How AI is shaping fundamentals of customer support
By Sam Makad
According to Gartner, in the coming years, customers will manage up to 85% of their relationship with enterprises without any human interaction. Indeed, the age of Artificial Intelligence (AI) is upon us. PWC states that business leaders believe AI will be fundamental in the future, with 72% considering AI technology to be a “business advantage.” Talking about its applications, AI can be successfully employed at all steps of a customer’s journey to provide an exceptional and convenient customer experience. While analysis of data remains a crucial area of AI application in businesses followed by on-site personalization, the use of AI-enabled chatbots and voice assistants for delivering real-time, omnichannel customer service is also increasing.
AI and Customer Support
The modern customer is omnichannel. You cannot push them back to a legacy customer service experience that requires them to wait endlessly for an email or to get connected with a customer support agent.
With AI, it is possible to offer 24/7 support to your customers in real-time, while also helping them to explore various self-service options that save time on both the ends. Besides, AI equips businesses to create more personalized experiences and anticipate customer requirements, even before they arise, leading to an improved brand experience.
Here are five ways in which AI is shaping the fundamentals of customer support:
1. Chatbots for Real-Time Customer Support
According to Chatbots Magazine, up to 67% of users prefer to use messaging apps to talk to a business. No wonder, chatbots are becoming an integral part of customer support for most companies, as they offer one of the simplest ways to resolve consumer issues.
Chatbots are ideal for killing the wait-times for your customers. They give the user an immediate response, offering real-time assistance, ‘round the clock, across multiple channels of communication. And, considering that excellent customer service is the new marketing trend, the future, definitely belongs to organizations that can ensure immediate, convenient, and personalized service for their customers.
In case you are wondering what your customers think about interacting with chatbots, Aspect reports that 61% of consumers believe that “chatbots in customer service are here to stay.” Millennials, who represent a large section of your users, are more than happy to interact with new technology and favor chatbots for the immediacy in conversations they offer.
According to The 2018 State of Chatbots Report, users depend upon a chatbot for the following:
- Finding an immediate resolution when dealing with an urgent situation (37%).
- Getting detailed answers or explanations (35%).
- As a means for getting connected with a human (34%).
Thus, using chatbots for customer support has dual advantages in terms of reduced costs and heightened customer satisfaction. Stats indicate that chatbots can reduce up to 30% of your customer support expenditure while speeding up the response times by answering 80% of straightforward and repetitive queries.
If you are thinking what differentiates AI-enabled chatbots from regular messaging apps, the answer lies in Natural Language Processing (NLP) algorithms that empower your chatbot to read, decipher, and make sense of human languages to respond intelligently to customers, leading them to the most logical response for their query; thus, paving the way for engaging two-way conversations. Also, AI chatbots can instantly handle up to 80% of high-volume, repetitive customer queries by leveraging machine learning to interpret inquiries and learn from new experiences.
2. Automation for Reducing Your Team’s Workload
Research indicates that chatbots will help businesses save over $8 billion annually by 2022 by taking up repetitive tasks and reducing the need for hiring and training large teams in customer-facing positions. In addition to reduced costs, integrating chatbots in your customer support slashes your team’s workload considerably, leading to happier and more efficient employees.
For example, a chatbot uses NLP to process your customers’ queries intelligently, answering a majority of the questions on its own, passing only qualified leads and complicated cases to your human team. Each query that is passed on is accompanied by a transcription of the conversation so that there is no need for repetition, that often adds to customer stress and frustration.
An AI-enabled system can also filter out support tickets by dealing with the redundant or repetitive queries on its own, and only passing on the ones that require deeper analysis to humans. Such a system not only improves the efficiency of your team but also ensures faster resolution for a larger number of customers.
In the above picture, you can see Yoko by Toshiba Finance, which is the company’s first line of customer support, in action.
3. Customized Experiences with AI-Initiated Triggers on Landing Pages
Do your pages generate a lot of traffic but show a dismal conversion rate? This means your site is experiencing a high bounce rate.
In other words, users visit your website but move on to something else, without taking any action. But can you turn around this lost opportunity to your advantage?
Indeed, you can, by offering customized experiences to your customers through AI-initiated triggers on your landing pages. A trigger can be defined as something that tells your bot to work. For example, it could be a visitor abandoning your site after browsing it for a few seconds. Here, your bot can offer a personalized coupon or discount to the user while they are quitting, to compel them into purchasing on the site. Alternately, it could also share a short survey asking the user reasons for abandoning the site.
Another example could be of a user revisiting your site. In such a scenario, a chatbot can start a pro-active conversation with a personalized greeting followed by some pre-determined questions to find out what the user is looking for. In case they respond, your bot can always share links to relevant information, saving the user a lot of time and hassle in the process while also learning more about their preferences.
However, remember that you don’t want to come across as spammy. Avoid suggesting unnecessary products to users and set intelligent triggers to help them along their purchase journey.
4. Detailed User Profiles for Personalization
Data unification is a must to create a single customer view that can be used for curating highly personalized offerings for your customers. An AI-based platform is adept at collating customer data from multiple sources, helping you to create segmented customer profiles that can add much more value to your marketing campaigns.
Walkers predicts that immediate query resolution will no longer be enough for customers by 2020. In addition to speedy customer support, they will also demand personalized conversations and expect brands to anticipate and address their needs proactively.
AI-based platforms make use of natural data from multiple sources to delve deep into every customer’s behavior and shopping patterns to perform predictive analysis and suggest products that best match their profile. With such a model in place, not only can you offer personalized offers to boost sales but also recommend alternative products to customers in case what they are looking for is not available or out of stock. By sending relevant marketing messages and throwing up useful suggestions to customers when they are browsing your site, you can successfully add more value to their customer journey while amplifying their interaction with your brand.
5. Streamlined Workflow with Machine Learning
Many customers get frustrated by repeating their issues to multiple customer support agents. Besides, it also adds to your call volumes, when a user has to jump from one department to another before reaching the one that is poised to help them.
With Machine Learning, support agents can segment their users and also assign them to the concerned department for better engagement and customer delight. It provides support teams with an automated process where the machines tag support queries with topics and extracts data or opinions from unstructured text, such as emails, support tickets, survey responses, tweets, etc. Thus, organizations draw actionable insights from the horde of comments on their social media or use it to sort pending support tickets and assign the tickets to relevant departments for faster and more efficient query resolution.
Take the example of a survey conducted by your company to gather what your customers think about your service. Here, getting the customers to respond may not be as difficult as going through each and every response provided. However, with Machine Learning, you can extract what customers dislike and like about their experiences and bucket them into granular themes and categories — identifying pain points in the customer journey.
With evolving customer expectations and the competitive business landscape, integrating AI into your customer support can be a tremendous competitive advantage if leveraged correctly. However, it must be understood that the aim of AI is not to replace customer support teams but to reduce their workload and make them more effective, as suggested in the points above.
What do you think about the future of AI in customer support? Share your thoughts in the comments section below. We’d love to hear from you.