. Artificial Intelligence reads data and processes it to detect the best representative present to address the customer’s needs. It then provides important background information before interaction with the customer which saves extra time and manpower increasing the efficiency of addressing the issue by the customer.
Many Customer facing platforms have chosen AI customer service technology which is helping them to provide a level of service which is beyond human capacities in an ideal time. AI customer service tech. assist in customer identification , call classification , call routing , chatbots and predictive personalisation.
AI for customer identification
It refers to measurement and calculation of the body for the service purpose of identification, authentication and access control. Physical biometric solutions scan and record human body parts such as human face , fingerprints , retina while Behavioural biometric solutions analyze human characteristics and personality such as voice, gait , or interaction with a device.
2. FACE AND VOICE RECOGNITION
Facial recognition verifies and identifies an individual comparison of facial features from a digital database of image or videos. An AI based algorithm can analyze characteristics like shape of jaw ,width of nose or the distance between eyes and then use this to find a match. Similarly, Voice recognition digitizes words with different data such as pitch , tone and cadence and then creates a unique voiceprint used to identify and authenticate the voice of the speaker.
AI helps in improvements in machine recognition capabilities and challenges .Using this data of biometrics agents can easily get info of customers and they can greet and present them significantly helping in maintaining good customer service. Some Companies use AI biometrics to verify product warranties of customers and provide them service free of providing receipts and documents. Insurance related companies use biometrics to lower the risk of frauds as AI biometrics are more reliable and cost effective and this feature creates expectations of more companies to take advantage of it in near future.
3. INTENT PREDICTION
It is the science of figuring out the customer’s next step requirement which can be predicted by analyzing signals such as clicks , views , purchases which helps in value added personalization before request put forward by customers. Predictive solutions determine intent by combining customer data with AI to provide the right next step to deliver the relevant and perfect customer support.
4. EMOTION ANALYTICS
It understands the mood or attitude of an individual by analyzing the verbal and non verbal communication made. It works on simple science that if someone is smiling he/she will probably be happy or if someone’s eyes are wide individual is shocked. Emotion Analytics is used to provide right priority and route customers to the right agent by classifying customers mood. For eg. If customers are found happy they are directed to agents for selling new products while if they are found angry they are routed to the customer retention team.
Virtual assistants or Chatbots are conventional AI customer service platforms. Many enterprises like Facebook , Apple , Microsoft , Google are readily engaged in building chatbots that can respond to their customers queries and problems and scale the quality of their customer service. These Virtual assistance can store unlimited amounts of data, predict customer behaviour all in the right time. Today, humans and AI based bots can collaborate to optimize relation and interaction with customers and it can be applied in ways like enhancement of human capacities and augmentation of human intelligence.
6. TEXT/NLP ANALYSIS
The application of computation technique to language used in the natural form- written text or speech to derive analytical insight is known as Natural Language Processing (NLP). This analysis is a tool for creating sense out of the multitude of opinions expressed on review sites, forums , social media and blogs everyday. NLP analysis allows companies to retract suggestions on product and online product review complaints.
7. PREDICTIVE MAINTENANCE
To predict maintenance and technical issues before they actually developed this technology is used. Some of the big companies have stated its profit by using this tech. Which is through increased elevator availability by employing real time diagnostics reducing service out time. Many other plus of this maintenance includes optimization of network performance and faster troubleshoot issues.
8. COMPUTER VISION AI FOR OBJECT/ISSUE RECOGNITION
This involves understanding the meaning and context of digital images and videos automatically through processing and analysis. It’s so accurate that it identifies an object with an image and classifies and distinguishes it from other objects. Also it identifies parts within the object. Computer Vision AI Technology reduces the workload of contact center agents by enacting enquiries of customers to self service channels where they are guided towards self resolution with visual interaction with visual assistants.
9. AGENT DECISION SUPPORT
Computer AI technology makes contact center reps’ job easier by creation of dynamic visual knowledge bases by enhancing agent decision making and company wide knowledge sharing .It enhances agent performance by the computer’s ability to provide real time resolution suggestions because of the agent and system collaboration during each customer interaction. The contact centers handling large call volumes found this model effective.
10. AGENT TRAINING
Some of the solutions to reduce agent training times are AI based call centers training tools such as gamification , visual assistance and self-monitoring which cut down agent onboarding time and ensure full engagement of reps from day one. Another solution such as Virtual Employee Assistants (VEA) help contact centers support their agents with on demand learning.
Agents compete with each other to outpace other reps in specific KPIs such as lesson learned , hours worked and to complete objectives , when Gamification is introduced. It worked as motivating agents which includes rewards for them such as recognition on leaderboards , physical rewards and small rewards like preferred shifts or free parking. The full transparency and comprehensive reporting ensuring a fair competition made gamification a success. A fair competition is ensured based on activity tracking by the platform such as timesheet submission and resolved cases.
12. PROCESS IMPROVEMENT
Upto 20 to 30 percent of the revenue each year is cost to organisations because of inefficient processes. AI helps companies harness their data that will drive the organisation forward by making useful decisions about process changes. Process improvement should be factually substantiated based on data analytics rather than relying on instinct or team decisions.
13. CLV OPTIMIZATION
How valuable a customer is to a company throughout the relationship can be tracked by a metric known as Customer Lifetime Value (CLV). One study has found the likelihood of a first customer to buy is 5-20% whereas it is 60-70% for an existing customer. To optimize CLV high level AI driven data analysts to pinpoint where in their life cycles customers are or to target customers with loyalty promotion is used. To get the data needed to continuously improve or to pinpoint areas of excellence, understanding CLV is required.
14. BUSINESS INSIGHTS
To gain insights from huge volumes of data in order to aid decision making companies are increasingly adopting AI. An AI driven analytics process based on transactional data found in their databases and holistic solutions are being utilized to automate business intelligence.
AI in customer service
The science dealing with the creation of human-like abilities of learning and reasoning is said to be Artificial Intelligence (AI). It is changing fundamentally the way of work across several industries. Where it is in retail , finance , manufacturing or law , customer service has formed part of those sectors for many years. According to some experts in the forthcoming years, it will be impossible to dictate the difference between human and AI customer service agents.
Some ways we can use AI customer service to help support team can be :
1. Faster Response – It’s frustrating for both agents and customers when it comes to waiting, especially when the answer should be easy to find. AI is able to simple queries through experience of live chat and can refer customers to helpful self service articles also it provides 24 hours service a day.
2. Reduced Research – As researching answers spends a lot of customer support team’s time, it makes customers wait for a response and also makes availability of agents less to other customers. AI can recognise trends in commonly asked questions as it stores frequent answers. This way it provide agents frequent answers and spare less time.
3. Stronger Customer Engagement – Scenario of today’s customer support is totally different from that of past as in old days of call center support team were measured on how fast they can deal with customer issues but nowadays engagement of customer support is online and lengthy business and this where AI help is needed by leveraging procured from CRM. AI can bring up customer information and make it accessible to agents.
4. Predictive Insights – With more relevant information to increase transparency and communication companies want to improve customer relationships. AI with predictive insights were used by companies to elevate their work of customer staying connected. AI can instantly scan previous inventory and previous products to recommend similar items to customers which is not possible by customer support agents.
5. Next Steps in the Conversation – With hundreds and thousands of messages and mails coming each day it may become quite hectic and uneasy job for customer support team to answer everyone issues, this is where AI technology can help agents when to escalate and which agent can handle the issue by using sentiment analysis. With this analysis , tickets can be categorized automatically in many categories like “frustrated” , “excited” helping agents to prioritize their work.
Will AI replace customer service?
AI is created to make customer support teams more effective and not to replace them. The use of AI increases efficiency of support teams by helping them to be more smart and strategic workers. There are cases when the support team faces urgency in some customer works and they guess it by analyzing their behaviour , simple mood or expression but it is impossible for AI to read it and work for urgent emotional ones by taking them in top priority ones. This incidence shows us that AI can’t really replace customer service completely but can go hand in hand with them.
AI customer service is doing great help in developing the overall experience of the users to have a smooth way of surfing and finding the right pieces of information and help. There are many AI customer service companies who are working day and night to bring lots of innovative ideas to better handle the users.