Among the various business functions, customer service was one of the early adopters of AI and machine learning. Customer Relationship Management or CRM is an essential part of any successful business. In the initial stages chatbots and AI enabled communication ensured that round-the-clock customer communication became a reality. Businesses interacting with their customers more, whether through a human customer service representative or a chatbot or through automatically generated email drip campaigns led to a lot more data collection about customers.
Recent studies have shown that 75% of customers believe that it takes too long to reach a human agent. In such cases, giving them 24×7 service using an AI empowered chat agent gives businesses a good boost and puts them ahead of several of their competitors. Customer service live agents can only handle one customer at a time. This means that with growth in the customer base, more agents will be needed. However with AI-driven live chats, businesses save on hiring more agents all the time by having chatbots respond to basic questions. When the questions become more complex, the chatbot with its NLP (Natural Language Processing) ability will map the customer to the human agent who can help solve the problem quickly.
Let’s look at some projections and forecasts for this domain at this time.
Global AI business is projected to reach $1.2 trillion by end of 2018, with customer experience solutions creating the most value.
Oracle estimates that by 2020, 8 out of 10 businesses would have already implemented or would be in the process of implementing AI driven customer service solutions.
Gartner research predicts that AI bots will power 85% of customer service solutions by 2020.
Again, according to Gartner, virtual agents will account for 46% of the global AI-derived business value by the end of 2018 and 26% by 2022, as other AI types mature and contribute to business value.
By 2020, 57% of customers will expect companies to know what they need before they ask for it and Forrester has already estimated that, by 2020, AI insights-driven businesses will steal $1.2 trillion per year from less-informed peers. The future of AI will likely be in new business creation and customer retention through advanced prediction capabilities.
Decision support / augmentation systems such as Dynamic Neural Networks (DNNs) allow organizations to perform data mining and pattern recognition across huge datasets not otherwise readily quantified or classified, creating tools that classify complex inputs that then feed traditional programming systems. Such capabilities have a huge impact on the ability of organizations to automate decision and interaction processes. Forrester estimates that DNNs will represent 36% of AI-derived business value by the end of 2018 and 44% by 2022, surpassing all other AI sectors.
Here are some good reasons why AI-driven, automated customer service is the future for many businesses:
You only have to train it once:
Hiring and training staff takes a lot of effort and costs a lot of money. In case of high employee attrition, which is typical for many call centres, the training costs for new people hired keep adding up. In case of process changes, there is no need to re-train the entire staff anew. Mostly, the software has to be re-configured and a lot of that process can be automated. Besides, these customer service platforms help free up employees from routine tier-1 support requests that they may be tired of working on. Instead, they can focus on the more challenging and creative tasks that support their customers’ complex needs.
Reliable 24×7 service, contributing to enhanced customer satisfaction:
Customer service powered by AI technology is far more reliable than the service provided by humans. Chatbots are free from many afflictions and biases that can negatively influence a human customer interaction. Besides they do not turn up late, come to work with a hangover or get angry. They are programmed to escalate calls to a manager if they cannot solve a customer query. A recent study showed that 42% of B2C customers purchased more after a good customer service experience, whilst 52% stopped buying after one bad customer service interaction.
Providing customer service through messaging application platforms:
In 2015, the number of people using messaging apps overtook social media. Since many individuals are increasingly using messaging apps to interact with brands, in addition to friends and groups, these constitute a new space for organizations to connect with existing and future customers. Businesses now have the opportunity to create new revenue streams using real-time, customized customer service bots within messaging applications. Among several others, the airline, clothing and tourism industries are already leveraging this space.
Personalization as the key to enhanced customer satisfaction:
Organizations collecting customer data can combine big data, machine learning and AI to deliver a high level of personalization for all customers. This can range from simple product recommendations based on past purchases, to websites redesigned in real-time to tailor to an individual customer’s reading level and browsing habits. Effective personalization has been seen to greatly improve customer service interaction experiences, enhance customer satisfaction, improve conversion to purchase metrics for those interacting and stimulate repeat buying.
High scalability and speed:
Technologies like these give businesses the power to scale their customer service pretty rapidly, even if they have limited resources. Upon launching a new product, businesses can put up interactive FAQs, which can resolve thousands of customer queries without the need for human interaction. Chatbots empower businesses to respond instantaneously to a surge in customer inquiries, simply by switching on additional servers which may be needed. With AI driven chatbots that continuously learn from every interaction, brands can quickly venture into new geographic markets, since they no longer need to hire and train staff who can speak the local language.
Use Case: AI Augmented Customer Service
This use case is about AI-augmented messaging. The company uses a “bot assistant to the agent” model for some of its customers. The idea is to avoid the frustration some people feel when dealing with a pure bot while using the technology to increase efficiencies. The company feels generalized bots don’t work, but specialized bots meant to handle the most routine and simple tasks do.
As per the company, around half of all customer service interactions are currently highly suitable for bots and so, they have implemented a system where humans and bots work in tandem. Very simple questions are handled directly by a bot, but as soon as the conversation becomes complicated the bot hands over the conversation to a human. The human handles the more difficult tasks and hands the interaction back to the bot for simple details. This maximizes the time human agents spend doing what only they can handle and also minimizes the time they spend on tasks that bots can handle. The system allows one agent to handle multiple interactions.